Communications, Signal Processing, and Systems

This paper introduced technique of current flame detecting system based on the CCD camera from which the color images are transferred into a computer, then the image processing algorithm is used to determine whether there is fire in the image sequence, the monitoring method is the most important in the whole system. The initiation of flame is a slowly process in which the image characteristics are very clearly, As the shape, area, and intensity of the flame in different time, each one varies every time. The image information of flame is analyzed in this paper, the regularity is summarized in color feature and dynamic characteristics, which is the main basis for the design of the identification algorithm. The color model is established based on the analysis of the characteristics of flame color, and the dynamic characteristics of the flame are identified according to the irregularity, the similarity and the stability of the flame, so as to provide the accurate basis for the flame detection.

The deployment of the next generation mobile networks increasingly relies on the deployments of small cell. In this paper, we propose and evaluate an optimal energy efficiency (EE) small cell deployment scheme to solve the problem of small cell deployment in Massive MIMO system, considering the impact of base station (BS) location, number of antennas and BS density on the EE of the system in different scenarios. Single-cell model and multi-cell model are considered. In the single-cell model, the system allocates the location of the small cell by minimizing the system power consumption when the scheme satisfies the target transmission rate constraint of the user. In the multi-cell model, the optimal BS density is obtained by deriving the EE expressions of different optimization parameters. The simulation results show that the scheme can achieve high EE and validate the effectiveness of the proposed scheme.

The change of telemetry data of spacecraft is usually caused by tele-command or fault, which conforms to the causality model of remote-control input and telemetry output under different conditions of spacecraft. Traditional expert system relies on static knowledge of experts to diagnose telemetry parameters. In order to solve the problem of rule-based expert system knowledge acquisition and less manual intervention, considering the characteristics of spacecraft telemetry, this paper proposes an expert knowledge acquisition algorithm based on successful data envelope line and conditional probability from two dimensions of analog and digital quantities respectively. Through data mining of historical telemetry, this algorithm achieves the threshold of analogue quantities and automatic extraction of causal rules at different stages of product life cycle. The experimental results show that the algorithm is effective and the simulation value is more accurate than the product design index and the redundancy of causal rules is less. After knowledge mapping, the algorithm can be applied in the spacecraft fault diagnosis expert system.

In this study, the error performance of a space shift keying (SSK) system with amplify-and-forward (AF) protocols on transmit correlated dual-hop channels over Rayleigh fading environments is analyzed, we obtain an average bit error rate (BER) expression in closed-form. The correctness of the analytical results is validated by simulations.

Joint Source and Channel Coding (JSCC) considers the source coding and channel coding of communication system to optimize the design. Firstly, an Unequal Error Protection (UEP) scheme based on the Rate Compatible Punctured Turbo(RCPT) code is proposed. The simulation results show that the UEP scheme is superior to the Equal Error Protection (EEP) scheme when the channel rate is fixed. Both the objective data and subjective video show that the UEP scheme is superior to the EEP scheme without increasing channel redundancy and achieves channel coding design based on source characteristics. Secondly, using UEP schemes with different bit rates, a channel adaptive JSCC system is designed. The system can adjust the bit rate allocation between sources and channels adaptively according to channel conditions, and realize the joint source and channel coding. Experiments show that the video restoration quality of this scheme is better than that of the single UEP scheme.

Ultra Dense Network (UDN) is an effective solution to the explosive growth of traffic in the future 5G networks. In this paper, a mean-field power allocation algorithm is proposed for UDN. It imbeds the power allocation decision problem into a Dynamic Stochastic Game (DSG) model. And then it finds the optimal decision by deriving the model into a mean-field game model. The simulation results show that compared with the other methods, the proposed method can achieve better performance in terms of the CDF and the Utility EE, and can also guarantee the Quality of Service (QoS).

In order to carry out the condition monitoring tasks in working process of gas turbine, a multi-channels data acquisition instrument was designed based on high-speed AD and FPGA, which can collect temperature, rotational speed and vibration signals in real time. The data is transmitted to PC through USB interface, then PC uses EEMD to analysis the vibration data and LabVIEW software to process and display data. At the same time the instrument has both on-line data processing module and storage module, and it can analyze data offline in special working environment. The instrument is characterized by good communication ability with host computer and strong anti-interference ability, so it can provide reliable state data for fault detection and analysis of gas turbine, and it is feasible and practical to carry out data acquisition and condition monitoring in a complex environment.

This paper examines the joint moving target parameter estimation in hybrid active and passive radar networks with sensors placed on moving platforms, which are composed of one dedicated linear frequency modulated (LFM)-based active radar transmitter, multiple frequency modulated (FM)-based illuminators of opportunity, and multichannel radar receivers. Firstly, target returns contributed from the active radar transmitter and multiple illuminators of opportunity are adopted to fulfill the radar purpose, resulting in a hybrid active and passive radar networks. Then, the CRLB for joint target parameter estimation is derived as the performance metric for the underlying system. Finally, the numerical results show that, the achievable CRLB can be decreased by exploiting the signals scattered off the target due to illuminators of opportunity transmissions.

This paper introduces a novel fiber Bragg grating (FBG) sensor for hull roll and pitch motion measurement. The sensor is mainly composed of three parts: differential hinge structure, fiber grating and mass block. When the hull produces a dip angle affected by external forces, the fiber gratings fixed on the left and right sides are deformated due to the tensile and the pressure force. The relationship between the deformation of the fiber gating and its wavelength is subjected to the proportional function. By using compensation algorithm of the demodulator, we can get the fiber wavelength and the inclination angle of the ship.

Maximum Stable Extreme Region (MSER) is the most influential algorithm in text detection. However, due to the complex and varied background of Mongolian text in natural scene images, it is difficult to distinguish between text and non-text connected regions, thus reducing the robustness of the MSER algorithm. Therefore, this paper proposes to extract the connected regions in the natural scene pictures by applying MSER, and then uses the convolutional neural network (CNN) to train a high-performance text classifier to classify the extracted connected regions, and finally obtaining the final detection results. This paper evaluates the proposed method on the CSIMU-MTR dataset established by the School of Computer Science, Inner Mongolia University. The recall rate is 0.75, the accuracy rate is 0.83, and the F-score is 0.79, which is significantly higher than the previous method. It shows the effectiveness of the proposed Mongolian text detection method for natural scenes.

Relaying is a common application of D2D communication, which optimizes system capacity and increases the coverage of mobile cellular networks on shared downlink resources. We established a network model of cellular base-stations and adopted the theory of stochastic geometry. Based on the model, the coverage probability analysis of the network is analyzed to select a specific user as the relay node, and the relay point uses the forwarding strategy of the decoding and forwarding. Subsequently, D2D communication can help the edge user to communicate with the base-station. The coverage probability expression of the downlink cellular network is defined, then the coverage probability of the cellular link, the base-station to the relay link, and the relay to the edge user link are derived. Simulation results show that with the increasing of density of the macro base-stations, the coverage probability of the whole network will increase and the final coverage probability will become saturated.

A time series analysis method for spacecraft telemetry data is presented in this paper. For spacecraft testing and on-orbit flight, this method can monitor the changes of telemetry data automatically and identify the failure modes of spacecraft. Using dynamic time warping (DTW) algorithm, combining historical data samples as well as fault cases with this method analyzes the similarity of telemetry data transformed into time series. By comparing the results of analysis with the results of DTW distance calculation, the relative deviation of data is measured and the abnormal data in fault mode is identified. The results show that the telemetry data analysis method based on DTW algorithm can effectively detect data anomalies and realize fault identification, which has a certain application prospect.

Considering the lack of three-dimensional localization scheme for spacecraft internal, a joint TDOA/AOA three-dimensional localization algorithm based on Wireless Sensor Network (WSN) is proposed in this paper. WSN is deployed in the spacecraft which is composed of reference nodes and unknown nodes, and the reference nodes’ position are known which help to locate the unknown nodes. Only six reference nodes are enough for the proposed method to localize all the unknown nodes within the WSN in three-dimension theoretically, and the synchronization of the network is not necessary, satisfying the low complexity requirement of the WSN. TDOA (Time Difference of Arrival) is adopted to estimate AOA (Angle of Arrival), and the angle is estimated by the hierarchical deployment of the reference nodes by which the complicated antenna arrays for AOA are avoided. A three dimensional coordinate is established by setting the plane of the reference nodes as plane XOY and the z coordinate is computed according to the angle estimated by the AOA. Finally, the unknown node is projected on the plane XOY, and the x coordinate and y coordinate are computed by trilateration localization tragedy.

An idea for lunar surface environment exploration system by using WSN (wireless sensor network) is proposed for long-term unmanned monitoring, and the large temperature difference between day and night, the loose soil structure of lunar surface and the space radiation intensity are considered. The system is composed of WSN, relay satellite of lunar, relay satellite of earth and earth station. An energy-balanced routing protocol is proposed to prolong the network lifetime. The communication protocol stack for lunar surface, lunar relay satellite, earth relay satellite and earth station is designed. The earth-moon communication technique based on relay satellite is proposed to guarantee real-time data transmission. Compared with the traditional technique, the idea proposed in this paper has advantages as: more detecting objects, larger detection range, longer detection time, higher reliability and lower costs.

The space station mission faces the data interaction requirements between the space station and multiple extravehicular astronauts. The traditional wireless communication mode with constant transmitting power will cause the interference and incompatibility of communication due to the different positions of the extravehicular astronauts. In order to ensure the communication link stability of all extravehicular astronauts, an automatic power control method is proposed. The extravehicular communication device located in the space station receives the real-time data of all extravehicular astronauts, and the signal to noise ratio is estimated. According to the evaluation results, the power is automatically controlled by the two ways of outer loop and inner loop. Finally, the signal to noise ratio of all the astronauts received by the extravehicular communication device is the same, ensuring the quality of extravehicular communication. The method is verified by building the testbed and carrying out experiment, and the result shows that the multiple signal to noise ratio received is almost the same, and the reliability for multiple extravehicular activity is improved.

Considering the large-scale of manned spacecraft and the increasing scope of EVA, a full-range and anti-multipath communications method for EVA is proposed to solve the problem of low coverage and severe multipath effect which cannot be solved by traditional method. Multiple antennas are evenly distributed around the manned spacecraft to ensure the full communication coverage of EVA. FDD (Frequency Division Dual) is adopted and different frequency is assigned to the forward link and backward link respectively. DS-CDMA (Direct-Sequence Code Division Multiple Access) is applied. Diverse spreading codes are distributed to each astronaut of EVA, and the problem of EVA communication interference for multiple astronauts is solved. In order to weaken the multipath effect brought by shield and reflection of manned spacecraft, a communication method by combination is proposed. Time diversity technique is applied that manned spacecraft transmits the forward message through multiple antennas in time staggered mode, and the astronaut of EVA is searching the maximum point in limited time by correlation of sliding window. The rest peaks are found near the original one, and the maximum ratio combining is carried out by the judge of peak value. Space diversity technique is also used that manned spacecraft receives the backward information of astronauts by multiple antennas, and all the peaks are found by the correlation through sliding window. The maximum ratio combining is implemented by the estimation. Simulation is made, and the result shows that by whole-scope communications method for EVA, the signal to noise ratio can be reduced 1–4 dB to realize the BER (Bit Error Rate) of 10–5 comparing with other methods, and it realize the full-range of EVA communication without interruption.

Visual indoor positioning has a wide application because of its good positioning performance without additional hardware requirement. However, as the indoor scenes and complexity increase, the offline database will inevitably become large and the online retrieval time will also become long, which make visual indoor positioning unpractical. To solve this problem, we propose a Semantic and Content-Based Image Retrieval (SCBIR) method. By dividing the offline database into semantic databases with different semantic types, the retrieval scope of the image is reduced, and the retrieval time is reduced. First, we use the semantic segmentation method to detect the semantics. Then we divide different semantic scenes in terms of the image order and basic pattern of the semantics in the scene. Finally we use the images belonging to each different semantic scene to build a semantic database, so as to achieve online accurate and fast image retrieval. The experiment results indicate that the proposed method is suitable for large scale retrieval database, and it can reduce the retrieval time in the online stage on the premise of ensuring the accuracy of image retrieval that is critical for visual indoor positioning.

In this paper, we proposed a channel estimation scheme for an off-grid massive MIMO channel model, with the consideration of carrier frequency offset at the BS antenna array. We first developed an off-grid channel model for the spatial sample mismatching problem. Then, an EM based sparse Bayesian learning framework was built to capture the model parameters, i.e., the off-grid bias and the CFO. While in the learning process, a damped generalized approximate message passing algorithm was introduced to obtain accurate needed posterior statistics. Finally, simulation results are exhibited to certify the performance of our proposed scheme.

Using a thermal-vacuum test and Monte Carlo simulation analysis, this paper examined the key technical performance parameters of the carbon-fiber heating cage and compared them with those of the traditional nickel-chromium alloy heating cage. The results indicated that the heating capacity and temperature uniformity of the carbon-fiber heating cage for spacecraft were better than those of the traditional nickel-chromium alloy heating cage, and that the electro-thermal properties of the carbon-fiber infrared heating cage met the requirements of the spacecraft thermal-vacuum environment.

Aiming at the problem that it is difficult to realize zero voltage and zero current switching (ZVZCS) in the current switching power supply, the resonance energy of the lagging arm is insufficient. In this paper, with TMS320F2812 as the control core, the DC/DC part of the switching power supply is designed by using the PWM phase shift control full-bridge ZVZCS technology in the series diode of the hysteresis arm of the converter circuit. The soft switch is well implemented in the case of load change. The MATLAB simulation results show that the soft switching power supply has the advantages of high output precision, fast dynamic response and small overshoot.

In monostatic Multi-Input Multi-Output (MIMO) radar system, Direction Of Arrival (DOA) estimation is important for target detection. However, conventional MIMO DOA estimation approaches suffers from the off-grid issue which refers that the real DOAs deviate from the predefined grid points. In this paper, a grid adaptive DOA estimation method is proposed to address the off-grid error and the improper initial grid problem for monostatic MIMO radar system. We construct a Bayesian learning framework with Laplacian prior to adjust grid and observation dictionary adaptively. Simulation results show the superior performance of the proposed method in terms of high angle resolution and robustness against the noise by comparing with the state-of-the-art DOA estimation methods in MIMO radar system.

Person re-identification (re-ID) has attracted tremendous attention in the field of computer vision, especially in intelligent visual surveillance (IVS). The propose of re-ID is to retrieval the interest person across different cameras. There are still lots of challenges and difficulties that are the same appearance such as clothes, the lens distance, various poses and different shooting angles, all of which influence the performance of re-ID. In this paper, we propose a novel architecture, called global deep convolutional network (GDCN), which applies classical convolutional network as the backbone network and calculates the similarity between query and gallery. We evaluate the proposed GDCN on three large-scale public datasets: Market-1501 by 92.72% in Rank-1 and 88.86% in mAP, CHUK03 by 60.78% in Rank-1 and 62.47% in mAP, DukeMTMC-re-ID by 82.22% in Rank-1 and 77.99% in mAP, respectively. Besides, we compare the experimental results with previous work to verify the state-of-art performance of the proposed method that is implemented by NVIDIA Ge-Force GTX 1080Ti.

Millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) has been regarded as an attractive solution for the next generation of communications. Restricted by the hardware and energy consumption, a hybrid analog and digital precoding structure is widely adopted. However, high-computational complexity is the fundamental restrictions of the most existing hybrid precoding schemes. To overcome these limitations, this paper proposes a high-performance hybrid precoding algorithm for partially-connected mmWave MIMO systems. Due to the special partially-connected structure, we decompose the analog precoding problem into a series of optimization problems. For each subproblem, we use the method of phase extraction to optimize one column of analog precoding matrix. Then the digital precoding matrix is obtained based on the least square algorithm. Simulation results verify that the proposed algorithm outperforms the existing ones.

Based on the research of multi-sensor fusion tracking, combined with the working characteristics of warning radar and secondary radar, this paper pro-poses a point fusion and track fusion structure suitable for its engineering application and a specific fusion process. The track fusion algorithm proposed in this paper not only approaches the point fusion algorithm in tracking accuracy, but also retains the advantages of distributed fusion structure and has broad application prospects. The effectiveness and superiority of the algorithm are verified by related simulations.

Directional modulation (DM) has been applied to linear antenna arrays to increase security of signal transmission. However, only the azimuth angle is considered in the design, due to inherent limitation of the linear array structure, since linear antenna array lacks the ability to scan in the three dimensional (3-D) space. To solve the problem, planar antenna arrays are introduced in the design, where both the elevation angle and azimuth angle are considered. Moreover, a magnitude constraint for weight coefficients is introduced. Design examples are provided to verify the effectiveness of the proposed design.

In this work, we propose a low-overhead characteristic learning mechanism for the time-varying massive MIMO channels. Specially, we exploit the common sparsity and temporal correlation of the channel. Firstly, using VCR and modeling the temporal correlation as an autoregressive process, we formulate the dynamic massive MIMO channel as a sparse signal model. Then, an expectation maximization (EM) based sparse Bayesian learning (SBL) framework is developed to learn model parameters. To achieve the posteriors of model parameters, approximate message passing (AMP) is utilized in the expectation step. Finally, we demonstrate the performance through numerical simulations.

Electromagnetic interference causes metrological performance degradation of intelligent flow meters due to the electronic components. Therefore, the electromagnetic compatibility (EMC) tests are particularly important to evaluate the performance of flow meters under interferences. Relative test methods have been presented in some works. This paper proposes a kind of on-line EMC test system for liquid flow meters. By using a compact liquid flowrate standard facility, the system can realize the actual flow calibration under electromagnetic interference. Besides, simplicity is another advantage of the system proposed in this paper. Finally, contrast experiments are carried out which reveal that the system has a clear advantage that the variation of the metrological performance of flow meters can be measured during electromagnetic interference.

Six-degree of freedom (6DOF) parallel robot for space mirrors positioning is one of the effective way to adjust position and attitude of the space mirrors and improve the image quality of space camera. In order to realize the 3D simulation of Kinematic for the space mirrors positioning 6DOF robot, this paper construct the 6DOF kinematic model and algorithms, and then the Human-Computer interaction interface is programmed based on MFC frameworks and 3D simulation interface is achieved based on OpenGL. The experimental results show that the simulation system can display the movement of the space mirrors positioning 6DOF robot precisely and verify the dynamics algorithms with a friendly interface.

Grid mismatch is the main drawback in grid-based sparse representation. For DOA estimation, off-grid problem degrades the accuracy of angle estimation. In order to solve this problem, a dictionary learning-based off-grid DOA estimation method is proposed. Firstly, we calculate the sampling covariance matrix, then based on covariance matrix model, we formulate the DOA estimation as a sparse representation problem with Khatri-Rao product dictionary. In the proposed method, two stage iteration strategy is utilized to address the off-grid problem. In the first stage, the coarse estimation is attained by the grid-based sparse DOA estimation; in the second stage, the dictionary perturbation parameter is learned based on gradient descent method for improving the accuracy of DOA estimation. Simulation results verify the effectiveness of the proposed method.

The electromagnetic environment of radar operation is increasingly complex, and active interference will have a great impact on radar performance. Side-lobe cancellation technology is an effective means to eliminate interference by auxiliary antennas. This paper introduces an adaptive beamforming algorithm to form the cancellation weight based on the secondary antenna. The weight convergence speed of several algorithms is analyzed, and the cancellation ability is analyzed, and a normalized least mean square algorithm is proposed.

This paper investigates the spectral efficiency (SE) of downlink massive MIMO systems, where we consider the Ricean fading channels and utilize the zero forcing precoder at the base state. An exact expression for the SE is derived and the tight lower and upper bounds are presented by utilizing the modified Jensen’s inequality. Our results show that as the number of transmit antennas grows to infinite or in the high signal-to-noise ratios regime, the lower and upper bound coincide, which are approximately equal to the exact expression for the spectral efficiency of system. In addition, we reduce the Ricean fading channels to the Rayleigh fading case, a tractable lower bound of SE is obtained, which is shown that our results cover a series of previous works as special cases. Finally, numerical results are presented to validate the theoretical analysis.

In this paper, an algorithm for removing outliers is proposed for low SNR signals. Firstly, the coarse separation of signals is performed by using the isolated point removal algorithm based on Euclidean distance, and then the coarsely separated data is finely separated by the LOF algorithm based on density detection. The remaining signal data after fine separation is clustered. Through simulation analysis, the algorithm can remove all isolated points at the cost of useful signal loss at low SNR, and the residual signal clustering effect is better.

The LED reflector of whole-reflection shadowless illumination was designed by flux compensation method. The theory of the Geometric optics and the Non-imaging optics were used in the design process of the reflectors. Based on LED’s characteristics, this reflector can achieve one uniform illumination spot at 1 m whose diameter is 200 mm. The illuminance is greater than 100,000 lx. The shadowless rate is also studied if there are occlusions. This reflector can meet the special requirements of shadowless lighting or signal transmission and coupling.

It is difficult to analyze and detect wideband Chirp interference signals, since the existing algorithms are constrained by hardware performance. Aiming at this problem, an anti-interference communication algorithm based on wideband spectrum sensing is proposed. Firstly, the signal is represented as sparse signal by discrete fractional Fourier transform (DFRFT), and Gaussian observation matrix is applied to measure the sparse signal. Then, the signal reconstruction is realized under the Bayesian framework. Finally, the frequency domain information entropy is utilized to make spectrum judgment of the signal, and non-interference frequency band is used for communication, so as to ensure safe and reliable transmission of information. The simulation results demonstrate that, in the case of less measurement data and low signal-to-noise ratio (SNR), the proposed algorithm achieves higher accuracy of signal reconstruction and better detection performance compared with the Bayes compressive sensing energy detection algorithm.

The performance of Multi-task compressed sensing for streaming signals is restricted by blocking effects caused by block sparse transformation. To solve this problem, a multi-task dynamic compressed sensing algorithm based on sparse Bayesian learning is proposed in this paper, which combines multi-task compressed sensing with sliding window based on LOT transform. Experiments show that the proposed algorithm has higher reconstruction accuracy and operation efficiency compared with its block DCT based version.

At present, most airborne radars have no volume scan capability, so the echo information detected is limited and it can be difficult to detect the thunderstorms in front of the aircraft completely. First of all, this paper proposes an airborne weather radar that adopts volume scan mode and takes the X-band ground-based weather radar data as the simulation source to obtain the airborne radar reflectivity volume scan data according to a simulation model. Then, based on the Storm Cell Identification (SCI) algorithm, this paper researches and proposes a thunderstorm identification algorithm applying to this airborne radar by modifying some threshold parameters, which has improvements on identifying thunderstorm cells. Finally, an example of thunderstorm identification based on the simulated airborne weather radar reflectivity volume scan data is given, which shows that the algorithm can effectively identify the thunderstorm cells in the scanning sector in front of the radar and get their attributes. It is helpful for monitoring thunderstorm and meaningful for flight safety.

As there is a high tendency of falling in the independent living of the elderly and the post-fall injury is very serious. It is necessary to get timely assistance when they fall. The main objective of this work is to build an FPGA-based hardware implementation of video-based fall detection system. First of all, the moving object model will be extracted through background subtraction based on Gaussian Mixture Models (GMM). Second, we judge whether there is a fall through the aspect ratio, the effective area ratio, and the change in the center of the human body. Finally, if the old person falls, the detection system will sound-light alarm and send message to the elderly family and community through GSM. The experimental results demonstrate the accuracy of this fall detection system is up to 95% indoor and this system satisfices the requirement of real-time.

Information security of Internet of Vehicles (IoV) has attracted much attention in recent years. In view of security vulnerabilities existed in automobiles, many countries launch guidelines and cybersecurity standards concerning IoV security and plenty of new techniques have been applied to combat threats. In this paper, a variety of attacks on IoV are summarized and classified, then artificial intelligence and game theory based security countermeasures for IoV are highlighted, and their protection mechanisms are illustrated. Finally, a few application cases of artificial intelligence and game theory based security strategies for IoV is analyzed, aiming to provide helpful reference for the development of IoV security techniques.

With recent advances in high-throughput sequencing, reading the human genome is not an arduous task anymore. The extensive collection of different types of omics data and possible causal relations between them have led the scientists to exploit specialized machine learning methods such as deep learning and perform integrative analysis of multi-source datasets. In this paper, we compare the performance of both generative and discriminative deep models based on their integration stage. First, we explain the architecture and mathematical point of view of these methods. Then we evaluate the performances of different models by applying them on two sets of cancer-related data to discover the effect of the integration stage on classification accuracy.

First introduced the working principle of pseudo-code ranging for aerospace spread spectrum ranging system. An advanced method of spread spectrum ranging based on on-orbit automatic correction is proposed. The ranging error is analyzed, and the measured data is used to verify the effectiveness of the method.

Learning Automata (LA) is an adaptive decision-making unit under the reinforcement learning category. It can learn the randomness of the environment by interacting with it and adaptively adjust its behavior to maximize its long-term benefits from the environment. This learning behavior reflects the strong optimization ability of the learning automaton. Therefor LA has been applied in many fields. However, the commonly used estimators in previous LA algorithms have problems such as cold start, and the initialization process can also affect the performance of the estimator. So, in this paper, we improve these two weaknesses by changing the maximum likelihood estimator to a confidence interval estimator, using Bayesian initialization parameters and proposes a new update strategy. Our algorithm is named as weight-assignment last-position elimination-based learning automata (WLELA). Simulation experiments show that the algorithm has higher accuracy and has the fastest convergence speed than various classical algorithms.

The Bayesian probabilistic observation model is established by using the interactive input of multi-system observation data. The positioning information between multi-system is directly interacted. The non-linear problem of the observation system is solved by the extended Kalman filter theory. Moreover, the system probability is updated in real time by using the filtering innovation and variance of each system, and the estimated results are fused with each weight to output. The simulation results show that the proposed algorithm has better stability and adaptability than the traditional location algorithm under the same observation conditions.

Owing to many advantages such as low losses in the feeder, high power handling capability, and high efficiency, waveguide slot antenna array has been widely used. However, the bandwidth of this kind of antenna is very limited. In this paper, 3 dB couplers are inserted to enhance the bandwidth of the waveguide slot antenna. To verify the validity of the bandwidth enhancement technique, A waveguide slot antenna array working at Ka band is designed, fabricated, and measured. Good agreement is found between the simulated and measured results, and the results show that the bandwidth is enhanced to 2.3 GHz (6.7%), which makes it suitable for satellite communication systems.

Turbulence is very hazardous to the flight safety, which generally can be detected by airborne weather radar. In newly specification DO-220A revised by Radio Technical Commission for Aeronautics (RTCA), standards of enhanced turbulence detection with airborne weather radar have been complemented. In the specification, it is stated that the characteristics of aircrafts should be taken into account in the turbulence detecting process. The aircraft response following a turbulence encounter is analysed in this paper, and then the characteristics of aircrafts are quantified by employing the load factor. Based on the quantified analysis, the vertical load factor is predicted based on both radar observation and the characteristics of aircraft. It can provide more accurate turbulence metrics for crews involved with different aircraft types. The simulation results demonstrate that the vertical load factor based turbulence detection process meets requirements of DO-220A. Furthermore, the research is important for the study of enhanced turbulence detection specifications documented in DO-220A.

This paper selects the precipitation data of three precipitation processes on July 2, July 8 and July 11 of 2018 obtained from the two-dimensional video disdrometer (2DVD) of Chengdu University of Information Technology (CUIT). By counting the raindrop size distribution, calculating the total particle density and the median volume diameter during the sampling time to analyze the change of the raindrop spectrum during the precipitation process, and then calculating the precipitation intensity and the radar reflectivity factor during the sampling time. Combining the above related parameters for analysis, the following conclusions are obtained: The three precipitation processes are mainly composed of small raindrops with a diameter of 0.1–1 mm; Unstable precipitation will lead to a large change in the total particle density and median volume diameter, and the total particle density will change by 2 orders of magnitude, and the median volume diameter will vary by 1 mm.

The influence on DPD with memory effect in frequency hopping communication system is analysed in this paper based on the theory of hopping communication and digital predistortion (DPD). An experiment system is designed and applied based on the theory analysis, and the test data is then analysed. The analysis results show that the memory effect of the power amplifier will cause a short time nonlinear distortion during the frequency hop. However, the memory effect of the PA will not create new frequency spectrum in the entire communication band. As a result, it is not necessary to consider the influence of the memory effect in hopping communication system and only the current frequency signal should be sampled in the DPD linearization modelling.

As a critical digital signal processing method, finite impulse response (FIR) digital filter is widely used in radar signal processing, synthetic aperture radar (SAR) signal processing, etc. Furthermore, an efficient FIR hardware implementation contributes to the practical application of these processing. However, as a computation-intensive operation, the multiple high order FIR digital filter consumes a lot of hardware resources when implemented in commonly used chips such as field-programmable gate array (FPGA). In this paper, a reconfigurable FIR digital filter architecture is presented, which can perform different order FIR filtering operation without FPGA re-programming. In the experiment, the proposed FIR digital filter architecture was implemented and validated on the Xilinx Zedboard Evaluation Kit. The experimental results demonstrate that this design has a low consumption of hardware resources and can achieve real-time processing performance for digital signal processing in the practical applications.

Spatiotemporal datum can provide geometric information and spatiotemporal distribution information of geographic space, and enable satellite users to master detailed spatiotemporal situation of spatial geography. In this paper, from the perspective of ground observation, considering the influence of several spatial factors, the high-precision spatiotemporal reference model including the description of the spatial reference system and its transformation model and the high-precision spatiotemporal reference model including the description of time reference system and time system, the transformation of coordinate system and the method design of astrometry correction are derived. This method can provide geometric information and spatial and temporal distribution information of high-precision geographic space and lay a theoretical foundation for subsequent space applications.

In this study, two types of in-body sensor antennas, which were designed for intelligent health monitoring systems, are studied and discussed. The impendence matching of two types of in-body sensor antennas are investigated. The transmission characteristics of in-body sensor antennas are explored. The traits of these two types of in-body sensor antennas are summarized. And the application range of these in-body sensor antennas is also proposed.

In view of the serious consequences of Slamming Loads on ships at high speed, a fiber Bragg grating acceleration sensor for measuring the Slamming Loads on bows is designed in this paper. It is mainly composed of the sensor shell, the sensor shell cover, the accelerometer sensitive devices and the hinge structure, which realize the automatic monitoring of the Slamming Loads on bows. The experimental results show that the sensitivity of the sensor is 295 pm/g in the frequency range of 0–100 Hz.

To improve device-free people recognition resolution by using WiFi signal, we research the composite method of WiFi preamble and radar chirp signal, and present an indoor designated area device-free people recognition trial using the composite preamble in this paper. We carry out a different heights human recognition with random position in a small designated indoor area, based on finite-difference-time-domain (FDTD) calculation. The simulations for 802.11a show that recognition resolution is improved and is better than original WiFi preamble signal. Meanwhile the attainable accuracy is about 94% in our test. The given method may be applied to device-free people queue and product management in shopping center, such as further distinguish adults and children, as well as toilet fall detection and other health monitoring fields in a private scenario.

A low noise amplifier (LNA) is designed, which can work stably at 2.45 GHz frequency. The noise figure (NF) is less than 1 dB and the transmission gain is greater than 14 dB. ATF54143 chip from Agilent is the core part of this LNA. ADS simulation software is utilized to analyze the noise figure and scattering parameter (S-parameter) and design the bias circuit combined with stabilization of the amplifier during the whole process. The inductance of transistor source in the schematic diagram is replaced by a short-circuit microstrip line. With the addition of negative feedback, the optimal design of stability and parameters in the circuit is completed. The circuit module is manufactured according to PCB layout afterwards. The test data illustrate that the actual parameters of the LNA satisfy the design requirements.

GPS is difficult to solve the problem of positioning in large indoor places such as stores and warehouses, so using WiFi for positioning has become the mainstream indoor positioning method. However, most of the location fingerprint methods used in WIFI positioning have some shortcomings, such as low fault tolerance and weak anti-noise ability. To solve these problems, a WIFI indoor positioning method based on Kalman filter is proposed. After obtaining RSSI values, the optimal distance is estimated by Kalman filter, and then the optimal position is calculated by triangular centroid method, Kalman filter is adopted to calculate the optimal value finally.

The topological structure of the spatial information network changes drastically, the resources on the space are limited, and the communication delay is long which bring great challenges to the construction of the spatial information network. In the analysis of the existing satellite network routing model, based on the DTN protocol, a low-orbit satellite network routing model on account of the price game is proposed based on the characteristics of satellite network. The model uses the price game to save the remaining storage space. The success of data forwarding is an important evidence for the routing node status as a satellite network for routing, which enhances the practicability of the routing algorithm. Simulation experiments show that compared with classical models such as Epidemic and FC, the proposed routing model has better performance in terms of delay, success rate, network overhead, etc., and the comprehensive performance is better.

In order to solve the high precision and high time efficiency TDOA and FDOA estimation of communication signal in multi-station location system, we proposed an estimation algorithm based on fine classification and combination. Through the design of different fine classification series number, the algorithm can reduce the operation time effectively, and the estimation accuracy of TDOA and FDOA are developed by adopting the quadratic surface fitting. Simulation results show that when the carrier to noise ratio(CNR) is −5 to 5 dB, the estimation accuracy of TDOA is 20–100 ns, the estimation accuracy of FDOA is 45–100 MHz, which are improved by 3–4 times compared with the traditional estimation algorithm. Meanwhile, the operation time is reduced by more than one half according to the different fine classification series.

An Adaptive DFT-Based channel estimation method for MIMO-OFDM system is proposed. It enhances the precision of channel estimation, improves the demodulation performance of the receiver, and reduces the bit error rate. The results of experiments show that the proposed method has more excellent estimation performance, and Adapts to different channel conditions and SNR conditions.

This paper proposes a novel image restoration method based on non-local total variation (TV). Firstly, the image is divided into two types of regions by the gradient L0 norm. The one regularized by the local TV term contains edges and flat regions, the other regularized by the non-local TV term contains rich image details. Then, in order to simplify complex numerical algorithms, we adopt the alternating direction method of multipliers (ADMM) algorithm to optimize the object function. Finally, we carry out comparative experiments with several recent state-of-the-art methods to verify the performance of the proposed method. Experimental results show that the proposed method has better performance in the efficiency and get a good balance between balance between easing staircase effects and retaining image details.

With the advent of 5G era, the research on the 5G has gradually become a hot topic in the world. This paper analyzes interference coexistence between the 5G and the EESS (Earth Exploration Satellite Service) system from 24.25 to 27.5 GHz, and gets the aggregate interference power in different areas. In addition, this paper has obtained the relationship between interference power and protection distance in urban area when the antenna of earth station is at different off-axis angles. According to the criterion of protection and the simulation results, the minimum protection distance for the coexistence of the two systems in the same frequency band can be calculated.

Directional modulation (DM) has been applied to sparse linear antenna arrays to increase security of signal transmission. In this work, we extend the DM design to sparse planar antenna arrays and provide the corresponding design formulations. In previous studies, group sparsity technique was used for sparse antenna array design, but no quantitative analyses were given. In this paper, both designs with and without group sparsity are provided, and the corresponding optimised antenna locations are shown explicitly. Design examples are provided to verify the effectiveness of the proposed design.

To the demand of accurate time delay (TD) for digital broadband signal, the equal-interval fractional delay (EIFD) filter and its linear interpolation method are studied. Based on the theory of multi-rate signal processing, the design method of EIFD filter is proposed, and the principle and the implementation structure are demonstrated. The EIFD falls on a number of delay grids, which means approximation to the general FD to the delay grids. Multiple groups of the EIFD filter are interpolated to form the final FD filter, based on the relationship between the required TD and the EIFD, to enhance the accuracy of the TD. It shows that the linear interpolation of EIFD filter can provide accurate TD for digital broadband signal, given a simulation test on the linear frequency modulation signal.

Aiming at the problem of low contrast and unclear edge of gray image in hyperspectral transmission imaging, the single-channel grayscale processing algorithm was applied to the simulated image based on the simulation experiment. The experiment shows that this algorithm improves the contrast to a certain extent and enhances the image edge and grayscale image quality. While improving the quality of grayscale images, this algorithm also triples the number of original images, providing a data enhancement method for heterogeneity detection using deep learning. Therefore, this experiment verifies the feasibility of the single-channel processing algorithm and may provide a method for multispectral transmission biological tissue images, it may be a data enhancement method that can be applied to deep learning for tissue image detection.

An efficient handwriting numerals recognition structure based on Convolutional Neural Network (CNN) with RMSProp optimizer algorithm and Adam optimizer algorithm is presented in this paper. The experiment is implemented on NIVIDIA’s Jetson Nano platform, where we compare the performance of CNN models with two different optimizer algorithms. Experimental results show that the training accuracy of the model using the Adam optimization algorithm is better than that of the model with the RMSProp optimization algorithm. The training accuracy is 98.25%. Adam algorithm has fast convergence speed and RMSProp algorithm.

Image recognition technology is becoming more and more widely used and is getting closer to people’s lives. This article applies the Jetson Nano embedded system and use the ImageNet dataset as a training set. Image recognition is implemented on the TensorFlow platform using Soft Max regression algorithm, and add CNN to improve the recognition accuracy.

Three dimensional (3-D) wireless localization is a significant technology for wireless networks. The two key factors of improving the accuracy of localization are the locations of beacons and the improvement of precision. Aimed at these problems, we propose a precise 3-D wireless localization technique based on the arrival angle Ranging (AOA) by smart antenna. In concrete, we use the linearization approach by Taylor-series expansions estimation for 3-D positioning to get the initial positions. Then we propose to use Precision-Weighted Aggregation to define the final estimated position of the object from the mean of the initial position estimates or the centroid of a polygon formed by those points. Simulation results show that the equilateral triangle system formed by the selected beacons is optimal among all systems with the minimum positioning error, and the average positioning error is about 0.15 m.

Considering the high complexity of fault diagnosis of heterogeneous wireless networks (HNNs), we propose a two-phase fault diagnosis algorithm based on convolutional neural network (CNN), which includes monitoring phase and diagnosis stage. In monitoring phase, based on the analysis of the causes of failures of HNNs, feature selection is used to select network parameters that have a great influence on network nodes. Then the timing distribution characteristics of network parameters are monitored and suspected faults can be diagnosed. Once suspected faults are diagnosed and the second stage diagnosis program will be triggered, the Operation Administration and Maintenance (OAM) system will request the detailed network KPIs data of neighboring base stations to provide more comprehensive diagnostic information. And CNN is used to confirm and locate the faults. Simulation results show that this proposed algorithm has good performances on short diagnostic delay, high fault recall ratio and precision ratio.

In recent years, wireless power transfer technology has been recognized by users for its unique advantages such as convenience and distance transfer. It has been popularized in life and developed rapidly. The paper designs a wireless power charging system with two kinds of charging modes. The system mainly includes an electricity grid charging module, a solar energy charging module, and a power switching device of two charging modes. Through experimental analysis, the system can realize the switching of two power supply modes and the wireless charging function, which has certain practical application value, and verify the wireless transfer characteristics through experiments. Finally, on the design and experiment of the wireless energy transfer system, the system characteristics are summarized. The development of the system is expected to be useful in real world applications.

In this paper, a fiber grating stress sensor for measuring the local strength of the hull is introduced. The optical fiber stuck between the fixed plates on both sides of the sensor is deformed when the part of the hull in which the sensor is mounted is subjected to an external force, causing the wavelength of the fiber grating to change. The local intensity variation of the hull will be solved by the wavelength variation of the fiber grating. The meter shows that the deformation of the fiber grating is linear with the wavelength change of the fiber grating. To this end, the compensation algorithm of the demodulator can change the stress of the hull structure and the wavelength of the fiber grating. Therefore, the sensor enables real-time monitoring of local stresses in the ship’s structure in harsh marine environments.

For passive bistatic radar based on uncooperative phased array radar, it is necessary to estimate the parameters of direct wave signal to achieve the time and frequency synchronization of the passive bistatic radar system. By using the template library of the radar waveform parameters obtained by the long-term monitoring and analysis of direct wave signals, a method based on template matching is proposed to estimate the direct wave parameters in real time, including carrier frequency, pulse width, bandwidth and time of arrival. The experiment demonstrates the process of direct wave parameters estimation, and the results verify the effectiveness of the method.

Target detection and tracking systems using illuminators of opportunity have received significant interest in the past few years. The passive bistatic radar system under investigation in this paper exploits non-cooperative navigation radar. In order to provide useful surveillance or cueing information, certain data must be collected from the direct-path signal of the illuminator. Detection of aircraft is an important step to demonstrate its potential in remotely surveillance. The PBR illustrates the detection of civil passenger aircrafts in the airspace by receiving a bistatic return when they are illuminated from non-cooperative emitters. The results show that target detections have been achieved from real data. “Air-truth” data obtained by a Mode S ADS-B receiver is used to verify the results of this bistatic system.

Full-link image quality simulation analysis is an important part of the satellite development process. It can not only predict the satellite image quality before launch, but also help to adjust the satellite related design according to the simulation results. This paper expounds the construction idea, system scheme and composition of each subsystem of the full-link image quality simulation system for optical remote sensing satellites and carries out serial full-link closed-loop simulation test on the preliminary simulation system to verify the reasonableness and feasibility of the system scheme. This system is constructed with functional modular design, while the model algorithm has good openness, good adaptability to different tasks and high application value.

The micro-vibration environment of optical remote sensing satellites is one of the main factors affecting the quality of in-orbit imaging and has become a research hotspot in the field of remote sensing. In order to ensure the in-orbit imaging quality of the satellite, this paper analyzes the requirement of the imaging quality on the vibration isolation and suppression of the micro-vibration of the satellite, and adopts the method of measuring the angular displacement of the main optical elements to analyze the impact of the micro-vibration on the imaging quality, analyzes and verifies the impact of the turning on of a certain type of satellite control moment gyroscope (CMG) group on the camera transmission.

As a large component directly exposed to space environment, the solar array suffers from the impact of space debris very significantly. The cumulative effect of space debris will lead to the performance decline of solar cells. The effect of space debris on the decline of output power of the solar array is often accompanied by abnormal attitude. This article takes the decline of output power of the solar array of a medium-high orbit satellite as an example, the main factors affecting the decline of output power of the solar array are analyzed. Based on the analysis of satellite attitude telemetry data and the verification results of simulation experiments, the effect of space debris impact on solar array and its mechanism are analyzed in detail. The influence of space debris on the output power of solar array is highly correlated with the attitude of the satellite, therefore, it is necessary to analyze the output power of solar array and satellite attitude data for guiding the design of solar array, as well as for on-orbit monitoring and early warning.

The existing literatures usually adopt the linear method to obtain the location error which is usually evaluated by geometrical dilution of precision (GDOP) in nonlinear passive location systems. However, the linear method is not always suitable in any condition, and the obtained GDOP may deviate the true values of location errors severely when the location system is of severe nonlinearity. Thus, a new nonlinear method for the calculation of location Error based on unscented transformation (UT) is proposed and verified in this paper.

With the rapid development of Space technology, on-board software plays a more and more important role in the spacecraft. Stack is an important storage resource for on-board software. If the allocation space of stack is not enough, it may cause stack overflow and software crash. Based on SPARC V8 architecture, this paper introduces a static method for detection of stack overflow. This method does not need to run the on-board software dynamically or design complex test cases. By directly analyzing assembly file generated by the compiler, the stack usage space and the call relationship of functions can be obtained. Taking the entry function of each task as the starting point of stack depth analysis, the function call path is traversed by the stack data structure, and the maximum stack depth of each task is finally calculated. An instance of a task stack detection shows that by analyzing the static assembly file, the maximum depth of stack can be obtained directly, the risk of stack overflow can be avoided, and the reliability and security of on-board software can be improved.

For the past few decades, the use of wireless communication devices have exponentially increased and invaded all domains. And the limited amount of spectrum bands can possibly cause an overcrowded or interference in communication between different users. The implementation of Cognitive Radio in the network can successfully deal with the huge demand and the lack of wireless spectrum. Cognitive radio energy detection is a famous method used to sense the state of the primary user PU. In this study, cooperative spectrum sensing together with double threshold have been used. Two thresholds were considered and compared to a test statistic an accordingly a decision regarding the presence and absence of the primary user is made. The simulation results shows that during cooperative sensing the proposed double threshold method performs better than both energy detection and conventional double threshold detection methods, by considering different parameters such as SNR, number of samples, probabilities of detection and false alarm.

Wireless body area networks (WBANs) are key means to provide real-time health detection. If WBAN was deployed that lacks inter-WBAN coordination in dense environment, it will reduce network performance seriously. Therefore, it was crucial to coordinate co-existing WBANs. Based on the idea of link resource allocation, self-generating topology coloring scheduling was proposed in this paper. The experimental results show that self-generating topology coloring scheduling is effective mitigation interference for inter-WBAN. In mobile WBAN scenarios, low interference and high frequency utilization are always maintained.

Parking is becoming a major problem in urban traffic, and intelligent parking system is an effective way to solve this problem. Intelligent parking system requires high accuracy of local information and strong real-time feedback, which have brought challenges to traditional cloud computing. In this case, a fog computing framework based on the idea of “layered processing” is proposed. The fog layer is close to the data source to ensure that requests and information from users and terminals can be quickly processed and responded by system. This paper designs a smart parking system with parking space and road condition information collection, road condition analysis, parking space reservation, road navigation and parking space guidance functions, and introduces two fog calculations in the intelligent parking system that based on the fog calculation framework, and introduces two typical application scenarios: video detection of license plates and parking spaces, driver parking space recommendations based on big data.

In view of the Tacotron Griffin-Lim algorithm in speech synthesis system recovery phase information of the obvious effect of the synthetic speech artificial processing, low protect boomed, this paper proposes a speech synthesis method based on Tacotron + WaveNet network architecture, the method is based on the sequence mapping Seq2Seq structure, first of all, the input text into one—hot vector, and introduces attention mechanism for MEL spectrograms, finally using WaveNet vocoder back-end processing network reconstruct the phase information of speech signal, so as to convert the input text into waveform. The test language of the experiment was LJ-Speech, and the experiment was conducted for English language. The experimental results showed that the average subjective opinion score MOS was 4.23, which was higher than Tacotron end-to-end speech synthesis method in terms of synthesis naturalness.

Spatial domain based steganography is well-known for its high embedding capacity, implement simplicity and computational complexity. However, it is vulnerable against digital image compression. In this paper, a robust spatial data hiding scheme is proposed by quantifying certain pixels with given integer as quantization value. In addition, inspired by reversible data hiding approach, a post-process aimed at recovering stego image is proposed as well. Experimental results show that, when applying various JPEG/JPEG2000 compression, bit error rate (BER) of extracted secret data is controllable which can be revised by error correcting code, moreover PSNR of recovered stego image maintains greater than 35 dB, which is fairly acceptable in human vision system.

With the development of Internet of Thing (IoT), high accuracy positioning is of a significant importance in indoor environments. In this paper we propose Losen, a real-time indoor localization system by integrating MEMS sensors of smartphone and PHY-layer information of WiFi signal. Losen consists of AOA-based localization module, MEMS sensors module and fusion localization module, which solves problem of the initial localization of the existing integrating system. Aim to reduce the time cost of calculating spatial spectrum, Losen employs synthetic aperture radar (SAR), which is used in radar filed, to estimate the AOA of the direct path between the smartphone and AP (access points). Then, Losen uses MEMS sensors to estimate relative location and fuses the location estimated by SAR for mitigating location errors caused by the movement of the person and serious multipath. Losen is a real-time indoor localization system, which fuses PHY-layer information and MEMS sensors. The extensive experiment shows that the proposed integration system achieve the 67% localization accuracy of 1.3 m.

The existing methods of low-resolution radar target recognition are based on feature extraction, which are difficult to improve the recognition rate and lack of generalization. In this paper, a direct target recognition algorithm for low-resolution radar based on focal loss is proposed. The algorithm using Convolutional Neural Network (CNN) automatically to obtain sample data deep essence characteristics, without feature extraction, realize the target recognition directly. In order to further improve the recognition effect under the condition of unbalanced samples, the focal loss function is used to calculate the error. By using focal loss, CNN can focus on the difficult samples in the training process to improve the ability to recognize difficult samples. Experimental results show that, the proposed direct target recognition algorithm for low-resolution radar based on focal loss than traditional based on weighted Support Vector Machine (WSVM) recognition algorithm of recognition rate increased by 7.95%, than CNN recognition algorithm based on cross-entropy loss function recognition rate increased by 5.17%. The experimental results fully demonstrated the effectiveness of the proposed algorithm and the superiority to traditional recognition method based on characteristic.

In this paper, discrete Fourier transform (DFT)-spread OFDM signal is studied and the performance is investigated, which is designed for an intensity-modulation/direct-detection (IM/DD) system. The results show that the peak-to-average power ratio (PAPR) of OFDM could be reduced effectively by applying DFT-spread. As result, the strong nonlinearity tolerance is obtained. The measured BER could meet the requirement of forward-error correction (FEC) limit well.

Aiming at the problem that the signal recovery accuracy of the underdetermined blind source separation algorithm is low, the mixed matrix estimation algorithm is improved by using the sparse characteristics of the signal time-frequency domain. By applying the eigenvalue decomposition detection single source point algorithm based on noise threshold to matrix estimation, instead of the single source point detection algorithm of the real time and real part of the traditional time domain, the signal and noise are connected, and the algorithm is improved. Anti-noise performance; then, The k-means algorithm is used to implement the hybrid matrix estimation. Experiments show that the improved algorithm is more accurate than the traditional algorithm under the same conditions, which is more conducive to subsequent signal separation.

The APTEEN routing protocol has been widely used due to its practicability. But it exists the problems of uneven network energy consumption, premature death of some nodes and low effective coverage of the whole network. To solve these problems, this paper uses the genetic algorithm to optimize the APTEEN routing protocol. By adding residual energy, distance from node to base station, distance from node to geometric center of the whole network, node degree and other selection factors to cluster heads selection, the genetic algorithm is used to select cluster heads for the first time, and the second cluster heads selection based on density adaptive algorithm. Some nodes are selected to sleep according to the position and degree of nodes. The residual energy of cluster head, the distance between node and cluster head, and the number of cluster members are taken into account when nodes join clusters, and the GA-APTEEN routing protocol is obtained through the above optimization. The simulation results show that the GA-APTEEN improves the lifetime, coverage and robustness of the network, reduces the energy consumption of the overall network system and avoids the phenomenon of the hot zone of energy.

APTEEN is a typical routing protocol for wireless sensor networks, but when clustering, cluster heads are randomly selected, which makes it easy to select nodes with low residual energy as cluster heads, thus forming network holes. To solve this problem, this paper uses particle swarm optimization (PSO) to optimize APTEEN routing protocol. When APTEEN routing protocol networking, considering the residual energy of the node, the location of the node and the energy distribution around the node, the particle swarm optimization is used to select the cluster head. The simulation results show that the optimized APTEEN routing protocol significantly prolongs the network lifetime and reduces the network energy consumption rate.

This paper introduces the classification and research status of wireless power transmission technology, analyzes the basic principles, key technologies and application scenarios, and forecasts the future development trend of technology. As a revolutionary progress of energy transmission technology, it will have a profound impact on energy interconnection in the future.

The motion of target rotation entitles different Doppler frequencies for scatterers located in cross-range domain. Due to this fact, we can produce an un-scaled target image by using the technique of inverse synthetic aperture imaging (ISA). However, the rotation also smears the image of the target since it can easily cause unwanted range migration and Doppler migration. This paper presents a new ISAR imaging algorithm based on sparse Bayesian Learning by using sparse probing frequency signals, which can easily solve the problem of range migration caused by target rotation. The source causing range migration is theoretically modeled in the mathematical signal model under sparse representation. Then sparse Bayesian learning is applied to automatically learn the sparse coefficients from the original radar data to form the focused and high resolution target image.

Recently, with the development of nano-technology, molecular communication has become a promising communication paradigm. In molecular communication via diffusion systems, nano-machines utilize the physical (concentration) or chemical (composition and structure) characteristics of message molecules to represent information. In order to restore the original information intended to be transmitted effectively at the receiver, the channel state information is essential, one of which is the relative position between the transmitter and the receiver. Previous studies focused mainly on one-dimensional distance estimation without considering the exact location of nano-machines. In this paper, four novel localization schemes based on trilateration method in a 2-D environment are proposed. The simulation results show that our proposed schemes can effectively locate the target nano-machine even under a low signal-to-noise ratio (SNR) and each scheme makes its own compromise between the accuracy and system complexity.

In order to reduce the calculation amount of the source code estimation algorithm of the Gerschgorin Radii, and improve the accuracy of the source number estimation under the background of low signal to noise ratio, small snapshots and white noise. Feature extraction of the signal and noise received by the antenna array is performed by using the characteristics of the noise vector orthogonal to the array pattern. A classifier based on Support Vector Machine is designed. The structure of the classifier and the related parameters of the optimal classification accuracy are determined by theoretical analysis and actual data testing. The validity and feasibility of the proposed method are verified by simulation data and actual data test.

Unmanned aerial vehicles have been becoming widely used tools along with the developing progresses of robotic, control and energy techniques. As a convenient carrier of payloads and apparatuses for specific missions, the replenishment of battery is significantly important for the using reliability and durability. To overcome the restrictive issue of conventional battery charging dependent on cables, the wireless electricity transmission based on electromagnetic coupling is investigated in this work. The non-contacted charging may enable more powerful facilities of unmanned aerial vehicles, and may further open up new application fields of drone scenarios.

In order to study the fine features and individual recognition of radiation signals, a method of individual recognition of secondary radar radiation source based on ITD method is proposed to solve the problem of poor anti-noise performance in current research work. This method USES the inherent time scale decomposition to describe the unintentional modulation characteristics of the radiation source signal and USES the fast entropy algorithm to measure the difference of the subtle characteristics of different radiation source signals. Support vector machine (SVM) was selected as classifier for classification and recognition. Experiments show that the proposed method can significantly improve the recognition effect and speed.

Vascular segmentation is the basis for medical diagnosis, surgical aid design, etc. The traditional Gaussian mixture model (GMM) can be introduced to well extract the main blood vessels, but the performance on small blood vessels is poor. Fortunately, gray intensity of blood vessels in different regions is different. Therefore, a Gaussian Mixture Model based Multi-region Blood Vessel Segmentation Method is proposed in this paper. Firstly, Nonsubsampled Contourlet (NSCT) transformed is employed to enhance the contrast of image. Secondly, the problem of optimal threshold selection for each region after GMM has been analyzed in detail by experimental method. Finally, adaptive filling filtering is performed on the integrated image to achieve noise reduction. The experimental results show that the proposed method can effectively reduce the missing classification ratio and improves the recall ratio. The proposed method is more suitable for situations where the color distribution is not uniform, or where small blood vessels need to be segmented but the demand of accuracy is low. It will have great significance for medical clinical applications.

In the process of Vehicular ad hoc network (VANET) infrastructure, Roadside Unit (RSU) can facilitate vehicle-to-vehicle (V2V) communications and enable communications between vehicles and the Internet. However, RSUs are expensive and immovable. Once installed on the side of the road, they cannot be moved. Therefore, as traffic flow changes, there always exists a certain amount of RSUs that will either be waste or shortage. Compared with the immovable RSUs, Unmanned Aerial Vehicles (UAVs) possess the advantage of convenient deployment and movement, which can move flexibly based on the changes of traffic flow. In this paper, we study the network coverage enhancement issue for VANETs by using UAVs, which serve as air base stations (BSs) for improving coverage and boosting connectivity. Specifically, the deployment problem is modeled as minimizing the amount of required RSUs and UAVs with the constraint of an enough coverage. A novel joint deployment scheme is proposed for RSUs and UAVs. The simulation results show that our deployment scheme can dynamically adapt to the changing traffic flow, and meanwhile guaranteeing coverage and cutting costs.

In this paper, the Wavelet transform is used to decompose the image into low-frequency components, horizontal components, vertical components, and diagonal components. Then, the low-frequency components are scrambled by constructing index sequences, obtaining the scrambled image by wavelet reconstruction. The chaotic sequence generated by the logistic chaotic system is used to encrypt the scrambled image, and then multiple sequences generated by Qi hyperchaos are used for the second encryption of images. The decryption process is the inverse of encryption. The encryption scheme is simulated on MATLAB, and the security analysis is carried out by key space, key sensitivity, histogram, information entropy, pixel correlation, etc., and compared with other algorithms, it is concluded that the algorithm can be applied to images encryption.

With the development of space technology, the functions of satellites are becoming more and more powerful. The number of intra-satellite telemetry and the requirement of possible inter-satellite telemetry transmission increase the complexity of telemetry acquisition and scheduling. The traditional acquisition mode and PCM telemetry format can no longer meet the increasing telemetry requirements. In this paper, a design scheme of satellite telemetry acquisition system is proposed, which takes the CMU as the core, the distributed network architecture as the system architecture, and follows the TM Space Data Link Protocol. It can realize the telemetry acquisition and scheduling download of complex satellite systems, and has scalability. It can meet the needs of most satellite and satellite systems for telemetry data download.

High-dimensional features have great advantages for the analysis and identification of radio fingerprint features. In order to enhance the classification and recognition capability of frequency hopping stations, it is usually necessary to increase the feature type and dimension to further improve the classification accuracy of the classifier. However, with the increase of the feature types and the dimensions, a large number of irrelevant and redundant features will be introduced, which leads to the increased classification time and the low classification accuracy. In order to reduce the feature dimension and remove redundant features, a FCBF feature selection algorithm based on normalized mutual information was proposed, named FCBF-NMI. The algorithm uses normalized mutual information instead of symmetric uncertainty as the correlation evaluation standard of FCBF algorithm, and analyzes the correlation between features and categories, deletes irrelevant and redundant features, and finally obtains optimal feature subset. Experimental results show that, FCBF-NMI can obtain the reasonable optimal features, on the base of guaranteeing the correct classification rate, the computing time can be reduced, and the effectiveness of feature recognition and the generalization ability of classification algorithms can be improved as well.

An integrated high-speed data injection and emergency measurement and control system is produced in this paper, which is based on forward link of relay satellite. In this system, the relay high-speed forward link is used as the satellite-to-ground transmission channel. The satellite receive the data and record it in the storage module. The data will be transmitted to each device through intra-satellite bus network according to the need of use. Through this system, the high-speed data injection on the ground can be completed, the injection efficiency can be greatly improved, and the requirements of functional maintenance and new additions can be quickly responded. And the ability of backing up TT&C upstream is acquired. When the multiple faults of transponder and remote control module can not be recovered, satellites can still carry out emergency TT&C for satellite through relay forward channel, which improves the survivability of satellite in fault state.

The moving vessels exhibit defocusing and other characteristics in SAR images, which brings great challenges to the target detection in the later stage. Combining spaceborne SAR images and AIS information to perform imaging correction analysis of moving vessels is the basis for efficient and accurate detection and identification of moving targets. Firstly, the performance of the moving target in SAR image is analyzed theoretically, and the correctness of the theoretical model is verified by simulation experiments. Then simulation and correlation analysis of the scene with moving target is conducted, with the combination of AIS information. In the case of a status with multi-vessel and multi-motion, the AIS information is introduced together with the SAR results to correct the moving targets and label missed vessels in the image. The results show that with the combination of AIS information and SAR image, it is able to obtain the relative focused position of the moving vessels without offset according to the motion state of the target, as well as efficiently screen the hidden vessels in the AIS.

In order to solve the practical application problem of the automatic target-scoring system based on computer vision under dynamic interference conditions such as flying catkins, this paper improves the traditional detection and removal methods of frame difference method and mean value method and proposes an effective flying catkins detection and removal algorithm based on time domain and brightness characteristics by fully studying the characteristics of flying catkins in target video and existing rain and snow removal algorithm. Experiments show that this method can effectively solve the problem of missed detection caused by the multiple moving states of flying catkins and realize the rapid flying catkins removal of the target video with good robustness and timeliness.

Discriminative Correlation Filters demonstrate superior capabilities, while still suffering from background clutter. The proposed context-aware correlation filter (CACF) framework effectively avoids the interference of background noise with the explicit incorporation of global context information. However, there is still sequential context information that is not considered. This work proposes a robust context-aware tracking based on hand-crafted features by adding a temporal regularization. The temporal regularization term provides temporal information for learning filter, which limits the mutation of the filter. Experiments on OTB-100 show that our tracker demonstrates excellent accuracy and significantly improves the robustness of CF trackers and those trackers in the CACF framework.

The classification describes common methods for estimating motor speed. Methods completely dependent on the physical parameters of the motor and the electromagnetic equation are easy to implement, but have poor robustness and anti-noise ability. Methods partially dependent on the physical parameters of the motor and the electromagnetic equation are introduced. Simulated and compared Model Reference Adaptive System (MRAS) and Sliding Mode Observer (SMO). Methods independent of the physical motor parameters and electromagnetic equations are introduced. Introduced common artificial intelligence algorithms. The applicability of various algorithms is summarized.

A new user-centered power allocation and interference merging scheme for virtual cells in ultra-dense networks is proposed in this paper, which can reduce system interference and improve system performance. The proposed scheme is divided into two steps. The first step is to allocate power to users in the virtual community to improve the utilization of system resources. In this connection, we propose a proportional power allocation algorithm, which reduces the complexity. The second step is to merge the virtual cells based on their interference strength. Since user access is generally based on RSRP (Reference Signal Received Power) rather than path loss, we use RSRP as the interference merging indicator. The interference merging scheme we proposed presents the interference strength of the virtual cell better. The simulation results show that the proposed proportional power allocation scheme has similar performance to the traditional proportional power allocation. At the same time, compared with the traditional path loss interference merging algorithm, our proposed interference merging scheme shows better performance.

Gesture recognition has been found to be a vital mission for a variety of applications, such as smart surveillance, elder care, virtual reality, advanced user interface, etc. Recently, an emerging sensing technology, namely device-free sensing (DFS), has been introduced to the domain of gesture recognition which only uses radio-frequency (RF) signals without the need to equip any devices or extra hardware support; thus, it would be a natural choice to fully leverage ubiquitous Wi-Fi signals in almost every modern building. Although the feasibility of using this technology for gesture recognition has been explored to some extent, we observe that it still cannot perform promisingly for some gestures which maybe look nearly identical in a certain instant. Therefore, in this paper, we conduct experiments with several typical hand gestures in the opposite direction based on a proposed Auto-Encoder/Decoder (Auto-ED) deep neural network to address gesture recognition in our case. Compared with several traditional learning methods, experimental results demonstrate that our proposed approach can best tackle the challenge of gesture recognition for identical motions, which indicates its potential application values in the near future.

Sleep plays an important role in human life activities, and Obstructive sleep apnea (OSA) is a very important factor. Sleep apnea is a common sleep-related respiratory disease. Polysomnography (PSG) is the gold standard for detecting sleep apnea, but PSG is a contact device, there will be a first night effect, and some people may even be disturbed by long-term incompatibility. This study proposes to extract the cardiopulmonary coupling (CPC) strength based on the Ballistocardiogram (BCG) signal to achieve the purpose of further improving the accuracy of OSA detection. We extract heart rate and breathing through the BCG signal. Then, the time domain features and frequency domain features of the heartbeat interval sequence over a fixed length of time are extracted. The coupling strength of the two signals is further analyzed to generate cardiopulmonary coupling characteristics. A classification model of sleep apnea is used to determine whether sleep apnea occurs within a fixed length of time. And, the accuracy can be further improved by adding the cardiopulmonary coupling feature.

Networked data link system connected by various communication links with space, air and ground nodes could be beneficial to efficient information sharing. This paper studies on an architecture of space-air-ground integrated data link networks, including system architecture, information flow process and protocol structure, which is conductive to data link system design.

With the increasing use of natural language processing, how to build a word vector which contains more semantic information becomes a top priority. Word vector is used to represent the most basic unit in the language-word, and is the basis of the neural natural language processing model. Therefore the quality of word vectors directly affects the performance of various applications. In continuous bag-of-words model, limited by the frequency of occurrence, some words do not get enough training. At the same time, based on the set of minimum frequency, some low-frequency words are ignored by the model. In this paper, we build similar clusters from the semantic dictionary and integrate it into CBOW model with the help of multi-classifier. We improve word vectors and use it to complete a semantic similarity comparison task. Compared with the original word vectors built by CBOW, the method we proposed got higher accuracy. It shows some semantic information are integrated and the word vectors of low–frequency words are improved.

In order to solve the problem of radar communication integration waveform signal separating and occupying more radar resources, the frequency diverse array transmit signal model and its waveform loaded with communication signal are studied and designed. Considering various factors, a frequency diverse array radar communication integrated transmission waveform with linear frequency interval is designed. The random communication signal is modulated to the frequency deviation between the frequency diversity elements, and the linear frequency interval is added between each communication signal. Therefore, the transmit signal can be easily demodulated at the communication receiver without affecting the beam characteristics of the frequency diverse array and the radar target location task.

This paper research on parameter configuration of deep neural network applied on speech enhancement. In recent years, deep learning based methods have become a hot topic in the field of speech enhancement. Parameter selection is important to the performance of deep neural networks and can even play a decisive role. From the perspective of engineering practice, this paper analyzes five basic parameters and their influence when applied on speech enhancement. Through detailed analysis and a large number of engineering experiments, we give a proposal on how to configure parameters of deep networks when doing speech enhancement.

In order to verify the correlation between infrared spectrum characteristics and storage stability of automotive gasoline, this experimental study was carried out. Using the method of 43 ℃ accelerating storage test, the gasoline was carried on the long-term storage test. By means of mid-infrared spectroscopy, quantitative analysis of the integral area of absorption interval on the stability index of gasoline was carried out. The results show that the quality decay model of gasoline established with the integral area of 648–628 cm−1 can better characterize the quality decay state of gasoline during storage.

The middle infrared spectrum characteristics of hydrocarbon compounds and blending components in automotive gasoline were determined by correlation analysis method. By using the method of 43 ℃ accelerating storage test, the gasoline was carried on the long-term storage test, Using the method of moving correlation coefficient, the decay condition of the functional group in the infrared spectrum of gasoline in the process of storage was investigated, and the decay characteristic range was determined.

Digital Predistortion (DPD) is one of the most widely used method for improving linearization of power amplifier (PA). Power amplifier with DPD can also be used to achieve high speed data transmission for micro-nano satellites. Since effectively reducing the cost and power consumption of power amplifier with DPD is very important for micro-nano satellites, the method of undersampling used in DPD is proposed. The undersampling method provides sufficient linearization with less sampling rate for DPD compared with traditional structure. The way of undersampling in the feedback path can dramatically reduce the power consumption and cost of the DPD system. The proposed solution is proved feasible in theory, the simulation results show that the proposed method of DPD system improves the third-order intermodulation of the power amplifier by about 19 dB. And the experimental results show that the proposed method achieves the performance equivalent to that using the conventional method of five times oversampling. The proposed method also makes DPD become practically implementable for micro-nano satellites.

In the realization of navigation constellation, time slot allocation to determine the main reason for the communication time delay, this article is based on double hybrid constellation published a time slot allocation new Settings, for scenario application, found the navigation constellation based on TDMA in unicast and broadcast application scenario, the stability of well beyond the traditional navigation constellation, prove the link between the star really realize TDMA algorithm than traditional optimization of navigation satellite time delay is small, high transmission efficiency, superior performance.

In this paper, an integrated detection and tracking (IDT) algorithm is developed for asynchronous moving radar network. The main idea of IDT is to adjust the threshold of each radar by utilizing the prior track information, while keeping the constant track false alarm rate property. The prior track information we utilized is the predicted distribution of the target location, which can be obtained through jointly estimate radar and target state. Simulation results show that the proposed IDT algorithm can improve the detection and tracking performance significantly, when compared with the existing works.

Once lossless compressed data is damaged in the transmission process, a specific fault-tolerant decompression algorithm is required to correct the error. A novel fault-tolerant decompression method for English text files has been proposed for error detection and correction in previous research work, which is trained with natural language model of English. In this paper, we transfer action scope of this fault-tolerant decompression method from English compressed files to Chinese text files. In order to apply the algorithm framework to Chinese compressed files, a N-Gram-based language model is built adhering to the compression coding rules and the grammar rules of Chinese, with which the prior information of source can be fully expressed, and the character encoding adaptation problem in the process of algorithm transfer-learning is solved. The experiment results demonstrate that the proposed algorithm can meet the requirements for fault-tolerant decompression of lossless compressed Chinese text files.

Ultra-wideband radar signals have broad application prospects in ultra-close-range detection due to its high resolution, strong penetrability and strong anti-interference. Ultra-wideband radar is an ideal detection method in non-contact medical monitoring scenarios. Based on UWB radar, the time domain sliding window feature extraction method and decision tree classification algorithm are used to classify and recognize the human motion state behind the obstacle. The decision tree is generated by inputting the sliding window feature extraction result of the radar echo data into the decision tree algorithm. The experimental results show that the proposed feature extraction algorithm and decision tree algorithm can classify the motion state of the human target behind the obstacle, such as the human walking in the radial direction, walking in the tangential direction, and standing in the ground.

Frequency modulation continuation wave circular synthetic aperture radar (FMCW CSAR) imaging can’t focus imaging,because of the target scattering coefficient to change caused by large synthetic aperture. The sub-aperture imaging algorithm effectively avoids such problems. However, the sub-aperture division needs to be further studied to ensure that it does not appear redundancy or data leakage. In this paper, the principle of sub-aperture division is theoretically deduced, and the expression satisfying the selection of sub-aperture is given. The simulation results show that the image can be well imaged in the sub-aperture range, but beyond the sub-aperture range, it is difficult to achieve good focusing imaging.

This paper proposes a signal processing method of passive bistatic radar (PBR) exploiting a non-cooperative pulse radar as an illuminator. Compared with other opportunity illuminators, the transmitting signal of a radar usually has a better ambiguity function, which leads to a higher range resolution, but there is a problem that radar transmitting signal parameters are unknown, and radar signal processing, such as pulse compression, coherent integration, cannot be conducted accurately. The proposed method estimates radar signal parameters from the direct-path signal, and uses estimated parameters to radar signal processing of the scattered echo. The experiments use the shore-based maritime surveillance radar as a transmitter and moving ships from the seaport as the targets. There is a good agreement between the measured data of the proposed method and the AIS data. Results of AIS verify the correctness of this experiment.

A systematic design of a quasi-real-time communication system for Low Earth Orbit (LEO) satellites is proposed and discussed, which utilized the Beidou Navigation Satellite System (BDS) short-message service. Interfaces of the BDS short message scheme is introduced, followed by the detailed descriptions of system structure and working process. Satellite Tool Kit (STK) software is then used to simulate the performances of the proposed system in two typical test scenarios. Results of available orbit intervals and time delay characteristics for different signal propagation paths are analyzed. Results show that the proposed system can serve the LEO satellite users with 99.9% available orbit intervals of the simulation period, and data can be transmitted to users in less than 17.2 s with 4 hops in the transmission path. The proposed system could find important applications in the design and construction of realistic communication systems for LEO satellites and future satellite networks.

With the exploitation of space technology, low earth orbit (LEO) constellations for global communications are becoming an increasingly attractive research topic. To provide programmability in LEO communications constellations, software defined networking (SDN) is an ideal choice over the traditional distributed IP architecture. In this paper, we addressed an onboard architecture where controllers and switches are both placed on the LEO satellites and supported by two different communication systems, one wide-beam radio frequency (RF) system and one free space optical system, to physically decouple the control plane and the forwarding plane. Simulated annealing algorithm is adopted to further determine the placement of the controllers to optimize the average flow setup time with weighted regional user data traffic rates. The performance of the proposed scheme is verified in an IRIDIUM-alike constellation through computer simulations with failure recovery time and flow setup time compared to other LEO SDN architectures.

To address the problem of detection and tracking for the near-space weak targets under range ambiguity, an improved approach is proposed using Dynamic Programming (DP) within the track-before-detect (TBD) framework. Firstly, the target information is accumulated along the ambiguous tracks by the DP method, which can significantly improve the target SNR and realize the detection of ambiguous tracks. Secondly, all the ambiguous tracks are divided into different matching groups in according to the track information. Finally, target range is unfolded using one-dimensional set algorithm, and the real track can be realized by coordinate transform. Simulation results illustrate the effectiveness of this approach.

This nowadays, the home care system of the Internet of things has become a technological development direction. This article designs a home care system of Internet of things based on Wi-Fi network technology. The CC3200 of TI company is selected as the system chip for collecting terminal nodes. In this paper, temperature acquisition, blood pressure collection, blood oxygen acquisition and electronic circuits are studied and designed. This paper gives the block diagram of the collection of terminal blood oxygen.

The kernel chip of wind pendulum control system is 32-bit STM32F407 micro control unit (MCU). MPU6050 gyroscope is utilized to detect the motion state of the pendulum rod in real time. After collecting and analyzing the motion data, pulse width modulation (PWM) is applied to four axial flow fans. The system can implement the functions of swing-up, stop-swing, drawing a line segment of specified length or deflection angle and drawing a circle of specified radius by laser pointer fixed on the wind pendulum. The design involves many mathematical and control methods, such as PWM control, PID algorithm and power series correction. As a result of the design and manufacture of hardware and the programming and debugging of software, many groups of test data indicate that the system has satisfied functionality and accuracy. Moreover, the system can realize its functions in the case of overcoming interference. The deviation is within the allowable range, which illustrates the robustness is satisfactory.

A parallel accessing schedule of multi-channel Nand Flash for signal processing system on board is proposed, which obtains high-performance in throughput rate with the combine of intra-channel pipeline and inter-channel interleaving. The massive data storage efficiency assessment model is established to calculate the bandwidth formula of the proposed method. The parameters which affect the parallel access rate of multi-channel Nand Flash memory are analyzed. The proposed architecture is implemented in a Virtex II FPGA, and is applied in the signal processing system on board. With 8-stage intra-channel interleaving and 4-stage inter-channel pipeline, the throughput of Nand Flash storage system can reach about 560 MBps for store and about 220 MBps for playback, which is more efficient compared with previous literatures. The results of the experiments verify the advantages and feasibility of the proposed method.

For joint Inverse Synthetic Aperture Radar (ISAR) imaging, the traditional methods all process the radar data matrix through vectored manipulations, which adds burden to the calculation and storage procession. In this paper, a novel algorithm of two-dimension (2D) joint ISAR imaging is addressed based on matrix completion (MC) theory. The ISAR observation signal model is established, and the echoed signal matrix is undersampled. After demonstration of the low-rank property of data matrix, 2D joint ISAR imaging is mathematically converted into the kernel norm optimization. The joint ISAR imaging can be achieved with the inexact augmented Lagrange multiplier (IALM) algorithm. Simulated data experiments verify the effectiveness of the proposed algorithm.

Phase center of GPS receiving antennas are significant error source in GPS precise orbit determination. The error source was influenced by all kinds of environment and was not be calibrate by testing in ground. This paper study a method for calibration GPS antenna phase center offsets (PCO) and phase center variation (PCV) by GPS pseudo-range and carrier phase. A lot of orbit by GPS receiver was compared with by each other, the GPS antenna phase center offsets was be eliminated from the GPS precise orbit determination.

This paper considers the migrating target detection problem under spiky clutter background. Ideal generalized likelihood ratio test (GLRT) detector for point target detection under spiky clutter needs iterative operations. By analyzing the reasons for the iteration, we design a non-iterative detector, i.e., Rao detector. The Monte-Carlo experiments show that, compare with the ideal GLRT detector, the proposed detector also possesses the constant false alarm rate character and achieve the same detection performance as GLRT without iterative operations.

We consider the problem of estimating range about unmanned aerial vehicle (UAV) swarm target. The main challenges are small radar cross-section (RCS) and high target density. In this paper, for better accumulation, we focus the original beat signal by Focus-Before-Detect (FBD) and obtain the velocity. Based on the velocity, we form a compensation matrix to eliminate the range migration (RM). Then, a novel range super-resolution algorithm based on the gridless sparse method is implemented that improves the range resolution to a great extent. Experimental results based on simulated and real measured data are carried out to demonstrate the accuracy of the model and the effectiveness of the algorithm.

Pedestrian dead reckoning (PDR) and WiFi fingerprint localization technologies have been widely used in the field of indoor localization. To reduce the limitation of the single localization technology, the PDR/WiFi integration method has become a widely accepted indoor localization solution. For indoor pedestrian real-time tracking and localization, due to the short time available to collect the received signal strength (RSS) and the high fluctuation of RSS, using only the RSS measurements as the RSS information will cause great localization errors. Therefore, this paper proposes an improved PDR/WiFi integration method to address the fluctuation problem of RSS for indoor pedestrian localization. The experimental results show that the localization accuracy of the proposed method outperforms the traditional PDR/WiFi integration method.

An adaptive ISAR-imaging-considered task scheduling algorithm for multi-function phased array radars is important to maximizing the overall performance of radar. However, most of the existing resource allocation method only considered the allocation of resource with slow-time dimension, which did not schedule the resource with fast-time dimension. Therefore, a novel radar resource scheduling algorithm is proposed in this paper, which can realize the resource allocation with slow-time dimension and fast-time dimension together. What’s more, the pulse interleaving technology is utilized to improve the radar performance further. The effectiveness of the proposed method is verified by simulations.

General aviation flights are always task-oriented and thus have more maneuvering than transportation flights. These complex trajectories have intrinsic difficulties to fit in the current common used flight plans, although which is suitable for transportation flights. A task-dependent flight plan is proposed to address this issue. The proposed flight plan can provide the general aviation operators, pilots and the safety administrators of the airspace a better description and interpretation of general aviation flights at the planning stage. By exploiting this kind of flight plan, a conflict risk assessment method is also proposed, aiming to quantitate the conflict risk level for a general aviation operation airspace. This method introduces uncertainty by employing time variance for the flight plans’ operation. Simulation results demonstrate that this proposed method is capable of providing measurements of conflict risk of the airspace as well as operational advisories.

A uniform model for conflict prediction and airspace safety assessment for free flight is presented. This model is inspired by classical electromagnetic theory. The free flights in the airspace are considered as charged particles in this model. By dividing the airspace into elementary grids, the electric static potential of the grid’s vertices is then used to predict the conflict between trajectory pairs. Furthermore, the average of vertices’ potential of grids can be interpreted as the safety level, in terms of conflicts between free flights, of the whole airspace. The application of the proposed model is not limited to free flight scenario, however, it can be applied in general aviation airspace and/or UAV operating airspace without difficulty. Two dimensional version of the proposed model is presented in this article. And numerical simulations show the effectiveness of the proposed model.

This paper investigates the power allocation for full duplex relay-assisted D2D communication underlaying wireless cellular networks. Firstly, we derive the outage probability of the full duplex relay-assisted D2D link in an interference-limited scenario. Then, we formulate the power allocation problem by minimizing the outage probability while fulfilling the interference inflicted on the cellular user is below a predetermined threshold. Considering the maximum transmit power constraint of the source user and relay user, we find that the optimal solution should be on three possible boundaries of the feasible region. For each boundary, we obtain the optimal solution. Finally, simulation results are presented to validate the correctness and feasibility of the proposed optimal power allocation scheme.

Image is used everywhere in our life and can bring us abundant information. Unlike general visual elements, text contains a wealth of semantic information. Obtaining the content of the image can help us better to understand the image. Therefore, it is crucial to recognize and understand text in scene images. In recent years, deep learning technology has developed rapidly and has played a leading role in the field of traditional optical character recognition technology (OCR) and it achieved good results. Based on this, this paper combined with the deep learning method further study the scene text recognition.

Spectrum sensing is the key of implementing cognitive radio technology. As a kind of machine learning method based on statistical learning theory, support vector machine has the advantages of global optimization, nonlinearity and good generalization ability. The use of support vector machines in spectrum sensing can solve the problem that parameters in spectrum sensing are difficult to determine by learning historical data. Aiming at the problem that the training time of the spectrum perception algorithm based on support vector machine is too long, this paper proposes a spectrum sensing algorithm based on twin support vector machine based on fuzzy mathematics and twin support vector machine. The algorithm extracts the leading eigenvector as the input features that can reflect the signal correlation and calculate the membership degree according to the proportion of the maximum eigenvalue. The twin support vector machine is used to solve the classification hyperplane. The algorithm complexity is only 1/4 of the SVM algorithm, which can greatly reduce the training time. The simulation results show that under equal prior condition, when the number of users and the number of samples are the same, the spectrum sensing algorithm based on TWSVM can obtain a lower minimum error probability than the SVM-based spectrum sensing algorithm and energy detection. As the number of users and the number of sampling points increase, the minimum error probability of the TWSVM-based spectrum sensing algorithm decreases.

In view of the existing wireless optical communication analysis, a single model of plane wave or spherical wave model is adopted when calculating turbulence model parameters without considering turbulence intensity and receiving aperture size, causing a large difference between two models among the actual gaussian beam, thus calculating the quoted error of scintillation index and signal-to-noise ratio (SNR) when using different models in different turbulence intensity in blue-green laser communication. The results show that the plane wave model has smaller error in weak turbulence, spherical wave model has smaller error in strong turbulence, but both models have larger error in medium turbulence. Therefore, plane wave model can be used to simplify the calculation in weak turbulence, spherical wave model can be used to simplify the calculation in strong turbulence, but any approximate models should not be used in medium turbulence.

Wireless power transmission (WPT) technology has been applied to many areas that driven by batteries. WPT system based on coupling coils is a popular method to realize WPT. In general, two coils are used in such system including a transmitting coil and a receiving coil. Two coils have to reach the same resonant frequency and impedance matching to assure high power transfer efficiency. In this paper, the influence of resonant frequency on transfer efficiency is studied. Inductance is closely related with frequency and capacitor. Hence, the influence of compensation capacitor on efficiency is also studied. Simulations have been done to perform the study. When the frequency is 130 kHz and the capacitance is 46.7 nF, the efficiency of the system reaches its maximum, and the efficiency of the system is 82.26%.

In order to improve the direction finding accuracy under low signal to noise ratio this paper presents a method for estimating the direction of multiple signals by using support vector machine. The signal subspace of the known direction signal is extracted as the input of the model. The fitting ability of the support vector regression to the nonlinear function is used to build the model, and finally estimate the directions of arrival. The method proposed in this paper does not need to perform peak search, which can improve the direction finding accuracy and direction finding speed.

When a bistatic inverse synthetic aperture radar (ISAR) system fails to collect complete radar cross section (RCS) datasets, bistatic ISAR images are usually corrupted using the conventional Fourier transform (FT)-based imaging algorithm. To overcome this problem, this paper proposes a new bistatic ISAR image reconstruction method that includes three steps: construction of the sparse dictionaries according to the range and cross resolution units on the imaging domain and echoes can be considered as the interaction between the two-dimensional distribution of point scatterers and the sparse dictionary, construction of the observation matrix and low-dimensional observation samples are obtained, and reconstruction of scattering distribution of target using nonlinear reconstruction algorithm. To validate the reconstruction capability of the proposed method, bistatic-scattered field data using the point-scatterer model is used for bistatic ISAR image reconstruction. The results show that the proposed imaging method based on the bistatic ISAR signal model spatial reconstruction combined with the compressive sensing(CS) theory can yield high reconstruction accuracy for incomplete bistatic RCS data compared to conventional FT-based imaging methods.

Medical image segmentation is a complex study due to its disadvantages such as noise, low-contrast, intensity inhomogeneity, and so on. A novel level set model was proposed in this study to segment medical images accurately. The kernel function used to determine the size of neighborhood of central pixel was modified by Laplace kernel function, which is insensitive to the choice of parameters and is more suitable for segmenting medical images. Compared with several state-of-the-art models, both visual and objective experiments can demonstrate the performance and superiority of the novel level set model.

With the development of communication technology, Multi-UAV formation work plays an increasingly important role in the military and civilian fields. In order to ensure the reliable and effective communication between drones, it is important to construct a Multi-UAV information interaction topology based on the performance of UAV communication equipment, the geographic environment where the formation located, the wireless spectrum resources and the quality of service QoS requirements. However, during the presently practical application of routing network planning, there are some weaknesses such as the degree of automation is not high enough, the cost of network planning is large, and the operation is inconvenient. As a result, in this article, we create the Multi-UAV simulation system by using the professional game engine unity3d to solve these problems. In this system, we construct a real terrain environment of Yantai (a city of Shandong province) and import multiple UAVs to solve the problem of automatic pathfinding simulation and build the communication routing network. Besides, we propose some adjustments for this system when the aircraft loses connection.

Video tracking technology is a hot topic in computer vision research. Video tracking technology is widely used, such as robot vision, intelligent traffic management, medical diagnosis and intelligent monitoring. Therefore, it is of theoretical significance and practical value to study video target tracking technology. In this paper, the background subtraction method and adaptive Kalman filter are combined to realize real time video target tracking. The experimental results show that the proposed method can improve the tracking accuracy.

The construction of measurement matrix is one of the core parts of compressed sensing. Its performance directly affects the signal sampling and reconstruction. Therefore, it is very important to design a measurement matrix with good performance. Aiming at the problem that the commonly used random measurement matrix is difficult to implement in hardware and practice, this paper proposes a measurement matrix of a chaotic system to construct compressed sensing and proves that the measurement matrix proposed in this paper can reconstruct the information well through simulation experiments and comparative experiments.

By seeking the optimal solution, the modal component and frequency center of the variational modal functions are determined, and the separation of the modal function is realized. This method effectively solves the modal aliasing problem of intrinsic mode functions in empirical mode decomposition. Based on this, Variational Mode Decomposition algorithm is applied to the problem of underdetermined blind source separation. The decomposed variational modal components are extracted in multiple Sequential Extractions to convert the underdetermined problem into a positive definite problem. Then the Independent Component Analysis algorithm is used to separate the source signal. Simulation results show that this method can effectively separate the mixed image information.

With the development of artificial intelligence, the use of machine learning technology to sort search results has become a very popular research field in recent years. In this paper, the correlation annotation dataset and the non-annotated dataset are combined with the singular value decomposition, and the new feature set is added to the training set, then the non-labeled data information is introduced in the training set. The differences in the ranked models trained by the new feature set before and after the experiment were compared. Experiments show that the feature set selected by SVD (singular value decomposition) helps to improve the accuracy of ranking effect.

With the development of science and technology, people gradually have a profound understanding about IoT technology, but due to the fast integration of IoT technology into life too fast in recent years, plenty of security risks are there brought about. Trojan virus in software level can be detected and cleared by software, but the Hardware Trojan (HT) on the chip has caused certain difficulty for detection it, and it will bring some troubles to users, like the increased power consumption of the chip or the risk of exposing privacy of the user. In this paper, the Hardware Trojan has been tested at different temperatures by means of the ring oscillator network (RON), with the implementation of the whole model is on the FPGA. In the end, it is found that the RON has a corresponding change in the detection degree of the HT at different temperatures.

As we all know, the traditional mechanical and electromechanical hybrid height measuring instrument which occupies the main position of the height measurement market has some insurmountable disadvantages, such as bulky body, large occupation area, high energy consumption and difficulty in maintaining the measurement accuracy. However, due to the high price and frequent maintenance, it is difficult for the sophisticated large-scale height measurement equipment to enter ordinary families. This makes it difficult for people to obtain accurate height measurement results conveniently and in real time. In recent years, although the electronic height measuring instrument has gradually entered the line of sight of people, its intelligent level is still not high, the price is hard for ordinary families to accept. In the face of such a situation, this topic designed a wireless communication intelligent voice height measurement system, is committed to promoting the realization of the dream of the contemporary society of all things Internet. Compared with the mainstream height measuring instrument in the market, this distance measuring system not only has an LCD screen display but also its biggest highlight is that it can realize WIFI wireless transmission. People can realize height measurement by the remote control of the instrument through mobile phone software, and feedback the height measurement result immediately. At the same time, the design can also achieve interaction with the computer, the height information uploaded to the computer for data analysis and storage. Another highlight of this design is that it has voice broadcast function, which can carry out voice prompt and voice broadcast of height value, greatly improving its intelligence degree. Also, the system combines temperature compensation and data error correction analysis in one, greatly improving the height measurement accuracy, to meet the needs of people’s daily life.

In recent years, the degree of concern about air pollution, especially for suspended particulate matter, has gradually increased. The suspended particles are solid particles that have a particle diameter of fewer than 100 μm such as PM1, PM2.5, and PM10 in the atmosphere. This design determines the theoretical basis for measuring particle concentration by laser scattering method using the principle of Mie scattering. Based on the Mie scattering, the light scattering characteristics of suspended particles were analyzed theoretically. The scattered light intensity distribution curve and extinction coefficient curve of single suspended particles under different characteristic parameters were discussed. The scattered light intensity distribution and incident light were obtained respectively. The design also has automatic garbage sorting capability. When people need to throw garbage, it can automatically open the cover. When the garbage bin is full, it will automatically alarm and remind. And it can automatically distinguish metal and non-metal and classify it to recycle metal resources and reduce waste. Meanwhile, solar panels can also be used to power solar energy, saving energy and environmental protection. To further improve the intelligence of the overall design, the product is equipped with luminescent material and an LCD on the outer wall of the garbage can, which can display the status and time of the current garbage bin, temperature, air quality and change its color according to the air quality. Moreover, the wireless communication function is designed. And the remote human-computer interaction is realized through the mobile terminal. The air quality information and the garbage bin classification situation are automatically uploaded to the mobile terminal.

For the false target jamming method of network radar, the data processing flow is analyzed. On this basis, the characteristics and weak links of the data processing algorithm of the networked radar system are analyzed. A method of false target disturbance based on phased-array multi-beam for the networked radar system is proposed. The paper analyzes its mathematical model, interference performance and applicable conditions, and provides corresponding technical solutions for the development of the simulation system.

Ultra-wideband has great advantages in indoor positioning. This paper introduces the TDOA technology commonly used in positioning systems. The two algorithms based on TDOA technology are emphasized: Chan and Taylor are described by mathematical modeling, and then simulated and compared. The algorithm localization results based on root mean square error are given.

In order to obtain better spectrum utilization under radiation confinement constraints, the radiation masking requirements outside the 3.1–10.6 GHz band, this paper chooses to study the random number algorithm and the least mean square error criterion algorithm commonly used in designing Gaussian pulses. MATLAB is used to simulate the power spectrum image, compare and analyze, and choose a better algorithm to meet the requirements under various radiation masking conditions.

Based on the STM32 processor, a network adapter is designed, which can realize data communication and protocol conversion between sensor and computing process node (CPN) in group intelligent projects. The adapter needs to be configured firstly, to know the communication parameters of the sensor and the corresponding storage location in the CPN. After that, the adapter begins to collect data, convert the message format, and send data to the CPN periodically. The results of practical project reveal that the proposed adapter is reliable and steady.

Improving the optical path scanning range of Fourier transform spectrometer (FTS) interferometer is a necessary for obtaining fine spectra. Translating optical path scanning is used to achieve large optical path difference (OPD). The permanent magnet linear synchronous motor (PMLSM) is used as the driver, and the position and speed are selected as the state variables to establish the state space model. This paper presents a model reference adaptive control (MRAC) algorithm, by designing a second-order system with the same order as the controlled object in advance as an ideal model. When the system is disturbanced, the control quantity is adaptively adjusted to eliminate errors between the actual system and the reference, and to achieve high tracking performances. Adding interferences to the system, the simulation results show that there is a delay of 0.1 s when tracking the reference position, the velocity has a super adjustment of 3.6%, and the velocity stability of the constant velocity range is 99.237%, which satisfies the requirement, indicating that the control strategy is effective.

UAVs are widely used in different fields of social life. With the increasing use of UAV, the main direction of future UAV technology development is “intellectualization”. Facing the problems of abundant information sources, large amount of information, interaction of various equipment systems and stability of communication signals, UAV can not only rely on manual operation, so it is important to strengthen the ability of process data to UAV. Nowadays deep learning is one of the hottest topics in the field of science and technology, and there are more and more researches based on the theory of deep learning, which provides a method for realizing artificial intelligence. This paper based on deep learning technology builds a neural network framework to study UAV path planning. Compared with the traditional UAV path algorithm, the neural network model is smaller and the recognition speed is faster. A detection and recognition method suitable for the network is proposed, which is applied to the design of UAV obstacle avoidance system to realize UAV’s recognition of the surrounding environment, obstacle avoidance and to ensure UAV’s flight safety.

In order to calculate the temperature and infrared radiation characteristics of space target, it is necessary to master the distribution and variation regularity of the orbit external thermal flux on target. Analysing the energy exchange relation-ship between the inside and outside of the space target in orbit, the numerical simulation method was used to simulate the temperature and infrared of a cone-shaped space target, and the influence of various parameters on the calculation results was quantitatively analysed. The results show that the angle β between sunlight and orbital plane has a significant effect on the temperature and infrared variation of space target, the calculation results are more sensitive to the change of β when β is larger. Considering the change of the earth albedo with latitude, the temperature and the infrared radiation intensity of 8–14 μm reduced by 1.73 °C and 0.27 W/sr respectively. The variation of albedo cannot be ignored when calculating the characteristics of space target accurately.

The traditional canny operator performs edge detection, which needs to artificially intervene in the variance of Gaussian filtering, and the choice of variance will affect the edge retention and denoising effects. When filtering out the noise, many edge details are lost. Aiming at the shortcomings of the traditional canny algorithm, this paper proposes an improved canny algorithm for edge detection. After multispectral image Gaussian filtering, the mixed and enhanced operation is made on multispectral image. This operation filters out the noise and retains the important edge detail information. In addition, when the gradient amplitude image is obtained, more edge information are obtained by changing the size of the sobel operator. The edge details of the multispectral image processed by this operation are more abundant and the detection is more accurate. The multispectral image is then subjected to non-maximum suppression and double threshold processing. Experiments show that compared with the traditional canny edge detection effect, the algorithm proposed in this paper has greatly improved the effect of edge connection and pseudo edge removal, and the objective evaluation and visual effect have been greatly improved than before.

The ground information port composed of distributed cloud data centers with interconnection is the core for spatio-temporal data access, processing and distribution in the space-ground integrated information network and can provide large-scale computation, storage and network forwarding resources. Thus, the performance improvement of the ground information port not only faces the traditional resource scheduling and task allocation problems, as the scale of business grows, its power consumption also explodes, which has become a bottleneck restricting the sustainable development of the ground information port. By introducing Software-Defined Networking (SDN) into the ground information port, this paper proposed an overall SDN-based green and efficient architecture for the ground information port. The SDN controller unifying scheduling and allocation of the whole network resources can not only maintain the efficient operation of the ground information port, but also effectively reduce the power consumption. This paper provides a feasible solution to solve the pain points in the industry.

The anthracnose is one of the most serious diseases in the growth period of mango. In order to take preventive measures timely, it is indispensable to calculate accurate statistics on the distribution density of anthrax spores on the farm, which has challenges in accurate instance segmentation of adherent spores. Based on the traditional watershed algorithm, which treats the image as a morphological topography and segments the image by finding the lowest and highest points on the topography, we proposed the marked watershed algorithm combined with morphological preprocessing to realize the segmentation of adherent spores. Firstly, the spore images are preprocessed with morphology technique. Then the gradient values of the spore images are calculated. The segmentation of spores is performed in the gradient image by the watershed algorithm with foreground mark and background mark. The experimental result shows that our proposal has a better segmentation performance for adherent spores than the morphological method and the level set evolution.

Nowadays, data query in the Internet of Things (IoT) is suffering from high latency as increasing amounts of data collected by devices every day. In this paper, we focus on data retrieval and present a novel storage method for fast retrieval by managing hot data separately. Experimental results show that our storage method has reduced the query latency by at least one order of magnitude compared with the existing typical method.

Symbolic simulation is one of the most important equivalence checking method, but it can not deal with large designs due to the limited capacity of BDD and SAT/SMT. Cut-points technique is used with symbolic simulation to verify the large size of designs. However, the existing approaches need the mapping information, which is not easily obtained in the verification process. This paper presents a new equivalence checking method against system-level descriptions without mapping information of variables. Our method first randomly simulate the designs under verification to generate potential cut-points set. If some output variables do not belong to the potential cut-points set, return not equivalent. Second, our method select and remove the potential cut-point pair from the set and slice the program according to it. Third, we symbolically simulate the slice and compare the results. If the corresponding potential cut-points are really equivalent, they are put into equivalent set. Finally, the process is repeated until the potential cut-points set is empty or some outputs are not equivalent. Our method can check the equivalence of designs without mapping information and decrease the verification size. The experimental results shows the effectiveness and efficiency of our proposed method.

Password attacks are classified into four types: known-plain text attack, cipher text attack, chosen-plain text attack, and chosen-cipher text attack. The chosen-cipher text attack is the strongest attacking mode among the four attack modes. In order to resist the most aggressive chosen-cipher text attack mode, this paper proposes an improved adversarial neural network encryption method. We call it CCA-ANC encryption algorithm which changes the attack mode of the attacker to use the chosen-cipher text attack (CCA) method that can give the attacker a stronger cracking capability so as to force both encryption and decryption to use a better encryption system generating a more secure encryption algorithm.

Based on Montgomery modular multiplication structure, this paper proposed and realized SM2 algorithm core module for IC cards, that is, scalar multiplication hardware structure of elliptic curves in prime field. Modular addition operation was improved so as to gain faster operation and anti-SPA capacity. Point addition and multiple point algorithm of Jacobi projective coordinates and Montgomery modular inversion algorithm were adopted in the structure. Experimental results indicated that, the structure had better performance and met the specific design indicators of the IC cards. Contact IC card communication protocol module (ISO7816 Protocol) was designed, and serving as the interface, it was tested by the contact IC card reader. In previous scalar multiplication designs, designers tended to focus on certain performance improvement of scalar multiplication, rather than a fixed product. This paper designed scalar multiplication encryption module, which was specific to the design of the interface of the contact IC cards widely used in life, so as to make the IC cards work more effectively.

In this paper, we establish a general heterogeneous cellular network (HCN) model based on stochastic geometry and employ the accurate signal-to-interference-ratio (SIR) approximation of the ASAPPP (approximate SIR analysis based on the Poisson point process) technique to investigate spectrum allocation problems for general HCNs. Firstly, a spectrum partitioning problem where each tier uses a dedicated band is formulated to maximize the area spectral efficiency. The solution is obtained by approximating the problem with the ASAPPP method, deriving the optimal SIR thresholds, and solving a simple linear program to give the optimal spectrum allocation. Without a spectral efficiency constraint for each tier, the area spectral efficiency is maximized if the entire bandwidth is allocated to a single tier. Secondly it is shown that the proposed algorithm can also be used to solve the spectrum sharing optimization problem, where bands can be shared between tiers.

A video human action recognition algorithm based on 3D DenseNet-BC is proposed. The convolution operation is used to acquire the characteristics of human action in video. Based on the connection mode of DenseNet-BC, network level connection is obtained to acquire high-dimensional features, thus constructing 3D DenseNet-BC for human action recognition in video. Tests were carried out on the data sets KTH and UCF-101 respectively. The experimental results show that the constructed network structure has a good recognition effect in the video action recognition task.

For the M × N encrypted color image size, data of at least M × N × 3 will be calculated, and the amount of data is too large, so that the operating system is also required to be very strict. Therefore, it is necessary to propose an algorithm that can encrypt some relatively small data to obtain a good encryption effect and use the data to reconstruct the original image. An image encryption algorithm based on PCA (principal component analysis) is proposed in this paper, which performs small variance reduction on the data of those dimensions, and then encrypts the obtained data. In this paper, the chaotic sequence generator uses 2D-Logistic to calculate the matrix generated by the chaotic sequence and the reduced-dimensional data, so as to obtain a good encryption effect.

A high-precision sensitive and portable “transmitter” of the hand gesture recognition system is designed in this paper. The MPU6050 sensor is used to collect the hand gesture data in real time. And the quaternion is obtained by the digital motion processing (DMP) unit of the sensor, and the Euler angle is obtained by the mathematical formulas operation. After that the complementary filtering correction algorithm is used to calculate the human hand gesture. Wireless modules are used to send data to the intelligent terminals and realize the function of wireless control of the intelligent terminals by hand gesture. Finally, the effectiveness and reliability of the scheme are verified by experiments. The experimental results show that compared with Euler angle method and direction cosine method, the attitude calculation method based on quaternion method and adding correction algorithm proposed by this scheme has the characteristics of less calculation, fast calculation speed and less drift error.

For the problem of image matching in visual indoor positioning research, the image quality is degraded due to the interference and influence of noises during the generation or transmission of the image, which ultimately leads to the problem of image matching rate and low efficiency. Comparing existing traditional denoising algorithms and wavelet transform algorithms, we use MATLAB for simulation to compare the effects of adding different noises and denoising with different wavelet bases. The results show that the wavelet image denoising improves the shortcomings of the traditional denoising algorithm to a certain extent, but there is still room for improvement.

We introduce the concepts of compression sensing and signal reconstruction, and then explained the minimum 0 norm and minimum l1 norm reconstruction algorithms. We extensively study existing reconstruction algorithms and take the advantages of existing algorithms to propose a new reconstruction algorithm. The normalized random Gaussian matrix is used as the measurement matrix. We chose three different sparse signals for comparisons, namely the three classic CS inputs including the time domain sparse signal, the frequency domain compressible signal and the sparsity unknown frequency domain compressible signal. Finally, a series of simulation results show that the proposed algorithm can achieve signal reconstruction with high probability and high precision.

It is difficult to extract a narrow road with a width of only a few pixels from a remote sensing image. In order to solve this problem, it is proposed to process the remote sensing image with super-resolution. This paper extends the details of narrow roads by using the Deep Convolutional Neural Network (DCNN) method. Next, some noise points or roads of error extraction are processed by topological structure. To verify performance of the experimental method, experimental research on open remote sensing image data set is carried out. The experimental result is to compare the original image, super-resolution image, and topology filtering. Experimental results demonstrate that the new method has better effectiveness and superiority over the original remote sensing image.

As a sign of atmospheric processes, clouds play a crucial role in regulating the earth energy balance, redistributing surplus heat and hydrologic cycle. Appropriate recognition method is essential for accurate ground-based cloud classification. This paper evaluates three kinds of learning strategies, i.e., end-to-end method, k-nearest neighbor (KNN) classifier, support vector machine (SVM) for multimodal ground-based cloud recognition. The experimental results demonstrates that SVM is superior to the other methods on multimodal ground-based cloud recognition.

This paper introduces the SAR (Synthetic Aperture Radar) load comprehensive test verification technology based on echo simulator. Firstly, the working principle of the echo simulator for SAR load test verification is introduced. Based on this, the SAR load test mode, test items and test methods are designed. The SAR composed of echo simulator and SAR fast view is given. Load comprehensive test setup Preparation and design plan. It has been applied in actual satellite testing.

For long-term real-time monitoring of human parameters, providing effective treatment for patients and reduce medical costs WBANs emerged, electrical devices microminiaturization makes WBANs come true. The network environment of WBAN is heterogeneous, nodes function and data types are various, however, in typical SPIN data priority is not considered. Limited resource is another challenge for WBANs, whole network energy balance is neglected by now available protocol. Aiming on provide low latency transmission for critical data and extend network lifetime, we proposed a optimization protocol for SPIN, data transmission and routing selection based on data priorities and residual energy. The outcome of simulation we performed show that the improved algorithm result in low latency and longer network lifetime.

This system mainly proposes a new data transmission method of modulation and demodulation in petroleum mining equipment. Before the signal is loaded. the signal is modulated by the method of frequency shift keying. The modulation signal is composed of 4 MOS tubes of H bridge, and then through the design of the full digital phase-locked loop for demodulation. This paper presents the full digital phase-locked loop design using Verilog and its implementation on FPGA. The system uses the hardware of quartus II to simulation, to prove the logical correctness of the design system, and feasibility.

Three-dimensional (3D) hierarchical nanomaterials assembled by nanosheets were synthesized by a facile hydrothermal method, without using any templates. Interestingly, it is explored that the sensor based on the 3D SnO2 hierarchical architectures reveals outstanding gas sensing performances towards ethanol. These excellent sensing performances mainly attribute to 3D SnO2 hierarchical architectures with a large resistance change occurred in air and ethanol gas. The structure and morphology of the resultant samples were investigated by X-ray diffraction (XRD) and scanning electron microscopy (SEM). The sensor sensitivity to 250 ppm ethanol is 11.6 at 240 °C.

Person re-identification (re-ID) could automatically match the same pedestrian across multiple cameras. In this paper, we review three kinds of person re-ID methods with the generative model and comprehensively analyze the applications of the generative model. We perform comparison experiments to verify the performance of the generative model on DukeMTMC-reID, and reveal the generative model could produce meaningful training samples and learn more discriminative features for person re-ID.

Extensible, stable and accurate indoor positioning technology is the main goal of future large-scale perception services. With the widespread deployment of wireless hotspots, the demand for location-based services is also increasing. Location fingerprint technology is one of the main localization algorithms in this field, because it does not need expensive hardware facilities and can be located through existing resources and software. With the increase of the number of wireless access points in fingerprints or the number of fingerprints in database, the complexity of location algorithm will increase, and it may be difficult to achieve location fingerprint for large-scale multi-building and multi-floor. Therefore, we introduce the relevant classification technology of integrated learning, and use the XGBoost positioning algorithm to design the classification of indoor positioning to improve the positioning accuracy and reduce the computational complexity. Finally, according to the simulation results, the performance of the algorithm is analyzed, and the localization effects of the algorithm and other localization algorithms are compared and analyzed.

Recently, iris identification technology have attracted much interests in the field of automatic biometric identification, taking uniqueness, stability and anti-counterfeiting of iris texture information and non-contact of iris recognition into consideration. However, the self-safety problem of iris recognition is caused by the safety of the iris itself. In order to enhance the security of iris feature data, an information hiding method based on adaptive multi-planet is proposed in this paper, in which, The iris feature is used as the secret information, the face image is the host information, and the data hiding algorithm of the iris feature template data is embedded into the face image according to the characteristics of the secret information and the host information. The algorithm has low computational complexity and large amount of information hiding, which realizes the hiding of biometrics to biometrics and enhances the security of biometric data. The simulation results show that The algorithm has strong concealment. The hidden algorithm has zero error rate and high computational efficiency. It does not affect the performance of the iris recognition technology itself. It can effectively protect the feature template data and enhance the security of the iris recognition system itself.

We synthesized a conjugated polymer based on phenolphthalein, oxazole and acid chloride (CZ-PT). The solution of this material can be processed with a conductive layer of an organic thin film transistors (OTFT). It uses a wide ap-plication space in the sensor field. Carbazole, phenolphthalein and acid chloride and their derivatives are important parts of nitrogen-containing aromatic heterocyclic compounds. It contains a large conjugated system and has strong electron transfer ability in photoelectric materials, dyes, drugs and supramolecular recognition. The field has a wide range of application scenarios. A copolymer prepared by a method such as Suzuki polymerization, which exhibits excellent solubility in a DMF solvent. The properties of the monomers and polymers of these studies was well characterized to investigate the relationship between structure and properties. The performance of the OTFT was also investigated, and the hole mobility of 1.19 × 10−2 cm2/Vs was obtained.

The traditional single-scale retinex (SSR) algorithm has good color fidelity advantage when the surrounding scale is large enough, but the image with uneven illumination is prone to local halo. To solve this problem, a new algorithm for smog images is proposed in this paper. Firstly, the smog image is processed by SSR to ensure good color fidelity. It is then filtered by a modified Gaussian homomorphic filter to eliminate halo artifacts. Finally, the CLAHE algorithm is used to enhance the edge details to achieve the final defogging effect. It can be concluded from the simulation results that the proposed algorithm can restore the image color and the image details while reducing the local halo compared to the traditional dehazing algorithm.

Gear fault diagnosis technology is significant in reducing casualties and economic losses caused by industrial accidents. Signal processing is an important step in the diagnosis of gear faults, which affects the accuracy of fault recognition seriously. Traditional signal processing method can be divided into three categories: time domain, frequency domain and time-frequency domain. For stationary signals, feature extraction methods can be divided into two categories: time domain and frequency domain. The time-frequency analysis method is more suitable for dealing with non-stationary signals, which can effectively reflect the distribution of non-stationary signals in the time domain and frequency domain. This paper focuses on elaborating various signal processing methods, analyzing their advantages and disadvantages, summarizing existing research results and problems, and looks forward to future research directions.

The hyperspectral image classification method based on recurrent neural network (RNN) regards the spectral values of all bands of each pixel as spectral sequences. But a one-way RNN can only focus on current input and past memory states, not future memories. And RNN itself has the problem of severe gradient vanish. In this paper, bidirectional gated recurrent units (BiGRU) is used for the classification of hyperspectral images. Bi-directional can not only integrate past memory state and future memory state, but also solve the gradient punishment problem of RNN to a certain extent. And the proposed method obtains better classification performance.

The purpose of pedestrian detection is to accurately locate each pedestrian belonging to the detection range from a specific scene. When combined with pedestrian recognition and pedestrian tracking technology, it has important applications in areas such as autonomous driving, human-computer interaction, intelligent video surveillance, and character object behavior analysis. The research progress of deep learning technology in the field of pedestrian detection is studied. The main problems and challenges of pedestrian detection are analyzed. The paper also summarizes the data sets and evaluation criteria of pedestrian detection. Provide reference and basis for comprehensive research in the field.

The low power spectral density characteristics of the direct sequence spread spectrum (DSSS) signals make it difficult to be detected in complex and variable electromagnetic environments. Especially when DSSS signals as an intrusion signal are transmitted in channels overlapped by strong power signals, the possibility of DSSS signals being detected is very low. The traditional DSSS signal detection algorithm is only based on Gaussian white noise, the research scene is single and the complexity of the algorithm is high. In this paper, we will propose an electromagnetic spectrum intrusion signal detection algorithm based on signal power information entropy. According to the characteristics of the DSSS signal, the signal power information entropy is used as a feature, and a single class support vector machine (OC-SVM) is used as a classifier for anomaly signal detection. The simulation results show that the algorithm has the advantages of robustness, high efficiency, and low complexity.

In this paper, a Wireless Powered Communication (WPC) based hybrid multiple access model is proposed. Users can harvest energy in the downlink and transmit signals to base station in the uplink. In order to implement hybrid multiple access, Non-Orthogonal Multiple Access (NOMA) is used for intra-cluster users, while Orthogonal Frequency Division Multiple Access (OFDMA) is used for inter-cluster users. Simulation results demonstrate that the WPC based hybrid multiple access scheme can effectively improve the spectrum efficiency and fairness.

In order to detect the safe driving area in a non-invasive and accurate way in practical application, the architecture of three-dimensional inverse opal (3DIO) ZnO and molecular sieve modified 3DIO ZnO gas sensor are prepared by simple controllable sacrifice template method. Its morphology is characterized by scanning electron microscopy (SEM) and its structure is characterized by X-ray diffraction (XRD). The sensing properties of the gas sensor are studied systematically. The results show that the response of the sensor possessed remarkable sensitivity to ethanol gas, even in high relative humidity (RH) conditions (~94%RH). The response of the molecular sieve modified 3DIO gas sensor is ~5.103 to 200 ppm for ethanol, which can effectively detect ethanol as low as 10 ppm. In conclusion, the molecular sieve modified 3DIO ZnO owns satisfactory gas sensing properties in detecting ethanol under high humidity.

FiberEUse is a €9.8 million research project funded by the European Union since June 2017 and collaborating with 20 partners from 7 EU countries. It aims at developing different innovative solutions towards enhancing the profitability of glass and carbon fiber reinforced polymer composites (GFRP and CFRP) recycling and reuse in added-value products and high-tech applications. There are three big tasks: (i) mechanical recycling of short GFRP, (ii) thermal recycling of long fibers (both GFRP and CFRP), (iii) inspection, repairing and remanufacturing for the end-of-life (EoL) GFRP/CFRP products. As one of the partners, the main objective of our work is to design a nondestructive testing (NDT) method for recycled/repaired/remanufactured CFRP products based on hyperspectral imagery (HSI). In this paper, we will introduce the use of hyperspectral imaging for erosion detection in different materials. Our previous work on metal corrosion estimation will be discussed first. Then, the idea of this work is carried out. The experimental setup of both works is illustrated and more details of our strategy are provided with future development direction.

Regarding to the deep-space missions which are faraway from the Earth, the conventional command-dependent mission operation and control are challenged due to the long delay of the uplink. Under this circumstances, autonomous planning and scheduling attracts more and more attention. As a critical subsystem of a spacecraft, data transmission system is a key node of autonomous planning and scheduling. This paper discusses the strategy of autonomous mission planning and scheduling for deep-space data transmission tasks. First, from the system-level analysis, four planning requests are proposed, which covered the problems of link establishment, antenna pointing, data rate and storage. Then, planning strategy models as well as the mathematical models are built-up for each request. The strategy model clarifies the planning elements which are “plan period”, “constraints” and “activities”. The discussed models are not separated, the relations and connections among the four models are illustrated to form a integrated planning logic.

As a promising solar cell type, a three-dimensional (3D) DSSC was fabricated based on the traditional DSSC structure using TiO2 nanotube array (Ti-NTA) photoanode prepared in a titanium wire spiral coil as an alternative material. The effects of preparation process parameters (electrolyte composition, oxidation voltage, oxidation duration, etc.) on the structure of Ti-NTA were investigated, and also the morphological and crystal structure analysis of the materials were carried out by scanning electron microscopy (SEM) and X-rays diffraction spectrum (XRD). Finally, a 3D DSSC was fabricated using Ti-NTAs as photoanode with optimal process parameter and performance of the DSSC was tested.

Blockchain technology is now a frontier field of high value with its unique technological advantages, innovative value concepts and wide application scenarios. The blockchain guarantees the integrity and anti-tamper modification of the stored transaction data through the hash algorithm. In the face of the hash algorithm, the centralized attack method, namely the brute force cracking method, the dictionary cracking method and the rainbow table attack method, is realized by using the collision principle. Based on the traditional method of salt-adding hashing, this paper proposes to use the commonly used Miller-Rabin prime number detection algorithm to generate random large prime salt to resist the attack of rainbow table. The test time comparison proves that this method plays an important role in the secure storage of blockchain data.

In this paper, in order to facilitate the hardware implementation, the hyperchaotic Chen system is discretized based on the Euler algorithm, and the Lyapunov exponent is used to verify that it has the same hyperchaotic characteristics with continuous one. A nonlinear controller is constructed for synchronization of two discrete hyperchaotic Chen systems with the same structure, then the synchronization of it is proved by Lyapunov stability theory. Finally, the secret communication scheme based on nonlinear feedback synchronization is used to transmit the voice signal, which verifies the feasibility and efficiency of it again.

With the gradual deployment of the fifth generation (5G) communication network, various challenging requirements are urgently expected to be met. One of the most important challenges is to ensure service quality under explosive traffic volume, which is putting pressure on the cellular network. In this paper, we aim to introduce the Unmanned Aerial Vehicles (UAV) to assist the existing base stations (BS), under an overloaded cellular network. Specifically, we suggest an Extended Kalman Filter (EKF) based technique to help the BS get the real-time locations of multiple UAVs. Then, a multiple access strategy is designed to arrange the users to communicate with the nearby UAVs, instead of the BS. Numerical simulation results show that the average network throughput can be significantly improved by introducing UAVs, showing the great potential of our UAV-assisted communication scheme in future 5G network.

Wireless Fidelity (WiFi) based fingerprint indoor positioning can directly utilize existing commercial WiFi devices, the deployment cost is low, easy to expand, and has good non-invasiveness, which has gradually become a hot spot of indoor positioning technology researchers. The positioning method of this paper combines the Received Signal Strength (RSS) ranging method and the location fingerprint method. On this basis, the Weighted K-Nearest Neighbor (WKNN) matching algorithm is used to match the fingerprint data in the location fingerprint database. In view of the strong problem of indoor wireless signal oscillation, this paper uses Kalman filtering method to process the signal strength value. The simulation is carried out under the MATLAB platform. The results show that the proposed method is superior to the existing K-Nearest Neighbors (KNN) and Nearest Neighbors (NN) algorithms in the same simulation environment, which significantly improves the indoor positioning accuracy.

This paper proposes a digital spacecraft overall power system design, simulation, analysis and verification mode, forming a unified simulation model based on a unified model, fully autonomously controllable, and the simulation results and power distribution map, grounding map and tasks time series is deeply integrated to realize the dynamic analysis and display of the whole satellite power supply and distribution system, as well as the digital flight of the ground test and the in-orbit flight process, and combined with a lunar probe model application example to carry out verification work. This has important guiding significance for the overall design and verification of the spacecraft.

The safe flight of both backup spacecraft and primary spacecraft without changing the facilities onboard is discussed based on the design and operation of Chang’E-4. Since the TT&C subsystem of Chang’E-4 is the backup spacecraft of Chang’E-3, the two satellites are designed and produced at the same time with the same hardware and software, and they fly at the same time around the similar place. By using the different tele command data rate, the different frequency, the shelter from the moon as well as the limit of the uplink power, the interference between the two tele command link is avoided, and the safety of the two satellites are maintained.

In this paper, we propose a modified Hough transform based TBD detection algorithm for radar weak targets using radar plot quality information. Firstly, a general description of the proposed method was presented. And then the quality of radar raw plots was redefined and its calculation algorithms were discussed in detail. Finally, a modification of traditional Hough transform using plot’s quality information was applied and more credible peak detections were conducted and outputted. The effectiveness of the proposed method especially in heavy clutter conditions was verified by simulation experiments.

Precipitation is the main sources of subsurface and surface water in the Tianshan Mountains, which play a vital role in the social and economic developments of Xinjiang, China. Researches showed that precipitation in the Tianshan Mountains is largely affected by climate phenomenon. In this paper, Ensemble Empirical Mode Decomposition (EEMD) and wavelet coherence analysis were used to explore the effects of climate teleconnections at annual to multidecadal scales on precipitation in the northern and southern slopes of the Tianshan Mountains. The results show that the annual scale of ISM strongly impacts the precipitation on both northern and southern slopes. The interannual scales of ENSO and NAO mainly affect the precipitation of the northern slope, and ENSO has a greater influence on precipitation than NAO. The influence of the multidecadal scale of the PDO on precipitation of northern and southern slopes are weak.

With the increase of types and functions of electronic equipment, the electro-magnetic environment of radar is becoming more and more complex, so it is very difficult to estimate the spectrum of electromagnetic environment. Since it does not need prior information of signal, cyclic spectral algorithm is very suitable for analyzing electromagnetic environment. The algorithm has strong anti-noise performance but high computational complexity in spectrum estimating of electromagnetic environment. This paper combines compressed sensing with the spectral correlation function to solve this problem for spectrum estimation. The simulation results show that the proposed algorithm can reduce the computational complexity of spectrum estimation while ensuring the estimation accuracy of radar electromagnetic environment.

KMV-Cast is a pseudo analog video transmission scenario which is robust and scalable in multicast video transmission. However, the bias of received metadata in KMV-Cast may cause blocking artifact in the image. Instead of using the H.264 deblocking filter which is of high computational complexity and is of limited benefit, a CNN-based filtering method is proposed in this paper to correct the luminance of each block and remove the noise from each pixel. CNN filter can recognize the edge of the image, automatically distinguish the true and false edges of the image, and then smooth the image. Compared with traditional filtering methods, it can retain details intelligently. And experiment indicates our method performs well and can significantly increase the PSNR of reconstructed video by an average of 5 dB.

The detection of moving targets in complex traffic scenes is the most basic and important technical means in video surveillance. In order to balance the speed and accuracy of object detection, this paper chooses You Only Look Once(YOLO) algorithm to extract foreground targets in video frames. Meanwhile, some steps are used to improve this algorithm. First, data is augmented during the pre-processing phase to ameliorate the imbalance of sample category. Then, re-clustering our own data set before training to get the corresponding anchor box size to enhance the accuracy of the final training model. In the training process, the focus loss is used instead of the binary cross entropy loss to further solve the problem of slow convergence rate and poor training effect caused by the imbalance of sample category. The improved YOLO algorithm is used to compare the training results with the original YOLO algorithm, and they are comprehensively analyzed by the model evaluation index. It can be verified that the improved YOLO algorithm maintains a faster training speed while it also improves the accuracy of training.

For radar imaging of multiple targets, it is necessary for the radar network to allocate the imaging task to different radars to achieve high image quantity under limited radar resource. In this paper, based on the Bi-ISAR imaging algorithm, a task allocation optimization method is proposed for multi-target inverse synthetic aperture radar (ISAR) imaging in radar network consisting of different bi-static radar. Simulation results indicate that the proposed method can effectively accomplish the multi-target imaging task allocation using the minimum time resource.

In target tracking for radar with Kalman filter, the process noise covariance matrix is usually selected experientially and assumed to remain unchanged throughout the tracking process. Although this method is effective on steady moving targets, in some practical situation, especially in the case of large maneuvering targets, we will meet some unsuccessful examples and fail to track those targets. In this paper, an improved target tracking algorithm based on the law of large numbers for maneuvering targets is proposed. During the process of Kalman filtering, the sliding window is used to select the acquired target trajectory data to estimate the process noise covariance matrix according to the law of large numbers. It means that the process noise covariance matrix can change adaptively with the movement of the target so that the filter can track the trajectory of the target more accurately. The simulation results show that the proposed tracking algorithm can produce smaller tracking errors than classical Kalman filter for targets with different motion models.

A new SVM training algorithm is proposed in the paper to improve the validity and efficiency of image annotation. These annotation tasks are related to one another due to the correlation among the labels. The model will implicitly learn a linear output kernel during training. Simulation results show that compared with independent SVMs training, Joint SVM improves classification accuracy and efficiency substantially.

Integrating cellular networks into coastal communications may significantly reduce communication cost and improve service quality for maritime users. However, particular ocean communication scenario brings new problem of modeling for cellular networks which are originally used for terrestrial communications. A tractably analytical model for coastal communications is proposed in this paper, through which the distribution of cellular link distances is investigated. Based on these analyses, network performance of different metrics can be obtained. As special cases, the network coverage, handover metric and resource partition are analyzed, which provide possible ways of evaluating and guaranteeing the network performance.

To meet the growing energy demand of the new spacecraft such as high resolution and radar remote sensing satellite and deep space probes, a incremental conductance peak power tracking method based on S3MPR circuit is presented in this paper. The shortcomings of traditional MPPT topology that low mass and power ratio, low efficiency are overcome. The incremental conductance method can be used to reduce the oscillation near the maximum power point. The principle and control processes of the incremental conductance method are introduced. A co-simulation platform and semi-physical experimental platform is built to validate the circuit and control method. The results show, the incremental conductance peak power tracking method based on S3MPR circuit can achieve effective tracking of the maximum power point of the solar array, and has good tracking efficiency, the simulation and experiment platform are reasonable and effective.

An effective algorithm named as Toeplitz fourth-order cumulants orthonormal propagator rooting method (TFOC-OPRM) is proposed. Firstly, the reduced-dimension fourth-order cumulants (FOC) matrix can be achieved via deleting the redundant information which is encompassed in the original FOC matrix, and then the Toepltiz structure is regained by utilizing the Toepltiz approximation technology. Finally, the DOAs of incident source signals can be estimated by exploiting the proposed orthonormal propagator rooting method (OPRM). The simulation results show that the proposed TFOC-OPRM algorithm can not only reduce the computational complexity significantly, but also achieve satisfying estimation performance.

In order to realize the diversity of the Internet of Things (IoT) system cloud platform transmission protocol and the reliability of data transmission and storage, this paper proposes a ONENET-based remote monitoring and control system for greenhouse environment. By designing the acquisition unit based on STM32 MCU’s air temperature and humidity, light intensity and soil temperature and humidity, the communication gateway, combined with the relay control unit and ONENET IoT cloud platform, realize remote monitoring and control of the greenhouse by computer or intelligent mobile terminal. The experimental data shows that the system has the advantages of high detection accuracy, simple structure and low cost, and can realize the function of remote monitoring and control of the greenhouse environment.

At present, the research on lunar communication mainly focuses on the analysis and research of communication node level and link level. There are few studies on multi-device broadband wireless networking and protocol. The existing point-to-point communication mechanism poses challenges to multi-device missions on lunar. Facing the increasingly complex mission requirements of lunar exploration and scientific research station, it is urgent to build an efficient multi-node broadband wireless communication system to ensure the smooth implementation of the follow-up missions of lunar exploration. It analyses and compares the current wireless communication protocol technologies, studies the foreign lunar protocol schemes, and presents the design of the lunar’s multi-device broadband wireless network architecture. It focuses on the design of networking protocol system, which is compatible with the existing earth-moon communication protocol system, and supports the broadband wireless communication between multiple devices. Through the research in this paper, we can realize the efficient interconnection of multiple devices and provide the technical basis for the construction of the broadband wireless networking infrastructure on lunar.

Pedestrians’ red-light snapping system is applied more and more in traffic control. The snapping technology based on face image recognition is widely used, but there are some defects such as low successful capture rate and small number of simultaneous tracking. In order to improve the capture algorithm based on face recognition technology, the MHT tracking algorithm based on upper body recognition is introduced to improve pedestrian tracking accuracy and system load performance, so that the tracking success rate is increased to 85%, and the number of simultaneous tracking reaches 25 people. At the same time, the image quality is judged by image Laplacian variance, face size and angle, and the face capture algorithm is optimized to improve the snap quality of the close-up photos of the face and improve the system availability.

The snapshot image mapping spectrometer (IMS) can acquire the datacube of a target in real time, and has the advantages of high light throughput, high temporal resolution, compact structure et al. This paper proposes a datacube reconstruction method for IMS, which is based on the geometric model of IMS. The simulation results prove that the method is effective and efficient.

This paper proposes a pre-tracking detection algorithm based on dynamic programming in UAV power patrol obstacle avoidance technology. The new method combines the basic principle of millimeter wave radar with the kinematics equation, models the target echo, and then simulates the algorithm based on the established model. Finally, the echo data of the wires collected by the drone is verified by the algorithm. The experimental results show that the pre-detection tracking algorithm can detect the power line better and more stably.

Noisy multicomponent linear frequency modulated (LFM) signals analysis plays an important role in radar signal processing and many analysis methods have been proposed. The maximum likelihood (ML) method, Lv’s distribution (LVD) and parameterized centroid frequency-chirp rate distribution (PCFCRD) represent three research directions for noisy multicomponent signals analysis. This paper aims to theoretically review and analyze these three methods. One numerical simulation is given to validate theoretical analyses and several discussions are given for realistic applications and future research.

In recent years, RSSI-based indoor positioning system has been widely used worldwide due to its low installation cost and wide coverage. However, due to the complex and varied indoor environment, the crowd is relatively dense, and the propagation of wireless signals is greatly disturbed. This leads to the fact that the RSSI-based indoor positioning cannot meet the requirements of people. In this study a wireless signal compensation model considering population density is proposed. This model can use image information to compensate the path loss of RSS signals to achieve accurate indoor positioning.

In recent years, mobile network data has an explosive growth. To adapt this demand and accelerate the development of new applications, the fifth generation of mobile communication networks emerged. At present, the vision and needs of 5G have been gradually clarified. How to integrate existing technologies and various potential new technologies to realize 5G network becomes the next research and development focus. In econometrics field, Granger causality test is a normal analysis tool for time series data based on autoregression, but it is not limited. It is also widely used based on the information theory conditional common information stage generalized Transfer Entropy (TE). In this paper, first Granger causality test is proposed on testing the correlation between two 5G channels, then transfer entropy algorithms is applied to forecast 5G channel coefficient. Then based on the forecasted channel, the energy allocation of the channel is performed by the Inverse Water Filling (IWF) algorithm. Finally, we demonstrate the high energy efficiency of the IWF on channel power allocation. The simulation further validates our theoretical results.

With the problem of environmental pollution becoming more and more serious, people pay more and more attention to indoor air quality, because more than 80% of the time spent in the indoor environment. This paper designs an indoor air quality monitoring system based on Internet of Thing cloud platform, which adopts modular design idea and realizes the development process of the system quickly. The architecture design of perception layer, network layer and application layer of the system is given. Sensors can be connected to the communication gateway as long as they conform to Modbus communication protocol. The WYSIWYG (What-You-See-Is-What-You-Get) method is used to develop the interface of the monitoring system. Finally, the system is tested and analyzed. The test results show that the system runs stably and accurately, and supports remote monitoring of web and mobile versions.

At present, the research about beamspace MIMO-NOMA system focus more on mathematical model. It is difficult to utilize this model in practice. To solve this problem, we decompose this system into four parts, that is beam selection, clustering, power allocation and precoding. Next we discuss the impact of beam selection and clustering on spectral efficiency (SE) and energy efficiency (EE) with a specified power allocation and precoding algorithm. The simulation result show that no scheme of system can outperform others in SE and EE in any environment.

Aiming at the problem of high complexity of the conventional parameter joint estimation algorithm and the more clutter interference in the measured data. This paper proposes a novel joint range and azimuth estimator using multi-chirp coherent accumulation (MCA) for short-range frequency modulated continuous wave (FMCW) radar. Specifically, we combine fast Fourier transform (FFT) and multiple signal classification (MUSIC) using MCA to estimate range and angle. We verify the performance of the proposed algorithm by measured data, and the results show that our algorithm greatly improve the accuracy of measured data estimation and reduce the complexity.

With the continuous development of high-speed wavelength division multiplexing (WDM) system, the transmission speed of light signals in optical fiber channels dramatically increases, which has caused the fiber nonlinear effect more and more prominent. As the previous methods to resist the influence were to some extent reliant on extra devices, to optimize the design of connection relationships in hybrid optical fiber networks might be a promising way to further improve the resistance ability of fiber nonlinear effect. To prove this, a simple WDM system including only one transmission path formed by two different types of optical fiber cables, i.e., G.652 and G.655, is established, based on which comparative simulations are performed to analyze the end-to-end nonlinear effect when the two types of optical fiber cables are connected in different order. Simulation results indicate that different connection orders of G.652 and G.655 cables correspond to different optical signal to noise ratio (OSNR) at the receiver side, demonstrating the difference in nonlinear effect accumulation in the end-to-end transmission and the importance on fiber link design in the WDM networks.

With the development of wireless networks and positioning technologies, location-based social networks (LBSN) have gained popularity. More and more people share experiences about points of interest (POI) through “check-in” behavior. Mining the check-in data can help people discover the POI they are interested in. However, the data sparsity of user check-in records and the cold start problem with users and POI pose serious challenges. In addition, POI recommendation need to consider the impact of multiple factors. In order to solve the above problems, we propose a POI recommendation method based on heterogeneous network representation learning, called HRPR. First, we propose to use the meta-path based weighted random walk method to generate node sequences and learn the representation vector of the user and POI by means of the skip-gram model. Then, we design a POI recommendation framework based on deep neural network. The experimental results on real-world Yelp dataset demonstrate the effectiveness of our framework.

Natural language processing is an important research direction and research hotspot in the field of artificial intelligence. Named entity recognition is one of the key tasks, which is to identify entities with specific meanings in the text, such as names of people, places, institutions, proper nouns, etc. Traditional named entity recognition methods are mainly implemented based on rules, dictionaries, and statistical learning. In recent years, with the rapid expansion of Internet text data scale and the rapid development of deep learning technology, a large number of deep neural network-based methods have emerged, which have greatly improved the accuracy of recognition. This paper attempts to summarize the traditional methods and the latest research progress in the field of named entity identification, and summarize and analyse its main models, algorithms and applications. Finally, the future development trend of named entity recognition is discussed.

In this paper, a full-duplex time- and wavelength-division multiplexing -radio-over-fiber (TWDM-RoF) system which can support a hybrid transmission of wired and wireless data is proposed based on an additional sub-central station (SCS). For the downlink, the TWDM technology is employed to transmitted wired and wireless services from a central station (CS) to a SCS with baseband data formats. For the uplink, one upstream optical carrier can simultaneously support both wired and wireless signals to achieve upstream transmissions. Better system compatibility, wavelength utilization and dispersion tolerance for bidirectional transmission links can be achieved in the proposed system. Finally, a demonstrated system with one 10-Gbps wired signal and two 2.5-Gbps wireless signals carried by a 28-GHz radio frequency (RF) signal is established. We validate the feasibility of this system based on the results of the bit error rate (BER) curves for both downlink and uplink.

In this paper, we propose a new energy efficiency maximization algorithm for AF relay OFDM-SWIPT system. Specifically, we divide the transmission time into two parts. In the first part, the base station transmits information to the user and the relay node. The relay node uses different subcarriers to implement SWIPT. Then in the second part, the relay node forwards information to the user. We formulate the above process, and then through some complex calculations to get the maximum energy efficiency.

Considering the spectrum utilization of cellular system and the overall communication capacity of the system, a device-to-device (D2D) communication system model which based on non-orthogonal multiple access technology (NOMA) is established, and the concept of “D2D group” which allows one D2D transmitter communicate with multiple D2D receivers simultaneously is introduced. In this model, D2D groups utilize NOMA technology for transmission, and multiple D2D groups can reuse the same sub-channel. Aimed at the co-channel interference caused by shared spectrum resources between cellular users and D2D groups, and the power allocation problem based on the NOMA principle in the D2D groups, an optimization problem of maximum the total rate of cellular networks is handled by two steps. Firstly, an effective resource allocation scheme based on weighted bipartite graph algorithm is proposed and then obtains the power allocation scheme of each D2D group by converting the power allocation factor into an unknown quantity. The simulation results show that the algorithm which is proposed can greatly increase the number of D2D user groups and the total speed of cellular system.

The difficulty of reconnaissance and jamming have been greatly increased as the result of the extensive use of Radar Netting system. So how to put effective jamming on the Radar Netting system becomes an important problem in electronic countermeasures realm recently. In this paper, we briefly introduce the concept of the Radar Netting and the characteristic of the Radar Netting; Second, we establish a distributed Radar Netting system which is composed of three radars’ models; Finally, we analyze the algorithm of track jamming on the netting radar and also give the formula and the jamming with the process of the algorithm. The effectiveness of the algorithm is verified by simulating.

The cluster feed beamforming network uses a ground-based beamforming technique. In view of the complicated link transmission in ground-based beamforming networks, the channel calibration for forward/backward links of ground-based beamforming networks is given in the paper. Aiming at the problem of channel amplitude and phase inconsistency in beamforming network, two solutions are given: point frequency coherent amplitude-phase detection method and code division coherent amplitude-phase detection method. Because continuous wave signal is poor to resist wideband interference and fuzzy to solve delay ambiguity, a channel amplitude-phase detection method based on composite orthogonal code sequence is proposed, and the performance of beam synthesis after channel calibration is verified by the prototype, which proves the validity of the method.

Based on the cluster-fed reflector antenna, a low-coupling cluster-fed reflector antenna design scheme is proposed to solve the problem of feed occlusion and beam scanning of the forward-fed reflector antenna. The accuracy test and feed coupling test of the prototype are carried out, and the test results meet the design requirements. Then we optimize the beam of the beam feed reflection surface, and the large-scale beam-forming problem is transformed into an optimization problem. An improved genetic algorithm for the target beam optimization is proposed, which adopts the envelope method to model the target beam optimization problem, thus reducing the control difficulty and computational complexity. Based on the original genetic algorithm, the methods of optimal preservation strategy, pseudo-parallel strategy and sharing niche algorithm are introduced to improve the genetic algorithm, to ensure the convergence of the optimization algorithm, increase optimization speed, and enable the algorithm to approximate the Pareto optimal solution.

In this paper, a cognitive simultaneous wireless information and power transfer (SWIPT) method based on decode-and-forward relay is proposed to study how the method performs energy collection and information reception to maximize the transmission rate of secondary network. In the proposed method, the relay user of secondary network collects energy broadcasted by the primary user through spectrum sensing, optimizing the power segmentation factor of secondary network in SWIPT. Then relay user uses the accumulated energy in the previous multi-slots and harvesting energy to help the source user of secondary network to transmit information to increase the total information transmission rate of the secondary network.

Various studies that address the distributed compressed sensing (DCS) problem are based on jointly sparse prior (Sarvotham et al Asilomar conference on signals, systems, and computers, pp 1537–1541 (2006), [1]). In this paper, we relax this condition and propose a data-driven method to reconstruct UWB soil echo signals from compressed sensing (CS) random measurements. To this end, we use a long short-term memory (LSTM) network architecture which takes in DCS measurements as input and outputs reconstruction signals. The proposed method is LSTM-DCS. On a dataset of UWB soil echo signals, we show that the LSTM-DCS significantly outperforms traditional DCS solvers.

In the original McEliece cryptosystem based on QC-LDPC codes, the decoding algorithm uses BF algorithm instead of BP algorithm, which reduces the time complexity but decreases the decoding performance. To solve this problem, we improve the original algorithm and use the Q-decoder which utilizes the correlation between the error pattern e and the matrix Q, making decoding performance much better. Finally, we analyze the security of the improved cryptosystem, finding it has a higher security level.

Multi-carrier modulation technology can improve spectrum efficiency in limited spectrum resources by modulating data to low speed parallel multiple subcarriers, and can provide reliable data transmission in an effective frequency band. As a member of multicarrier modulation, orthogonal Frequency Division Multiplexing (OFDM) can resist certain channel fading, but it has the disadvantages of low energy efficiency and spectral efficiency, sensitive to carrier frequency offset and high out-of-band radiation. In order to solve this problem, scholars have further explored in the field of multicarrier modulation, and proposed OFDM technology based on index modulation (OFDM-IM) and filter bank multicarrier technology (FBMC). Compared with OFDM, although the complexity of OFDM-IM and FBMC is improved, OFDM-IM has higher energy efficiency. The on-demand adjustment between system bit error rate (BER) performance and spectral efficiency can be realized by configuring the number of active subcarriers in each subcarrier group. FBMC has higher spectral efficiency, can resist a certain carrier frequency offset, and the out-of-band attenuation is greatly reduced. Therefore, the research on OFDM-IM has very important theoretical significance and application value.

With the increasing use of high orbit satellites, the global navigation and positioning system (GNSS) for high orbit satellite positioning has become a hot research topic. In this paper, the GNSS-based high-orbit spacecraft orbit determination algorithm is built to simulate and verify the system, and the system state obtained by system-level simulation is verified in real time. Traditional STK simulation, parameter changes need to be manually adjusted, so this article uses Labview to control the STK from the bottom, automatically set the parameters, to facilitate related research, so as to verify the visible stars, DOP deserves specific values and dynamic trends.

Power amplifier (PA) nonlinearity has been one of the crucial constraints to the performance of radio frequency (RF) communication systems. The distortion caused by amplitude-phase modulation (APM) results in severe performance degradation. In this paper, we study the effect of PA nonlinear distortion on bit error performance in Multiple-Input Multiple-Output (MIMO) wireless communication systems, and develop a new method based on Support Vector Regression (SVR) to compensate the nonlinear distortion. Under the condition that the receiver has no knowledge of PA distortion parameters, we propose a receiver compensation technique which involves estimating the points of the distorted AM-AM curve based on training using SVR. The proposed scheme can realize a model-free estimation. Simulation results show that, for $$4 \times 4$$ MIMO with 16-QAM, the proposed scheme is effective to deal with the nonlinear distortion caused by PAs.

Based on the geographical position on the South China Sea, this paper puts forward a strategy of setting antennas on islands and constructing 2 × 2 MIMO (Multiple Input and Multiple Output) system with two distributed GEO (Geostationary Earth Orbit) satellites. The fixed orientation of antennas can ensure the stability of communication, and the signals transmitted by the islands can cover the sea area near the islands, and provide communication services to the passing ships. The geometric parameters of antennas can be adjusted to achieve the maximum line of sight channel capacity.

Civil Aviation Administration of China (CAAC) proposed to enhance the global tracking and monitoring capability of China civil aircraft after the MH370 incident. In order to meet the urgent needs of global tracking for civil aircraft in China, the design scheme of flight data processing software for global flight tracking system was presented in this paper. Based on the features and operational process of global flight tracking, the overall framework and interface design of the software were given in detail. In order to meet the performance needs of the real-time processing of emergency mass data, the database access technology based on stored procedure was adopted and the realization method of flight data processing software was given. The engineering application and the test results shows that the software can well meet the functionality and performance requirements for the flight data processing of global flight tracking system, which provides technical reference for improving the global tracking and monitoring capability of China civil aircraft.

In this paper, a face recognition method based on deep learning is studied and implemented. By adjusting the hierarchical depth and structure of the typical convolutional neural network model ResNet, a new network model structure is designed, which uses the LFW face detection benchmark. The database is used for confirmatory experiments. The experimental results show that the overall accuracy and model size of the system have a good performance.

The Time-Triggered Ethernet (TTEthernet) had been under considerations to be adopted in aerospace or spacecraft avionics domain, because of its time deterministic to allocate Time-Triggered messages belonging to each strictly periodic virtual links (VLs) into scheduled time windows according to off-line generated timetables. An on-line time window allocation method for Time-Triggered messages using a heuristic method called As Soon As Possible (ASAP) was present in this paper. Under a topological configuration with multi-stage connected TTEthernet switches and full-duplex accessing end systems, an optimal path for each on-line scheduled VL can be selected firstly as a result of searching orderly set of physical links which make the allocation more easy. If the smallest sum of occupied time-slots and the smallest number of hops along candidate paths become an incompatible dilemma, a criterion was addressed to make a reasonable choice. With shifted and masked time-scales of each selected physical links, some non-occupied time slots are reserved for the on-line scheduled VL, which is going to achieve these slots unless other VLs’ conflicted selections. Cases in MATLABTM language had been developed and studied to make this method verified.

This paper proposed an algorithm to predict soil pH value using ultra-wideband (UWB) radar echoes. Compared with the existing work, instead of classifying soil pH value via echoes, this paper predicted soil pH value using machine learning (ML) method—extreme gradient boosting (XGBoost) at the first time as far as we know. In this experiment, we collected a total of 7 types of soil UWB radar echoes with different pH values. The echoes were split into train set and test set. The prediction results were compared with actual pH values via mean squared errors (MSE). Analysis results show that this method can achieve a very low MSE that is $$3.6\times 10^{-7}$$.

Heterogeneous Integrated networks is an inevitable trend in the development of next-generation networks, in which a key issue that must be considered and addressed is how to enable any user to obtain QoS guaranteed services at any time and any place. In the heterogeneous integrated networks, we consider a heterogeneous network scenario with overlapping coverage of IEEE802.11b wireless access points and macrocell base stations and 5G, and we use the convex optimization method to maximize the total system capacity while effectively satisfying the minimum rate constraint of the delay-limited type and the proportional fairness of the best-effort type. The numerical results shows the performance enhancement with the bandwidth and power joint allocation can be achieved in 5G heterogeneous integrated network.

This paper mainly designs a vehicle positioning algorithm based on single base station, which can both consider the angle and distance between the vehicle and the base station. The known vehicle location environment is free of any occlusion. The propagation loss prediction model is used to measure the distance between the base station and the vehicle according to the known transmitting power and receiving power. The uniform antenna array are installed on the base station, and the signal angle is obtained by ESPRIT algorithm. Finally, the coordinate system is constructed by using geometric model, and the coordinates of vehicles are calculated according to the triangular formula. If the speed or acceleration of the vehicle is known, the coordinates of the vehicle in the next second can be obtained through physical knowledge, so as to achieve vehicle location. By comparing with the literatures, the positioning scheme designed in this paper is more accurate than that in the selected literatures.

With the development of mobile communication and information technology, various wireless access technologies have emerged in the private networks, which are heterogeneous in the access and services. Aiming at the allocation of heterogeneous wireless private network resources, this paper presents a resource allocation algorithm based on multi-master and multi-slave Stackelberg game, and designs a user utility function mainly based on bandwidth and private network operator utility function for network service capability. And a distributed iterative algorithm that needs local information is used to obtain the perfect balance of the sub-game of the game model, and finally obtain the optimal bandwidth strategy of the user and the optimal network service capability strategy of the private network operator.

This paper studies the system performance of a multiuser dual-hop satellite relaying network where the threshold-based decode-and-forward (DF) is adopted at the relay. Specifically, we can first get the maximum signal-to-noise ratio (SNR) formula of the satellite relaying network. Then, by supposing that both of the uplink and downlink channels are submitted to Shadowed-Rician (SR) fading, the exact expression for the system outage probability (SOP) of the satellite relaying network is derived. Moreover, we also obtain the asymptotic SOP with a high-SNR to provide more insight into the performance in terms of coding gain and diversity order. Finally, numerical examples not only demonstrate the correctness of the performance analysis, but show the impacts of the system parameters on the SOP of the satellite relaying network.

Possessing the capability of breaking through the limitation of geographic conditions, Space-Terrestrial Heterogeneous networks play a significant role for global coverage capacity, which however pose new challenges to the network architecture design. In this paper, we first propose novel architectures for the considered space-terrestrial heterogeneous networks. On this basis, some key technical issues in the developed architectures are comprehensively discussed, including protocol design, Integrated Routing and so on, which pave the way for extensive applications of the Space-Terrestrial Heterogeneous networks.

In order to achieve the absolute angle in the measuring system, the coarse channel and fine channel data of the absolute round inductosyn need to be fused. Firstly, this paper propose two coarse and fine data fusion methods based on round inductosyn, specifically analyzed the advantage and disadvantage of the two methods. Secondly a improved look-up-table method was applied in the double-channel angle measurement system and was validated in the high and low temperature environment experiments. The experiment results show that the data fusion algorithm is correct and reliable, and can still obtain accurate absolute angle under high and low temperature condition.

With the advancing of space technology, space network attacks more and more attentions in both academic and industry. In a network, flow control is the process of managing the rate of data transmission between two nodes to prevent a fast sender from overwhelming a slow receiver. Traditionally, flow control mechanism used in Internet allows the node to drop coming packets when the node does not have enough buffer to store. Undutifully, it will induce lots of power waste. However, powers is the most resource in the space environment. In this paper, we propose a lossless flow control to save power. Instead of dropping packets, LFC sends the congestion information to the source node by backpresure. Finally, efficient analysis results are presented to prove the efficiency of the proposed mechanism.

In order to adapt to the dynamic changes of the network environment, it is necessary to select the most suitable network for each session to serve the heterogeneous network and achieve network load balancing at the same time. Based on the heterogeneous network composed of PDT and B-TrunC, and based on deep Q learning algorithm, the network selection Markov decision process (NSMDP) is adopted. Based on Markov decision-making process, we establish a network selection problem and propose an algorithm for wireless access network selection in heterogeneous network environment. The algorithm considers not only the load of the network, but also the business attributes of the initiating session, the mobility of the terminal and the location of the terminal in the network. The simulation results show that the algorithm reduces the system blocking rate and achieves the autonomy of network selection.

At present, China’s police mobile communication network mainly includes Police Digital Trunking (PDT) and Broadband Trunking Communication (B-TrunC) networks. It is an urgent task to build a heterogeneous network of broadband and narrowband networks under the background of mobile Internet. Vertical handover technology is an indispensable key technology in heterogeneous networks. According to the characteristics of private network communication, this paper divides the services into four types, and then calculates the subjective weight and objective weight of each network attribute. And a Technology for Order Preference by Similarity to an Ideal Solution algorithm based on Kullback-Leibler divergence (KL-TOPSIS) is proposed to sort candidate networks. Simulation and numerical results show that this algorithm has better vertical handover performance under different services.

With the rapid development of deep learning, face recognition also finds its improving dramatically. However, facial change is still a main effect to the accuracy of recognition, as some complex factors like age-invariant, health state and emotion, are hard to model. Unlike some previous methods decomposing facial features into age-related and identity-related parts, we propose an innovative end-to-end method that introduces a deformable convolution into a deep learning discriminant model and automatically learns how the facial characteristics changes over time, and test its effectiveness on multiple data sets.

With the development of public security private network communication, the traditional narrowband Police Digital Trunking (PDT) network is difficult to meet the requirements of image and video transmission. As a broadband trunking network, Broadband Trunking Communication (B-TrunC) fills the gap of broadband wireless network in private network communication. How to realize vertical handover between two heterogeneous networks in terminals has become the focus of research. In this paper, a subjective weight method based on Triangular Fuzzy Number (TFN) and an objective weight method based on Rough Set Theory (RST) are proposed. Among several attribute parameters affecting network performance, the weight of each attribute is determined, which provides a theoretical basis for network vertical handover.

With the rapid development of the Internet of Things (IoT), the demands for location-based services (LBS) are increasing day by day. At present, most of the indoor localization technologies require targets to carry terminal devices, which limits the practical application of indoor localization. In this paper, we propose a deep learning-based device-free localization system using ZigBee. The system employs ZigBee nodes as sensor nodes, which can locate the targets through measuring received signal strength (RSS) among these sensor nodes. In the off-line phase, we collect the RSS data of some specific locations and construct a localization model through training a deep learning convolutional neural network (CNN) model. In the on-line phase, we are able to calculate target location coordinates with the trained CNN model. The experimental result shows that the mean error of the proposed deep learning-based device-free localization system is 0.53 m, which could be a technical solution for human target localization in indoor environments.

This paper introduces a modified genetic fuzzy tree (GFT) based nodes moving strategy in heterogeneous WSN. Different from the former work [1], there are two kinds of nodes with different sensing range. In order to testify the tracking performance of the GFT moving strategy in this kind of WSN, we put forward a weighted Centroid localization algorithm. Simulation results show that the modified GFT moving strategy still suits for this kind of WSN. The tracking performance of the heterogeneous WSN improves a lot by using the moving strategy.

In complex indoor environments, non-line-of-sight propagation, multipath fading and shadowing effects can have a significant impact on indoor positioning, resulting in large positioning errors. Aiming at the problem of low positioning accuracy in wireless indoor positioning algorithm, this paper combines the advantages of RSS and CSI features to propose a wireless indoor positioning algorithm combining RSS and CSI features. Firstly, CSI data is filtered in time domain to diminish the impact of complex indoor environment on positioning accuracy. Secondly, use the principle of coherent bandwidth to decrease the CSI data dimension. Finally, the relationship between RSS and CSI is fused by confidence degree to determine the final position estimate. The experimental results show that the time domain filtering can reduce the environmental interference effectively. Compared with the algorithm of positioning using RSS or CSI only, the fusion algorithm has higher positioning accuracy. At the same time, the coherence bandwidth principle is used to lower the dimension, which reduces the complexity of the fusion algorithm.

Aiming at the problem that the time of current on-board computer processing complex computing tasks is tight and the data transmission rate between on-board computers is low, a high-performance on-board computer based on S698PM processor and time-triggered Ethernet is designed. The on-board computer uses a CPCI internal bus with a data transmission rate of 1 Gbps, which matches the 1 Gbps data transmission rate of time-triggered Ethernet, so that the computer has a high data throughput performance. In this paper, the principles of the computer design and the block diagrams of key modules are introduced in details, and the actual test results are given. The results show that the computing performance and the bus communication speed of the on-board computer are improved by 1–2 orders of magnitude compared with the current existing computers, and the design goal is successfully achieved.

In target tracking, it is difficult to ensure that a target can be detected by both radars and infrared sensors simultaneously. In this paper, the dimensional-reduced particle swarm optimization (DRPSO) algorithm is proposed to find the optimal deployment for radars and infrared sensors when tracking multiple targets in a 3D area. Since DRPSO prevents premature convergence during optimization, an optimal deployment with higher tracking ability is obtained within fewer iterations compared with classical PSO.

Due to current information transmission and interaction capabilities of the satellite main bus are difficult to meet the intelligent development trend. An integrated design based on the time-triggered Ethernet protocol is proposed. A method for fast generation and verification of CRC in the case of cut-through transmission is designed to further ensure communication quality. The network scheduling difference between variable length frame and fixed length frame forward communication is compared by SMT modeling and calculation, which show this design method has improved the network scheduling capability. In addition, this paper proposes further ideas on the issue of health monitoring under the Time-Triggered Ethernet protocol and cut-through switching.

Based on SURF feature detection operator, image feature points are extracted, matched and mosaic Wavelet transform is used to fuse the registered images and eliminate the edges between stitched images. Overall, the splicing effect is well.

In the distribution system, voltage sag/transient rise will cause unexpected power load outage. Dynamic Voltage Restorer (DVR) is usually used to compensate the grid voltage. DVR detection unit should have good dynamic performance to detect voltage amplitude and phase changes in order to trigger DVR in the rapid compensation of distribution network voltage fluctuations. In order to improve the power quality in power system, a DVR control strategy based on double loop vector decoupling control is proposed to improve the dynamic performance of DVR in compensation process. The experimental results show that the proposed control strategy can correct voltage sags more quickly than the traditional method, make DVR achieve better dynamic performance, and ultimately improve the power quality of the grid.

Facial micro-expression commonly has an extremely short duration and subtle motion. At the same time, the micro-expression databases are rarely available for research. These are not conducive to directly introduce the deep neural network which is currently outstanding in the field of image recognition into facial micro-expression recognition. Recently, some researchers use the video motion magnification technology to magnify micro-expressions and achieve a good performance of enhancing the recognition results. This paper follows this idea and introduces an innovative way to adjust the amplification rate adaptively instead of the previous manual way. In addition, we use the extended continuous frames, instead of a single apex frame, to extract a concatenate feature map for the final classification. We have demonstrated through a series of experiments on the CASME II database that our method can effectively improve the accuracy of facial micro-expression recognition.

Mobile edge computing (MEC) is envisioned as a promising technology for enhancing the computation capacities and prolonging the lifespan of mobile devices. In this paper, we study a single mobile user MEC system in which each base station is integrated with a MEC server in executing intensive computation tasks. The mobile user’s computation tasks can be partied into several parts to be offloaded to different MEC servers simultaneously. The mobile user’s energy consumption is minimized with the constraints of power and latency, and an iterative scheme is proposed to solve the optimization problem. The numerical results demonstrate the effectiveness of the proposed scheme.

This paper proposed a new method to classify soil with different pH values and humidity based on GRU-RNN via ultra-wideband (UWB) radar echoes. Five categories of UWB soil echoes with different soil parameter, the pH values and water contents, are collected and investigated by GRU-RNN. And the simulation experiment results indicate that compared with LSTM-RNN, GRU-RNN has a better classification performance and has a shorter execution time. This can be an evidence that GRU-RNN method is more suitable for the study of other soil parameters.

Laser communication has the characteristics of wide bandwidth, high data rate, and low power consumption, which is an important way to realize the information exchange of large amount. However, the transmission quality is deeply affected by atmospheric absorption, scattering, turbulence, and background light, which bring certain challenges to its reliability. This paper firstly summarizes the mathematical models of the main factors, and simulates the error rate of the incoherent optical link under the influence of beam wander, beam scintillation, and beam spreading. Finally, the paper further proposes a full-element BER analysis model including the influence of atmospheric turbulence, absorption and scattering, and background light.

Outage probability (OP) is important performance metric of broadband satellite communication networks. This paper investigates an amplify-and-forward dual-hop satellite relaying system operating above 10 GHz, where the satellite links which mainly affected by rain attenuation are assumed to following the double-lognormal distribution. By considering the satellite pattern and path loss, we obtain a new analytical expression for OP of the considered network. Numerical results have demonstrated the derived formula.

Multi-sensor management for multi-target tracking is a theoretically and computationally challenging problem. A multi-sensor management algorithm is proposed within the partially observed Markov decision process (POMDP) framework, and cardinality balanced multi-target multi-Bernoulli (CBMeMBer) filter is applied to track targets. The novelties lie in evaluating the threat degree of the targets using the analytic hierarchy process (AHP) and the threat-based sensor selection algorithm. Considering distance, speed and heading of targets, we use the AHP to evaluate the threat degree of targets at each sampling time. Based on the threat level of targets and detection distance of sensors, the sensor-target matching algorithm is proposed. Numerical studies are presented in the dynamical system. The simulation results show the feasibility of the method.

The basic theories of compressed sensing and measurement matrix are reviewed firstly, and then the equivalent conditions of the Null Space Property and Restricted Isometry Property for measurement matrix, the incoherence is introduced, including the theory and mathematical proof. On this basis, the construction methods and properties of several commonly used measurement matrices (random Gaussian matrix, Bernoulli random matrix, and Toeplitz matrix) are introduced. The time-domain sparse signals are used for simulation analysis. Simulation results show that the sparse signals can reconstructed when the measurement dimension M satisfies certain conditions. Considering the hardware implementation and storage space for matrix, and with the idea of circular matrix, this paper proposes a pseudo-random Bernoulli matrix. The simulation results show that the proposed matrix can realize reconstruction of sparse signal and is hardware-friendly, moreover, the required storage space is small.

According to the deposition rate collected by the crystal probe of the electron beam evaporation system, the output voltage of the crystal film thickness controller is used to control the voltage at both ends of the filament of the electron gun, so as to adjust the output power of the electron beam. According to the change of deposition rate, the closed-loop feedback control of PID in genetic algorithm is used to change filament voltage to stabilize deposition rate. Experiments show that by using this method to study the rate control algorithm in the process of electron beam evaporation, the surface of SiO2 film used in the production of experimental coatings can maintain a certain smoothness, and a relatively stable evaporation rate can be obtained. Other plating systems and different coatings used can be modified by appropriate parameters according to the algorithm, which can greatly improve the rate control effect.

This paper realized the interpolation and three-dimensional mosaic of Doppler weather radar data. Currently, the Adaptive Barnes interpolation method is generally recognized as an effective algorithm for weather radar. The smoothing scheme of this algorithm are improved to keep good characteristics of the raw volume data. Compared with the commonly used interpolation methods, the improved smoothing scheme of the Adaptive Barnes interpolation can get better CAPPI data without over-smoothing effect.

A type of vehicle diesel engine was taken as the research object, the numerical simulations of intake, fuel injection, mixture formation and combustion characteristics were carried out by a software, which including the drop model, vaporize model and breakup model etc. The distribution of fuel-air equivalence ratio and static temperature are computed and compared, in order to research on combustion property with different fuel in the vehicle diesel engine.

The NOMA technology uses the power domain non-orthogonal multiplexing method to enable multiple users to occupy the entire frequency band simultaneously to transmit signals. In order to maximize the total transmission rate of the system, an effective method is to use the genetic algorithm for NOMA power allocation. In this paper, the NOMA downlink system model is constructed, and the objective function and constraints are analyzed. A NOMA power allocation strategy based on genetic algorithm is proposed. The algorithm distributes user power based on the criterion of maximizing total transmission rate, therefore the algorithm has random search capabilities and relatively low search complexity. The simulation results show that when the system transmits power or multiplexed users is fixed the proposed algorithm outperforms the fixed power allocation algorithm in the total transmission rate. The total system transmission rate of the genetic algorithm is similar to the full space search algorithm. As the number of multiplexed users increases, the computational complexity of genetic algorithms is much lower than that of full-space search algorithms.

We propose a BERT-based data augmentation for labeled sentences called Aug-Bert. New sentences are generated by stochastically selecting words and replacing them with other words predicted by Aug-BERT. After a two-stage training, including CLP and L-MLM, BERT can be fine-tuned to be Aug-BERT. Aug-BERT can predict stochastically selected words according to both the context and the label. Depending on the ability of BERT’s deep bidirectional language model and the information of label incorporated via label-segment embedding, Aug-BERT can generate sentences of high quality. Experiments on six different text classification tasks show that our methods outperform most of the others and can improve the performance of classifiers obviously.

As a candidate wireless communication technology for future indoor scenarios, visible light communication (VLC) not only has the function of green illumination, but also can transmit data at high speed without occupying licensed spectrum resources. Therefore, it can offload the data traffic from the existing radio-frequency (RF) network. However, due to the rectilinear propagation of the visible light, the VLC coverage cannot always satisfy the traffic service. Several factors such as transmitted power, number of users, transmitted/received angle, and multiple access schemes affect the VLC coverage probability. In this paper, we utilize the user quality of experience (QoE) as the evaluation metric, and further, propose a new coverage model named as QoE probability coverage model which is defined as the LED coverage area projecting on the ground that the user can achieve a satisfying QoE with a certain probability. We investigate how the factors including non-orthogonal multiple access (NOMA) scheme, number of user, user density etc. affect the QoE probability coverage area. The research results may guide the design of the VLC network deployment, multiple access protocol and handover schemes.

Most of the garbage bins in public places are traditionally fixed, which are limited by the number of garbage bins, limited capacity, untimely cleaning and uneven placement, and other factors, causing inconvenience for people to use garbage bins [1]. This paper designs an intelligent garbage bin. Based on NB-IoT communication technology, it realizes the functions of self-moving and collecting garbage, self-detecting garbage capacity in the bin, temperature and humidity monitoring and so on. The design is divided into three parts: intelligent robot module, sensor circuit module and NB-IoT communication module. The intelligent robot module is mainly responsible for the mobile and obstacle avoidance function of the intelligent garbage bin, and realizes traversal and surround in the setting environment scene [2]. The sensor module is the core of the hardware system, which realizes the functions of obstacle avoidance evaluation, capacity monitoring, heat source induction and so on. The platform establishes contacts to feedback the contents of barrels, temperature, humidity, electricity and other information in a timely manner. This design combines convenience and practicability, and can improve the sanitary condition of public places.

In order to built a safe and stable power station, this paper provides a defect identification method to detect the quality of crimp. We detect the difference between the inserting position of steel corem and aluminum wire in the strain clamp and measurement the length and other characteristic parameters. It is meaningful for the evaluating crimp quality, and benefits for a qualitative analysis of the quality of wire crimping.

With the increasing demand of users, the ground network cannot meet the user experiences. Considering the advantages of satellite network, an innovative network is presented, which integrate satellite networks, the Internet and mobile wireless networks into one network. In this paper, the architecture of future space-based network is proposed, which can be divided into physical structure and virtual structure. In addition, in order to form this network, a three-layer and two-domain technical architecture is discussed finally.

In this paper, the complex (DFT) modulated filter banks is described. Based on the research evidences and findings collected, it mainly focuses on 4 parts: the detailed history of complex (DFT) modulated filter banks part, complex (DFT) modulated filter banks, Matlab programs implementation part and the part of applications in details generally. In order to explain the impact of these aspects on the complex (DFT) modulated filter banks, evidence based on experts’ opinions and identification of facts was used.

This paper uses Discrete Fourier Transform spread spectrum (DFT-Spread) technology to extend the frequency domain of ortho-subcarriers in orthogonal frequency division multiplexing (OFDM), which can effectively reduce the PAPR of coherent optical orthogonal frequency division multiplexing CO-OFDM system. Based on DFT-Spread-COOFDM system theory and simulation analysis, shows that this method not only can effectively reduce the PAPR of coherent detection system CO-OFDM signal, at the same time, compared with the traditional OFDM system, analysis it only adds to the complexity of a Fourier transform (FFT), the amount of calculation than selective mapping traditional (SLM), partial transmit sequence (PLS) technology to reduce PAPR algorithm has lower complexity.

With the boost of the quantity of antennas at the base station (BS) in massive multiple-input multiple-output (MIMO) systems, the channel capacity and spectral efficiency are also increased. Conventional channel estimation method, such as the classical minimum mean square error (MMSE), which involves the matrix inversion in large size with enormous computational complexity, especially in massive MIMO systems due to large antenna arrays. To degrade the complexity caused by the inversion of the matrix, a low-complexity channel estimation scheme is proposed based on the improved symmetric successive over relaxation preconditioned conjugate gradient (ISSOR-PCG) method to avoid computing the matrix inversion directly. A simple way is also introduced to address the optimal relaxation parameter for the proposed scheme, by utilizing the channel asymptotic orthogonality in massive MIMO systems. Analysis shows that the proposed channel estimator is able to degrade the complexity effectively compared with MMSE channel estimator. Simulation results illustrate that the proposed scheme can obtain near-optimal performance to the classical MMSE estimation method and outperforms other baseline schemes with increased number of iterations.

Based on the publicly opened SAR dataset of ships, methods for ship classification have been presented in this paper. For comparison, a joint feature based method for ship classification for SAR is described first. In this method, features for SAR ship classification are concluded, in which density of RCS and main-structure feature have been proposed to discriminate ships. Afterwards, a modified LeNet based method has been presented for SAR ship classification, by restricting the size of convolutional window and layers according to the properties of SAR. Experiments are conducted on the real measured data to show the effectiveness of the methods above. And by comparing the methods, the proper method for SAR ship classification has been concluded, as well.

In this paper, a backpropagation (BP) neural network algorithm and a multiple factor regression (MR) algorithm are presented to improve the performance of the prediction of wheat growth. By applying the BP neural network algorithm and the MR algorithm, the corresponding Leaf Area Index (LAI) and Soil Plant Analysis Development (SPAD) values can be regressed from the Thematic Mapping (TM) data. The experimental result demonstrates that the designed framework has a better performance, which can effectively predict desired parameters and provide a promising solution for the crop growth monitoring. For finding a better solution for the crop growth monitoring, the performance of the BP neural network and the MR algorithms have been investigated.

Location information is a crucial requirement in underwater acoustic sensor networks. Since ToA-based localization is a commonly used method among plenty ranging-based localization techniques, the problem of improving the performance of ToA ranging ought to be carefully considered. In this paper, we propose an improved ToA ranging scheme for localization in UASNs. Based on a two-step process, this scheme can acquire accurate detection of the earliest arrival time and estimate of clock offset. Experimental results with real sea-trial data confirm that our proposed scheme has excellent performance in improving accuracy of ToA ranging from two aspects, and this method is then employed for underwater localization.

Multi-layered satellite network is one of the effective methods for dealing with the problem of global network coverage. However, due to its complicated structure and frequent handover of satellites, the signal transmission can be influenced, resulting in the randomness of the quality of service (QoS) of links. This puts forward a challenge to the link performance evaluation. To tackle the challenge, this paper uses network calculus to analyze the performance of inter-satellite links between the three-layered satellite network, which is composed of high, medium and low orbits, and finds that the multi-layered satellite network has a better performance compared to the GEO architecture under low average channel transmission rate. In addition, the performance of three-layered satellite network, which decreases with the inter-satellite distance of LEO increasing, achieves the minimum network delay when the MEO orbital height is about 7000 km. Our analysis results provide reference to the establishment of the inter-satellite links of the three-layered satellite network.

This paper studies the sensor geometry design problem of sky-wave time-difference-of-arrival (TDOA) localization systems under non-line-of-sight (NLOS) scenario where signals are reflected by ionosphere-layer before arriving at sensors. Traditionally, the optimal sensor geometries for line-of-sight (LOS) scenarios have been derived. However, ionosphere-layer heights (IHs) are generally inaccurately known. IH errors can severely degrade localization performance but the joint estimation of IHs and target location is conventionally an ill-conditioning problem. To solve this problem, we propose a grouped sensor geometry, which enables the joint estimation of IHs and target location. In this way, we improve the robustness against IH errors in sky-wave TDOA localization. Theoretical analysis and performance comparison validate that the superiority of our proposed grouped sensor geometry.

In the existing Non-Orthogonal Multiple Access power allocation algorithm, the iterative water-filling algorithm is a commonly used algorithm, which has good performance but high complexity. In order to reduce the complexity, this paper divides the power allocation problem of Non-Orthogonal Multiple Access into two steps. Firstly, the water-filling algorithm is used to complete the power allocation between sub-carriers, then the greedy algorithm is used to allocate power to the superimposed users in the carrier. Many elements in the candidate power allocation coefficient set that are impossible to be optimal solutions are deleted by using the sum of power distribution coefficients of each user and the product of throughput, which effectively reducing the complexity. The simulation results show that the proposed algorithm has a slightly lower performance than the iterative water injection algorithm, but it effectively reduces the complexity. The performance of the proposed algorithm is better than other traditional algorithms, and a good compromise between system performance and complexity is achieved.

Accurate attitude information is essential and crucial for autonomous underwater vehicle (AUV) to achieve the purpose of precise control. However, there is an error between the measured value and the real value due to the influence of noise on sensor data acquisition. To obtain high-precision attitude information, this paper presents a data fusion method using adaptive Kalman filter to fuse data of multi-sensor which is integrated gyroscope, accelerometer and magnetometer. An adaptive fuzzy logic system (AFLS) is utilized to improve the fusion accuracy in the state estimation. The stability, static accuracy and dynamic tracking of the adaptive Kalman filtering algorithm are tested and analyzed through experiments. The experimental results show that the improved covariance adaptive Kalman filtering algorithm can fuse the measured values of the three sensors in attitude detection system effectively, and significantly suppress the angle drift caused by the accumulated error of the gyroscope and the influence of other noises in Multi-Sensor attitude determination system.

With the development of technology, the demand for better image resolution is growing. The common method to achieve higher resolution is to increase the aperture of the lens. However, for satellites, resources are limited and the cost of launching is high, which makes it difficult to implement optical systems with larger apertures. In this paper, methods are analyzed, where by applying the optical synthetic aperture imaging technique for a group of small satellites, similar resolution of a single large aperture system is achieved. Difficulties and suggestions are given and fully discussed.

Continuous Active Sonar (CAS) has some advantages over conventional pulse active sonar, such as larger time-bandwidth product, longer correlation durations, continuous detection and tracking. Recently two kinds of coded-modulated pulse trains were designed as the transmitted waveforms for CAS. The linear frequency-modulated (LFM) waveform is modulated via the Costas sequence to form the LFM-Costas pulse train. Another coded-modulated pulse train is composed of the generalized sinusoidal frequency-modulated (GSFM) waveform. This paper aims at designing a new coded-modulated pulse train by modulating the GSFM waveform with the Costas sequence. By comparison with GSFM and LFM-Costas pulse trains, the simulation results show that the new GSFM-Costas pulse train can provide better detection performance including thumbtack ambiguity function shape and low cross-correlation property, as well as the better performance of reverberation suppression.

Shadow removal from natural images is always a challenging but intriguing issue. In this paper, we develop a novel region based hierarchical model to address this problem. Firstly, an automatic shadow detection method is applied to obtain the shadow region and then roughly decompose it into umbra and penumbra areas. A hierarchical framework is then adopted to relight umbra and penumbra separately. For umbra arrears, each pixel is recovered via a lighting model on the basis of the characteristics of shadow-free regions. Afterwards, we present an improved brute force filling image in-painting strategy to relight complex penumbra areas using the information of neighboring patches which are selected from recovered umbra and lit regions according to their similarities in texture, gradient and distance. The comparing experimental results with three canonical and other state-of-the-art approaches have demonstrated the superiority and versatility of our method in generating high quality shadow-free images with consistent illumination.

Attention mechanism recently shows promising performance on varies of natural language processing tasks including natural language inference. We propose a collaborative attention mechanism based on the structured self-attention and the decomposable attention, which mutually benefit each other and provide both dependent and independent information of the sentence pairs. The model performs well on natural language inference tasks while having a relatively light-weight structure. Experiments on the SNLI dataset indicate that the approach enhances the accuracy and obtains improvements compared with the pro-posed methods and the individual two models, implying that it learns a better way to represent the textual semantic.

The traditional multiple-input multiple-output (MIMO) linear array of inverse synthetic aperture radar (ISAR) need to scan in height dimension in order to get the three-dimensional imaging of the target in the near field. In this paper, based on vortex electromagnetic wave MIMO linear array, a method is proposed for ISAR radar three-dimensional imaging, and the principle is designed for the radius of uniform circular array (UCA). Simulation results indicate that the proposed method can accomplish three-dimensional imaging of the target.

Based on the contradiction and existing problems of power system, the paper analyze and introduce the concept and connotation of the future smart power grid, and combined with the development of smart power grid, study these veral main ways of storage technology. Finally, the developing direction of smart grid energy storage technology is proposed.

Existing multiple track initiation methods based on Hough Transform have flaws, such as huge computation load in dense clutter environment and performance degradation in measurement error. To improve those defects, we propose a robust Hough Transform-based track initiation method for multiple target tracking in dense clutter. The whole proposed method is divided into three steps. The first step is to screen data through the grid-based velocity test. The second step is to cluster the screened data into groups based on eight connectivity principle of grids. The last step is to respectively make Hough Transform on each group data to achieve track initiation. The experiment results illustrate the proposed algorithm outperforms traditional algorithms on Hough transform based track initiation.

This paper first introduced the necessity of developing the space omni-directional plasma detector and its detecting target, and then presented the physical design, structural design, electrical performance, mechanical performance and other constraints and design requirements under this background. Then, in view of the omni-directional plasma space detector, in order to ensure the realization of space plasma detection, analysis, transferring function and could accept the space environment simulation experiment examination, this paper designed structural and improved and, by establishing a finite element mechanics model, gave the acceptance level mechanics simulation, including the detector’s response of modal analysis and frequency analysis. At last the mechanical test data proved its rationality. Finally, the application of composite material on the space omni-directional plasma detector is proposed. Maybe it would become a direction to improve the design.

To solve the problem of autonomous management of lithium batteries with balanced control energy in GEO satellite, this paper analyzes the requirements of the main management of lithium batteries, and presents the design scheme of the software system for the autonomous management of lithium batteries, which includes charge and discharge management of lithium batteries, mode conversion, Autonomous balanced management; self-overvoltage, over-temperature and over-current charging protection of the software, and bus overvoltage protection. The design and implementation of the system is verified on a GEO satellite. The design and implementation of the autonomous management software system proposed in this paper can provide reference for the autonomous management of lithium batteries with balanced control function for the follow-up GEO satellites.

In high frequency surface wave radar (HFSWR), Doppler frequencies of the vessels targets may overlap the first order Doppler frequencies from the sea clutter interference. Therefor it is hard to discover the vessel targets and estimate direction of arrive (DOA) of the ship targets. In this paper, the target direction finding method based on fractional Fourier transform (FRFT) was introduced for uniform acceleration/deceleration target, which used the frequency peculiarity difference between the targets and the first order sea clutter. Before the target DOA estimation, sea clutter were suppressed by FRFT method. The effectiveness of the method was verified by the simulations. The result demonstrated our method can estimate the DOA of targets in first order Doppler spectrum of sea clutter.

This paper proposes a clustering routing protocol in wireless sensor networks, which combines non-uniform clustering and inter-cluster multi-hop routing denoted by Adaptive Unequal Clustering Routing Protocol (AUCR). In this protocol, the energy of the candidate cluster head is self-enhanced, and the surrounding node density and the average energy of the nodes within the cluster radius are used to calculate the time of the cluster head. After clustering, each cluster head reaches the sink node by forwarding control information, and the sink node generates the routing table through the artificial bee colony algorithm to complete the data transmission. The cluster head dynamically adjusts its cluster size parameter through the data transmission process and the information exchange between the surrounding cluster heads, and adjusts the cluster size of the common nodes in the cluster by broadcasting. Simulation results show that adaptive intelligent clustering protocol can quickly adapt to network conditions, and can reduce node energy consumption, enhance network balance, and extend network life cycle.

For to know the performance of dual linear polarization SC radar and its consistency with CINRAD-SA detection data, the echo reflectance data of two kinds of radar are compared and analyzed. Using reflectivity data of two radars at the same time in the same rainfall event, two radar data were interpolated into a 3d grid at the same height to compare their intersecting areas by using the method of region lattice point comparison. Study on the scatter characteristics and probability distribution of two radar reflectivity values in intersecting regions. The results show that echo reflectivity characteristics of the perpendicular bisector in two radars have good consistency, but the echo data value detected by dual linear polarization SC radar is larger than that of SA radar; Under 25 dB, the detection value of dual-line polarization SC radar is greater than that of SA radar; The echo reflectivity data of SC radar are less discrete. The rainfall calculated by using the Z-R relation, R(KDP) and R(ZH, ZDR) method of the dual-line polarization SC radar is all higher than the observed values of the ground rain gauge, and the difference between the observed values of the rainfall calculated by the R(KDP) method and the rain gauge is relatively small. In order to improve the effect of quantitative precipitation estimation, further quality control of polarization parameters and echo data is needed.

In cooperative communication system, relay node may consume more energy for relaying the information from source node, resulting in decreasing the stored energy for its own transmission. Recently, simultaneous wireless information and power transfer (SWIPT) has been presented to collect radio frequency (RF) while decoding the received signal. In this paper, A SWIPT based on relay and subcarrier joint allocation for OFDM cooperative communication is proposed to maximize the transmission performance of the relay node while guaranteeing the relay performance, via collecting the RF energy of the source signal. A joint optimization problem for subcarrier and power allocations is proposed, which can be worked out by a repeated optimization arithmetic. It can be seen from the simulation results that the system can change the spectrum efficiency.

In recent years, with the massive increase in mobile smart devices, the demand for mobile services has also increased dramatically. In order to improve the network performance, full-duplex device-to-device (D2D) communications have drawn significant research interests. In this paper, a power control problem for underlay full-duplex D2D communications is formulated, which maximize the transmission rate of the full-duplex D2D link while fulfilling the minimum rate requirement of the cellular user. By using difference of convex (D. C.) programming, we can transform the problem into a convex optimization problem and find the optimal solution by using the iterative algorithm. Simulation results are given to prove the effectiveness of the algorithm.

In recent years, device-to-device (D2D) communications and full-duplex communications have attracted great attention. In the underlay mode, D2D users reuse the same spectrum resource with the cellular user, and thus the problem of the co-frequency interference between full-duplex D2D users and cellular user is particularly prominent. This paper proposes a power control algorithm based on max-min weight criterion, which transforms the non-convex optimization problem into the convex optimization problem by using D.C. programming. The optimal result of power control through finite numbers of iterations can improve the transmission rate of the whole system.

Although the LT codes have obtained much attentions, but how to design degree distributions can make LT codes with optimal decoding performance is also a hard issue which have not been solved very well. As most of the practical degree distributions are based on ideal degree distributions, and ideal degree distributions were designed under the asymptotic conditions, which can not make the practical LT codes with optimal decoding performances. Different with the asymptotic ensembles, under the finite length conditions, the degree distributions in the decoding process are dynamic. In this paper, we analyze the BP decoding process of LT codes, and then provide the change trends of output degree distributions by quantitize the BP decoding process of LT codes.

Existing reading recognition methods of the pointer type meter have problems of inaccurate target segmentation, high manual dependence of dial detection and low accuracy. This paper presents a self-tuning parameter pointer type meter reading recognition based Gamma correction. In detail, the proposed approach firstly processes the image by Gamma correction with the contrast to output the high contrast image. Then the presented method makes the circle Hough transform by automatically adjusting the parameter to segment the dial. Next, the algorithm further processes image by OTSU, refinement and Hough line transform to segment image and extract pointer. Finally, it calculates the slope of the detected line, and combines the lookup method with the formula method to interpret the indicator. Experimental results show that the proposed method can realize the automatic detection of the dial and accurate segmentation of the pointer, and the obtained reading is more close to the actual reading. The algorithm is more stable and reliable.

In millimeter wave (mm-wave) communication systems, large antenna array can be employed for higher data rates. Hybrid digital and analogue beamforming design is adopted to reduce the digital signal processing (DSP) power consumption and the circuitry complexity. In this paper, we investigate the multi-pair massive MIMO two-way relay networks with hybrid processing architecture. Furthermore, the power scaling scheme is also considered. We got some conclusions that when the number of antennas at relay station M goes huge, the asymptotic spectral efficiency can be obtained. We also compared three cases of power scaling schemes and obtained the optimal case specially when M tends to considerable large. We can draw the conclusion that when $$M\rightarrow \infty $$, the Case 2 (only the transmit power of users scaled by M in power scaling schemes) is the optimal case compared with the other two cases. Finally, the Monte-Carlo is employed to demonstrate the validity of analytical results.

Speckle suppression in synthetic aperture radar (SAR) image is one of the important steps in SAR image processing, in which spatial domain based algorithm has been widely used. However, such filtering algorithm does not consider the local orientation information and could not preserve the edge details well. To solve the problem, an improved frost filtering algorithm based on the four rectangular windows is presented. An edge strength maps (ESM) of the four rectangular windows is introduced into the frost filtering algorithm, to keep the proposed algorithm adaptively to match the local geometric structure of the multiplicative model. Meanwhile,the weight of each pixel near the edge local window is calculated. This algorithm could solve the problem of direction deficiency in variation coefficient, and preserve the edge details well. The experimental results show that the proposed algorithm has better results on speckle suppression and edge preservation than some existing algorithms.

Waterline extraction is of importance for safe navigation and environment protection. However, the existing pixel-based method failed to get accurate edges due to the similar features between land and interference. This paper proposes a minimum ratio with the mean and standard variation as similarity measure, and presents an improved region merging criterion which based on superpixels for waterline extraction. Utilizing the Envisat and Radarsat images, experiments show the proposed method can effectively extract the waterline and demonstrate better performance in contrast with some existing pixel-based algorithms.

In coastline detection, for synthetic aperture radar (SAR) images, active contour model based on gamma distribution faced unsatisfactory results especially in weak edge region. To solve this problem, we present a coastline detection algorithm with active contour model based on inverse gaussian distribution. Assuming that speckles of SAR images obey inverse Gaussian distribution. The energy functional based on the inverse gaussian distribution is constructed by the maximum likelihood estimation method, and then the active contour model is obtained by introducing the level set function and the length term. Through mathematical derivation, the level set evolution equation is obtained, which is used for coastline detection in SAR image. In experiment, single polarized envisat-1 and envisat-2 of SAR images are utilized. Experimental results show that the proposed algorithm has better detection capability than Gamma model.

The facial expression recognition occupies an increasingly important position. In this paper, the static emoticon images are analyzed, and the geometrical priori based weighting strategy is used for feature fusion. The disadvantages of the preliminary features extracted by the traditional methods lack the discriminant characteristics. An improved Linear Discriminant Analysis (LDA) algorithm based on intraclass divergence matrix correction is proposed. The data is sent to the Generalized Regression Neural Network (GRNN) classifier for identification and has achieved good results.

With the development of modern wireless technologies, large bandwidth signals and large antenna arrays are widely adopted. It facilitates the high resolution detection and tracking of objects, which is of great importance in numerous applications, such as autonomous vehicles, internet of things, and so on. In this paper, we mainly focus on the target positioning and tracking problem based on the “non-linear" measurements, namely the range and angle, where both the location and the contour information are considered. The random matrix based theory is adopted for this extended target localization issue. Both single and multiple targets scenarios are considered. Numerical results validate our analysis, and show that the proposed tracking frameworks can work for real applications such as the autonomous vehicle and so on.

In the 5th generation mobile communication, a heterogeneous network architecture based on control/user (C/U)-plane split will be utilized in high-speed railway communication system. In this network, handover between two adjacent macro cells involves two types of handovers which are macro-macro base station (BS) handover and small-small BS handover. It will increase the complexity of handover, bring more communication interruption and reduce handover success probability. To alleviate the problems, we propose a coordinated multi-point (CoMP) based inter-macro cell handover scheme. Two antennas are deployed at the front and the rear of the train. And the front antenna is connected to target small BS firstly by using CoMP technology. Then handover is triggered according to a simplified trigger decision. Because the target small BS has been connected, only macro-macro BS handover should be performed. Simulation results show that the proposed scheme improves the handover performance.

The differences of onboard faults characteristics and severity result in different models, methods and interfaces of fault diagnosis. Thus, FDIR (Fault Discovery, Identification and Recovery) systems usually use a hierarchical architecture in centralized or distributed styles. It is difficult for the centralized FDIR to guarantee the timeliness and coverage of fault diagnosis simultaneously, and the distributed one would bring safety problems. Both of them only focus on the health states of spacecrafts, while not considering its own reliability and reusability. Taking advantage of the above two, the architecture proposed by this paper keeps synthetic views of the spacecraft health states at higher levels and distributes local FDIR at lower levels to improve the timeliness and coverage of fault diagnosis simultaneously, which is based on the hierarchical architecture of spacecrafts and fault severity levels. To ensure the safety and reliability of the FDIR system, a highly decoupled runtime model is proposed. To improve the reusability of the architecture, a unified FDIR model is proposed, which includes hierarchical programming interfaces, etc.

With the development of modern educational methods, new modes and platforms for college students’ learning have appeared. Through the scientific statistics and analysis of students’ learning behaviors, we can find the regular pattern contained in these data, discover students’ interest goals and predict learning effects, and provide students with targeted and personalized learning guidance. At present, the common analysis methods are mainly K-means clustering method. Considering the support vector machine has higher classification accuracy, this paper proposes a support vector machine analysis method based on Fisher-Score feature selection for students learning behavior analysis. Firstly, through the Fisher-Score feature selection, the key features in the learning behavior are selected, and the honor features unrelated to the learning effect are removed, and then the data analysis is performed by the SVM classifier. The verification results show that our method has better accuracy.

Network traffic text classification plays an important role in network security. Traditional classification methods based on machine learning, such as supervised learning algorithms and semi-supervised algorithms, are insufficient: classification mode is too simple, unable to adapt to diverse classification requirements; text feature selection method is simple, text classification lacks diversity, and classification accuracy is low. And the classification speed is slow, not suitable for environments with high traffic and real-time. Multi-instance learning classification can describe the characteristics of the sample more accurately and comprehensively, and can improve the classification effect. In this paper, we combined the multi-instance learning classification with principal component analysis (PCA) to select text features of data sets, and removed the redundant and uncorrelated features in the original data, obtained a better classification accuracy.

In order to study the development law of lightning induced overvoltage on transmission lines, based on the theory of lightning electromagnetic field, Taylor’s formula is used to develop the first two expressions of the lightning current double exponential function, and the correction factor is introduced to offset the error of the latter items when the expansion is performed. This expression gives the analytical solution of the vertical electric field. Combined with the field line induction equation of the transmission line, the D’Alembert formula is used to solve the field line induction equation, and the scattered field voltage on the line is solved, and then the induced overvoltage is obtained. The correct parameter value is selected by simulation to verify its correctness.

The major bottlenecks of the frequency division duplex (FDD) systems are the overheads of downlink channel state information (Downlink-CSI) estimation and feedback. To address the aforementioned problems, this paper enables reconstructing Downlink-CSI directly from the information of uplink channel state (Uplink-CSI) by proposing a convolutional long short-term memory network (CLSTM-net) scheme. More specifically, the CLSTM-net extract features from Uplink-CSI by utilizing the temporal and spatial correlations existed in the Uplink-CSI and the Downlink-CSI, and then maps the features to the reconstruction of Downlink-CSI. Experiment results verify the superiority of the proposed CLSTM-net based method.

The current collision avoidance implementation is based on unilateral static information, but not the bilateral movement information of the two vessels. In this paper, we utilized the Machine Learning (ML) Mechanism Integrated Vessel Networks (MLMIVN) to collision avoidance cooperatively for the two vessels especially for the (Maritime Automatic Surface Ships, MASS). The device onboard has the capability of big data analysis and edge computing and Vessel Networks is based on Device-to-Device(D2D) communication. The safety and economy of collision avoidance route can be improved by training historical navigation data. First, we put forward the concept of cooperative collision avoidance that considering the motion state of each vessel, and a two-step-turn cooperative collision avoidance method is utilized. Then a improved genetic algorithm combined with K-Means algorithm is used to train the big data.

In order to obtain a wide field of view and high spatial resolution hyperspectral image, image splicing has been widely studied. This paper proposed a weighted fusion algorithm to address the phenomenon of color inhomogeneity in the overlapping regions of two spliced images, which is caused by different light intensity or different camera angles of scanning. Different from the traditional direct average fusion methods, the proposed weighted algorithm conducted the fusion of the overlapping regions with adaptive weights change. The simulation results show that the proposed weighted average fusion algorithm can effectively eliminate the seam between two parts of the assembled image, making the transition of the fusion area more naturally [1].

Target detection has become an important research direction in hyperspectral imagery (HSI) processing. In this paper, aiming at the phenomenon that different bands have different abilities to distinguish materials, a spectral weighting detection algorithm is proposed. Firstly, relative distance between different categories as the spectral separability criterion is used to estimate the distinction ability of each band. And then different bands are endowed with different weighting coefficients. Finally, the RX and LPD algorithms are used to test the efficiency of the proposed spectral weighting method. The experimental results show that the detection algorithms based on spectral weighting have better performances than the traditional RX and LPD algorithms.

To analyze the various non-functional properties of the AADL (Architecture Analysis and Design Language) model, many model transformation processes transform different AADL elements to different Petri nets. Unifying these transformation processes into a single process can greatly facilitate architects analyzing multiple properties simultaneously. The difficulty is that the specific elements in specific Petri nets lead to different transformation rules of different transformation processes. Some studies transformed AADL model to Petri Net Markup Language (PNML), the interexchange format of different kinds of Petri nets, to realize the unification, but only supported the transformation of part of the AADL architecture model elements. This paper proposes a framework for analysis of non-functional properties of AADL model, improving the unification work by supporting more AADL elements transforming to PNML. We establish the transformation rules mapping elements in AADL error model and behavior model to PNML. In addition, we transform AADL properties to tool specific information in PNML to generate specific Petri nets.

The covariance estimation of dynamic system control models has applied both to estimator design and controller performance monitoring. Many algorithms has been proposed to estimate the unknown noise covariance of dynamic systems, such as maximum likelihood estimation (MLE), Bayesian estimation, covariance matching, correlation technique. The MLE method that maximizes likelihood estimation of the noise covariance matrix for the given observation sequence has a larger time overhead. This paper solves this problem by proposing MLE based on golden section. Each iteration of this algorithm will reduce the convergence interval to 0.618 times of the previous one. The length of convergence interval will be exponentially reduced. Simulation results show that the proposed algorithm has a more stable and faster convergence speed than the gradient-based MLE in both linear and nonlinear examples.

Artificial Intelligence (AI), which is designed to analyze huge amount of data, is introduced into wireless network analysis for assistance. Because the amount of data in this field is extremely massive, feature selection is a critical process. Compared to correlation based feature selection techniques, causal inference based Linear Mixed Models (LMM) can identify features with direct and fixed effects resulting from causal variables. However, Correlation based Feature Selection (CFS) does not give interpretable results and lacks justification. In this paper, an improved LMM is proposed for feature selection and used to analyze the performance of a wireless network. We introduce the $$L_2$$ normalizer into the parameter estimation process of an LMM to regularize the standard model. Then, we utilize the results of the network analysis to construct a quality of users prediction model and use the improved LMM algorithm to select features and perform prediction. The experimental results prove that our proposed feature selection model outperforms other methods with respect to interpretability and prediction accuracy.

The main contribution of this paper is an approach for introducing more context to improve the accuracy of the traditional SSD (Single Shot Multibox Detector), which is one of the top object detection algorithms in both aspects of accuracy and speed. We augment SSD with a multi-level feature fusion method at shallow layers for introducing contextual information to improve accuracy, especially for the detection of small objects, calling our resulting system SFSSD for shallow feature fusion single shot multibox detector. In the feature fusion module, features from different layers with different scales are concatenated together, followed by some down-sampling blocks to generate new feature pyramid which will be fed to multibox detectors to predict the final detection results. For the Pascal VOC2007 test set trained with VOC2007 and VOC2012 training sets, the proposed network with the input size of 300 $$\times $$ 300 achieved 75.4 mAP (mean average precision), while the network with 512 $$\times $$ 512 sized input achieved 79.7 mAP. Our SFSSD shows state-of-the-art mAP, which is better than those of the conventional SSD, Fast R-CNN, Faster-RCNN, ION and MR-CNN.

With simultaneous wireless information and power transfer, the energy of RF signal can be used effectively for the self-sustained operation of wireless devices. In practical applications, power splitting method can be used for simultaneous wireless information and power transmission (SWIPT) between base station and multi-user. Different user systems have different energy consumption, so users with insufficient power need more energy transmission to supplement their power. Nowadays, the beamforming strategy of simultaneous wireless information and energy transmission does not consider the problem of insufficient power at the terminal. This paper proposes a energy state feedback method to optimize simultaneous wireless information and power transmission system. Through the joint design optimization of beamforming and power splitting, the power transmission of users with insufficient power is added, and the minimized transmission power optimization is done under the conditions of satisfying SINR and energy harvesting constraints by utilizing the channel state information and node power information of each node. This optimization model is difficult to solve. In order to solve this problem, the concept of reserve group is introduced. When the optimization model cannot be solved, the users with the worst channel state of the node are put into the reserve group. These users do not participate in the optimization first. After the new channel state information is obtained, the optimization model is established again. If the problem can be solved, the transmission power of base station doesn’t reach the maximum power, some users in the reserve group is added to continue to optimize, if it has reached the maximum power of base station transmission, it is not optimizing, so as to maximize the optimization of all users and improve system performance.

With the development of blockchain technology, blockchain technology has strong vitality in various industries due to its characteristics of dispersion, anti-mite modification and traceability. Blockchain technology allows trusted parties to trust transactions and realize value streams. Since there are different values in different chains, will the values of different chains also flow? This is a problem that needs to be solved in the cross-chain interaction. Under the scenario for cross-chain interaction between private chain or alliance chain between enterprises, this paper proposes an alliance chain model that can realize cross-chain interaction between enterprises. On this basis, it analyzes the difficulties of cross-chain interaction technology and analyzes the model. At last, we summarize the paper.

In recent years, blockchain has emerged as a new decentralized infrastructure and distributed computing paradigm with the increasing popularity of digital cryptocurrencies such as Bitcoin. Blockchain technology is a decentralized, de-trusted, open and transparent distributed data storage technology that can reduce the costs of trust and achieve secure and reliable data interaction. Big data has been widely concerned and studied because it contains great value, however, because big data sets usually contain a lot of privacy-sensitive information, extensive and in-depth research on privacy protection of big data has not been realized. Under the premise, reasonable storage of big data is an urgent problem to be solved. Under the premise of analyzing blockchain structure and access control, this paper proposes data security storage protection architecture based on blockchain technology and access control technology, and analyzes the architecture in detail, and finally summarizes the paper.

Dynamic migration of virtual machine (VM) is the main feature of the cloud environment, and trusted computing is one of the core technologies to solve the security problems of the cloud environment. Aiming at the security problem of VM and virtual trusted root (VTR) migration in the cloud environment, an active dynamic trusted migration scheme built upon the Chinese active immune trusted computing (AITC) is investigated in this paper, where migration architecture and protocol are included. To validate the effectiveness of the scheme, experiments are carried out and promising performance is shown in comprehensive experiment results.

Aiming at the malicious attack problem in FM positioning, based on the characteristics of FM broadcast signal, a kind of FM Multiple hybrid Strategy Filtrate Localization (FMFL) is proposed: according to the current FM signal characteristics, the reasonable distance error range of each anchor node is determined, which is effective. The effect of joint attack and other methods on the performance of the positioning algorithm is reduced. At the same time, in order to improve the positioning accuracy of the algorithm more effectively, the positioning process is divided into two stages: anchor node validity verification and positioning calculation. The experimental results show that this method can obtain higher security and positioning accuracy in the presence of multiple malicious attacks.

The problem of mobile sensor network location has always been a core technical problem that needs to be broken in the application of IoT. The existing location method is difficult to effectively solve the problem of the location of unknown nodes in the mobile environment. Although the Monte Carlo method is more suitable for dynamic sensor networks than other existing positioning algorithms, this algorithm still has bottleneck problems such as low positioning accuracy and low security. This paper proposes a high-security Monte Carlo positioning method based on FM signal characteristics (FM-MCL). Compared with the traditional KNN, SVM and MCL algorithms, FM-MCL significantly improves the security performance of the algorithm. The positioning accuracy of the algorithm makes the positioning accuracy still less than 20% in the environment of malicious attack.

Coherent state based quantum optical communication is able to offer high speed secret key rate with relatively easy operation by the use of mature classical optical communication infrastructure. While it involves the discrete modulation format, the phase noises due to the operation of those classical coherent optical communication devices may impact the security. We analyzed the security with respect to coherent state based quantum cryptography by taking a concrete 4 state modulation into account in this paper. Our numerical simulation results revealed that the phase noise resulting from the discrete modulation degrades the secret key rate remarkably.

With the development of mobile communication and multiple access technology, SCMA (Sparse Code Division Multiple Access) technology, as a new access technology, can improve the overload rate, gain coding gain and maintain low complexity because of the limited spectrum resources in the new demand scenario. Firstly, according to the principle of SCMA, this paper will introduce the superiority of LDS technology, the uplink and downlink model of SCMA technology and MPA algorithm of receiver. Secondly, based on the standard of bit error rate, a feasible design method of SCMA codebook is studied in this paper. In the design of codebook, it is mainly designed for 15 users, 6 chips, overload rate of 2.5 scenarios. In the design, we mainly use the method of phase rotation optimization, and design these two schemes according to the design method of copying LDPC codebook. And the bit error rate is analysed.

We study the spectrum coexistence of Fixed Satellite Services (FSS) uplinks and Fixed Services (FS) feeder links in Ka band (27.5–29.5 GHz) which is allocated for FS systems primarily. Firstly, the spectrum sharing framework is proposed. Subsequently, based on the interference analysis between these two systems, a novel power control is investigated to assure FS performance. Finally, a carrier allocation mechanism is applied resulting in a higher per beam throughput.

Convolutional Neural Network (CNN) is popular deep learning framework with vast applications in image classification, segmentation, object detection etc., and has attracted attention of the machine learning community at large. In this publication, we aim to propose a model for classification of fruits. Our model is novel as it applies the concept of local connectivity of patterns in neural networks and learns low level features while preserving information about the geometry of objects and shapes. We demonstrated the effectiveness of our approach on a fruits dataset with 63 classes. The obtained results effectively demonstrate the local representation capacity of CNNs. We achieved test set accuracy of 96.63% and training set accuracy of 96.42%, which effectively exemplify the effectiveness of CNNs for this class of problems.

In the original version of the book, the affiliation of authors have been updated from “Harbin, China” to “Heilongjiang University, Harbin, China” in the chapter “Research on Image Encryption Algorithm Based on Wavelet Transform and Qi Hyperchaos”. The correction chapter and book have been updated with the change.