Communications, Signal Processing, and Systems

Traditional WLAN typically relied on signal strength for handoff that is not sufficient for fair selection of physical access point (_PAP) and also causes the poor performance of network and load imbalance. To deal with these issues, we proposed a mobility management scheme on the basis of logical AP (_LAP) that keeps a connection with the mobile terminal (MT) during handoff either triggered by the user or software-defined network (SDN) controller for seamless mobility. The proposed scheme was implemented on a real testbed in WLAN environment, and the evaluation results demonstrated that it could provide a rather good seamless handover without throughput degradation and load imbalancing between _PAPs, with an efficient handoff procedure to allocate the best AP in the neighbor region.

With the continuous improvement of social economy as well as science and technology, along the continuous development of Internet technology, Internet has become an indispensable and important part of the learning life and social work of people; moreover, with the increase of network ports, the giant network traffic will lead to network congestion, that is also the main reason which affects the network performance and restricts the development of the network. In this paper, the author puts forward that a kind of routing algorithm which may optimize the routing could be obtained by means of combining with correlation function, calculating the hop number of nodes as well as occupation situation of cache, which is able to reduce the network transmission delay and network congestion via combining with alternate routing.

Fingerprint-based positioning in Wi-Fi environment has caught much attention recently. One key issue is about the radio map construction, which generally requires significant effort to collect enough Wi-Fi Received Signal Strength (RSS) measurements. Based on the observation that the Micro Electromechanical System (MEMS) can automatically calibrate the target locations without complex equipment, we propose an efficient radio map construction method based on the technology of multi-sensor. Different from the conventional methods, the proposed one first relies on the gait detection approach and quaternion-based extend Kalman filter algorithm to estimate the velocity and heading of the target. Second, the Pedestrian Dead Reckoning (PDR) algorithm is used to calculate the current location of the target in a real-time manner, and meanwhile the data from Wi-Fi module are collected to generate the fingerprint database. The experimental results show that the proposed method is effective in positioning accuracy and efficient by saving the time and energy.

In order to realize the attitude determination in high accuracy and stability of BeiDou Navigation Satellite System (BDS) receiver carrier, a BDS/INS tightly coupled attitude determination algorithm was proposed. First, the error model of BDS system was put forward. Then, an extended Kalman Filter System with the double differences carrier phase as the main observation and the error state equation of INS as the system state equation which can ensure the attitude in a high precision were designed. Last, a platform was set up for testing the effectiveness of the algorithm with a single-frequency BDS receiver and an inertial sensor. The results show that the algorithm can effectively improve the measurement accuracy and output frequency of the attitude.

Due to the limitation of hardware resources in satellite onboard switches, this paper proposes a three-level deficit-round-robin-based variable-length scheduling algorithm, which could improve the throughput as well as saving the hardware resources. The circuits occupy 6607 slice registers and 8092 slice lookup tables while used in a 16 $$\times $$ 16 switch fabric, which can meet the requirements of triple modular Redundancy. The scheme can meet the requirements of 160 Gbps switch fabric with 16, 10 Gbps ports, and the design is implemented in a Xilinx xc6vlx240t FPGA. Typical simulated results are presented to show the availability of the scheme.

A shared memory output queuing structure used in satellite onboard switches is presented in this paper, where DDR2 memory is chosen as the main storage resource. The DDR2 memory can improve the storage capacity and the ability to resist flow fluctuation. The whole shared memory switch fabric is realized with Verilog HDL, simulated with ModelSim SE 10.0b and occupied 8921 LUTs and 77 Block RAMs in a Xilinx xc4vsx55 FPGA, which indicates that the key resources consumption can meet the requirements of triple modular redundancy. When the data is 64 bit wide and the system clock is 100 MHz, the peak throughput of the switch fabric can reach 6.4 Gbps.

Considering the hardware resource limitation in satellite onboard switches, a Credit-based Dynamic WRR (CDWRR) Scheduler is presented in this paper. The scheduler can improve the throughput under nonuniform traffics and afford good support for multicast traffics. It can also guarantee the service priority and ensure the fairness concurrently. With the 128 bit data width and 100 MHz system clock, the peak throughput of switch fabric mentioned in this paper is 12.8 Gbps, which can meet the requirement of the 10 Gbps input port for the satellite onboard switches. The CDWRR scheduler is used in a Combined Input Output Queued (CIOQ) switch structure and realized with Verilog HDL. The scheduler occupies 2251 LUTs and 3 Block RAMs in a Xilinx xc6vlx130t FPGA, which indicates that the key resources consumption can meet the requirements of triple modular redundancy. Typical simulated results show that the scheduler works correctly.

Software-Defined Wireless Network (SDWN) aims to build a flexible wireless network infrastructure that can support future Internet services. In this paper, we present WLAN architecture to take advantage of OpenFlow that provides the global view of the entire network including wireless network configuration, resource allocation, and flow control policies to make the load balanced network environment. We build simulation environment through Mininet-WiFi to analysis throughput and jitter values of associated stations. The results demonstrate that proposed architecture can divide the load between APs that increase the average throughput of associated stations.

Under the circumstance of high reliability demand in satellite onboard switches, an Iterative Round-Robin with SLIP (iSLIP) matching scheduling algorithm with load balancing suitable for Combined Input and Output Queuing (CIOQ) switch is presented in this paper. The implementation of load balancer improves the system reliability and the ability of recovery from failure. The iSLIP algorithm with the function of load balancing is used in a 16 $$\times $$ 16 CIOQ switch, and the whole switch fabric is implemented in a Xilinx xc7vx690t FPGA. Typical simulated results are given and analysized.

A conjugate cyclic adaptive beamforming algorithm is proposed with the conjugate symmetric constraint. By using the symmetric structure of uniform linear array, it can be first proved that the conjugate cyclic correlation matrix is centro-Hermitian matrix, and the weight vector of the linearly constrained conjugate cyclic adaptive beamformer has a conjugate symmetric structure which is particular to conjugate cyclostationary signal. Then, the conjugate symmetric constraint for weight vector is added to the original algorithm, and the weight vector is derived from the proposed algorithm by recursive least squares. Compared to the traditional algorithms, the proposed method can achieve a higher steady state output SINR and a faster convergence speed. Moreover, in our method, the number of variables in the update equation are reduced effectively by half, which leads to significantly improve the overall performance. Simulation results demonstrate the effectiveness of the proposed method.

A cognitive automatic identification system (CAIS) employing some promising technologies, such as spectrum sensing and OFDM, has been investigated by us in recent 4 years. In the CAIS, the normal location messages and security video information will be loaded by employing the OFDM. However, OFDM signals have a high peak-to-average power ratio (PAPR), which causes signal distortion. Lots of the PAPR reduction techniques have been presented in the literature, among which, a technique of dynamically selecting sequences has been taken considerable suggestion, but its high computational complexity and bandwidth expansion impedes practical implementation. In this paper, transforming sequence of Frank-Heimiller (TSFH) is proposed for the first time, which is with the ideal correlation properties; then we propose a dynamic spreading code allocation (DSCA) based on the set of TSFH and artificial bee colony algorithm (DSCA-TSFH and ABC) scheme to obtain low PAPR. Simulation results show that the proposed DSCA-TSFH and ABC algorithm is an efficient one to achieve significant PAPR reduction, with a low computational complexity.

As sparse arrays cost fewer elements, they are less expensive than dense arrays and could be applied widely in antenna and sonar deployment, two sparse cylindrical arrays are proposed in this paper. According to the characteristic of cylindrical array, it can be seen as a linear array whose elements are the identical circular arrays. Therefore the co-prime linear array and nested linear array could be combined with circular arrays. Based on the beam pattern of uniform cylindrical array, 1D and 2D beam patterns of co-prime cylindrical array and nested cylindrical array are derived respectively. In addition, when more than one sources are coming from arriving directions, the performance of sparse arrays are analyzed and compared. The simulation results show that the new proposed sparse cylindrical arrays not only reduce the number of elements, but also improves the resolution in comparison with an equal length uniform cylindrical array.

D2D communication is considered to be one of the key technologies in LTE-A network even in 5G communication system. Spectrum allocation problem is an important part in the study of D2D communication. Considering the spectrum allocation problem under D2D communication scenario, this paper proposes a system model with graph theory and adopt the concept of interference weights. A novel spectrum allocation algorithm ACO-GA combined algorithm is proposed. This algorithm combines ant colony algorithm and modified genetic algorithm based on the theory of graph coloring. Simulation results show that ACO-GA combined algorithm performs superior than traditional ant colony algorithm and genetic algorithm on spectrum efficiency and interference cost.

As we access data widely in our daily life, the present situation is that there is a lot of demand for data access in the process of moving while there used to access data in the static context. Under this situation, using the mobile relay nodes (MRN) in conventional cellular communication to support auxiliary transmission communication can effectively improve the ability of data transmission in the process of mobile performance. In this paper, we discuss that under the driving on the highway scenario, with the using of mobile relay nodes, we chose the whole vehicle as the communication transfer object and the middle vehicle carry as mobile relay node (MRN). Under the condition of considering the link capacity, this paper analyzes the traffic flow with the demand of the mobile relay nodes (MRNs) and conveys a digital expression. Simulation and probability analyses indicate that for three different traffic flows, it is expounded to the change of traffic to the mobile relay nodes (MRNs) demand changes.

Dynamic time slot frame ALOHA algorithm is currently the most widely used anti-collision technology in radio frequency identification (RFID) system. Based on the traditional ALOHA algorithm analysis, we propose an improved algorithm. The algorithm is based on a packet adaptive ALOHA anti-collision algorithm (PA-ALOHA). First, the reader scans and counts the time slots which are randomly selected by tags, and sends it to each tag. The tags, then, adjust accordingly the time slot that enable the reader to skip idle slots and collision slots, adaptively allocate effective slots, and then identify tags rapidly. The algorithm employs packet and dynamically adjusts the frame size and other strategies, in order to reduce the time of processing slots. Simulation results show that PA-ALOHA algorithm improves the efficiency and stability of the system and reduce the transmission overhead. Especially, when the number of tags is over 1000, the algorithm throughput is still above 70% that has been greatly improved system efficiency than conventional ALOHA algorithm.

This paper analyzed and contrasted multiple tags inventory performances of the two international air Interface standards and Chinese national standard in RFID. Given the innovation of technology and design ideas of the Chinese national standard, the problems of inventory were analyzed in the three standards. Afterwards, the performance was simulated by software and hardware platforms, and the performance of Chinese national standard [1] is better than EPC (EPCglobal Class-1 Generation-2 UHF RFID Protocol) [2] or ISO 18000–6B (Information Technology-Radio Frequency Identification for Item Management-Part 6: Parameters for Air Interface Communications at 860–930 MHz) [3].

Full-duplex relaying (FDR) can receive and transmit simultaneously over the same frequency band, thus enabling a significant increase of spectral efficiency and has attracted much research interests. In this paper, we investigate the ergodic capacity of multi-hop decode-and-forward (DF) FDR systems, in which the relay nodes suffer not only from self-interference but also from inter-relay interference. We consider two cases for the inter-relay interference, i.e. one relay node knows perfect channel state information (CSI) from all other relay nodes or only knows the CSI from the previous relay node. The ergodic capacity upper bounds are derived for each case, respectively. Finally, we present numerical results to compare the ergodic capacity of multi-hop FDR with multi-hop half-duplex relaying (HDR).

This paper considers transmit multi-beamforming for colocated uniform linear array (ULA). First, the colocated ULA emitting sum of independent orthonormal baseband waveforms by each element is briefly introduced, and its transmit beampattern expressed by the sum of sub-beampatterns of all the waveforms is formulated. Then the beamforming algorithms using the criterion of minimum integrated sidelobe level (MISL) for different types of steering angle-space are proposed. The algorithms can be summarized by optimizing the weighting coefficients of each waveform, which controls one sub-beampattern, to form a beam based on parallel MISL beamformers. And each beamformer is then converted to a Rayleigh quotient (RQ) minimization in which the optimal closed-form solutions can be obtained. Finally, numerical comparisons and computational complexity analyses are provided to validate the MISL beamformers.

Circular synthesis aperture radar (CSAR) is an efficient way to achieve high-resolution imaging and a common way to realize 3D imaging. In this paper, a multi-antenna receiving (MAR) technique for terahertz radar is presented. In the vertical direction, one antenna transmits radar signal while multi-antenna receives echoes scattered from targets. Data fusion based on threshold selection method is used for different antenna imaging results to get a better image. Imaging quality of the horizontal plane by the proposed technique is remarkably increased. The simulations verify the effectiveness of the proposed technique.

In the process of high-precision liquid level measurement, radar has to solve the problem of ultra-close liquid level’s high-precision survey sometimes, such as the distance of just a few tens of centimeters. For single-channel radar, the echo signal is a series of real data; and its spectrum will become no longer simple when the liquid level is very close. Since for real data, the positive and negative frequency components will superimpose on each other in its spectrum. In this case, the existing high-precision liquid level measurement algorithms are no longer effective. In order to solve this problem, the authors investigate a new high-precision liquid level measurement method. In this method, the echo signal is windowed first; and then a corresponding interpolation algorithm is designed aiming at the window type; finally, iterate the interpolation algorithm, and the high-precision ranging result is obtained. Experiments show that this method can realize the high-precision ranging of ultra-close liquid level. This method can be used for high-precision ranging radar, such as liquid level meter radar, which is of great practical significance.

Synthetic aperture radar (SAR) images play a quite important role in military and environmental monitoring. But the SAR image was greatly affected by coherent noise, which affects its application in the subsequent image analysis. In most of the SAR image de-noising algorithms in hand, the same operation is applied to the whole SAR image, which leads to artificial texture or edge blur. In order to overcome this shortcoming, this paper proposed a new SAR image de-noising method based on nuclear norm minimization (NNM) fusion algorithm. The noisy SAR image is de-noised by two different algorithms, and two de-noising images are fused to final de-noising image based on nuclear norm minimization fusion algorithm. Experimental results show that the proposed algorithm not only effectively improves the visual effect and objective indicators of de-noising image but preserves the local structure of the image better.

A new subspace learning method called neighbourhood feature space (NFS) discriminant analysis is proposed for high range resolution radar target recognition. An intra- and extra-class NFS is formed for each sample, and the within- and between-class scatter matrices are redefined according to the point-to-space difference, respectively. An additional weight is introduced to emphasise samples near the class boundaries and de-emphasise samples far from the class boundaries. Both the between-class separability and within-class local geometry preservation are considered to seek a discriminative subspace for classification. Experimental results of the measured high-range resolution profile data demonstrate the effectiveness of the proposed method.

In this paper, synthetic aperture radar (SAR) images are investigated theoretically and experimentally for a high two-dimensional (2D) resolution (cm level). In situ measured data of two soda cans at different spatial location are collected using a PulsON 410 ultra-wideband (UWB) radar with a pair of improved directional planar antennas. The images are obtained using two SAR imaging algorithms: Time Domain Correlation (TDC) and range Doppler (RD) respectively, and the results of RD are preferable to images via TDC. Compared to the simulation, the measurement results can provide more accurate targets location information.

Two model-free algorithms for soil moisture retrieval based on singleton type-1 fuzzy logic system (T1FLS) and non-singleton T1FLS are investigated in this paper. We reformulate the traditional soil volumetric water content (VWC) retrieval problem into a signal recognition issue of ultra wide band (UWB) echoes. We forecast UWB echoes of different VWCs employing both singleton T1FLS and non-singleton T1FLS. Their convergences are measured via root mean square error (RMSE). After that, the forecasting signals in T1FLS are retained as the templates. The testing UWB echoes with an unknown VWC are compared with 5 templates of different VWCs, and classified into one of the VWCs with the minimal RMSE. Monte Carlo simulations not only show that the correct VWC recognition rates are robust at different SNRs, but also indicate that the non-singleton T1FLS is superior to singleton T1FLS in performances of both convergence and VWC recognition rates.

UWB radar with high-range resolution and strong penetration ability can be used to separate multiple human targets in a complex environment. The through-wall human being detection with UWB radar has been relatively mature in the current study. This paper extracts the characteristic parameters which are related to the human targets from the received signals as the sample data. And used machine learning based on the KNN (K nearest neighbor) classification algorithm to identify and classify the through-wall human being status. Experimental results showed that the KNN classification algorithm effectively distinguished three statuses of through-wall human being and reached the prospective goal.

The purpose of this research is to increase the target recognition rate based on high-resolution range profile (HRRP) by the method of information fusion. We fuse the HRRPs of the same target from different radars with varying waves via methods of both the weighted mean and the arithmetic mean. Then, we carry out target recognition with Bayesian classifier. The result shows that the target recognition ratio using the fused HRRP is higher than that of single.

Radar signal processing is usually involved in a large amount of complicate tasks processing and repeated computations. Hence, DSP chips are always used in this field because of its real-time signal processing capacity. In this paper, an optimization scheme of radar pulse compression algorithm is addressed based on TMS320C6678 DSP platform. First, radar echo pulse compression algorithm is realized on DSP platform according to the MATLAB code. Then we use compile options optimization, lookup table optimization, library function optimization, algorithm optimization, and cache optimization to further increase the code efficiency. Exactly processing times are given before and after every optimization method. It can be seen from the experiment results that the processing time is distinctly decreased after optimization. The optimization methods proposed in this paper are proved to be effective.

In this paper, we investigate theoretical transmission capacity limit in the uplink hybrid wireless network under infrastructure mode. Massive MIMO is considered to improve network capacity. Multi-user MIMO is preferred over Point-to-Point MIMO for Massive implementation to achieve improved scalability. For ad hoc mode, without infrastructure support, Massive MIMO is not practical to implement in each user device due to the limitation of complexity. Another perspective of this paper is to include the fading effect on capacity. Under favorable propagation condition, Massive MIMO greatly mitigates small-scale fading effect between each user and base station antenna. Outage capacity over large-scale fading channel is derived in both low SNR and high SNR scenarios.

A novel deployment for multi-cell cooperative cellular network based on the two-dimensional (2D) coprime array, and analysis on its sum-rate capacity are proposed. Taking advantage of that the 2D coprime array system can provide O (N2) degree of freedom by using only N physical sensors when the second-order statistics of the received data is used, we show that the derivation procedure of average sum-rate capacity for the cooperative cellular network is still valid for the coprimed distributed base stations (BSs) in the non-fading and Rayleigh fading channels. Simulations further validate these theoretical results.

As the broadcast nature of wireless communications, information security becomes a more challenging issue in contrast to traditional wired channels especially for capabilities limited wireless sensor networks. On the other hand, we can exploit the randomness of wireless channels to authenticate devices by comparing their channel properties. In this paper, we proposed a performance enhanced physical layer authentication scheme for wireless sensor networks by collaboration of multiple sink nodes. A binary hypothesis testing based authentication method is proposed to identify nodes by directly comparing the current and previous channel frequency responses to decide whether the transmitter is malicious node or the legitimate node. Besides, we collaboratively integrate the decisions of different sink nodes to improve the authentication performance. The simulation results show that our proposed collaborative authentication scheme improves detection performance greatly.

Secret key agreement from reciprocal wireless channels has been the hot spot of research which focus on the security at physical layer. In future 5G systems, full-duplex (FD) communication will be one of the key technologies, whose one ability should be security. However, most key agreement models are the half duplex (HD). Due to the FD wireless transmission merit, transmitting simultaneously receives the signal from the other. Therefore, in this paper, we investigate the key generation problem in the FD relay channel, in which there is no direct channel between the key generation terminals. We propose an effective key generation scheme that achieves a substantially larger key rate than that of HD situation. It turns out that, for the application of key agreement, the FD approach enables advantages over the conventional HD setups with less self-interference (SI), but enable advantages under a certain SNR regime while lost the advantages in the high SNR regime and has an upper bound due to the non-negligible SI. Meanwhile, key rate in FD relay network lost its advantage when SI is higher.

The ZigBee network is widely studied and deployed recently because its low cost and simplicity features. However, the power consumption issue needs a further improvement since the application requirements are not fully satisfied. The emerging 5G communication technology is characterized by the smarter devices and the native support for the M2 M communication. On that basis, the 5G terminals are capable of joining the existing ZigBee networks and have the potential to improve the data transmission. In this paper, we investigate the performance of the ZigBee networks in the 5G environment for different scenarios. To make the 5G devices optimize the communication, the improved topology updating mechanism for 2 scenarios are investigated. The performances are evaluated based on the simulation results, it is shown that our scenarios lead to better performances with higher packet delivery ratio, less hop counts from ZigBee devices and fewer packets sent per ZigBee node. And the effect on 5G node mobility is also studied.

With the spread of wireless sensor network, the increasing number of sensors and the cross-regional spread of wireless sensor network, a large member of sensor data is being produced. Therefore, this paper aims to improve storage and query efficiency based on the data storage structure characteristics of distributed file system HBase. Extenics primitives is used to integrate the heterogeneous data sets which stored in the database of HBase, cross-regional wireless sensor network data and global data storage management directory of double layer distributed storage architecture, thus enhance the efficiency of storage and access. And this paper realize the real-time memory system of data by simulation.

In wireless sensor networks, the critical issue is the energy efficient utilization of sensor nodes, which can be enhanced through employing clustering techniques. But cluster head (CH) performs intensive assignments, brings about unbalanced energy dissipation and premature death of nodes. An ant-colony based Double Cluster heads Adaptive Periodic Threshold-sensitive Energy Efficient Network protocol (ADCAPTEEN) is proposed for solving the disadvantages in this paper. In which, a master cluster head (MCH) and a vice cluster head (VCH) are selected in each cluster. MCH is selected randomly as same as APTEEN, and VCH is selected according to the pheromone concentration by MCH. The tasks, data collection, fusion, transition, etc., can be performed by the double cluster heads (DCH). This method can reduce CH’s node energy dissipation. Simulation result in OPNET shows that, the proposed algorithm is much better on network lifetime than traditional APTEEN to a large extent.

In order to improve the energy efficiency of cognitive radio sensor network, this paper first introduces the TEEN routing protocol into cognitive radio sensor network. The efficient method is joins the process of idle channel detection in each round of TEEN routing protocol to adapt to the dynamic spectrum environment of cognitive radio sensor network. Simulation result shows that compared with LEACH, TEEN routing protocol increases the energy efficiency and extends life cycle of cognitive radio sensor network.

For network routing protocol’s lower security issues, Trust Sensing based Secure Routing Mechanism (TSSRM) with the ability to resist various attacks is proposed, at the same time we present an optimized routing algorithm, which will provide reliable guarantee to improve the network routing protocol security performance. The simulation results show that TSSRM could realize the low cost and high security level of wireless sensor networks.

Wireless sensor network (WSN) is an important branch of modern communication system, and it plays a significant role in human life and production. Due to the number of sensor nodes, wide distribution area and complex environment, the energy consumption caused by battery charge or replacement is much higher than that by redeployment. To prolong the effective work duration, a network lifetime maximization algorithm is proposed to reduce the calculating time and maximize the lifetime at the same time. In the mathematical model, the combinations of sensor deployment, activity scheduling, data routing, and sink mobility are considered. Simulation results show that the proposed algorithm can effectively extend the lifetime of WSN and improve the network performance as well.

Aiming at improving the efficiency in collecting the data of gas wells’ working environment, in accordance with surveillance camera system, we hereinafter try to state in this paper that how ZigBee network will be used to collect production data, to upload surveillance video through WiFi network, and how ZigBee nodes will be utilized to get the data of temperature, oil pressure, casing pressure, flow rate, and other gas wells data. Different nodes data will be uploaded to the System Gateway Center with a coordinator node. With the aid of GPRS, all data will be conveyed to the remote data center through the public network. The operator interface of gateway can display the data and the current time of each gas well. The built-in webcam is designed to collect video information in the gas wells transmitted to the terminal devices. So the operators will understand the site situation via Android APP or the Web page connected to WiFi network. And APTEEN algorithm is built to adapt real-time environment for emergency response as well as application environment for continuous data acquisition.

Aiming at the practical requirements of equipment maintenance material warehousing (EMM), the paper introduces double-layer nested Internet of Things into its management. First, the double-layer nested Internet of Things architecture is analyzed in the application of EMM warehousing. Then the paper designs the business model and describes the function made up by external layer and local layer. Based on these models, the implementing method of the Internet of Things overall is designed. At last, the method on the analysis of the law of the EMM consumption is studied.

To improve throughput and decrease delay in the wireless multicast networks, this paper focuses on the alteration from routing tree to network coding subgraph. A Steiner-tree-based algorithm (STBNC) is proposed to form a 2-cut-set network coding subgraph. Random linear network coding can be employed in the outcome topology. Simulation results show that in terms of power cost and delay, the algorithm in this paper involves better performance than traditional D algorithm in ultra-dense situation with large amount of nodes and destinations. The algorithm utilizes the flexibility of multi-antenna channels in 60 Ghz.

The Space Information Network (SIN) is a full-spatial, full-time, full-frequency, multi-users-oriented information network which has the characteristics of complexity, heterogeneous, and openness. In this paper, we propose a software-defined space information network (SDSIN) to solve above-mentioned problems. This architecture based on the core idea of Software-Defined Network (SDN) that separate control and forwarding panel, and takes full use of the global coverage properties of Geostationary Orbit (GEO), powerful computing capacity of ground station, the predictability and regularity of constellation and the forwarding capacity of inner-satellite links (ISLs). Thus the network can allocate and optimize network resources from a global perspective so as to achieve flexible and efficient network configuration and management, and realize direct and effective control of spatial information network.

This paper introduces the concept of distributed generation resources and smart grid, and analyzes the impact of distributed generation parallel operation on three aspects of network loss, voltage and relay protection.

As the complexity and functionality of electronic equipment increases over time, testing of modern day electronics has brought about the need for more complex stimulus and measurement capabilities. STD (Signal and Test Definition) standard provides the accurate definition for signals and the libraries of predefined basic signals which may be reused and extended. The signal synthetic method based on STD is illustrated in this paper. To support the signal synthetic mechanism, a signal composition component is developed, that enabled the automatic generate and synthesis of basic signals and related to the real measurement facilities. The description of signal composition components based on IDL (Interface Definition Language) is given out.

Bees algorithm belongs to swarm intelligence category. It is a good algorithm for dealing with continuous optimization, though its main disadvantage is its six parameters being defined by users. Parameter setting is a notorious problem in swarm intelligence. This paper aims to tackle this issue by a parameter tuning strategy. Parameters are extensively studied with different combinations. Moreover, a popularly used numerical function set is taken in experiment with different dimensions. Experimental results are discussed and analyzed. The best ten parameter combinations are identified in terms of average number of function evaluations reaching global optima. They are useful for users to solve their real-world problems.

A damage detection method for complex structures is proposed based on model updating and modal flexibility curvature difference in this paper, which includes two steps. At first, the model updating is used for preparatory detection. In the step, detection problem is transformed into an optimization problem by a nonlinear least-square objective function with bound constrains and the trust-region approach is used to solve the minimization problem and identify the damaged unit group. Second, the method based on modal flexibility curvature difference is used to detect the location of structural damage accurately. In order to verify the effect, simulation and experiment on a framework structure are carried out. The proposed method is applied for damage detection. The simulation and experimentation results show that the proposed method is feasible for damage detection on complex structures.

Body area network (BAN) is a key technology of solving remote medical, where protecting security of vital signs information is a very important technique requirement. This paper presents a data security scheme based on ECG characteristics, investigating methods of the circuit-level data encryption and dynamic key refreshment due to the obtained ECG signal characteristic value. The proposed scheme is constructed from the inherent features of the BAN system, adopting vital information from security protection, which has advantages of high strength, low cost, and easy implementation. Therefore, the scheme helps to design low-power sensor nodes and provides theoretical support and engineering implementation.

Big Data has been expanding rapidly, concept drift in the data stream receives great attention and has been one of the research focuses. To solve the problems caused by dynamic nature in the data stream, this paper proposed a method of concept drift detection based on fuzzy C-means algorithm and a cumulative update mechanism, a clustering model is established which can not only detect the concept drift in time, but also avoid the problems caused by frequent updates. The results of Experiment on synthetic and real-world data show the efficiency of the proposed algorithm.

Turbo encoding and decoding system plays a key role in improving the reliability of digital communication, and it has been widely used. Based on in-depth analysis of the Turbo code encoding and decoding principle, a comprehensive analysis of the performance of Turbo code system, a simulation scheme and result of Turbo system are given, and based on practical application and simulation results to optimize the performance of Turbo code system.

During the first step of cell searching in Time Division Long Term Evolution (TD-LTE) system, the symbol timing synchronization is the most import part, it can not only enhance the performance of anti-frequency-offset but also reduce the complexity. In this paper, we propose a novel Primary Synchronous Signal (PSS) timing synchronization algorithm. Based on the fast convolution and Overlap-save, the proposed algorithm achieves the joint estimation of the symbol timing synchronization and coarse frequency offset in frequency domain. The theoretical analysis and simulation results prove that the proposed algorithm can not only reduce the complexity, but also improve the performance of anti-frequency-offset for PSS timing synchronization in TD-LTE system.

Currently, with the significant development of the personal terminal’s processing rapidly, vision-based indoor positioning has become a hot area of research. Compared with the traditional algorithm, this method is deployed with lower cost. In addition, it provides more robust positioning results and extra visualized services. The positioning results of the traditional method relies heavily on the density of position fingerprinting. Location accuracy could be improved when the fingerprinting is concentrated. However, this causes a greater time delay because of the bigger database and vice versa. This paper proposes a vision-based indoor positioning method based on landmarks to solve above problems. It reduces time complex degree by using SURF-based object image feature matching and improves the location accuracy by adding extra homography matrix and projection constrain information. It leverages several landmarks instead of redundant images in database. Moreover, additional priori information, such as homography matrix constraint and multiple calibration lines’ projection relations from landmark to image, could optimize the location results smoothly.

In the Long Term Evolution (LTE) system, the conventional detection algorithm generally use 1, 2, or 4 transmitting antennas to decode the PBCH. Although the power detection algorithm based on the combination of the PBCH and Secondary Synchronization Sequence (SSS) is featured with low detection complexity, the performance of this algorithm may be much poor under the low Signal Noise Ratio (SNR) condition. In this paper, we perform the repetition of cell reference signal for different antenna ports to detect the number of antenna ports. Simulation results demonstrate that the proposed algorithm can reduce the detection complexity, as well as enhance the anti-noise capacity for antenna ports detection.

A new geometric localization approach that is able to locate a stationary tag in non-line-of-sight (NLOS) indoor environment using only the time-of-arrival (TOA) measurements is presented. The novelty of our work resides in converting the NLOS problem into line-of-sight (LOS) problem based on the following three steps, so that the localization algorithm for LOS conditions can be applied in NLOS conditions. First, the geometric TOA model for NLOS is developed, considering the effects of reflection and diffraction. Then, the equivalent anchors can be established based on the geometric features of the real indoor environment. Finally, paths estimation algorithm is derived to estimate the actual signal propagation paths, as well as the position of the tag. Simulation results demonstrate the effectiveness of the proposed method.

In order to analyze the IP label data from mobile communication gateway, this paper creates the network based on IP label data. By using the degree analysis method, the paper analyses the data and gets IP network degree distribution condition, fits deviation analysis and extracts the import node to obtain the size of the IP network and the network boundary. Then, the Eigenvector centrality analysis method is used to calculate the core of interconnected nodes. The IP label data network is partitioned and three pure data set is achieved according to the important nodes in combination with label propagation algorithm. Each divided data set can be used for further data analysis, including analyzing the user behaviour through a purer user behaviour data extracted from the complex network data and stripping the invalid data from the data set.

Most indoor localization methods only focus on the relationship between the user locations and environmental layout, while ignoring the relations among different user locations. Thus, we come up with an idea of collaboration to reduce the impact of noise on localization performance. First of all, according to mutual information between the target user and collaborative ones, we construct the geometric figure for different user locations. Second, the candidate marker reference points with maximum overlap are selected for solving a specific localization problem. Finally, the extensive experiments conducted in both the indoor straight corridor and lab demonstrate the effectiveness of the proposed approach with the average localization error within 2 m.

Dynamic tracing is a continuous process to estimate and predict the motion state of moving target using observable data. Probabilistic methods such as Bayesian filtering, Monte Carlo box (MCB) and particle filtering (PF) make use of historical data and state transition distribution as the posterior distribution function of sampling particles. In this paper, particle swarm method is added to sampling process to move particle to regions with higher posterior distribution. This optimization significantly enhances positioning accuracy and shortens localization time compared with conventional MCB and PF methods.

Because single GPS technology cannot realize localization in a dense urban environment, and in view of Chinese digital television terrestrial broadcasting (DTMB) signals using with fixed transmitters, wide coverages, high positioning accuracy, and low cost, this paper suggests a location method based on DTMB. Because the observation model of the DTMB’s location system is gauss non-linear, this article contrasts and analyzes the tracking effect of two kinds of environment: a line-of-sight (LOS) environment and a non-line-of-sight (NLOS) environment. Simulation results show that: the tracking trajectory is in line with the true trace, due to the interference of the NLOS environment; the error of NLOS is bigger than LOS; and the tracking accuracy of NLOS is better than LOS. This all proves the feasibility and superiority of a location system based on DTMB.

In order to further improve the positioning precision of the DV-Hop node localization algorithm in wireless sensor network (WSN), this paper proposes a modified particle swarm algorithm (MPSO) to optimize the result. The algorithm is based on particle swarm optimization algorithm, meanwhile overcomes the disadvantage of the PSO easily falling in local optimum. To adaptively balance local search capability and global search capability, MPSO updates each particle’s inertia weight and acceleration coefficients self-adaptive, and sorts the particles. The simulation results show that the average localization error is lower than the standard DV-Hop algorithm and the DV-Hop algorithm based on PSO algorithm (PDV-Hop).

A novel inductorless optical receiver analog front end (AFE) design is demonstrated to require less chip area and is suitable for both low cost and high-speed optical communication applications. The optimized transimpedance amplifier (TIA) has a differential regulated cascode (RGC) topology, with a novel zero-pole canceling technique. The proposed limiting amplifier (LA) using Cherry–Hooper topology and negative Miller capacitance broaden the bandwidth. Based on the IBM 7WL 0.18 μm SiGe BiCMOS process, the post-simulation results show a total transimpedance gain of 107.1 dBΩ and −3 dB bandwidth of 17 GHz. The chip consumes 132 mW power dissipation from a single 3.3 V supply and occupies the core area of only 110 × 340 μm2.

A robust nonlocal means with local James-Stein center pixel weight (LJSCPW) filtering algorithm has been proposed to enhance the performance of distributed optical sensing signal, and the simulation results show the proposed method has no signal distortion even if the input parameter noise level is inaccurate.

In this paper we proposed using a robust method, probabilistic non-local means (PNLM), to enhance the performance of optical fiber sensing system. The simulation results show PNLM significantly improves the signal SNR. Comparing with the traditional Non-Local Means (NLM), the PNLM filtering can effectively remove system noise without signal details distortion.

We studied the performance of coherent FSO system. The results showed that the terrestrial air turbulence increases the symbol-error-rate, eventually degrade the performance drastically.

Active network synthesis is important for circuit designer to find new circuits with desired performance. In this paper, a synthesis method for synthesizing active band-stop filters is presented, which starts from voltage transfer function and linked infinity variables to describe nullors in both nodal admittance matrix (NAM) and port admittance matrix of the circuit to be synthesized. Then circuit topology is derived by nodal admittance matrix expansion. The Tow-Thomas band-stop filter circuit and Åkerberg-Mossberg band-stop filter circuit are synthesized by nodal admittance matrix expansion on the same port admittance matrix. A design example of band-stop filter verifies the effectiveness of the circuit design method.

In this paper, an effective matrix reconstruction-based two-dimensional (2-D) direction-of-arrival (DOA) estimation algorithm is addressed. In the proposed algorithm, the coherency of incident signals is decorrelated through two equivalent covariance matrices, which are constructed by utilizing cross-correlation information of received data between the two parallel ULAs and the changing reference element. Then, the 2-D DOA estimation can be estimated by using eigenvalue decomposition (EVD) of the new constructed matrix. Compared with the previous works, the proposed algorithm can offer remarkably good estimation performance. In addition, the proposed algorithm can achieve automatic parameter pair-matching without additional computation. The theoretical analysis and simulation results demonstrate the effectiveness and efficiency of the proposed algorithm.

For analog signals comprised of several, possibly overlapping, finite duration pulses with unknown shapes and time positions, an efficient Sub-Nyquist sampling system is based on Gabor frames. To improve the realizability of this sampling system, we present alternative method for the case that the windows are exponential reproducing Gabor windows. Then, the time translation element could be realized with exponential filters. In this paper, we also construct the measurement matrix and prove that it has better coherence than Fourier matrix. Simulations prove that the sampling proposed in this paper, the sampling system require quite low sampling frequency and has nice reconstruction performance.

Conventional DOA estimation approaches suffer from low-angular resolution or relying on a large number of snapshots which are unavailable in numerous practical applications such as underwater array processing. The sparsity-based IAA can work with a few snapshots and has high resolution and low sidelobe levels, but it is only applied to narrowband signals. To solve the above problem, a new FrFT-IAA method was proposed to estimate the DOA of wideband chirp signals with high resolution based on a few snapshots. First, the wideband chirp signal was taken on the Fractional Fourier Transform (FrFT) under a specific order so that the chirp wave in time domain could be converted into sine wave with a single frequency in FrFT domain. Then the steering vector of the received signal can be obtained in FrFT domain. Finally, IAA algorithm was utilized with the obtained steering vector to estimate the DOA of the wideband chirp with a few snapshots. The simulation results demonstrate the effectiveness of the proposed method.

The electrocardiogram signal consists in a character of smaller amplitude together with a larger interference range and the reconstructed signal, according to the classical compressed sensing theory, cannot be accurately conveyed by the signal. To solve this problem, compressed sensing based on the wavelet transform was stressed on. We carry out a compressed sensing algorithm based on wavelet transform, thus is to use the wavelet decomposition to separate the electrocardiogram, to reduce the noise pollution, to compress and reconstruct the high-frequency coefficient and to recover the signal by inversing the wavelet transform. Meanwhile, analysis on the data effect was also made. The result of the simulation shows that it obviously proves the noise suppressing effect on combining wavelet transform with compressed sensing to recover the signal. The integrity of useful information is enhanced, as well as obtaining a higher signal-to-noise ratio.

Aiming at weak signal detection based on large parameter stochastic resonance (LPSR), frequency-shifted and rescaling stochastic resonance (FRSR) is used to obtain the weak feature of signals submerged in noise. An improved variable step algorithm is combined to the FRSR in this paper, we use the variable step to take place of a traditional fixed value after shifting and rescaling the frequency. It has been shown marked detection efficiency of the method by the results both in the simulation and engineering application.

With continuous progress of social economy and science and technology, the continuous developing internet technology has become an indispensable and important constituent part in people’s school life and social works. With the increase of network port, the network capacity is gradually decreasing, for which the data information and data transmission speed are also influenced to some different degree. Through the usage of network coding, the above problems can be solved. Furthermore, the handling capacity and reliability of network processing can be improved effectively. When compared to the traditional algorithm of coding-aware routing, the algorithm of wireless based on inter-flow network coding is proposed in this article as it has better handling capacity and reliability.

A novel decoherence algorithm, called fourth-order cumulants-based improved Toeplitz matrices reconstruction (FOC-ITMR) method is presented to estimate two-dimensional (2-D) direction-of-arrival (DOA) of coherent signals. The FOC-ITMR method fully utilizes the information of received data between the whole two parallel uniform linear arrays (ULAs) and the changing reference element based on FOC. Compared with the previous works, the proposed algorithm can achieve excellent decoherence performance. The theoretical analysis and simulation results demonstrate the effectiveness and efficiency of the proposed algorithm.

Compressive sampling is a promising solution to reduce required sampling rates for signal reconstruction. In many scenarios, such as cognitive radio and modulation recognition, there are only expecting to acquire useful features rather than original signals. To reconstruct these features from compressive measurements, Compressive Sensing (CS) requires features to be sparse and have a one-dimensional relationship with those measurements. Since most of features are nonlinearly transformed from signals, selecting one with high sparsity and then building a linear mapping between it and measurements become the main challenges. This work proposed a new method to find sparsity representations for signals based on their intra-structure. With this method, two common features, autocorrelation function and fourth order time-varying moment are respectively expressed as another two sparse representations called structure-based sparsity representations. Simulation shows that these representations can work effectively in reducing reconstruction iterations, computing consumption, and memory cost for sensing matrices.

A time error mathematical model of nonlinear response and noise is established in order to achieve low time error of correlated double sampling pulse-width-modulation (CDS PWM) pixel. By analysis and computer, the time error under fixed pattern noise (FPN), reset noise, shot noise, reference noise of pixel-level comparator, an integrated model is established. By means of the model, appropriate selection of high/low reference voltage of pixel-level comparator can decrease time error of CDS PWM. These results serve as a guideline for the design of CDS PWM pixel.

A frequency-locking scheme with wide adjusting range in homodyne coherent receiver is presented and experimentally demonstrated. FPGA is used as the core of the control unit to detect and adjust the frequency difference of local light and signal light. With the beat frequency detection, the frequency difference can be well controlled within 2 MHz, and the data signal can be recovered well.

In modern signal processing, sampling is a key step. The number of sampling points directly affects the computation of subsequent signal processing. Nyquist sampling theorem uses twice the highest frequency of signal to sample the signal. In fact, for sparse signal, not all of the points are necessary. FRI is a highly efficient sampling method, but FRI can merely deal with discrete signals. By the improved methods of FRI, FRI theory can be extended to process continuous ECG signals. What’s more is, sampling scheme put forward by this paper can change the number of points according to the application. If the requisite degree of accuracy is low, less points are needed. Finally, simulation experiment shows that this method can not only reduce sampling rate greatly, but also can ensure the accuracy of recovery.

Enhancing the robustness to changes caused by facial aging in automatic face recognition system is still an important problem worth researching. Compared with the external factors, such as illumination, posture and expression, facial aging which can produce variations in both shape and texture of the face has more complex effects. In this paper, we propose a method based on Local Gabor Binary Patterns and Convolutional Neural Network (LGBP-CNN) to improve the performance of age invariant face recognition problem. For each face image, this method first extracts shape, texture and local neighbor relationship features with multi-orientation and multi-scale Gabor filters as well as local binary patterns (LBP) operators. Then, we utilize one kind of Deep Learning model-convolutional neural network which has shown brilliant performance on face recognition area to avoid the dimension curse problem brought by Gabor filtering and further extract features. Such kind of method has robustness to changes of illumination, posture, expression, shape and texture by combining Gabor transform, LBP and convolutional neural network. Experiments are implemented on the FG-NET database and the results can outperform the state of the art ones, which verify the validity of the proposed method in age invariant face recognition problem.

This paper according to the background of a game project of family service entity robots quickly follow, introduces the problems existing in the most human recognition methods first. Then proposes a laser scanner as a hardware device, takes the method for recognition by random forest algorithm through the extraction of multiple different characteristics of the human lower limbs. And the experiment results show that the method is feasible and the recognition rate is high.

Influence maximization problem is to find a small set of influential nodes in a social network, whose activation of information propagation can be maximized using one of the propagation models such as independent cascade model. This paper proposes a new method to solve this problem based on discretized linear automaton algorithm and simple greedy algorithm. Every allowable node in the social network is regarded as an action and the goal of the learning automaton is to select an optimal subset of actions. To speed up the convergence, firstly, the problem space is reduced by excluding the nodes lacking influence; then, the original learning process is divided into two parts: the sampling process and the learning process. Besides, a new scheme for determining the best parameters is presented, which is more applicable to practical problems. The obtained results show that the new proposed algorithm is efficient in real-life social networks. The results are close to the ones obtained by the greedy algorithm in terms of accuracy, and the new method greatly improves the speed of selecting the optimal subset.

The updating scheme for Learning Automata (LA) is important. Thathachar and Arvind first proposed parallel operation of leaning automata (LA), which was a promising mechanism that could maintain the accuracy while reducing convergence time. In this paper, we implement this mechanism which helps to break the limit of the convergence speed of single LA. In contrast to existing scheme, the proposed scheme eliminates the worst performed LA in the sequence of interactions, until there is only one LA left. We compare Last-Position Elimination-Based Parallel Learning Automata (LEPLA) scheme with the classic one incorporating two pursuit schemes, discretized generalized pursuit algorithm (DGPA) and discretized pursuit algorithm with reward-inaction (DP_RI), respectively. Simulations prove that the proposed scheme gets an evidently higher accuracy and faster convergence than the classic ones.

Gesture recognition is an indispensable part of the human–computer interaction technology. In this paper, the research on dynamic gesture recognition technology based on multi feature fusion is studied. While identifying the posture using SVM, the dynamic gesture track feature is extracted and recognized using Gaussian pyramid optical flow algorithm. Then we’ll get the final recognition results by information fusion on decision level. Finally through the contrast experiment to prove the dynamic gesture recognition algorithm in the paper has higher gesture recognition rate.

Ultra-wideband signal (UWB) has been used in communication, location, and identification. In this paper, a novel target identification method is proposed. The UWB received signals is processed by time-frequency analysis method: S-transform. S-transform is an extension of short time Fourier transform and continuous wavelet transform, and it has good time-frequency characteristics. Then we use improved artificial bee colony (ABC) algorithm to optimize the penalty factor and kernel parameter of support vector machine (SVM), and finish the target identification. In view of the basic artificial bee colony algorithm has the problem of slow convergence speed. We propose a probability selection method based on quadratic function to optimize the algorithm.

Dynamic hand gesture recognition plays an important role in human–computer Interaction. This paper proposes a novel method for dynamic hand gesture recognition using wireless signals. Through the analysis of wireless frame structure, the preamble’s signal of 802.11a is collected through Software Defined Radio platform and reserved as the data source. In addition, more than one time-domain feature sequences perform unique shape for different dynamic hand gesture. These sequences are split into single cycle (time-series) and the unavoidable electronic interference is reduced through discrete wavelet transform. At the same time, due to fuzziness of dynamic hand gesture, the amplitude and duration for the same dynamic hand gesture are not exactly same. Therefore, the parallel HMM models which represent for different hand gestures and features are built for recognition. The result shows that the average recognition rate is about 90.5% for dynamic hand gesture recognition.

Automatic target recognition (ATR) generally refers to the autonomous or aided target detection and recognition by computer processing of data from a variety of sensors such as forward looking infrared (FLIR), synthetic aperture radar (SAR), inverse synthetic aperture radar (ISAR), laser radar (LADAR), millimeter wave (MMW) radar, multispectral/hyperspectral sensors, low-light television (LLTV), video, etc. SAR is a coherent active imaging method that utilizes the motion of a radar mounted on a vehicle such as an aircraft (airborne SAR) or a satellite (spaceborne SAR) to synthesize the effect of a large aperture radar. Motivated by the unique character of fuzzy logic system, simultaneously handling numerical data and linguistic knowledge, and the promising knowledge-based approach, we propose an FLS-based approach to SAR ATR.

Deep neural networks are very powerful models for machine learning tasks. However, suffering from overfitting and gradient vanishing problems, they are difficult to train. We proposed a method of gradually pruning the weakly connected weights to train deep neural networks and an effective strategy to identify the weak connections. Our method can improve the conventional stochastic gradient descent and can get even better performance than the widely used dropout method for deeper models.

In this paper, we propose a novel Ideal Local Structure Learning (LSL) for unsupervised feature selection method, which performs local structure learning and feature selection simultaneously. To obtain more accurate information of data structure, an ideal local structure with block diagonal constraint is introduced. Furthermore, a simple yet effective iterative algorithm is presented to optimize the proposed problem. Experiments on various benchmark datasets demonstrate the superiority of LSL compared with the state-of-the-art algorithms.

Person re-identification is a process of matching person images of same identity across nonoverlapping camera views at different locations and times. In this paper, we introduce how to use the kernel trick to improve the performance of large margin nearest neighbor (LMNN) classification for person re-identification. Since the classification ability of LMNN is weak for those person features with nonlinear distribution, KLMNN combining kernel trick and LMNN is introduced to extend linear distance metric to nonlinear cases. Three kernel-based methods and two indicators are applied to evaluate the performance of KLMNN.

People relation extraction is a significant topic in information extraction field. While in traditional study, the feature of extraction lexical and semantic was attached importance to, and the function of classifier was neglected, furthermore, there is great difference between microblog language materials and that of tradition. When it mentioned traditional classification algorithm, its low correctness and the inaccuracy to identification of fuzzy sample become the reason of being used little. In this paper, the traditional classification algorithm was improved. Using SVMDT-Random Forest and we designed, the fuzzy sample classifying ability increased, which remedied the shortcomings of SVM and Random Forest effectively. By testing the microblog language materials, the result indicated that this method can improve the performance of people relation extraction.

With the development of Web2.0 era, as local information publishing and social networking platform of Twitter, microblog has become an important medium for people to share and propagate information. Sentiment classification for microblog has also become research hotspot in natural language processing field. By analyzing existing sentiment classification features and complex sentence patterns of microblog and directing at defects of current microblog sentiment classification in feature selection and extraction, this paper combined semantic relation between complex sentences and sentence features of complete sentence based on proposing features of sentence-level fine-grained embedding features and semantic features under complex sentence pattern so as to conduct effective analysis of microblog sentiment features under complex sentence context. It used SVM classification model to conduct comparative experiment, and results indicated that feature selection method proposed in this paper could improve performance of microblog sentiment analysis.

To improve the false reject rate of discriminator for automatic target recognition based on synthetic aperture radar, we propose a new target discrimination method based on a modified manifold support vector machine (SVM). Covariance matrix features which combines texture features and their correlation information are used, and the distinguishability of these features are proved to be good by our experiment. An iterative manifold SVM discriminator is designed to better match the covariance matrix features in the non-euclidean space. The center of the hypersphere in SVM instead of the Karcher mean is selected as the base point by a novel iterative algorithm. Experimental results on RADARST-2 database demonstrate the superiority of the proposed method.

In this paper, we focus on the feature representation methods under the framework of bag-of-words model (BoW) for scene character recognition. We investigate three kinds of methods: the original BoW methods, the stroke-based methods, and the context-based methods. Specifically, various feature representation methods are introduced, their merits and demerits are explored, and existing problems are discussed. Finally, we empirically evaluate them on several widely used databases (Chars74k, ICDAR2003 and SVHN).

In recent years, research based on the Motor Imagery (MI) and physical training of stroke rehabilitation therapy has become a hot topic. Among them, brain–computer interface (BCI)-based electrical stimulation (ES) and MI synchronously induced electroencephalo-graph (EEG) analysis is one of the research directions. This paper integrates MI with ES, and introduces the steady-state somatosensory evoked potential (SSSEP) on the MI, and then fuses features. The purpose is to achieve better accuracy, reducing training time, and enhancing target body imagine attention. We use ES and MI synchronous-evoked EEG analysis for rehabilitation training strategy in the process of synchronization. Based on the time–frequency diagram and brain topographic map, we make analysis on event-related desynchronization (ERD), time–frequency characteristic of the steady-state evoked potentials (SSEP), energy characteristics, and distribution regularity of brain regions of each task model. We apply the short-time Fourier transform (STFT), common spatial pattern (CSP), and filter bank common spatial pattern (FBCSP) to classify task pattern recognition on the basis of support vector machine. The results show that based on the lead less the FBCSP has certain superiority, the classification accuracy of ES versus ES&MI achieved 95% or more.

With the gradual increase of accuracy requirement in the digital human identity authentication, biometric technology has been developing rapidly, where fingerprinting is most widely used. In this paper, according to the shortcoming in the actual fingerprint image acquisition, research on fingerprint image enhancement is based on improved Gabor filtering algorithm. Finally, by comparing the experiments, we prove that improved algorithm proposed in this paper can better achieve the minutiae enhance conducive accurate extraction of fingerprint features.

This paper introduces the Munsell color system and Pointer color system, and uses the algorithm to calculate the three-dimensional color gamut volume. In addition, we have tested a Toshiba display and got the data in three-dimensional color gamut, then hold the gamut that eyes can perceive as a denominator, and calculate the color gamut coverage respectively. This study provides some reference value for extending color gamut in three dimensions later.

With the rapid development of display technology, modern display equipment future development goal is diversified and widen the color gamut Miwa et al. (2013) ITE Winter Annual Convention, pp. 5–5: [1], Manders, Qian (2015) J Soc Inform Display 23(11), 523–528: [2]. In order to find a unified approach to evaluate different types of displays, more and more scholars tend to the three-dimensional color space to evaluate the color effect on the display. However, due to the three-dimensional color, gamut of display shows irregular shape, so the calculation is relatively complicated, and it always needs a long time. In this paper, the convex hull projection algorithm of three-dimensional color gamut based on the triangular surface has been proposed; it has been greatly improved computation time.

Image registration has been an attractive research area of image processing; it has wider applications in many fields such as pattern recognition, image fusion, computer vision, and other practical problems. Due to the multi-resolution features, wavelet becomes the focus of research new algorithm and improves the traditional matching algorithm. Based on the study of traditional Harris corner detection algorithm, combined with wavelet transform, proposed an image registration algorithm based on the wavelet edge detection and Harris corner detection.

In the classic two-step technique based camera calibration methods, pinhole parameters and lens distortion parameters are estimated together. The calibration results are affected by the coupling of these parameters. Image pixel coordinate expressed lens distortion models are derived, which are modifications to existing image physical coordinate expressed lens distortion models and reduce the coupling between linear parameters and distortion parameters. Camera calibration method is proposed based on the new distortion models and used to calibrate binocular vision cameras with a three-dimensional target. The experiment results show that compared with traditional camera calibration method, the proposed method is feasible and can efficiently improve calibration accuracy.

From the basis of studying in SIFT (Scale-Invariant Feature Transform) operator and wavelet transform, a multi-scale image mosaic algorithm is proposed. The proposed mosaic algorithm maintains the advantage that SIFT operator is invariant to scaling and rotation transformations, combining with the wavelet transform image fusion method, it enhance the anti-noise ability, maintains a certain stability to brightness change, improved the joint effect.

The Wiener filter is widely used for image denoising. Although good performance can be obtained, the parameters such as the sizes of window and etc. still limit the performances of algorithm. To deal with this problem, the Stein’s unbiased risk estimate (SURE) approach has been used in related methods. Although the proposed method employs the SURE strategy, unlike previous approaches the interscale and intrascale information of wavelet coefficient is adopted to improve the Wiener filter. Compared to the related state-of-the-art wavelet-based algorithms, the proposed method shows good performance but very simplicity.

To validate the performance of a certain type of anti-tank missile under altiplano environment, the operate principle of control system is analyzed first, and then the simulation model based on Simulink is established. The stability and dynamic performance of the missile is analyzed through the curve of flight and attack angle. Toward the instability of control system under altiplano environment, the emendation network is designed, and it is testified that the stability of control system is improved.

For the currently the most widely used data storage memory is DDRII SDRAM which is used in the high-speed, high-precision, and high-memory depth of the data storage and communication system, using ISE software and calling the IP core of Xilinx to create MCB, adopting the Verilog HDL to achieve a common DDRII SDRAM controller interface for universal design and application based on Xilinx’s FPGA chip and industrial standard. This paper deeply analyses the working principle of DDRII SDRAM, focusing on the read operation, write operation and refresh operation and validates the design of accuracy and stability in the latest Xilinx Virtex-5 series of FPGA platform.

Decoupling control is one key component during the design of autopilot for spinning missiles. However, in addition to the strong dynamic coupling effect, its parameters vary very fast over a large range, which induces great challenge to the design of autopilot. To address this issue, the classic complex summation decoupling control method and the newly developed robust gain-scheduling control method are both studied in detail, which are then applied to the design of autopilot of the spinning missile. The results from comparative studies show that the complex summation decoupling control method yields poor tracking performance and robustness; while the robust gain-scheduling control method performs much better during the whole trajectory. It is indicates that the robust gain-scheduling control method is very applicable to the spinning missile with strong dynamic coupling, and large fast time-varying parameters.

Based on the principle of vector fitting and the theory of impedance matching, a method of antenna impedance matching is proposed for electromagnetic systems. A method of fitting of measured frequency domain responses named vector fitting is proposed. The S parameter obtained at certain frequency is transformed to Z parameter, then method of Smith chart is used to achieve impedance matching of the corresponding Z parameter at certain frequency. Finally, the method is used for frequency of 315 MHz antenna impedance matching. The simulation results verify the effectiveness of vector fitting in impedance matching of radio frequency (RF) domain.

In this paper, one hydraulic equipment detection system on certain launching device has been established applying data acquisition, virtual instrument, and signal analysis technologies. This detection system can detect pressure and flow parameters on-line, and has such advantages as such convenient operation and high accuracy.

Map-matching is a process of matching the track point of GPS to the digital map. The existing map-matching algorithm is based on the high sampling rate so that the algorithm has a low precise matching rate when sampling interval time increases. For that reason, this paper came up a special map-matching algorithm aiming at GPS track point with a low sampling rate. By considering many aspects like the geometry of road network structure, topological structure, mutual influence between the neighboring points and so on, the algorithm improves the accuracy of matching and determines the best matching results of GPS tracking points by probability calculation. Finally, it has been proved by experiment that the algorithm has good run time and accurate matching result.

Aiming at the question which is hard to describe the simulation and training system state transition, the state transition method is put forward based on the FSM. The paper introduces the conception of the FSM, describes the system state process transition, establishes the state transition modeling, and then introduces the application by this method. The application shows that this method is suitable for the system which has finite states; the software code which adopts this method has good reusable, high efficient design and easy maintenance cost.

In the process of constructing the ATS, developing of test program is an important and time consuming job. The traditional development approach of test program is in manual way. It makes the development cost very high, development cycle very long and portability very poor. Next, the automatic generation of test program will be a trend. Based on the study of ATML document characteristics, an approach to convert ATML documents into formal representation is proposed. First, the way of data type conversion is studied. Then based on the characteristics of entity documents, the formal way of entity conversion is studied. Finally, the method of converting the test description document into the formal representation of process control is studied. The effectiveness of the proposed approach is demonstrated by an example.

This paper aims at the development of maintenance training system of anti-tank missile equipment. It uses technique of large-scale real-time circuit simulation, technique of virtual maintenance and half substance simulation to present the whole structure of maintenance training system of anti-tank missile equipment and design the model of layered architecture of federation based on HLA/RTI to support the whole structure. It can supply firmly base for developing anti-tank missile equipment maintaining training system. The whole design of maintenance training system and layered architecture of federate model have better ideas for similar system.

Asynchronous Start Permanent Magnet Synchronous Motor has slots at both sides, so the formation mechanism of its cogging torque is more complex. To avoid influence on analyzing cogging torque by rotor slots, this paper propose a method of making the magnetic motive force generated by rotor bar equivalent to magnetic motive force generated by the permanent magnets, obtaining the analytical expression formula of such motor’s cogging torque, to analyze the impact of the rotor tooth width, rotor slots number and rotor teeth shape, and other rotor parameters on motor cogging torque, and gives the method of reducing asynchronous start permanent magnet synchronous motor cogging torque; uses the effectiveness of the method proposed by finite element analysis verification and conducts the comparative analysis of the motor’s performance before and after optimization. The results showed that selecting the appropriate rotor tooth width and rotor slots number can effectively weaken the asynchronous start permanent magnet synchronous motor cogging torque.

The traditional attitude measurement methods have many constraints when they are applied on the traditional ammunition. The advantage of geomagnetic technology has yielded that magnetic sensors are smaller, steadier, and more sensitive when they are contributing in the attitude measurement system. The paper gives solving algorithm with the information not only about building of the solving model but the actual processing. First, the basic assumptions and the reference frame are given, so as the local geomagnetism analysis. Then transformation of coordinate system is carried out and the roll angle is worked out by the range analysis. From the experiment, the results demonstrate that the algorithm is a sample method and it can reach a high level that error angle is less than 5° (3σ). The algorithm has the well foreground and potential application in the future traditional ammunition improvement.

Different from the traditional electromagnetic material, the electromagnetic metamaterial has tremendous application potential in antenna designing, superlens and stealth fields because of its negative properties in permeability and/or permittivity. In this paper, a novel structure of electromagnetic metamaterial with middle-frequency band is designed and analyzed. The unit of this novel structure is composed of a copper wire and a resonator which are distributed in both sides of a substrate respectively. In this case, the resonators realize the negative permeability, and the copper wire realizes the negative permittivity. The simulation results indicate that the effective refraction index is negative from 360 to 450 MHz. The advantage of this structure is that its unit is compact and easy to form artificially periodic array.

Equipment is designed for detecting soft battery modules and is used for detecting whether the battery tab reaches its designated position. The system configuration requires visual positioning, a contact probe, a Cartesian robot connected to a host PC, a PLC controller, and C# software for language design. The system has characteristics including strong customization and simple operation.

With the development of economy and technology, the types and quantity of vehicles are increasing which lead to security problems in storage. To solve this problem, a remote intelligent monitoring system for vehicle storage condition is designed. It has the advantage of real-time monitoring, and sending alarm information once detecting abnormal status of vehicles. Based on the characteristics analyzed by gyroscope sensing data, a high-level precision of security decision function is proposed. The function can further enhance the system’s ability of early warning and increase the security level for garaging traffic tools.

Most family does not implement intelligent management of the most commonly used medicine. Little research teams designed the family medicine chest intelligent management. This article is based on this background, committed to the development of smart phone App managing medicine and cloud management platform software system. The mobile terminal will also develop Android and Apple’s dual-platform software. The medicine information is obtained by scanning the bar code. The backstage medicine information database maintenance and pharmaceutical science information releasing based on cloud-based platform architecture with more prominent security and scalability. The results show that the software can automatically remind medication and warn expired medicines. It can also well extend family health science knowledge.

Infrared dim target detection has been one of the key technologies in the Infrared Search and Track (IRST) System field. According to the phenomenon that the dim target could extend into a fake crossed target when the IRST system filtering with the front wire grid, this paper developed a dim target detection experiment software based on the human eye time-limited model. With this software, we could do various dim target detection experiments by changing the scene background, the shape of the dim target with equal energy, the SNR and Gaussian noise variance, etc. Through the detection experiment with six kinds of target, we could verify that in a certain condition, the dim target detection probability would be better when filtering with front wire grid.