Communications, Signal Processing, and Systems

This paper investigates uplink massive MIMO communication scenarios in dynamic Internet of things (IoT) networks. In this paper, dynamic IoT mainly consists of the Internet of vehicles (IoV) and the original IoT network. Because the speed of vehicle is very fast, the number of users is constantly changing in the IoT network, which leads the structure of the IoT network to change. We mainly consider how to obtain the channel state information (CSI) of active users. Due to active users and inactive users, the system model is considered a sparse structure. This structure inspired us to give an algorithm suitable for the sparse structure and obtain more accurate channel state information of dynamic IoT networks, though these numerical results, under the premise of guaranteeing performance, can greatly reduce the complexity of the algorithm.

High-accuracy detection of environmental pollution (DEEP) schemes to measure a variety of pollutants arouses great interests in industry and research communities. This paper proposes a novel DEEP approach to improve the detection precision by using machine learning theory in which an RBF network for detection is optimized by genetic algorithm. Specifically, this cooperative scheme employs more appropriate relationship in the networks, which can accelerate the convergence of the algorithm and also can enhance the precision. Simulation results demonstrate that the proposed method outperforms conventional schemes in terms of environmental pollution detection accuracy, as well as monitoring different pollutants.

Imbalanced data classification is one of the problems that emerged when classifier learning algorithms used in the worlds of business and industry. This paper proposes the methodology to improve the performance of imbalanced data classification. We balance data sets by using synthetic minority oversampling technique (SMOTE); noise generated by new data sets is eliminated by Tomek links (T-Links), support vector machine (SVM), k-nearest neighbor (KNN), and logistic regression (LR) which are selected as the base classifiers to improve classification by using stacked generalization, and the final result is generated by weighted voting. In the experiments, six UCI datasets are tested, and the experimental results show that the method is highly representative and can effectively improve the classification ability.

In the field of artificial intelligence, learning automaton (LA) is a self-adaptive decision-maker which plays an important role in reinforcement learning (RL). Games of learning automata are stochastic games with incomplete information that have received frequent usage. Traditional learning automata schemes using in games are parameter-based schemes which exist a tunable parameter (stepsize) changing with different environments. In this paper, we proposed Bayesian method-based parameter-free learning automata (BPFLA) for two-player stochastic games with incomplete information. The parameter-free property indicates that a set of parameters in the scheme can be universally applicable for all configurations of games. Besides, simulation results demonstrate that BPFLA has much faster convergence rate than traditional schemes using games of learning automata with equal or higher accuracy.

Learning automaton is considered as one of the most potent tools in reinforcement learning. The family of estimator algorithms is proposed to improve the convergence rate of learning automaton and has made significant achievements. However, the estimators perform poorly on estimating actions’ reward probabilities in the initial stage of the learning process. In this situation, a lot of rewards would be assigned to nonoptimal actions. Thus, numerous extra iterations are required to compensate for these wrong rewards. To further improve the speed of convergence, we propose a new P-model absorbing learning automaton using a double competitive strategy to update the action probability vector. The proposed scheme overcomes the drawbacks of the existing action probability vector updating strategy. And, extensive experimental results in benchmark environments demonstrate that the proposed learning automata perform more effectively than the most classic learning automaton $$SE_{RI}$$ and the current fastest learning automaton $$DGCPA^{*}$$ .

The convolutional neural network has been proved to be the state-of-the-art technique in image classification problems. In general, the improved recognition accuracy of the CNN is often accompanied by the increase of structure complexity. However, apart from the accuracy issues, computational resources and operating speed need to be considered on some occasions. Therefore, we propose an efficient compression scheme based on learning automata, which are usually used to choose the optimal action as a reinforcement learning method in this paper. Our proposed method can help the trained CNN to delete insignificant convolution kernels according to the actual requirements. According to the results of experiments, the proposed scheduling method can effectively compress the number of convolutional kernels at the expense of losing weak classification accuracy.

Existing works have revealed that dictionary learning can effectively preserve the label property when applied to signal reconstruction and face recognition. As time goes, the methods based on dictionary learning become increasingly popular due to their superior accuracy and efficiency. Based on this, an improved dictionary learning model is proposed in this paper to find the balance between the time cost of operating the algorithms and the residuals generated when reconstructing signals with the learnt dictionary sparse codes. Demonstrated by the results of experiments, the proposed method intends to determine a more reasonable sparse dimension, which can not only obtain desired classification results but also decrease much of the redundancy in experimental computation.

Due to the variety of poses, lighting, and scenes, dynamic face detection and calibration pose a big challenge under unconstrained environment. In this paper, we use the inherent correlation between detection and calibration to enhance their performance in a deep multi-task cascaded convolutional neural network (MTCNN). In addition, we utilize Google’s FaceNet framework to learn a mapping from face images to a compact Euclidean space, where distances directly correspond to a measure of face similarity to extract the performance of facial feature algorithms. In the practical application scenario, we set up a multi-camera real-time monitoring system to perform face matching and recognition of collected continuous frames from different angles in real time.

The popularity of the Internet and the rapid development of the massive data promote the expansion of the field of cloud computing applications, which absorb the grid computing, utility computing, and other distributed technology, through the network sharing platform resources to improve the throughput of data processing speed and computing power. As the core of cloud computing virtualization technology, through the construction of multiple virtualization platforms, in order to achieve space expansion, backup, and other operations to achieve the purpose of efficient integration of resources, but in practical applications, with the increase in the size of resources, physical and virtual machine load balance contradiction has become increasingly prominent, if such problems are not a reasonable solution, both a waste of resources, but also interfere with the application performance, thus affecting the user experience. In this paper, we study the process of blocking technology proposed based on load balancing under the conditions of virtual machine scheduling strategy, and through experiments to verify its reliability and efficiency.

According to classification framework of classical neural network, contemporary neural network, and soft computing, the basic concepts of the feedforward neural network (MLP, BP, and RBF), the feedback neural network (Hopfield, Boltzmann, Elman), the self-organizing neural network (SOM, ART, CPN), deep and extreme neural network (Deep learning, extreme learning), the novel neural network (SVM, PNN), and soft computing neural networks combined with various methods are introduced, and the research progress and typical applications of the neural network in fault diagnosis are given. The existing problems and future development directions of fault diagnosis are also discussed.

Convolutional neural networks (CNNs) have been widely used in image classification task, which is based on the huge amount of image samples. However, the insufficiency of cloud sample numbers brings obstacles to classify clouds using CNNs. In this paper, we propose to apply Wasserstein generative adversarial network (WGAN) to generate virtual cloud samples via supervised learning. Afterward, we fine-tune a deep CNN model to evaluate the classification performance under different number of virtual cloud samples. The experimental results demonstrate the feasibility of the proposed method.

Optical character recognition (OCR) is considered as one of long-term and hot research topics due to the fact that OCR technique can change the documents from paper to computer-readable format by consistently growing. However, the recognition accuracy of current OCR technique is required to improve some special applications such as in reimbursement of value-added tax (VAT) invoices. This paper proposes two OCR techniques by using deep convolutional neural network (CNN) and residual network (ResNet), respectively. According to our test dataset, the formerly proposed techniques can reach up to 97.08%, while the latter can increase to 99.38%.

SDN is a new type of network architecture. The core technology of the SDN is to separate the control plane of the network device from the data plane so as to achieve flexible control of network traffic. Such structure and characteristics have put forward higher requirements on the security protection capability of the SDN controller. However, there are still less researches on malicious applications for the SDN network architecture. This article aims at this problem, based on the analysis of the existing malicious application detection methods and on deep learning technology proposed by a detection method for SDN malicious applications. Finally, under the TensorFlow deep learning simulation environment Keras, 30 SDN malicious samples were studied and tested. The experimental data show that the detection rate of this method for malicious applications can reach 89%, which proves the feasibility and scientificity of the program.

The era of multimedia big data has a profound and extensive impact on the field of public security. The application of multimedia data and big data technology has brought new opportunities to the construction of public security system, as well as new challenges. This paper summarizes the new characteristics of various public security risk events, such as violent terrorist attacks, serious criminal offences, major group events, and network crimes, and analyzes the main problems existing in the application of big data technology in the field of public security. The progresses and trends of some essential technologies are analyzed.

Pedestrian detection is an essential and challenging problem in machine vision and video surveillance signal processing. To handle the high cost of training-specific discriminative classifier for pedestrian detection, we focus on the learning of suitable features for pedestrian detection representation. A deep neural network is presented in this paper to resolve the above issue. Our pedestrian detection method has several appealing properties. First, the learning of features is much more efficient under the configuration of the proposed framework due to the reduction of training classifier. Second, a K-Nearest Neighbor (KNN) method is adopted to solve the comparison between the regions of interest and the templates. Third, due to the less dependency of the classifier, the performance across different datasets overcomes most traditional ones. Finally, we perform extensive comparison across different public datasets and compared with corresponding benchmarks.

Character recognition and classification is considered one of the most important parts of current LPR systems. Because of low recognition quality and poor robustness of traditional character recognition techniques, those techniques were gradually replaced by powerful deep learning modules such as convolutional neural networks. Convolutional neural networks (CNNs) show satisfying ability in character recognition and outperform most of other available models. Since Chinese license plates contain Chinese characters in addition to ordinary alphanumeric characters, a robust, powerful, and efficient CNN is needed to accomplish character recognition task efficiently. In this paper, we have proposed an efficient CNN model based on Darknet architecture to perform character recognition. Through convolutional and max pooling layers, features of input character images will be extracted and then sent to softmax layer for classification. To avoid overfitting problem in the training process, the dropout regularization technique is adopted. We have used a dataset of 84,000 character images for training and testing our model. The experimental result shows satisfactory outputs and eventually achieves test accuracy of 99.69%.

Being one of the most fundamental embranchments of deep learning theory, scene categorization technology has been extensively researched because of its great value in engineering application, especially in the field of remote monitoring and intelligent fault detection. To bridge the gap between theoretical accuracy and practical performance of relevant classification models which is mainly caused by nonstandard labeling information, this paper builds a normative dataset composed of 10,000 high-quality manual labeled images from the power sector, and proposes a high-performance multi-label classification model utilizing deep convolutional neural network (CNN) inspired by Inception-v4 [1] on this basis. Experiments demonstrate that the model proposed achieves an accuracy of 94.125% on the test set and thus can be deployed into practical intelligent surveillance scenarios.

For a few years, plenty of face recognition algorithms, such as deep learning, have been in hot pursuit with the trend of the technology, while dictionary learning algorithm is still out of the woods for the sake of its higher robustness to occlusion and light. In this paper, we propose to learn a discriminative structured dictionary with constraint named as multi-label to suppress representations for different classes, as well as Laplacian Eigenmaps to encourage the representations for the same class to be close to each other. Demonstrated by the results of the experiments, our proposed dictionary learning methods intend to achieve better classification performance and higher computational efficiency compared to the existing algorithms.

With the continuous development of science and technology, many application fields of data belong to the data stream type. Data stream classification is one of the most important analysis methods of data stream processing. The neural network algorithms have no complicated models and reasoning, and have great advantages in data stream classification. In this paper, data stream classification, neural network algorithm, and improved BP neural network algorithm are studied. The neural network toolbox provided by MATLAB is used for data stream classification and simulation.

One of the most important reasons for productivity decline and accidents is work fatigue. Work fatigue research has become more and more important in modern society. This paper proposes a method to detect fatigue, Build new features, propose new compensation methods, and combine the existing models to make the method adapt to the complex environment. As a result, it effectively improves the work fatigue detection efficiency and accuracy under the production environment.

To solve the beamforming problems of small-scale array, this paper describes a new method using power pattern function and genetic algorithm. Compared with the traditional antenna beamforming methods which utilize the envelope of the radiation pattern, this method can control all the sidelobes quantitatively and form the desired array pattern. For a small-scale array, its envelope is difficult to be quantified and obtained. The proposed method in this paper is more intuitive and reliable to control the sidelobe levels. Using the power pattern optimization method, it can obtain an improved array pattern and fast convergence in small-scale arrays design. Numerical results are presented to verify the convergence and computational efficiency of the proposed method.

The traditional text sentiment analysis directly inputs syntactic feature or word vector to model. It fails to consider the characteristics of time series in sentiment. Considering that product reviews are short text, this paper comes up with the method of windowed word vector and classifier fusion in the decision layer. The results indicate that the proposed method can achieve better performance than several existing methods.

Due to the nonuniform scale variations and severe occlusion, most current state-of-the-art approaches use multicolumn CNN architectures with different receptive fields to tackle these obstacles. We design a single-column network to verify the necessity of multicolumn network, and we find that under similar number of parameters and size of receptive field, single network is able to perform as well as multicolumn network. Following that, we propose a single-column network called FFnet based on residual block. FFnet is a fully convolutional network and easy to train. We perform extensive experiments on Shanghaitech dataset and the UCF_CC_50 dataset, and the results show that our method achieves a better performance than Switch-CNN with nearly half number of parameters, and a closing performance to the state-of-the-art model CP-CNN with almost one-tenth parameters.

The development of cooperative intelligent transportation systems brings new challenges to wireless communication technologies, where the channel estimation becomes more and more important. In this paper, a novel data-driven channel estimation method based on deep learning framework is adopted. Based on the feedforward neural network, the VNET neural network based on the convolutional neural network is proposed. The simulations and practical measurements are also provided to verify the performance advantages. The results show the achieved performance advantages of the proposed VNET-based method, which is shown to be an effective solution.

Evacuation path has an important influence on emergency evacuation efficiency at open space, but the evacuation path based on the shortest path is not the best choice. Such environmental variables as coverage effect of 5G signal is an important basis for intelligent guiding signs to dynamically guide the stream of people for evacuation, with bigger intervention effect with a choice of evacuation path of crowd, influencing the science of planning of evacuation path. In the paper, Yulin open block of Chengdu City is taken as the object of empirical study, and layout of guiding signs is adjusted using fuzzy control and intelligent agent technology to improve distribution of the stream of evacuated people in the space, screen out the evacuation path with high pass rate, and low congestion rate. Research result shows that the effect of the evacuation signs at the existing congestion point is not obvious at all. Fuzzy control can optimize spatial distribution of the stream of evacuated people by adjusting and controlling the degree of familiarity of evacuated crowd with different exits.

We present the problem of click-through-rate (CTR) for search advertising in ALiMaMa, which displays user information, item information, shop information and trade results. Traditionally, people use logistic regression (LR) to predict it. However, because of the lack of learning ability and the sparse feature matrix, the prediction results are always not so satisfying. In this paper, we mainly propose some feature engineering methods based on gradient boosting decision tree (GBDT) and Bayesian smoothing to obtain a wonderful feature, which has more useful information and is not so sparse. Also, we use xgboost (XGB) instead of LR as our prediction model. The proposed methods are evaluated using offline experiments and the experiment results prove that the log loss drop near $$5\%$$ after using these feature engineering methods and XGB. Obviously, it is an excellent performance.

In the field of forensic science, image segmentation is required as a basic and significant stage in forensic image analysis. It is very important to segment the stamp impression image with a complex background precisely. This paper puts forward a feasible and efficient approach for complex background stamp impression image segmentation based on Fuzzy C-Means (FCM) algorithm. The fuzzy feature of forensic image can be handled efficiently using Fuzzy C-Means (FCM) algorithm in the forensic science field. The results of the experiments demonstrate the validity and accuracy of Fuzzy C-Means (FCM) algorithm.

The quaternion-valued least mean squares (QvLMS) adaptive algorithm has been proved valid for the adaptive filtering in quaternion domain. However, there have been few researches on its performance. This paper firstly deduces the energy conservation relation in quaternion domain and then analyzes the steady-state performance of QvLMS algorithm in stationary and non-stationary environment by using the quaternion energy conservation relation. The relevant expressions are deduced and then the step-size range which can guarantee the algorithm convergence is obtained. Simulation results demonstrate the rationality of the analysis.

With the arrival of the era of big data, a large amount of data stream generates in the real world. However, the existence of concept drift has brought great challenges to data stream classification. Therefore, this paper proposed an ensemble classifier system based on fuzzy integral to solve the above problem. And after the experimental evaluation, we can approve the proposed algorithm outperforms other algorithms in terms of classification performance and the ability to adapt to new concepts efficiently.

Statistical parameters speech synthesis typically relies on context-dependent Hidden Markov Model (HMM) that is based on decision tree clustering. However, the shortcomings of clustering decision tree, restricted to a feature rigid subdivision model space, results in smooth speech parameters generated from HMM. In this paper, Deep Neural Network (DNN) is put forward to replace clustering decision tree, and we propose a post filter-parameter-based speech synthesis improvement algorithm. This method enhances the formant region of synthesized speech spectrum by selecting the most optimized filter parameter according to the flatness of spectrum. The experimental results show that DNN effectively can modify the deficiency of two smooth parameters. Furthermore, the improved post filter algorithm increases the naturalness of synthesized speech.

Machine Translation is an important part of Natural Language Processing. The model based on convolution neural network and attention mechanism (Fcnn model), which was proposed by Facebook in 2017, has been successful. We use this Fcnn model as the baseline model of this subject, and on the base of this model, we try to improve it by the combination of bytes pair encoding method, model ensemble method. In this issue, we use Bilingual Evaluation Understudy (BLEU) as a criterion to measure the quality of translation. After testing, these methods can improve the translation quality of the model. Finally, the overall translation quality increased from 0.28 of the baseline Fcnn model to 0.32.

Erasure codes can improve the reliability of modern Distributed Storage Systems (DSS) by preventing data loss and nodes failure. Regenerating code is a class of erasure codes that allow for repairing of failed nodes. However, regenerating code increases the amount of the participating nodes and its coding parameters are difficult to determine. In addition, it has huge computational overhead and low repair efficiency that prohibit its applications. Hence, we first propose a fractional regenerating code combined with prior replication with uncoded repair. Simulation results show that it can reduce repair bandwidth and computational complexity by increasing the number of high prior nodes. Second, we formulate the problem of computing multiple failure repairs cost using the proposed code as a redundancy scheme. We model the problem as an Integer Linear Programming problem (ILP) and solve it by Genetic Algorithm $$\_$$ Particle Swarm Optimization (GA $$\_$$ PSO) algorithm. We present results of repairing bandwidth cost for our proposed algorithm in two scenarios to evaluate the effectiveness of the solution approaches. Simulation results demonstrate that GA $$\_$$ PSO can get smaller repair bandwidth cost than GA.

In view of the problem of edge blurring and slow reconstruction speed in the existing super-resolution reconstruction of learning-based images, this paper proposes an improvement to the original learning-based reconstruction algorithm and applies Markov random fields to image super-resolution. In the rate reconstruction, during the dictionary training phase, training image blocks are randomly selected, and the texture part of the image is learned and reconstructed. The bicubic interpolation method is used to enlarge the image structure part and color information, the final reconstructed image and the interpolation-amplified image are merged. That is the final result. The experimental results show that the peak signal-to-noise ratio (PSNR) is used to objectively evaluate the image reconstruction effect, and it is concluded that the algorithm of this paper reconstructs the image better, and the reconstruction time also has a certain increase.

As intelligence is popularized in the life of people and technology develops continuously, intelligent plant become the main development direction of the modern enterprises. By taking intelligent plant of small-sized traditional factories as the case, the paper explains the conception of intelligent plant, shows the necessity of intelligent plant and establishes the systematic frame of an intelligent plant. A small-sized factory is constructed intelligently based on analysis of the systematic frame of the intelligent factory; a large database is established through an MES system, and various information flows are settled, so that intelligence of production management is realized, intelligent productions are established, an intelligent logistics system develops, and then intelligent improvement exploration of small-sized plant can be finished.

Rapid collection of data sources, varying in volume and structure poses a challenge for scientists to establish a practical approach to manipulating heterogeneous data sources. A multimodal learning and an integrated analysis make it possible to extract much worthwhile information from a collection of multiple simple raw data. Therefore, data integration can lead to a more reliable and robust result. High-throughput sequencing technologies, especially next-generation sequencing, leave us with multi-platform genomic data such as gene expression, SNP, CNV, DNA methylation, and miRNA expression. In this paper, we represented a multimodal deep neural network to exploit the mutual information between three different modalities to classify breast cancer patients into two groups based on their survival rate. Experimental results indicate that our method improves the classification accuracy and performs better on imbalanced data compared to the other single-modal state-of-the-art methods.

Deep Neural Network-based methods have recently shown an outstanding performance on object detection tasks in 2D scenarios. But many tasks in real world requires object detection in 3D space. In order to narrow this gap, we investigate the task of detection and localization in 3D human body in this paper, and propose a multi-view-based deep learning approach to solve this issue. The experiments show that the proposed approach can effectively detect and locate specific stuff in 3D human body with high accuracy.

Deep neural networks have present state-of-the-art results in many different machine learning tasks. In the traditional machine learning task, we train models on the formerly prepared dataset. However, in the real-world scenarios, training data are always collected in an incremental manner, in which new samples and new classes will be added to the training data gradually. Since the traditional training method with stochastic gradient descent will suffer from catastrophic forgetting problem when training on the new data set, in this paper, we proposed a new scheme to train deep neural networks incrementally. We first train the deep model on the original dataset with a weight-pruning manner, then on the newly added training data, we train the former pruned weights while remaining the former trained core-part weights unchanged. Experiments on MNIST demonstrated that our method is efficient and can even get better performance than training from scratch on the whole dataset in the traditional manner.

The Doppler spectrums of radar echoes of targets can reflect the change of the instantaneous velocity of targets. Therefore, it can be used for analyzing the motion state of the target and classifying them. Besides, deep learning is widely used in the classification of images. This paper proposes a deep learning based method of classifying targets in sea clutter. First, we introduce the motion model of targets and analyze their Doppler spectrum, based on which, we stimulate the time–frequency images of targets’ radar echoes. Since clutters in echoes usually obey Weibull distribution, we add Weibull clutter (Mezache and Soltani) to a novel threshold optimization technique for far-away detection in Weibull clutter using fuzzy neural networks, 2007, [1]) to the echo signals. Then we classify targets with different networks using NVIDIA DIGITS, based on the images and analyze the results of classification.

In China, infrastructure constructions of the city are developed continuously. The state of urban community safety and its capability are an important sign of its quality and civilization. High-precision bridge deformation detection is eagerly needed to ensure the safety of city facilities. In this paper, BP neural network is applied for high-precision 3D modeling of point cloud data obtained by 3D laser scanner along the Shanghai maglev train. Based on the 3D laser scanner technology, the deformation of the maglev train’s bridge can be usually monitored. After analyzing the experiments on monitoring the bridge deformation of the Shanghai maglev train, a certain deformation effect when the maglev train is passing can be monitored. So, we will get a great deal of data from the Shanghai maglev train safety information.

In recent years, with the continuous development of Visible Light Communication (VLC), its application fields are constantly expanding. In view of the tradition various gating locks have some shortcomings, especially in terms of safety needs to be improved. The visible light communication technology and access control system are joined to design a Hamming-encoded visible light communication access control system in this paper. The hardware design of this access control system is given in this paper and the system of Hamming coding is analyzed. Through MATLAB software simulation, the results show that the access control system with Hamming code can effectively reduce the bit error rate, and can provide about 2 dB coding gain. In the experiment, the system works well just like simulation. Hamming code greatly improves the performance and safety of the visible light communication access control system.

In order to improve the efficiency and accuracy of volume measurement of the large coal pile, a set of portable 3D laser scanner is designed. This device uses the 2D laser scanner as the measurement module supplemented by the high-accuracy sensor, to scan the 3D morphology surrounding the coal pile. Based on characteristics of the scanner and structural features of the coal pile, the data preprocessing arithmetic is optimized, which synchronously completes the scanning point noise reduction during the measurement process and saves the time required for the 3D modeling. Spatial interpolation is adopted to reconstruct the 3D model, which fills in the cavity at the coal pile top, filters excessive scanning points at the bottom, and improves the efficiency of the 3D modeling and volume calculation. As shown by the experiments, only a few minutes are required to complete the 3D morphology measurement of the coal pile, and it will generate the 3D model and volume within 20 s after the measurement.

In this paper, a new type of hull structure long base stress sensor is introduced. When the ship changes under external or internal forces, the fiber Bragg grating fixed between two bases is deformed. The deformation of the fiber grating and the wavelength change of the fiber grating are basically linear. By demodulating the end of the compensation algorithm, we can get the fiber wavelength and the ship structure stress. Therefore, the actual structural strength of the hull is accurately mastered, which provides a stable and reliable device for long-term detection and health monitoring of hull structure in the field of ocean ships.

Based on the analysis of structure and common control scheme of photovoltaic grid-connected system, a solar photovoltaic (PV) cell simulation model is built according to the internal structure and output characteristics of PV cell. A control method of photovoltaic MPPT which can track photovoltaic points and the maximum power of photovoltaic cells is expounded. This method can be used to improve the efficiency of photovoltaic cells to the greatest extent. Finally, the principle and control strategy of the photovoltaic grid-connected system are analyzed in detail. According to the instantaneous value control method of grid-connected inverter, some requirements of grid connection with low THD and high power factor are achieved.

The output power–voltage curve of photovoltaic array under uniform illumination is a single-peak value, the voltage–current U-I curve is a single-step type. Once the photovoltaic array is obscured by clouds, mud, trees, etc., the photovoltaic array appears like partial shadows, and the P-U curve output of the photovoltaic array will appear like multiple peak values, and the U-I curve appears multiple steps, which will reduce the output power of the photovoltaic array. By using MATLAB/Simulink to build the model of the photovoltaic array under the shadow condition and carry out several simulations, we can get some characteristics of the P-U curve and the U-I curve, and summarize the rules. It provides a theoretical basis for the maximum power point tracking (MPPT) under the shadow condition.

A novel polarization splitter is proposed in a photonic crystal fiber with orthogonal dual core. The device is designed by using index matching coupling theory. The proposed splitter exhibits some features, which has short splitting length (the minimum splitting length is 0.3 cm), low loss (the silicon fiber has been widely used in optical fiber communication) and so on. Moreover, it is easy to fabricate over its counterparts because the right core is equivalent to the left core rotated by 90°. It is extremely useful for the future applications of the broadband polarization device.

In this paper, the detailed strain characteristics of a selective-filling birefringent photonic crystal fiber (SF-PCF) were analyzed theoretically. The SF-PCF was achieved by selectively filling two symmetrical air holes around the fiber core of a PCF with a high index liquid. The group birefringence and phase birefringence presented unique characteristics different from the traditional index-guiding birefringence fiber. Hence, the transmission and sensing characteristics of the Sagnac interferometer based on the SF-PCF presented diversity and high sensitivity. Finally, the highest strain sensitivity of −9.56 pm/με was theoretically achieved.

In this paper, the double negative metamaterial is designed and analyzed using finite element method. Most of the structure of metamaterial is applied in radar stealth technology and superlens. Compared with the traditional material, the metamaterial can focus on electromagnetic waves, which can improve the wireless power transfer (WPT) system. In previous research, the metamaterial always operated in GHz and THz which is not suitable for the WPT system. To address this problem, a novel structure of metamaterial which works in MHz is proposed. The simulation results indicate that the novel structure of metamaterial can achieve negative permeability and negative permittivity at about 50 MHz.

This paper presents a direct method to generate a transmission zero in evanescent-mode waveguide filter by adding a shorted waveguide shunted in the coupling region between two resonators. First, a traditional evanescent-mode waveguide filter with series coupling topology is designed. To introduce a transmission zero to enhance the stopband performance, a shorted waveguide is shunted in the coupling region between two adjacent resonators. Adjusting length of the shunted waveguide can easily adjust the position of the transmission zero. To maintain the bandpass performance of the whole filter, the coupling region needs to be tuned to produce suitable coupling strength as the same as the original one. A fourth-order example is designed and measured, measured results show good accordance with the simulated ones, validating the proposed method in this paper.

With setting up and tearing down of dynamic requests, there will inevitably be spectrum fragmentation in elastic optical networks, leading to a higher blocking ratio. However, conventional algorithms only consider current spectrum fragmentation in the 2D-spectrum auxiliary graph, which determines whether requests can be accepted when they arrive. To overcome this problem, we propose an innovative routing and spectrum assignment algorithm considering the future fragmentation, which determines whether requests can be accepted when other online requests leave. In this algorithm, we first introduce a time variance metric to measure the occupied holding time of both the current request and its adjacent online requests, and then propose a model with the minimal time variance in the 3D-spectrum auxiliary graph, which tries to ensure all the leaving time is similar around the current requests. Finally, we verify the advantages of the proposed algorithm in terms of blocking ratio, fragmentation ratio, and spectrum utilization ratio through simulation results.

Software-defined Elastic Optical Networks (SD-EONs) combine software-defined network (SDN) with elastic optical networks (EONs) to further improve the spectrum utilization of routing and spectrum assignment (RSA) algorithms. However, some existing RSA algorithms usually only consider the hops when solving route subproblem, whereas the link spectrum status is also very important. At the same time, they usually neglect the vertical fragmentation between neighbor links during the spectrum assignment. Therefore, we propose an innovative hop and consecutiveness-based routing and fragmentation-aware spectrum assignment (HCR-FSA) algorithm with a joint consideration of hops and spectrum consecutiveness in routing selection and pays attention to both horizontal and vertical fragmentations during spectrum assignment. We implement our HCR-RSA algorithm in RYU controller to evaluate its performance. The experimental results show that our algorithm achieves better blocking probability and fragment degree compared with the previous works.

In this paper, a wideband chaotic light based ring-and-spur long-reach passive optical network (ring-and-spur LR-PON) is proposed and numerically demonstrated. The bidirectional long-haul secure communication between the optical line terminal (OLT) and the optical network unit (ONU) is realized, in which the bandwidth of the chaos carrier is more than 20 GHz. As a result, the data rate can reach up to 10 Gb/s for each wavelength channel. The proposed network has potential practical values, owing to its advantages such as providing a higher bit rate, achieving a higher level of real-time security, survivability, etc.

This paper proposes a method to determine the color gamut boundary of the color system by theoretical calculation based on the colorimetric parameters of three primary colors and the reference color white. Gamut mapping is the fundamental way to improve the chromatic aberration between different color systems, and the determination of gamut boundaries is the prerequisite to achieve gamut mapping. In this paper, the frame of a three-dimensional color gamut of the system is established by using the chromaticity parameters of the three basic color of the system and the base white, and then the theoretical construction of the three-dimensional color gamut of the system is realized by the interpolation method. In this paper, the 3D gamut boundaries of the conventional gamut and the Rec.2020 WCG gamut are completed by using this method. The calculation results show that the construction method in this paper has a small amount of computation, short time required, high accuracy, and universality.

In this paper, a new color gamut extension mapping algorithm based on the image is proposed by using the theoretical three-dimensional color domain boundary. The algorithm can be compatible with the existing conventional gamut video system and give full play to the reappearance advantage of WCG Rec.2020 display devices. In this paper, the mapping relationship between the two color gamut boundaries of Rec.709 and Rec.2020 is determined by the tone angle and the space position angle, and the extension coefficient lookup table of the conventional color gamut extends to the Rec.2020 WCG gamut is obtained. Then the color gamut expansion between the two system gamut is realized by the bilateral filtering and the lookup table assignment. In addition, this paper selects two images from the TID 2008 image library, and carries out data analysis and index evaluation on the color gamut mapping algorithm proposed in this paper. The results show that because the relative position between pixels of the source image is taken into account, the algorithm preserves the color features of the image better.

In view of the fact that existing Fiber Bragg Grating (FBG) accelerometers cannot achieve high-precision signal and high vibration frequency measurement at the same time, this paper proposes a novel type of FBG accelerometer based on the hinge structure. A novel mechanical structure of the sensor is designed, the elastic system is composed of a mass block and a flexible hinge, and the strain of the hinge amplified by the mass block is reflected by the fiber grating. Based on the analysis of the working principle of the new sensor, the influence factors of the resonant frequency and sensitivity of the sensor are studied. The mathematical model of the elliptical flexible hinge is used to optimize the design parameters of the sensor structure. The finite element analysis results show that the new structure sensor achieves the desired effect and it has a high resonance frequency (356 Hz) and sensitivity (284 pm/g).

Pressure sensor can be used from industrial production to modern life with fast-growing technology and high-pursuit performance. Because of the advantages of strong magnetostrictive effect, high response speed, noncontact drive, and high electromechanical coupling, giant magnetostrictive thin film are not only used to miniaturize the sensor dimension, but also makes the sensitivity improved significantly. The induced voltage value of sensor detection coil can be determined by the intensity of external pressure and magnetic field. Meanwhile, the variation rate of magnetization depends on the differential magnetic susceptibility and the magneto-mechanical effect change rate by Villari effect, so a hysteretic nonlinear magneto-mechanical coupling model is established based on the J-A model and the law of energy conservation in this paper. The COMSOL simulation results show that the magnetization curve of the model can describe the magnetization trend and hysteresis characteristics better, and the relationship between input pressure and output voltage can be predicted, which verifies the correctness and accuracy.

To enhance the development efficiency of test programs in Automatic Test System (ATS), a framework of test application development platform based on ATML (Automatic test description language) standard is built in this paper. ATML-oriented test application development platform contains configuration tools, test flow description, ATS resource description, and runtime system. ATML is used to describe ATS-related information and the test flow based on signal definition. By leveraging the ATML description documents, runtime system achieved the automatic matching of the test resource and test requirements, and generated the test programs automatically. The platform is extensible, and the test program sets can be transplanted to other platforms.

School violence has become more and more frequent in today’s school life and caused great harm to the social and educational development in many countries. This paper used a MEMS sensor which is fixed on the waist to collect data and performed feature extraction on the acceleration and gyro data of the sensors. Altogether nine kinds of activities were recorded, including six daily-life kinds and three violence kinds. A filter-based Relief-F feature selection algorithm was used and Radial Basis Function (RBF) neural network classifier was applied on them. The results showed that the algorithm could distinguish physical violence movements from daily-life movements with an accuracy of 90%.

Channelization RAID storage system requests multichannel data transmission and high transmission bandwidth. We design a data caching system which is inserted between fore-end data source interface and the back-end RAID interface on a FPGA implementation. The caching system uses DDR3 as the external memory because of its large storage capacity and high storage rate. It uses a special channel management system and only needs three clock cycles to complete the read–write scheduling of different channels. The caching system provides the AXI4-Lite interface, so it can be dynamically configured by the AXI4-Lite bus. After testing, the caching system can satisfy the request of multichannel storage task.

A novel method of multiple switched flat-top beam is proposed for solving the problem of unequal radiation in different directions of the same beam in the multiple switched beam smart antenna in this paper. Taking 4-element Quasi-Yagi antenna array for an example, the feed currents of 4-element flat-top beam antenna array are calculated by using Woodward-Rosen synthesis method. An improved 4 × 4 Butler Matrix feed network is designed by using the double-layer dielectric structure. The antenna array is simulated by HFSS simulation software. The simulated results show that, compared with the beams of uniform antenna array, the gain fluctuations of the flat-top beam antenna array are reduced by 6.4–25.9 dB in the main lobe, the sidelobe levels are reduced by 2.7–17 dB, and with the characteristics of small size and wide bandwidth.

The design of a portable multifunctional charger is presented in this paper. The charger supports two charging modes of 220 V alternating current and 12 V DC. The 12 V lead–acid battery is used to support AC 220 V output, DC 12 and 5 V output. The maximum power point tracking and battery charging curves are controlled to extend the life of the product and improve the performance of the product. Bluetooth can be connected to the mobile phone or pad to control the battery output and monitor the battery state. The design can be used in the field of home tourism.

Nios II is applied in direct current motor servo system as core microprocessor of FPGA system in this paper. A SOPC system is created by Qsys and Quartus II. Feedforward-feedback control algorithm is adopted and Fuzzy-PID is taken as the control algorithm of feedback. The position loop, velocity loop, and current loop control in the system improve both dynamic performance and static characteristic. Fuzzy adaptive PID control algorithm are achieved by Nios II, and simulated in MATLAB/Simulink. The whole system’s analysis, synthesis, simulation and configuration are completed by Quartus II. Results show that this method improves both system responding speed, and tracking precision in DC motor servo system.

According to the testing demand of a certain type of equipment, the current design scheme of constant current source is compared and analyzed, and the overall scheme is determined by considering the factors such as cost and index. Next, it is designed and hardware selected of power supply circuit, constant current circuit, and control circuit, and the hardware circuit is built. The system software design flow is analyzed. Finally, the system is tested and the results show that the design requirements are met.

In this paper, a compact hybrid-integrated circular polarized double-ring antenna is proposed based on the Yagi-Uda concept. With two cross-slotted patch hybrid coupler integrated inside the square ring, the proposed antenna can effectively radiate a circular polarized wave with lightweight and more compact size. With six other rectangular rings aligned at the right positions to act as wave directors, a gain up to 10 dBi can be achieved. The proposed antenna is thus especially suitable for satellite communications where weight and size are major constraints.

In order to analyze the effect of car center of mass on the steering stability of front-wheel steering vehicles and four-wheel steering vehicles, and based on the two-degree-of-freedom vehicle dynamics model, this article uses Matlab/Simulink to carry out the joint simulation of its steering characteristics. Based on the two conditions of front-wheel steering and four-wheel steering respectively, the stability of vehicle before and after the change of center of mass at different vehicle speeds is discussed and analyzed. The result shows that vehicle stability is greatly reduced after the center of mass moves. But when at low-speed, the steering stability after the center of mass movement of the four-wheel steering vehicle is still better than that of the front-wheel steering vehicle of the normal parameters.

The reflector of one surface for Light-emitting diode (LED) is designed based on flux compensation method. It is easier to be fabricated compared with freeform lens. Moreover, the reflector can effectively reduce material absorption loss. This design method is simple and other researchers can understand and repeat more simply. The designed reflector also has a small exit angle for uniform illumination. It has high illuminance uniformity and flux efficiency, which are more than 90% and 95%, respectively. Reflectors designed here can be used in optical signal coupling, display cabinets, and other optical transmission applications.

This paper introduces the design and fabrication of a low-power and high-precision electronic scale with resistance strain gauge as the weight sensor. According to the demand of commercial measurement, the paper compares and proves the various design schemes, chooses the cantilever-type resistance strain sensor with the STC89C52 single chip, and uses the 24-bit high-precision A/D converter chip HX711 to convert the signal to A/D. The system has the advantages of high integration, fast response speed, high precision, low power consumption, and strong anti-interference, which lowers the cost and improves the performance and reliability of the whole machine.

This paper takes the PIN diode as the main object of analysis, theoretically analyzes it’s operating principle and the type of damage occurring under the impact of the high-power microwave, and proposes that the damage characteristics of the device should be based on the time and energy.

Against the situation that traditional armored vehicle information acquisition device can have multiple models, variable structures, long manual test cycle, complex test steps, low reliability, and other drawbacks, a configuration-based automatic test system for armored vehicle information acquisition devices is designed to realize the various performance testing. The system uses a combination of hardware and software virtual instrument technology which provides the ability to flexibly configure the test information. In addition, the test cases can be automatically configured and executed; the test results are automatically generated.

In this paper, an enhanced dynamic Neuro-space mapping (Neuro-SM) method is proposed with emphasis on transistor modeling. By modifying the dynamic voltage relationships in an existing nonlinear model, the proposed Neuro-SM produces a new and more accurate model than the nonlinear model as well as the static Neuro-SM. Compared to the existing dynamic Neuro-SM, a new sensitivity analysis technique is derived to speed up the training of the proposed model with dc, small- and large-signal data. The validity and efficiency of the proposed Neuro-SM method are demonstrated by modeling examples of a GaAs high-electron-mobility transistor (HEMT). Suitable value of time delay parameter which is equal to one divided by 3 or 5 times of the largest frequency considered in simulation is suggested and demonstrated by the modeling example.

The paper presents a design for open-loop carrier synchronization which is suitable for short burst communication at low SNR and it is easy to implement on FPGA platform. The proposed open-loop carrier synchronization is based on V&V and FFT algorithm which estimate both the frequency offset and the phase offset. We also implement a post-processing to solve the problem of phase ambiguity. The simulation results show that our proposed algorithm can work efficiently. Besides, the algorithm is implemented with a Xilinx XC7VX690T FPGA chip which achieves good performance under the condition of large frequency offset and low signal-to-noise ratio.

This paper presents a sector-shaped microstrip patch antenna at an operating frequency of 1.4 GHz. In order to achieve wide beam and the normal radiation pattern, the radiation patch adopts sector. The shape of patch is the main factor affecting the performance of microstrip patch antenna. Besides, our feed network realized by electromagnetic coupling is achieved by a new method–T-shaped probe, which avoids the additional inductance introduced by the usual probe feed. Thus, the radiation efficiency of antenna is improved. In addition, the antenna has the characteristic of miniaturization. The electromagnetic simulation and optimization design of this proposed antenna are carried out using the CST Microwave Studio software package. The simulated VSWR of antenna is less than 1.5 at the working frequency. In this paper, we consider the microstrip antenna applying in some fields.

The operating frequency band of high-frequency surface wave radar (HFSWR) is generally 4–9 MHz, which makes the operating frequency band of the antenna a wideband. However, it is difficult for some commonly used antennas to meet this requirement, and it is necessary to widen the antenna. In this paper, a lumped monopole antenna is designed for high-frequency surface wave radar. Genetic algorithm is employed to optimize the loading position and impedance. The operating band of the loaded antenna is wider and can meet the performance requirements of the HFSWR transmitting antenna, and the antenna size is slightly smaller than other antennas in the same band.

A graphene frequency multiplier is proposed in this paper. The nonlinear characteristics of graphene are similar to the reverse parallel diode. In order to improve the efficiency of GFM, we use the method of recovering the fundamental and fifth harmonic wave which utilizes the way of branch recovery. According to this mechanism, an S band graphene frequency multiplier is designed. During the working frequency between 500 MHz and 3 GHz, the minimum conversion loss of −20.81 dBm can be obtained at 830 MHz and the input power is 18 dBm.

In order to actively respond to the national strategy of green lighting, this paper designs and implements a smart street lamp management system based on solar street lamps, which uses multilayer distributed structure. With the embedded ARM microprocessor as the core, the bottom terminal combines with NB-IOT transmission technology, sensor technology, GPS positioning technology, electricity collection technology, and so on. The system is equipped with a good human–computer interaction platform, and collects information to the top-level database terminal with the network transmission technology of narrow band, realizing the effective collection and storage of city streetlight information. Through scientific and effective control and management of urban street lighting facilities such as line control, point control, and spot survey, the intelligent street lamp management system saves energy and improves work efficiency, and improves the modern management level and scientific means of urban lighting facilities. The system which has good stability and can effectively manage the solar streetlights was tested on the spot at Nantong University, so it has a good value of application and popularization.

A reactor robot for object salvaging underwater was designed to reduce the labor intensity of operators under the nuclear radiation environment. The control system of the robot, composed of control system hardware and control system software, was designed to be compact and radiation resistant. The control algorithm of the robot based on current, velocity, and position feedback of each motor makes the robot achieve the goal of salvaging exactly and rapidly. In order to verify the stability of the control system, the salvaging foreign matter experiment was tested in nuclear base. The result shows that the control system of the robot is stable enough.

In order to improve the recognition rate of mechanical vibration signals of high voltage circuit breakers, a feasible new fault diagnosis method is proposed in this paper. Firstly, the empirical wavelet transform (EWT) is adopted to decompose the original multi-component signals into a series of intrinsic mode functions (IMF). Secondly, the envelop energy entropies of these IMF components are calculated as signal features. Finally, establishing the optimal support vector machine (SVM) classifier by particle swarm optimization (PSO) method. Using this EWT-PSO-SVM model to identify the unknown samples, the results show that the EWT method can effectively reduce modal aliasing problem, and the recognition rate of EWT-PSO-SVM model is higher than EMD-PSO-SVM model, these results verify the feasibility and superiority of the proposed EWT-PSO-SVM fault diagnosis method.

Due to the use of a fixed-size state transition set, the traditional dynamic programming Track-Before-Detect (DP-TBD) algorithm significantly reduces the detection and tracking performance of maneuvering targets. This paper proposes a DP-TBD algorithm with an adaptive state transition set (ASTS-DP-TBD). The algorithm improves the search efficiency of the maneuvering target by introducing Kalman filtering and target state transition probability in the traditional algorithm. In addition, this paper also optimizes the termination decision strategy of the algorithm, which significantly improves the detection performance. Simulation results show that the proposed algorithm in this paper has better detection and tracking results than traditional algorithms for maneuvering targets.

The paper mainly analyzes the development status and the development trend of the intelligent manufacturing technology in the machining field at present, presents the necessity of development of intelligent manufacturing, and creatively processes a new method of a blade with multiple space and compound angles so as to provide reference for development of intelligent manufacturing in the machining field in China.

Nios II is applied in three-phase AC-voltage regulator energy-saving system as the core microprocessor of FPGA system in this paper. A complete voltage regulating main topology circuit is designed. Only three groups of triggering signals are required to control the six thyristors’ conducting angles. A SOPC system is created by Qsys and Quartus II. The PID control algorithm is used to adjust the output voltage in real time according to the load change to save energy. The generation of high-precision triggering signals and PID control regulator functions are realized by Nios II, and simulated in MATLAB/Simulink. Experiments and in-field tests have shown the feasibility of the proposed scheme. The whole system’s analysis, synthesis, simulation and configuration are completed by Quartus II. Experiments show that the method is simple and reliable, and achieves a better energy-saving effect.

With a single RF front-end to equivalently realize RF transformation of multiple antenna elements, single RF array is an important way to reduce the manufacturing cost of massive MIMO systems. Time-division-multiplexing-based single RF array is suitable for uplink receiver array. However, there are some bottlenecks in broadband applications, such as very fast switching speed requirement of RF switch, wide bandwidth requirement of analog front-end and low signal fidelity, which stop the wide use of this technology. To overcome the above bottlenecks, a time-modulated-array-based broadband single RF receiver array architecture is proposed in this paper, and the theory of time-modulated array along with the idea of signal reconstruction is also introduced to broadband the single RF array. With these methods, the performance requirements of RF switching speed and analog front-end are expected to be reduced significantly while the signal fidelity degradation can be safely ignored. The experimental results show that the proposed architecture can improve the bandwidth of the system with traditional RF switching elements.

In view of advantages and disadvantages analysis of software communication architecture (SCA), general open architecture (GOA) and avionics software system structure (ARINC653), a new type of integrated system software architecture which is suitable for launch vehicle was designed in this paper. According to the characteristics of the launch vehicle software, the new software system architecture including four layers, such as resource service layer, operating system layer, middleware layer, and application layer. Each layer is independent to each other, and all layers can integrate the functions of the traditional launch vehicle software system. The new software system architecture can support flexible functional expansion and tailoring, which enables it to quickly adapt to the requirements of different types of launch vehicles, and greatly reduce the repetitive design work.

The eMMC (embedded multimedia card) device was originally applied for high storage devices on mobile devices by a driver provided a good internal programming interface to the hardware. This article analyzes the read and write characteristics of eMMC devices in high-speed mode. It is proposed to improve the read and write performance of eMMC devices by using frequency replacement algorithm and double buffer as the basic improvement scheme.

This essay analyzes the measurement of the power factor in the drive motor system of the frequency converter. Due to the fact that it is inconvenient to utilize the power analyzer to acquire the motor power factor directly. A method is proposed. We can collect the DC bus signal waveform. Then we the could reconstruct motor stator three-phase voltage and current to test indirectly the motor power factor. In addition, there are some simulations and data collected from experimental device in order to verifying accuracy. Congruously, the experimental results show that within a certain error range, the motor power factor can be measured by voltage and current reconstruction.

Rapid electrokinetic patterning (REP) is an optoelectronic technique to manipulate particles. The number of particles can be adjusted by varying REP parameters such as frequency and voltage. An experimenter using REP may want more or fewer particles than are collected in a certain experiment. We use evolution of life curve to analysis REP system, and use nine screens to analysis system. Currently an experimenter may try to vary the experiment parameters to acquire the desired number of particles. This approach is cumbersome. Our goal is to set up an automatic REP system. It can help us to acquire the particles in an automated way. Therefore, we used subfield analysis to set up the automatic rapid electrokinetic pattern system.

Due to the influence of special topography and meteorology conditions, the southwest ATC secondary surveillance radar has a unique characteristic which consists of a complex interference condition and a difference in data character compare with other areas. In order to analyze the blind area of the radar coverage and the surveillance of unreasonable target movement in this area, a radar target data analysis technique based on geometrical method is proposed in this paper. The data analysis technique consists of a horizontal position discrimination region method and a vertical altitude discrimination interval method. The technique can be implemented by the air traffic control department with a rational analysis of critical data components in the radar data. Therefore, by using such technique to achieve the purpose of improving the capability of critical data monitoring and maintenance, providing reasonable issue detection, and to ensure the safety of civil aviation operation.

The utilization of millimeter wave (mmWave) frequency has shown great potential for future wireless cellular systems. Compared with conventional fully digital precoding, hybrid precoding achieves a high performance with less radio frequency (RF) chains and lower hardware complexity. In this paper, we investigate the hybrid precoding for multiuser communication in a downlink mmWave massive MIMO system. First, we present a hybrid precoding scheme designed based on zero-forcing (ZF) precoding. Then, we derive the spectral efficiency of this hybrid precoding scheme and get its closed-form expression which can be represented by the digamma function. Finally, simulation results demonstrate the validity of the analytical result.

With the development of satellite load and very large scale integrated (VLSI) circuit technology, spaceborne real-time synthetic aperture radar (SAR) imaging systems have become a solution for rapid response to hazards. Through analyzing the algorithm pipeline flow as well as introducing the storage-computation model, a balanced and high-efficiency 2-D data access technology based on cross-mapping data storage method has been achieved to suit the large point processing for real-time spaceborne SAR system. A prototype based on NetFPGA-SUME board with Xilinx XC7VX690T is given to verify the performance of the proposed design. Taking Stripmap SAR imaging of 16384 * 16384 granularity raw data (5 m resolution, 25 km width) as an example, the imaging based on chirp scaling algorithm takes 6.63 s, which is better than some other real-time processing methods.

To generate the excitation signal of radar synchronous motor which can obtain the azimuth and elevation of radar antenna, high-speed DSP was used to generate SPWM by the arithmetic of asymmetry rule sampling. The advantage, theory, and mathematic model of SPWM were deeply analyzed. Based on TMS320LF2407A, the process of obtaining SPWM was given. Finally, the test waves were analyzed. The result of experiment shows that it has advantages of high precision, real time, and simple structure to use DSP on the generation of SPWM.

Dual-polarization Doppler weather radar variables such as differential reflectivity (ZDR), differential propagation phase shift (ΦDP), specific differential phase (KDP), and the co-polar correlation coefficient (ρHV), which are closely related to the characteristic of hydrometeors. The precision of these variables determines the accuracy for quantitative precipitation estimation (QPE) and hydrometeor classification. By analyzing the relationship between the polarimetric radar variables and the signal-to-noise ratio (SNR), it is found that when the SNR decreases to a certain threshold, polarimetric variables become more sensitive to the decrease in the SNR, and the precision of these variables become deteriorated with the decrease of the SNR. This threshold usually is closely related to the radar performance, for the X-band dual-polarization radar used in this paper, the SNR threshold is about 20 dB. The results of this study can provide some reference for radar system performance evaluation and data quality control.

The atmospheric structure constant of refractive index is an important parameter to describe the turbulent characteristics of the atmosphere. Using the ROBS data obtained by the Sichuan Provincial Meteorological Bureau in the Yanyuan, Dayi, Xinjin, Xindu, and Longquanyi districts, the real-time sampling of ROBS data combined with the hourly precipitation data provided by conventional meteorological observation stations was used for statistical analysis. Study the change trend of atmospheric structure constant of refractive index in the precipitation process in different regions. The results show that the convective movement during the precipitation process and the drag effect of the precipitation particles on the surrounding atmosphere, with the occurrence and development of the precipitation process, atmospheric structure constant of refractive index has a significant increasing trend, indicating the significance of precipitation. The vertical velocity and $$ C_{n}^{2} $$ are consistent with the change of precipitation, which can better reflect the change of precipitation intensity. The $$ C_{n}^{2} $$ value corresponding to the maximum precipitation time is two to five orders larger than that before and after the occurrence of precipitation.

The production efficiency of small and medium auto parts manufacturing enterprises has been restricted by unreasonable production processes arrangement, high equipment load rate, high quantity of work in process (WIP), etc. To cope with these problems, this paper proposes a novel modeling method to analyze production line. The production line model is established by the method that combination of object-oriented Petri net (OOPN) and time transitions. Then the proposed model is validated based on incidence matrix and state equation analysis method, by which the bottleneck of the production line is identified. Simulation results are provided to validate that the proposed method can accurately reflect the production process and the bottleneck.

In this paper, a radar electromagnetic environment sensing method based on the cyclic spectral algorithm is discussed, which can be used to acquire the spectrum information of radar signals and distinguish them. This paper uses the second-order cyclostationary detection algorithm based on the spectral correlation function (SCF) to obtain the cyclic spectral. The estimation of SCF is and the estimation precision by calculating deviation and variance of SCF are displayed. In the simulation, a scenario of radar electromagnetic environment is presented by transmitting Linear Frequency Modulation signals (LFM) and Amplitude Modulation signals (AM). Simulation results indicate that the cyclic spectral algorithm can not only sense the spectrum information of signals but also judge the type of signal. Therefore, the bandwidth of the interference information can be detected. The simulation results show that this method is highly preferred for radar electromagnetic environment sensing even under low signal-to-noise ratio (SNR) circumstance.

Based on the basic theory of laser communication in the atmosphere, the effects of wavelengths, transmission distance, and visibility on communication link are studied; considering the restriction parameters in the satellite-ground communication link, the outage probability, fade statistic, intensity fluctuation and bit error rate of OOK/BPSK modulation are derived; the link budget is discussed; the results show that mid-far infrared wave has better transmission performance in atrocious atmosphere compared with the near-infrared wave, which could become an optimization choice in satellite-ground downlinks.

Liquid antennas can be easily reconfigured, which means that the antennas can work at different frequency by adjusting the height of liquid surface. This paper presents that seawater antennas can be used in high-frequency surface wave radar. First, performance of seawater antennas is analyzed using Ansys HFSS. Then the measured results of a real seawater antenna are presented. The results show good agreement with the simulation and can be used in a range of 4–15 MHz.

In the process of shipping, container overboard falling accidents occur frequently. To detect the submerged containers, this paper proposes an underwater acoustic sensor network based detection system, which performs the positioning task through a multi-beacon nodes cooperative method to improve the robustness and accuracy of the system. The simulation results show that the proposed multi-beacon nodes cooperative positioning system can effectively solve the problem of link break between the sensing nodes and the beacon nodes due to the severe underwater environment and extend the detecting area with improved positioning accuracy.

Internet of Things (IOT) technology is another leap forward for the world information industry following the computer and the Internet, providing an unprecedented opportunity for the rapid development of agricultural modernization. This article describes the concept and architecture of agricultural Internet of Things (IoT), analyzes the key technologies of agricultural IoT from the three levels of information perception layer, network transport layer, and application layer. It analyzes the key technologies of IoT and proposes the Agricultural Internet of Things System architecture design. And pointed out that the middleware server software architecture, through the test MQTT agreement, identify the appropriate message mechanism. The development of agricultural internet of things will play a greater role in accelerating the integration of informatization and agricultural modernization and promoting the development of agricultural modernization.

Internet of things in the field of marine fisheries develops very fast. The current fishery servers have the problems of insufficient concurrency capability, low stability, small scalability, and difficulties in maintaining, which cannot meet the requirements of stable data transmission in high concurrency mode. Since most fishing equipment is embedded and has limited computing resources, remote monitoring and control systems for fisheries require that communication modules can be easily integrated into the equipment and occupy a small amount of equipment computing resources. This article introduces the MQTT protocol subscription/publishing features, and designs the cloud server software architecture.

In the paper, we detect 2D surfaces and recognize 3D structures in images, and generate occluded part of three-dimensional structure. We detect objects vanish points and geometric segmentation lines, and generate the vector and normal plane. We measure the objects boundaries which have been detected. In the process, we have found the best linear segmentation positions, generated buildings, and other 3D models in room. We propose some effective assumptions to recognize the object model from images by geometric reasoning. At the same time, we put forward structure prediction technology combined with the volume reasoning by parameter representation of spatial objects. We detect scene model relationships of each other by combining image-rich appearance with geometric features. We proposed image geometric modeling grammar framework according to previous discriminative classifier. It is used to represent the physical structure of the visual component. The framework has broken the traditional probability texture context grammar tree model. We proposed spatial context theory and model generation rules. Finally, on the public existing data sets, we proved that we only use structure prediction and linear segmentation of scene to recover 3D structure by a lot of experiments, comparing to restoration algorithm using whole image structure, ours method can produce more credible scene entity models and space constraint relationships.

This paper proposes a method about the intelligent supervision system of farm based on “Internet + BDS + GIS”. Using Internet of Things (IOT) to monitor and dispatch the management of farm machinery. Based on BDS/GIS/GPRS and communication technology, the system can realize intelligent monitoring and management for farms. The system consists of four subsystems: agricultural machinery monitoring and dispatching management system, automatic driving system of agricultural machinery based on BDS, information management system and data management system. The GPRS is used to achieve instant connection, and it can also improve the channel utilization rate, transmission rate, and the quality of communication service. The agricultural machinery monitoring and dispatching management system, automatic driving system of agricultural machinery based on BDS, and information management system are based on the positioning module of the BDS, which can obtain the position information and ensure the continuous stability of the service. GIS technology is applied to farm monitoring and management, to realize real-time monitoring of farmland information and the collection and analysis of data fast, efficient, and comprehensive, as well as the monitoring and management of related personnel, machinery, and livestock. GIS makes the management more scientific and intelligent.

The connectivity of vehicle-to-vehicle (V2V) communication is critical for efficient data sharing among vehicles. However, the vehicles moving in opposite direction show worse connectivity. To address this issue, a platoon-based V2V connectivity enhancing scheme under bidirectional highway scene is proposed in this paper. First, a typical bidirectional highway scene is investigated to ensure a comprehensive analysis of the connectivity probability among vehicles. Second, a time correlation expression of vehicle movement is derived based on the platoon-based expected lifetime model which is presented. Finally, compared with one popular dynamic clustering scheme in bidirectional road scenarios, the simulations validate the superior performance of the proposed scheme.

Intelligent water regimen monitoring system is required to design for protecting people’s safety and property in real time. It is bound to unstoppable that the traditional water level detecting system using manpower is replaced by automatic, intelligent monitoring system. This paper designs an intelligent water regimen monitoring system, which contains remote monitor stations and monitoring center. The remote monitoring station contains an embedded data acquisition module in order to collect environmental data including water level and image periodically. The collected data is then sent to a server over the Internet via cellular network, which is stored on a database and processed to determine early warning of flood in the area based on historical data. The design system not only solves measuring accuracy problem, but also increases working efficiency.

In order to solve the problems in attitude stabilization control of quadrotor UAV, an attitude stabilization controller based on Active Disturbance Rejection Control (ADRC) is proposed. First, the dynamics model of quadrotor UAV is introduced, and then the attitude stabilization controller based on ADRC is designed for quadrotor pitch, roll and yaw channels, respectively. The simulation results show that the designed controller can meet the control precision and rapidity requirements of the quadrotor.

This study proposes a new fuzzy adaptive PID control strategy to enhance the performance of a tail-sitter UAV in translational flight. First of all, the dynamic model of UAV is built, and then the interference of model parameter perturbation and the disturbance in translational flight is analyzed; Finally, the fuzzy adaptive PID controller is designed to solve the problem of model uncertainty. The simulation results show that the controller has good tracking quality and robustness.

This paper considers the problem of renewable energy and spectrum allocation for a fog-based IoT network where the fog node can request energy from multiple renewable power suppliers (RPSs) to serve the end devices. We consider that RPSs of different relay nodes can form coalitions. RPSs in the same coalition can better coordinate their price strategy. Then, we analyze an independent RPS’s incentive to join a coalition or stay independent, and a nonindependent RPS’s incentive to deviate to join other coalitions or stay in present coalition. Then, in this paper, we achieve the Nash stable coalition structure, and the corresponding energy pricing for the structure. Finally, we give simulation results, and results show that RPSs, the fog node, and spectrum owner can benefit from coalitions.

With the development of the Internet of Things (IoT) industry and the arrival of the 5G era, edge computing is considered to be the more suitable computing technology for the IoT. In this paper, we propose an edge-computing-based M2M data aggregation wireless transmission system powered by efficient renewable energy allocation servicing for the edge devices. The pricing scheme problem is formulated as a Stackelberg game between the operator and multi-RPSs. Simulation results show how the previous pricing scheme and bandwidth of each node affect the renewable energy storage levels of each RPS and his own profit. The results also show the operator’s optimal service price scheme and the equilibrium renewable energy storage level of each RPS.

At present, modern manufacturing and management concepts such as digitization, networking, and intelligence are widely used in the industry. Industry automation and information have been unprecedentedly improved, and therefore the entire life of industrial production link involves massive amounts of data, and the status monitoring data of industrial machine have large, multiple source, heterogeneous, and complex data characteristics. What is more, the traditional processing methods and tools could not meet the requirements for massive data, and may miss the best time to repair machine. So, to resolve the challenges that the industrial sensory big data faces, this paper proposes the sensory data collection and storage based on Hadoop platform.

In the industrial network, the trend of combining different communication systems has been observed in the past years and it will continue in the future. Nowadays, with the rise of Wireless Sensor Network (WSN) and Low-Power Wide-Area Network (LPWAN), wireless technology is gradually infiltrating into the field of industrial control. This paper proposes an industrial data acquisition and transmission system using ATMEGA328P microcontroller based on Narrowband Internet of Things (NB-IoT) technology. System realizes the integration of heterogeneous networks and seamless acquisition and transmission of data, and enables remote real-time monitor to the sensor nodes distributed in the smart factory.

This paper designs an indoor location and environment information testing laboratory monitoring management system. The system includes four parts: a development board based on STM32F103RET6 provided by China Mobile, W5100 Internet module, the Wi-Fi-RFID-WSN sensing part, data server, and Android client. Users can locate and monitor assets such as apparatus equipment of labs, monitor environmental information, make it convenient for both self-help studying or research and management, etc. This system has low power consumption, good scalability, and client interface simple operation, which can automatically identify where things with RFID tags, real-time query laboratory environment information, through the client application user can set up the environment parameters threshold, and start the transfinite alarm. Moreover, testing results of the effectiveness of this system are proposed in the end.

With the rapid development of the Internet, more and more network technologies are used for the taxi service. At present, a number of popular online taxi-hailing platforms have emerged in the taxi market in China, which have played a significant role for the convenience of hailing taxi. The appearance of the traditional online taxi-hailing platforms has provided new ideas for resolving previous conflicts, and they also have some defects in management and profit model. To address these problems, we propose a scheme for the online taxi-hailing platform based on the blockchain technology, aiming at improving the security and fairness of traditional online taxi-hailing platform.

The traditional Chinese flower arrangement as a national intangible cultural heritage has high historical value and artistic value, with the increasing demand of user’s emotional needs for interactive experience, its popularity, and interaction are relatively limited. In the information age of the rapid development of new media technology, using digital technology to protect and inherit this excellent intangible cultural traditional art has far-reaching significance and it has high theoretical value for developing the history and innovation of traditional flower arranging art. Based on the augmented reality technology of intelligent mobile terminal, the article integrates the digital resources of flower arranging art from the perspective of satisfying the user’s emotional needs, restore the art of flower arranging classic works, and enhance user interaction experience. Build an operable digital virtual flower arranging application system that transcends time and space, the combination of traditional flower arranging art and new media communication platform can form a three-dimensional digital “new ecosystem” for the dissemination and protection of intangible cultural heritage.

To solve the problem of increasing nodes data in blockchain, we propose an optimization model of storage capacity based on the consortium blockchain (OMSCCB). We have divided blockchain network into groups by the characteristics of partial decentralization of the consortium blockchain in the OMSCCB. In each group, the full node is selected according to the hardware performance and the accumulated reliability evaluation of the node. The full node is responsible for storing the blockchain data, and other ordinary nodes segment storage of the blockchain data according to the consistent hashing. And then, we have designed a replacement strategy of full node to ensure that the OMSCCB can resume normal operation when the network is attacked. The experimental results show that the storage cost of the system can be reduced effectively, and the reliability of the system can be guaranteed at the same time.

Aiming at the storage problem of blockchain, a space storage optimization scheme based on the InterPlanetary File System (IPFS) is designed. The scheme mainly achieves the purpose of space optimization by summarizing the blocks regularly, excluding the expired Transaction Output (TXO) that has no effect on verification. The Unspent Transaction Output (UTXO) will be saved as a file on the IPFS network during optimization. In this way, the miner node can quickly get the file and start mining. The ultimate goal is to optimize the storage space for the node and to increase the synchronization efficiency of the newly added miner node. The simulation experiment of the proposed optimization scheme is carried out. The experimental results show that the scheme can summarize the historical transaction data regularly, eliminate the Spent Transaction Output (STXO) which has no effect on the verification transaction, so as to effectively reduce the node storage space.

Territorial ocean safety and ocean development make it important to establish a large-scale, long-term, and low-energy integrated ocean monitoring sensor network (OMSN). In this paper, we introduce the high attitude platform-based ocean monitoring sensor network (HAP-OMSN) architecture and the basic ant colony optimization (ACO) algorithm first. And then, we propose an improved ant colony optimization algorithm for the node deployment of the HAP-OMSN architecture. Finally, we solve the multi-types node deployment (MTND) problems in HAP-OMSN using this algorithm. The final experiment results indicate that the improved ACO algorithm has good efficiency to find optimal solution.

D2D multicast communications can reduce communication cost and decrease interference in Internet of Vehicles (IoV). In this paper, the resource allocation algorithm for Device-to-Device (D2D) multicast communication reusing LTE network uplink resource is studied to improve the reliability of networks. The optimization objective of the algorithm is to minimize the outage probability of D2D multicast group links on the basis of guaranteeing the constraint of cellular link quality. To solve this optimization problem, a low-complexity heuristic algorithm is designed for spectrum resource allocation and power control. By comparing performance through simulation, it can be found that the outage probability of the heuristic algorithm designed in this paper can be very close to that of the Hungarian algorithm, but the computational complexity is greatly reduced compared with the latter.

There is a considerable pressure for random access networks to access numerous devices with limited preambles, while the different delay requirements of diverse applications exacerbate this situation. In this paper, we propose a service ranking scheme to ensure that delays of different applications are within a reasonable range. We classify applications by latency requirements and dynamically partition preambles for serving these applications. In particular, distribute queue is used to coordinate delay-critical applications. Delay-tolerant applications are partially prohibited when congestion occurs. The average success delay is minimized under the preambles constraint. Simulation and numerical results show that the proposed scheme can effectively reduce the average success delay by 30% while guaranteeing the success ratio of delay-critical applications.

Along with the development of the communication network technology and computer technology, the diversity of information in modern society and the geographical area scale has placed limits on the single unmanned aerial vehicle (UAV) in the tasks, so that cooperative mission of multiple UAVs will be the main form to be utilized in the future information society. Interference and energy limit are two key issues in the network, so we propose a novel opportunistic interference alignment (OIA) scheme with wireless energy harvesting (EH), which can eliminate the interference while optimizing the energy efficiency. Extensive simulation results are presented to show the effectiveness of the proposed OIA scheme with wireless EH.

Nowadays, a hybrid of communication technologies including a variety of wired and wireless technologies have been used in smart grid. Each wireless communication technology has its advantages and disadvantages. Short-range links, high power consumption, high cost, or a mesh topology are the main characters for wireless technologies. Due to wireless connection problems in mesh topology and a large amount of infrastructure required to build a reliable network, the costs of deployment for smart grid are still amazing. Recently, a new wireless technology Low-Power Wide Area Network (LPWAN) has challenged typical wireless connection. It is characterized by low-cost and long-range transmission technologies, and enables power efficient communication over very long distances using a simple topology. In this paper, we introduce the new wireless technology to implement the applications in smart grid, and discuss its advantages and the potential opportunities and challenges that can bring to smart grid.

Research on sensory networks for marine environmental information acquisition has received more and more attention. However, the underwater wireless sensor network has the characteristics of limited bandwidth and energy consumption. Therefore, by using the sparseness of sensor node data in frequency domain and space domain, this paper proposes a dual-domain compression sensing (DCS) low-energy ocean environment information collection scheme. The scheme uses the sparsity of the frequency domain for information recovery, thereby further saving the control overhead of the sink node’s downstream sending address frame. Through the result of simulation, it is found that the scheme proposed in this paper is better than the previous IDMA multiple access detection scheme in terms of energy consumption.

The blurred synthesis evaluation arithmetic based on analytic network process and systemic security evaluation target system is designed, and the main technology and system realization of cyberspace security evaluation system are presented, which is realized based on fight theory’s attack and defense affection evaluation and systemic security evaluation technology. The technology and system can provide a systemic security evaluation to information system target’s security and put forward security resolve scheme suggestions, which have important effect and meaning on accelerating China’s cyberspace war ability.

Compatibility and interoperability are the main developed directions for the future of GNSS. The B2a signal transmitted by BDS-3 will interoperate with GPS L5 signal and Galileo E5a signal. The performance of interoperable signals will directly affect the performance of user receivers. This paper systematically analyzes and assesses the interoperability of B2a/L5/E5a signals. By briefly introducing and analysis the signal systems and key technologies interoperability, the theoretical basis of the performance assessment of the interoperable signals is laid down. The interoperability performance is mainly assessed from two aspects: signal character and service performance. In terms of signal character, the same carrier frequency, chip rate, and signal bandwidth are selected for the interoperable signals, and the similar signal modulation method is adopted to realize low power consumption, small size, and low cost of the receiver terminal. Seeing from the service performance, through the unification of coordinate and time reference systems, interoperable signals achieve better performance in the accuracy, continuity, and availability than the single-system signal, and achieve the design goal of interoperable signals.

This paper investigates the physical layer security in underlay cognitive radio networks with multiple buffer-aided decode-and-forward relays, where an eavesdropper can intercept the data transmission from the source and relay nodes. Different from existing works, we propose a new buffer-aided relay selection scheme in multi-relay scenario instead of a link selection scheme in one relay scenario. Moreover, finite data buffers are assumed to be available at every relay to avoid selecting the best source-to-relay and relay-to-destination links concurrently, and the secrecy outage probability is derived. The proposed scheme is evaluated using simulations and theoretical results, which show that proposed scheme has performance advantage over the conventional unbuffered relay selection scheme.

With the development of the financial industry, the number of credit cards has greatly increased. However, credit card fraud is still a major concern for financial security. Credit card fraud detection is a typical imbalanced classification problem, in which fraudulent cardholders are far less than non-fraudulent cardholders. The training on imbalanced samples will cause that the classifier ignores the minor fraudulent samples. To solve this problem, a generative adversarial network (GAN) based classification method is proposed for credit card fraud detection. GAN consists of a generative model and a discriminative model, and the two models are trained in a competitive way to get the Nash equilibrium. Specifically, the generative model tries to fit the real distribution of the non-fraudulent samples. The discriminative model determines the probability of a sample belongs to the distribution of non-fraudulent samples. To improve the discrimination performance, the fraudulent samples are also used in the training of discriminative model, which is different from the traditional GAN training scheme. The experimental results show that the recall reaches 82.7%.

CVSS is one of the most representative quantitative assessment algorithms for security vulnerabilities. The calculated values of vulnerability harm according to the given index and formula. However, there is still insufficient objectivity of CVSS. Expert System is a kind of expert system for vulnerabilities; the severity of vulnerability can be assessed by experts based on the Expert System. The objectivity of Expert System and CVSS is analyzed, and CVSS is revised based on Expert System. The relationship between the above systems and the CWE, and between above systems and Product is analyzed, respectively, in the process. A new way of using Expert System is put forward based on CWE, namely, CWE Cycle Sorting Algorithm, in order to sort the average of vulnerability severity. Meanwhile, CWE Sort Factor is put forward based on CWE Cycle Sorting Algorithm to modify CVSS. The result is closer to the Expert System in terms of objectivity.

Appropriate traffic coordination at intersections where multiple roads merge plays an important role in modern intelligent transportations systems. In this paper, we try to propose an efficient traffic coordination framework using multiple collision sets. Aiming at the essentially non-convex problem, we try to reformulate the original problem into a mixed binary integer quadratic programming one by proper relaxations. Low complexity solutions are also given afterwards. Numeric results show that the traffic throughput at intersections can be significantly improved compared to the existing investigations.

Because of the security of the symmetric cryptosystem, a secure communication scheme based on PEG-LDPC codes has been put forward in this paper. The secure and reliable integrated communication is based on hiding and changing generator matrix randomly and simultaneously under the premise of ensuring error correction ability of LDPC codes. The performance of LDPC codes constructed by PEG algorithm is excellent, and this algorithm can construct an LDPC code with arbitrary code length and code rate.

Various mobile devices facilitate the users’ life, but the issues are brought into privacy focus by the individuals. This paper aims at the protection of intimate relationships among users. We consider the intimacy of user relationships based on similar sub-trajectories between the users. Then, we propose a $$k_{mn}$$ -anonymity protection model. We generalize from two aspects: location and time. The first is location generalization. The range that user pass within the time that the location point stays is the generalization region, and the corresponding location point in the region’s trajectory is represented by the generalization region. When the location generalization is not enough to satisfy the $$k_{mn}$$ -anonymity, then we use time generalization. Finally, the performance of our algorithm is evaluated by the experiment and the validity of our algorithm is verified.

Communication has become a very important part of people’s work and life. Wireless communication technology has the characteristics of low cost, good scalability, and easy use, but it is vulnerable to eavesdropping and other security threats, and eavesdroppers illegally receive through wireless channels. Traditional wireless communication system security methods need to be implemented at the upper layer through authentication and cryptographic techniques. In recent years, as an important complement to traditional security mechanisms, information theory security principles, transmission signal security technologies, spread spectrum and frequency hopping encryption technologies, and channel coding Research on physical layer security technologies such as encryption technology and modulation encryption can effectively protect information during wireless transmission and prevent eavesdroppers from illegally receiving information.

Cyber Range has become a very important means to support tasks such as network security technology validation, network weapon testing, training of network attack and defense and network risk assessment. However, Cyber Ranger faces many security threats from internal and external environments. In order to establish an adaptive security protection system, threat modeling is needed to analyze potential threats and provide security solutions. In this paper, we present a novel threat modeling method for Cyber Range. Based on ontology and knowledge graph, our research focuses on the design of threat ontology, knowledge base, and unified description specification. Typical cases are given to demonstrate our approach. This study could serve as groundwork for further Cyber Range researches including security architecture, situation awareness and intelligent decision-making.

A new method to detect credential spear phishing attack for the network is introduced in the conference of 26th USENIX Security Symposium. First, on the basis of the researching for the processes and the principles of spear phishing attack, and the overall structure of its detector, the Directed Anomaly Scoring technology is analyzed in the paper. Second, the selections of scalars in subdetectors are defined. Third, the spear phishing attack detection method of detector and the methods of traditional detection are compared and analyzed. And then, the obvious advantages of the detector are discussed. The prospection of the spear phishing attack detection development is also given at the end of the paper.

In most existing computer systems, data transmission and preservation in the form of plaintext are vulnerable to various attacks. In this paper, we use Parralelized memory Confidentiality and Integrity Protection technology (PCIP) algorithm to ensure the confidentiality and integrity of memory data. On the basis of PCIP, we use PCIP Bonsai Merkle Tree (PCIP+BMT) to protect the counter values of off-chip to reduce system delay and overhead. PCIP is that uses counter mode encryption to encrypt data while adding redundant data for integrity checking. Finally, we use the SimpleScalar Tool to simulate the PCIP and PE-ICE algorithms. The results show that PCIP is encrypted more effectively than the PC-ICE. Compared with the Hash algorithm, it can reduce the system delay and reduce the internal memory overhead. The tree mechanism adopted in this paper reduces the impact on system performance.

In the information age, people’s various behavioral data are collected in large quantities. The sharing of information makes it convenient for some scientific investigations, but there is a leakage of personal privacy at the same time. The current research on privacy preservation is mostly based on relational tables or social network graphs. This paper focuses on semi-structured data, which is often ignored in privacy preservation. We propose a new privacy guarantee called X-km-anonymity and propose a bottom-up heuristic algorithm that provides protection by satisfying X-km-anonymity. We verified the feasibility of the algorithm through a reliable utility analysis method on the simulation data.

This paper focuses on the automatic detection of illegal U-turn and recording the number of license plates. The algorithm mainly includes the position detection of vehicles’ heads and tails, the marking of vehicles, the license plate recognition, and the judgment of illegal U-turn. The positions of heads and tails are detected by monitoring the headlights and the taillights in RGB color gamut. Most of the headlights are white and the taillights are largely red. In order to avoid the impact of white vehicles on results, the HOUGH Transform and face detection are utilized to determine the position of the steering wheel and the driver, which complement and verify the detection of head position. The edge of the input image needs to be enhanced to improve image quality. It will be judged whether the moving vehicle has had an illegal U-turn. The effectiveness of the algorithm is verified by simulation with MATLAB. Through the analysis of experimental results, it is demonstrated that the algorithm designed in this paper is feasible and achieves the desired design requirements.

In order to solve the stability problem of communication network, network traffic instability is prevented, which is caused by local node or link failure. First, based on Lyapunov theory, a random queue model is established for communication network nodes to clear the traffic. Then, the factors are analyzed, which affects the network traffic stability. Finally, it proposes a dynamic scheduling multipath routing transmission algorithm to clear the network traffic steadily. Simulation and experimental results under the most unfavorable conditions of network stability, the performance of dynamic scheduling multipath route is verified to clear the traffic algorithm, which indicates that it is superior to traditional shortest path route dredging algorithm in ensuring network stability. It is proved that the dynamic scheduling multipath routing transmission algorithm can well suppress network turbulence by Lyapunov theory in the case of local network element failure, which ensures communication network stability.

Quantum secure direct communication is a new communication mode in the free space. Quantum secure direct communication utilizes single photon with the information to transmit ciphertext in the quantum channel directly, and it distinguishes from Quantum key distribution. Its outstanding characteristics are as follows: super velocity of light, high power capacity, high security, and anti-interference, so quantum secure direct communication can break the traditional secret communication frame to ensure the absolute secure communication. However, the imperfection of optical devices can leave loopholes for quantum attacks. Based on free space, this paper puts forward one kind of attack detection method in the field of quantum secure direct communication. This paper introduces quantum bit error rate analysis and decoy-state photon transmission rate analysis for designing security detection process to effectively detect the eavesdropping and improve security.

In view of the lack of maintenance of elevators in China, this paper builds a statistical model of elevators’ remaining service life based on elevator operation and maintenance management data, plots the remaining service life curve of elevators, and implements the prediction of preventive maintenance intervals from time to time and scrap time (i.e., the safety remaining service life) for elevators, and verifies the reliability of the model through empirical analysis.

With the development of social economy and the improvement of people’s living condition, elevator has become an indispensable tool because of its fast and convenient features. But the safety problem caused by elevator has brought great threat to people’s life and property security, which aroused wide concern of the society. Therefore, developing the research of elevator safety evaluation has important social significance. The research of this paper mainly include the following three aspects: (1) The research of elevator safety evaluation index system. According to the operation data and the existing research results, we used the analytic hierarchy process to determine the multilevel structure of elevator safety evaluation index system. And both expert scoring method and entropy weight method were used to calculate the weight of index system, then the weight was combined by the integrated optimal method, to obtain the optimal weight of each index. (2) The research of elevator safety evaluation methods. First, the safety grade of whole elevator system and each index were divided. Because of the complexity and fuzziness of elevator system, we used the fuzzy mathematics and extension method, combining the matter-element theory. Then we used the correlation function to assess each index’s security state. Finally, we established an extension fuzzy comprehensive evaluation model for elevator safety. (3) Example analysis of elevator safety evaluation methods. We took operation data of elevator in M city, for instance, analysis and MATLAB to establish safety evaluation program. The results show that the safety evaluation method is operable and effective.

The combination of commercial development and artificial intelligence services becomes more and more important. Chatbot is considered one of the effective techniques by using information retrieval (IR) and natural language processing (NLP). In this paper, we collect a series of business-promoted chat data and conduct a series of cleanup and classification of these data sets. Since the speech of different people is random, the similarity is calculated by using a combination of the retrieval model and the generated model, and then the final answer is generated using long-short term memory (LSTM) training and prediction. Finally, we use the TF-IDF weighting method to improve the dialog. Experimental results show that the proposed method can communicate with humans and answer real-time questions.

Aiming at the hidden danger of harmful gas leakage and insufficient oxygen supply in the operating environment of oil tanks, this paper designs and implements monitoring and warning system for dangerous gas in the oil tank. The system uses the multilayer distributed structure, combines with various gas detection sensor technology and wireless communication technology, and adopts the STM32 microcontroller based on ARM Cortex-M3, with LoRa spread spectrum MESH ad hoc network module, 4G module, and OLED display module, and so on, to realize the collection and transmission of the gas concentration data in the oil tanker and display them on the upper computer software in real time. According to the field test of Zhongyuan shipping Automation Co., Ltd., the system meets the design requirements. It has the characteristics of low cost, flexible distribution, safe and practical and so on, which has a very good value for promotion.

With the advancement of the aerospace industry, the reliability of integrated circuits that can overcome the impact of the radiation environment continues to increase. In this paper, two kinds of radiative effects of integrated circuits are focused on the total ionizing dose effect and the single-event latch-up effect. In response to these two effects, the anti-radiation reinforcement technology of the logic gate standard cell was adopted at the corresponding design level. The design of the schematic and the circuit simulation are performed, including transient simulation and static simulation in this paper. In addition, the standard cell’s logic information is verified and integrated into the standard cell library. A guard ring is designed on the layout of the standard cell so that it has radiation resistance. The layout of the standard cell is abstracted and the file containing the standard cell physical information is exported. The verification results and physical information of the standard cell demonstrate that the proposed technique can achieve the effect of anti-radiation.

The traditional crib has the simple function of carrying the baby to sleep and play, and the baby falling asleep also needs the parents to shake the crib manually. According to the above situation, the intelligent crib-based on Android is designed and implemented. The system takes STM32 MCU as the core, combines sensor technology, wireless communication technology, and video monitoring technology, uses MQTT server, and develops an app with Android studio. Parents can not only view the baby’s condition by remote video, but also check the baby’s fever, bed wetting, and control music play, shake the crib through the mobile phone interface. The design is convenient and effective to lighten the burden on parents to take care of infants and has high application value.

The intelligence of power grids has made the relationship between distribution networks and the Internet more and more compact. Therefore, in order to cope with the various threats in the situation of smart grid, it is necessary to study from multiple perspectives. Among them, attack scenario reconstruction is a more effective method of network security defense. However, the existing attack scenario reconstruction technology is not combined with the actual situation of the power grid. In this paper, we proposed a grid-based attack scenario reconstruction framework which is based on big data. The framework consists of KNN-based attack data classification and state machine-based attack scenario restoration. In addition, we also implemented prototypes and evaluated the effectiveness and availability of databases provided by IDS in China Grid Corporation. The results show that the framework proposed in this paper improves the efficiency and accuracy of analyzing attacker strategies.

In the command and control system, the modeling design of the command and control console occupies an important position. It not only gives people a direct visual experience but also affects the speed and reliability of specific operations. The status of the command and control console was combined. The main design elements were given. The outstanding problems were analyzed. Proposals for development were put forward.

In response to the emergency command vehicle’s on-site rescue and maintain orders requirements, the research overview, level, and development trend of domestic- and foreign-related technologies about the emergency command vehicle such as anti-sniper, anti-roadside bomb (car bomb), anti-laser radiation, anti-radar radiation, anti-electronic interference, and so on were analyzed. The composition of the comprehensive defense capabilities for vehicles on the highway is that our military’s new emergency command vehicle should be proposed.

This paper expounds the connotation of cognitive electronic warfare system and development status at home and abroad, analyzes the framework of cognitive electronic warfare system, and introduces three closed-loop processes: cognitive reconnaissance, cognitive countermeasure, and cognitive effectiveness evaluation; finally, to explore the realization approach of cognitive electronic warfare system, proposes a cognitive electronic warfare platform architecture, and focuses on the analysis of the functional requirements of the platform and key technologies.

The geomagnetic field is a vector field, and its elements in different regions can match the latitude and longitude uniquely (Fairfield and Mead in J Geophys Res 80(4):535–542, 1975) [1]. Therefore, an intuitive understanding of the geomagnetic information on exploration area is vital for geomagnetic navigation or flight path planning (Matteo and Morton in Radio Sci 46(4), 2016) [2]. The method proposed in this paper will get the geomagnetic information, which reflects the geomagnetic characteristics more intuitively after visualization. In addition, the characteristics of three geomagnetic models, EMM, WMM, and IGRF, are also compared and analyzed, which will be used for reference when planning the flight path (Kim and Chang in Adv Space Res 61(8), 2018) [3].