This PDF file contains the front matter associated with SPIE Proceedings Volume 12716, including the Title Page, Copyright information, Table of Contents, and Conference Committee information.

In view of the fact that a single type of floating vehicle data can not fully reflect the traffic state of the road, this paper proposes a method to judge the urban traffic state based on multiple floating vehicle data. According to the operating characteristics of different vehicles, integrate the advantages and disadvantages of three common floating vehicles, fuse the data, and then use clustering algorithm to divide the traffic state, so as to more accurately reflect the real-time traffic state of urban roads. The experiment shows that this method has a good discrimination effect on the urban road traffic state. It can provide reference for urban road status discrimination and traffic management.

Traffic signs are one of the main carriers of road information, the reflectivity of traffic signs at night directly affects the driver's access to road information. Fast and accurate traffic sign reflectivity detection method is conducive to the efficient operation of road maintenance operations to protect the lives of motorists. This paper proposed a novel method for detecting the reflectivity of traffic signs, which proposed the concept of sign’s luminance value. By collecting photos of traffic signs on the road at night through the detection vehicle, using Yolov4 algorithm and Deeplabv3 algorithm for sign target detection and image segmentation respectively, then the signs were converted into grayscale images after grayscale processing, and the difference between the grayscale value of the sign and its background was calculated to obtain the luminance value of the sign, and the luminance value was used to represent the reflective performance of the sign. This method was used to conduct the actual test experiment of road traffic sign reflectivity, and compared with the sign reflectivity results obtained by the traditional retroreflective coefficient detection method. Through data analysis, it was found that the test results of this method have significant correlation with the test results of the traditional method. The traffic sign reflectivity detection method proposed in this paper obtains more accurate detection results and avoids the disadvantages of the traditional retroreflective coefficient detection method, such as complicated operation, high instrument requirements, low detection efficiency, and failure to consider the impact of the actual installation position of the sign on the driver's visual recognition, and is more suitable for engineering practice.

In this paper, a staged channel estimation and reflection coefficient design method is proposed for the Intelligent Reflecting Surface (IRS) aided multiple-input and multiple-output (MIMO) systems. To achieve the channel state information (CSI) estimation between the IRS and the base staion (BS), the IRS and the user, the user and the BS, a principle component analysis (PCA) based scheme is proposed. Based on the estimated channel, a DNN network is proposed to realize low-complexity IRS reflection coefficient estimation, which effectively improves the signal-to-noise ratio (SNR) at the user side. Simulation results for IRS-aided MIMO systems are provided to verify the accuracy of the proposed schemes on the channel estimation.

In order to solve the problems of unbalanced sampling of track points in the track point distance-based ship track clustering algorithm, the abnormality of individual track points affecting the clustering effect, and the difficulty of accurately describing the spatial characteristics of ship tracks with the latitude and longitude data of track points, a Kmeans ship track clustering algorithm based on the similarity of track image features is proposed. The algorithm defines a similarity measure based on trajectory image features. The method converts ship trajectory latitude and longitude time series data into ship trajectory image data, extracts trajectory image features using ResNet-50, and uses the Euclidean distance between trajectory image features as a method to measure the similarity of ship trajectories. Using the similarity measure based on the trajectory image features, the ship trajectories are clustered by the Kmeans algorithm. Experimental results show that the proposed algorithm improves the accuracy by 10% over the traditional DTW-based K-centroid clustering algorithm, and can cluster a large number of complex ship trajectories, and the clustering results are consistent with the actual traffic flow.

When vehicles are in driving environments such as urban roads and tunnels, the global navigation satellite system (GNSS) signal can fail to achieve positioning due to occlusion. Cooperative positioning can effectively improve the accuracy and coverage of vehicle positioning. In this paper, we address the vehicle localization issue in GNSS-Loss driving environments and with random packet loss due to wireless communications. We propose an improved cooperative localization algorithm based on belief propagation, which first linearizes the measurement model of distance by statistical linear regression method, then performs belief propagation based on the linearized model, and solves the packet loss problem by expanding the dimension of the vehicle state. From the simulation results, the algorithm can effectively reduce the impact of packet loss and improve the vehicle localization accuracy.

Transparency is a basic parameter to describe the optical properties of water. In recent years, the rapid development of water color remote sensing technology, especially the improvement of the accuracy of water color remote sensing technology and related retrieval algorithms, makes it possible to remote sensing of water transparency. In this paper, a variety of satellite data are used to carry out the retrieval research of water transparency, which is mainly based on the theory and model of atmospheric radiation transfer, and directly obtains water transparency by using remote sensing retrieval of out-of-water radiance or remote sensing reflectance. Then, a variety of satellite retrieval products are used for fusion, and the fusion results are evaluated. The results show that the integrated product in the paper is superior to the product provided by the satellite data website, and the calculation is simple and can be popularized.

The differential protection of distribution network has good selectivity and quick action, but it is difficult to apply in areas where the laying rate of optical fiber is not high. The emergence of 5G communication technology with low delay and high reliability provides a new communication solution for the application of distribution network differential protection technology. However, the 5G distribution network differential protection has a large amount of differential current data in its application, which leads to the problem of large data flow and high cost of 5G communication terminals. This paper proposes a scheme of adding differential protection data compression transmission technology to the 5G terminal, which not only does not affect the differential protection service, but also saves the communication data flow, providing a reference for the wide application of 5G technology in differential protection in the future.

Jupiter is one of the targets for deep space exploration, but it has a harsher radiation environment than Earth. In order to reduce the damage of the radiation environment to the spacecraft, the radiation shielding scheme should be optimized. In this paper, the main optimization objective is to reduce the total radiation dose after shielding, and we balanced the areal density, thickness and shielding effect. Combined with genetic algorithm and MULASSIS, the optimal design of multilayer radiation shielding scheme of Jupiter spacecraft is studied. Through this method, the optimal shielding scheme - Mg-Pb-Mg-Al four-layer shielding structure is obtained. Compared with the single-layer shielding scheme of Galileo and other spacecraft, the optimal multi-layer shielding scheme can reduce the material mass by at least 14.4%. This design method could also be combined with the actual Jupiter exploration mission, providing a reference for the shielding design of deep space exploration spacecraft in the future.

Aiming at the problems that the human motion recognition method based on machine vision and sensors is easy to reveal user privacy and inconvenient to carry, this paper proposes a human motion recognition method based on passive RFID and multi-model fusion. By wearing a flexible liquid metal tag on the human body, the effect of different actions on the RSSI signal of the receiver is utilized, The approximate component and detail component of the signal extracted by wavelet transform are used as fusion features to represent human motion, and the four models of KNN, DT, SVM and LR are fused to construct the Blending model with KNN, DT and SVM as primary learners and LR as secondary learners for motion recognition. The experiments show that the accuracy of the Blending model for the five movements of standing, sitting, walking, running and falling is 97.29%, Compared with individual learners, it has better recognition effect and greatly improves the convenience of human motion recognition.

To address the problem that metal reflection, ambient illumination and different defect size affect the defect detection accuracy in metal defect detection, an AINDANE-Faster R-CNN based metal flat detection method under complex illumination conditions is proposed, which firstly adopts a adaptive and integrated neighborhood dependent approach for nonlinear enhancement(AINDANE) to preprocess the defect images and improve the image brightness to highlight the detail features such as color,profile and texture of defects, and then ResNet50 network is utilized as the defect semantic feature extraction network for the Two-stage Faster R-CNN model. In addition, this paper also constructs a dataset of three defects of metal aluminum plate under low light and uneven light conditions, such as scratches, oil stains and pits, and the method achieves a mean average accuracy of 92.01% on the defect dataset. Compared to existing one-stage surface defect detection methods, the algorithm in this paper is optimal.

For the nonlinear discrete-time Multi-agent system of unknown dynamic models, there are cooperation and competition between agents, and the problem of data quantification in communication, a model-free adaptive iterative control (MFAILC) algorithm is proposed. First, the method of compact form dynamic linearization (CFDL) is used to transform the agent system into a model with time-varying parameters, and the quantizer is applied to quantize the data in the process of processing, and the cooperation-competition relationship between multi-agents is considered in algebraic graph theory, on this basis, the MFAILC control algorithm is designed and the convergence of the proposed algorithm is proved. Finally, the simulation results verify the effectiveness of the proposed algorithm.

The camera image recognition and active wearable sensor scheme are classic methods in fall detection, but the camera scheme has the problem of privacy leakage, while the active wearable sensor scheme has the problem of wearable comfort. This paper proposes a method of human motion recognition using passive sensors and fall detection using hybrid depth learning model, and designs an intelligent human motion sensing clothing based on passive RFID tag positioning technology. The experimental results show that using passive RFID tag as a sensor scheme can achieve efficient and accurate fall detection, and achieve a recognition rate of 99.5%.The proposed method does not require additional equipment, realizes non-invasive monitoring, and is comfortable to wear and inexpensive. It can be widely used in other fields such as medical health, family safety, old-age care and so on. It is of great significance to improve the practicability of fall detection.

Mung bean seed variety identification is extremely important for ensuring agricultural productivity, safeguarding farmers' interests, and controlling the seed market. Hyperspectral techniques have the advantages of easy operation, non-destruction and rapid detection. However, it usually generates a large number of data, causing a low identification efficiency.This paper adopts the hyperspectral method and machine learning model to conduct experiments and analysis in the feature extraction which is the key to the identification of mung bean seek varieties. The experiments input the original spectral data into the RF classification model and use the SPA, CARS and LAR algorithms for spectral feature extraction, and the constructed SPA-RF, CARS-RF and LAR-RF discrimination models all have a substantial increase in running speed, of which the CARS-RF accuracy and recall are 0.988, with no decrease compared with the original RF model; the RMSE is 0.391, which is 0.029 greater than the RF model; the shortest running time is only 29% of the original RF model, and the best modelability. The results of the experiments demonstrated that the CARS-RF model chosen in this study successfully identifies different types of mung bean seeds while also reducing equipment costs, facilitating the production and fabrication of specialized equipment, and significantly enhancing identification efficiency.

In the cloud-side aggregated computing scenario, the base stations, routers and gateways deployed by edge servers are publicly accessible infrastructures, whose security is not guaranteed, and users' private data are vulnerable to huge threats, requiring means to safeguard user privacy. In an edge cloud, a large number of services will be deployed due to the need to support numerous edge-end devices, and selecting the optimal service combination solution from the large number of services is an imminent task. The existing cloud service providers can hardly take the responsibility of protecting users' privacy, and none of the users will actually own their data, so a data access control scheme needs to be designed to protect users' data from illegal access and tampering.

Time-series data are trendy and periodic, and have high data dimensionality, the current clustering methods cannot effectively target these characteristics. A recurrence plot variational auto-encoder deep clustering (RPVAEDC) model based on recurrence plot and variational auto-encoder is proposed to address the characteristics of time-series data. The time-series data are first transformed into recurrence plots to reveal their trends and periodicity; then the recurrence plots are fed into a deep clustering model for feature extraction and dimensionality reduction, and the distribution of the transformed data is normalized by variational auto-encoder; then the clustering results are obtained by adding a clustering layer to combine the auto-encoder reconstruction loss and clustering loss. It is experimentally demonstrated that the silhouette coefficient scores are achieved significantly better than other clustering algorithms on the public data sets.

The Internet of Vehicles has become a crucial component of contemporary transportation as a significant subset of the Internet of Things. The demand for periphery computing is increasing as vehicle intelligence and connectivity continues to advance. However, the task unloading of onboard edge computing encounters several obstacles, including limited computing power, communication delay, etc. This paper proposes a task discharge scheme for Internet of Vehicles edge computing based on the MADDPG algorithm to address these issues. The scheme employs a multi-agent reinforcement learning algorithm to accomplish cooperation and communication between vehicles and optimizes the task allocation strategy to improve the efficiency and performance of onboard edge computing. Simulation results indicate that, in comparison to other algorithms, this algorithm can significantly reduce the system's overall execution latency and possesses strong adaptability.

Traditional routing protection schemes usually adopt fast rerouting to deal with single-point failures in the network, which usually require a large amount of computation and communication, as well as specialized hardware or software, resulting in high latency and large overhead. In this paper, we propose a fast rerouting algorithm FRUSPOF based on SR technology for single-point failures. For possible failure nodes between each node on the shortest path tree and the root node, we construct segment label sequences to avoid the failure nodes. When there is no failure in the network, the shortest path is used for packet forwarding. When the network fails, the path calculated based on the SR sequence is used for packet forwarding. This algorithm can deal with single-point failures in the network and has a small additional overhead. We validate the effectiveness of the proposed fast rerouting method through three real networks in this paper.

The channel coding technology, being an important part of the Airport Aeronautical Mobile Communication System (AeroMACS), ensures the reliability of the communication link transmission. In this paper, by analyzing the basic principle of channel coding and the current state of the technologies of AeroMACS, we compare various channel coding, and propose the technical path of AeroMACS in WiMAX and 5G modes, which makes up for the deficiency of Turbo code and improves the error correction performance by transmitting ACD data by convolutional code and AISD data by LDPC code. Simulation results show that on short and long messages, convolutional code and LDPC code can greatly improve the reliability of airport air mobile communication system while obtaining high coding gain. This scheme will enhance the performance of AeroMACS system and provide better data transmission service for civil aviation safety communication.

Unmanned Aerial Vehicles (UAVs) have been widely used for data collection due to their high flexibility and ability to provide line-of-sight links. In existing studies, it is generally assumed that ground users can transmit only once, while in real systems, ground-based IoT devices can enhance the communication range by transmitting the same data multiple times. Therefore, this paper considers the UAV data collection problem in two different retransmission modes and models it as maximizing the number of users that can be served by the system for a determined amount of UAV onboard energy. Since the problem is a mixed integer nonlinear problem, it is difficult to solve directly. Therefore, it is decomposed into three subproblems by alternating optimization and solved by successive convex approximation (SCA). The simulation results show that the number of users that can be served by the system can be effectively increased by considering the retransmission of ground users.

LDPC (Low Density Parity Check Codes) is a low -density puppet test code applied to channel codes. Among the existing LDPC decoding algorithms, the LLR-BP decoding arithmetic is too complicated to calculate the calculation due to the containing a large number of bilateral positive cut functions. Therefore, this article proposes a LDPC decoding algorithm that combines the Modified Weighted Bit Flipping Decoding Algorithm (MWBF) with the Logarithmic Likelihood Ratio Based Confidence Propagation Algorithm (LLR-BP). In the process of the LLR-BP decoding iteration, the introduction of the MWBF algorithm not only does it reduce the complexity of iteration and decoding, but also avoids the traditional weighted bit-flipping decoding algorithm (WBF). Excessive effects of code performance.

The integration of 3D vision technology has profoundly impacted various aspects of our lives, with point clouds emerging as the predominant geometric representation in the field due to their simplicity, ease of processing, and low acquisition costs. However, limitations in 3D scanning hardware often result in sparse, noisy, and unevenly distributed point clouds, which impede the effectiveness of subsequent 3D vision tasks. To obtain high-quality and precise point clouds, researchers have introduced upsampling algorithms that enhance point cloud representativeness through densification. In recent years, deep learning has supplanted traditional algorithms that rely on a priori data, owing to its robust learning representation capabilities and data-driven nature. Consequently, deep learning algorithms for point cloud upsampling have become a highly valuable research direction within the 3D vision domain. This paper delves into recent advancements in deep learning algorithms for point cloud upsampling by first defining the problem and analyzing the technical challenges from six perspectives. It then provides an overview of the relevant benchmarks in point cloud upsampling and traces their developmental history. The paper further examines the technical issues in the three critical steps (point cloud local feature extraction, feature space expansion, and coordinate reconstruction) of the overall deep learning algorithm for point cloud upsampling. It categorizes the prevalent technical solutions, discussing their merits and drawbacks, and offers optimization strategies. Lastly, the paper presents a forward-looking perspective on potential research directions in this field from four angles.

Traditional fractional-order controller (FOPID) parameter tuning methods are mainly based on amplitude margin and phase angle margin in the frequency domain, and there are problems such as low parameter tuning efficiency and low accuracy. This paper proposes a parameter optimization method based on the multi-objective particle swarm optimization algorithm MOPSO. By rotating the Hankel matrix to approximate the fractional-order operator, the system fractional-order differential equation satisfied by the fractional-order controller parameters is transformed into an algebraic differential equation Using MOPSO to optimize controller parameters. Experimental results show that the design method improves the dynamic performance of the system and makes the system have good robustness.

The all-fiber photoacoustic system can work properly in the harsh environment of electromagnetic interference and has the potential advantage of achieving a wide range of distributed detection. However, the ultrasound signal of all-fiber system is affected by signal mode mixing, which will have an impact on defect identification and localization, and the time varying filtering based empirical mode decomposition (TVF-EMD) algorithm is used to solve the effect of mode mixing. According to different modes with different signal characteristics, this work proposes a defect identification and localization method based on an all-fiber photoacoustic NDT system. After experimental testing, the method can identify and locate the number and shape of defects in a thin metal plate with a bar-shaped penetration defect of aluminum alloy 6061 of size 50*50*0.1 cm³.

Paper investigates an enhanced approach on YOLOv5 method that solves the challenges posed by complex environmental backgrounds, intensive repeated target detection, and varying pedestrian sizes. In the backbone network, feature extraction mainly depends on the texture information and shape of the target. We use deformable convolutional network (DCN) to replace the traditional convolution.;In Non-Maximum Suppression(NMS),The Generalized Intersection over Union(GIOU) used by original network is in place of through Distance Intersection over Union Loss(DIOU) solve problem of the densely populated repeated test.We use crowdhuman training data set to evaluate the effectiveness of the algorithm.We observed that the detection accuracy of the improved Yolov5-DCN model was 83.8%, which was 1.6% higher than that of the basic model. Moreover, it can effectively improve the accuracy of pedestrian target detection in dense scenes, especially for the detection of dense occluded targets, and the effect is significantly improved.

In SoC design, in order to enable data exchange and communication between IPs integrated into different on-chip bus standards, a corresponding bus bridge is required to enable their interaction. This paper describes the design structure and implementation method of the Wishbone-AHB bus bridge based on the widely used AMBA bus and Wishbone bus, completes the protocol conversion of different bus standards, realizes the matching and timing adjustment between different bus interface signals at both ends, and performs the functional verification under the iverilog tool, and the simulation results show that the bus bridge design meets the requirements. The simulation results show that the design of the bus bridge meets the requirements.

Relay technology plays an important role in wireless communication as an effective method to improve network coverage and communication quality. Polarization is an important characteristic of electromagnetic waves. Single-user MIMO multi-hop relay channels show significant polarization selection characteristics, which have a huge impact on the transmission quality of MIMO relay channels. This paper analyzes the channel capacity of a point-to-point single-user polarized MIMO multi-hop relay network and reveals the polarization dependency of adjacent hop channels and multipath propagation links. For a single-user polarized MIMO relay channel network constructed based on the COST 2100 channel model, a hybrid algorithm based on adaptive simulated annealing particle swarm optimization (BSAPSO) is proposed to optimize the polarization configuration of the antenna array, thereby improving the channel capacity of this relay network. The performance simulation results show that the BSAPSO can improve the link capacity of the polarized MIMO multi-hop relay network significantly compared with the other three groups of polarization selection optimization algorithms.

Vehicle-to-vehicle communication technology (V2V) can improve road safety by exchanging information with the surrounding environment. At present, both LTE-V2V and IEEE 802.11p can support the necessary security service requirements, and have been widely studied and tested. However, with the rapid development of intelligent connected vehicles, supporting only the necessary security services can no longer meet the needs of enhanced applications represented by autonomous driving, which poses new challenges to vehicle networking technology. The third-generation partnership project (3GPP) has carried out the second phase of research and standardization of cellular vehicle networking (C-V2X), focusing on new enhanced applications and performance improvements. Moreover, the IEEE 802.11bd task force is also developing the next phase of the V2X standard. This paper mainly evaluates the physical layer performance of these two technologies in V2V, namely NR-V2X Sidelink (SL) and IEEE 802.11bd technology. The results show that NR-V2X SL is superior to IEEE 802.1bd technology in data transmission rate when only considering the performance of physical layer in V2V communication. In terms of transmission distance, NR-V2X SL can achieve stable transmission at a longer distance. In terms of transmission delay, IEEE802.11bd can achieve lower transmission delay. In addition, the influence of variable subcarrier spacing (SCS) on the transmission performance of NR-V2X SL is also studied. The results show that NR-V2X SL has lower transmission delay and transmission rate with the increase of SCS.

Target recognition of obstacles in the highway is a challenge problem. Most of the abnormal events on the expressway are pigs, traffic cones, stones, etc. This question of radar target recognition includes: echo signals detection and echo signals processing. The echo signals of this paper were produced by impulse UWB radar. And the signals were processed with feature extraction by The PCA (Principal Component Analysis) machine learning algorithm. The PCA is an unsupervised method by calculating the covariance matrix of the data and performing eigenvalue decomposition on the covariance matrix. The engineering research in this paper shows that extracting “information principal components” of obstacles can greatly help to identify the target on the expressway. The research results can be widely used in time sensitive target recognition.

The haploid breeding technology of maize can shorten the breeding cycle and is an important technology for modern crop improvement. However, selection of maize haploid seeds is often done manually, resulting in loss of time and labor. It is of scientific value to study the selection algorithm of maize haploid seeds with high accuracy and strong generalization ability. In this paper, CNN and SVM were combined, and CNN-SVM model was used to classify maize seeds. The optimal CNN model is obtained by adjusting the number of convolution layers through experiments. In the training process of SVM, the cuckoo search algorithm is used to optimize the value of hyperparameter C and hyperparameter gamma, so as to improve the training efficiency and classification performance of SVM. The performance of CNN-SVM model was compared with CNN-KNN, CNN, HOG-KNN, HOG-SVM, SURF-KNN, SURF-SVM, Pixel histogram-KNN, Pixel histogram-SVM. Experimental results show that the CNN-SVM model is superior to other models, and the accuracy of maize seeds classification is 98.5%.

For organ ultrasound examination, it is very important to accurately obtain the standard section of classified organ ultrasound images. In this paper, a method based on ResNet-cbam is proposed to identify the classified ultrasonic standard plane. By collecting thyroid ultrasound images, which are divided into TPTI transverse section isthm us, TPRT transverse section right thyroid lobe, TPLT transverse section left thyroid lobe and lateral thyroid lobe longitudinal section. After image denoising and enhancement preprocessing, several models are first used for experiments, which show that ResNet-cbam has the best classification and recognition effect. By constantly adjusting the ResNet-cbam model structure, the number of iterations comparative experiments on changing the learning rate and activation function show that the best experimental effect is when Resnet18-cbam and the learning rate lr is 0.001 and the activation function is relu() function. Finally, the accuracy of classification and recognition is 89%, which proves that ResNet-cbam can recognize the standard section of thyroid well.

Intelligent applications such as digital recognition have problems in the process of training data and model inference, such as idle resources, low efficiency, high power consumption, etc. This paper mainly solves the problem by optimizing the LSTM model. The solution of the optimization model mainly includes the improvement of the matrix vector multiplication of the LSTM model and the improvement of the pruning algorithm. The improvement of the matrix vector multiplication of the LSTM model can reduce the calculation amount of the operation unit, Improve the recognition speed of the digital recognition system. The improvement of the pruning algorithm can reduce the resource consumption of the model parameters in storage, and can recognize more pictures and improve the recognition rate. The results of LSTM network implementation through training show that the speed of the system is about 250 times that of digital recognition using only the microcontroller core, and about 7.5 times that of general CPU.

The radio occultation (RO) events can obtain the global atmospheric delay information, effectively make up for the deficiency of ground-based observation, and then realize the real-time dynamic positioning based on the precise point positioning (PPP) model. With the development of low-earth orbit (LEO) constellations, a large number of LEO constellation plans with communication or navigation functions have been proposed. If these LEO constellations can be applied to detect occultation events and build space weather models, the full monitoring space-based without large-scale station building on the ground can realize. In this paper, the LRO events between satellites in the LEO constellation are simulated, and these satellites can be used as both transmitting satellites and receiving satellites. The effect of the LEO constellation’s orbital altitude and inclination on LRO events is also analyzed through simulation. The results show that the LEO constellation’s orbital altitude has little influence on the number, and the occultation events spread from the equatorial region to the polar region with the increase of the LEO orbit inclination from 0° to 75°, the number of LRO events increased. The simulation results can provide some references for the occultation exploration plan of the LEO constellation.

A universal automatic Field Programmable Gate Array (FPGA) simulation verification platform based on Universal Verification Methodology (UVM) is proposed in this paper. Based on the standard UVM architecture, this platform realizes the fast automatic simulation verification of FPGA algorithm functions. The platform mainly includes the test target sub-board, universal master control platform, control and processing computer, and universal test software platform. It realizes the hardware acceleration simulation based on the real target object, gets rid of the dependence of the FPGA test and verification on the single hardware platform, forms the standardized and modular verification test method, and eliminates the verification difference caused by human factors. Finally, the verification process of the platform and the results of the running test are compared and explained by concrete simulation examples.

As an effective technology for precise detection of weak signals, phase-locked amplifiers are widely used in various fields. Field programmable gate array (FPGA) can improve data processing speed by virtue of its large-scale parallel data processing capability and pipeline technology, so it has obvious advantages in digital signal processing. The design of digital phase-locked amplifier based on FPGA can reach a high level in speed and accuracy. The digital low-pass filter in the traditional phase-locked amplifier is a combination of adder and multiplier in structure. Because in FPGA, the multiplication operation needs to consume a lot of system hardware resources, this paper proposes to use the distributed algorithm based on table segmentation technology to realize the digital phase-locked amplifier, which optimizes the problem of the large resource occupation of the traditional multiply-plus phase-locked amplifier, and solves the problem of the shortage of multiplier resources, It saves on-chip resources of FPGA and makes the phase-locked amplification technology easier to implement on FPGA.

In recent years, Unmanned Aerial Vehicle (UAV) has attracted much attention as a communication platform with great potential. Benefiting mobility, UAV aerial base stations can be deployed quickly and flexibly, effectively establishing line-of-sight communication links. However, due to the limitation of its onboard energy, the UAV network's durability has become a significant challenge. This paper uses ground supply facilities to recharge the system in a replacement manner continuously and studies how to deploy UAVs and energy-saving scheduling adaptively. Firstly, A UAV 3D deployment scheme is designed based on the K-means algorithm. Under the constraint of the maximum tolerable delay, it is dynamically deployed according to the user's location information, including the number and coordinates of drones. Secondly, by monitoring the energy of UAVs, the energy-saving scheduling of UAV groups is carried out in a centralized manner. Finally, the scheme's feasibility is verified through simulation experiments, which significantly improves the UAV utilization rate and effectively reduces the system energy consumption.

In order to solve the problem of low network coverage due to random deployment of nodes in wireless sensor networks (WSN), this paper proposes an Improved Fruit Fly Optimization Algorithm (IFOA) and uses the network coverage as the algorithm optimization objective function. First, a Tent chaotic mapping is used to initialize the population so that the initial population individuals are diverse. Second, a linear decay step strategy is used to balance the global search in the first stage of the algorithm and the local search in the second stage. Finally, the optimal fruit fly individuals are perturbed by the Cauchy mutation to prevent the algorithm from falling into a local optimum. And the algorithm is applied to the wireless sensor network coverage optimization problem. Simulation results show that the algorithm effectively improves the network coverage, reduces node redundancy, and makes the network have higher coverage performance.

As a big agricultural country, realizing agricultural modernization is the only way for development in China. In recent years, with the progress of agricultural science and technology and the development of rural economy, new generation technologies such as sensors and Internet of Things have provided a new model for the upgrading and innovation of traditional agriculture and the development of smart agriculture. Combining NB-IoT wireless communication technology and IoT cloud platform, this paper designs a new type of agricultural greenhouse environment monitoring system. This system is mainly composed of sensing module, data processing module, transmission module and application processing platform, the hardware circuit design includes STM32F103C8T6 data processing module, NB-IoT wireless communication module and various sensor interface circuit, and the software design includes acquisition terminal main program design, NB-IoT wireless communication design and sensor program design. It realizes real-time acquisition of air temperature and humidity, soil humidity, light, CO₂ concentration, PM2.5, wind speed and raindrop sensors in greenhouse, and establishes communication connection with IoT cloud platform through NB-IoT network.

Downhole TV plays an important role in oil exploitation, among which it is a key part of downhole TV. At present, most of the downhole TV industry uses armored cable for data transmission, but with the development of technology, it has begun to be equipped with optical fiber in the field working environment, and continuous tubing also began to be equipped with optical fibers. Based on this situation, this paper introduces a system design of photoelectric multi-mode remote transmission. This paper first introduces the principle of data coding and demodulation. After analyzing the characteristics of data transmission, TC-PAM coding modulation mode is adopted, and during the cable transmission, the cable has adaptive technology to improve the decoding accuracy of the decoding terminal during high-speed data transmission in the deep well test channel environment. Then, the working principle and system composition of teletransmission section are introduced, and the design and system design requirements of photoelectric composite cable, the selection of core modules and the design method of temperature and pressure resistance of the system are also given. Finally, the field environment test verifies the stable operation of the system and the feasibility of the design method.

With the rise of Internet of Things (IoT) technology, the technical requirements for indoor positioning technology are gradually growing. Indoor high-precision positioning technology is obtaining widespread attention from industrial areas, especially the field of safety management for hazardous operators. Ultra-wideband (UWB) technology has become one of research focuses for indoor positioning technology research because of its outperformances treating with multipath resistance, interference resistance, owning high penetration and high accuracy. With the improvements of the management standards, enterprises increasingly need precise positioning in personnel security management, movement track and activity area restrictions. To outline the current development status of the state-of-art in positioning technologies, this study introduces the development and algorithms of UWB positioning technology and related applications in the fields of personnel security, providing a comprehensive reference for personnel positioning and security monitoring. Finally, we introduce an effective positioning approach based on a hand-ring UWB tag, which is capable of real-time monitoring of life safety indicators, such as heart rate and blood oxygen. This hand-ring UWB tag can monitor the both position and human-body healthy status, and will be widely used in various fields in the future.

In recent years, our army has attached great importance to the development and use of equipment support information systems. In the field of radar equipment support, some equipment information support subsystems have been successfully developed. However, there is still a lack of systematic research on a series of issues such as what content, functions, and elements should be included in the radar integrated support information system, and how various subsystems should coordinate their work. In view of this, firstly, the functional requirements of the radar integrated support system are analyzed. On this basis, the architecture of the radar integrated support information system is proposed, and the structures and functions of three main subsystems, namely, integrated management, integrated support network database, and simulation training, are described in detail. Finally, the engineering implementation of the information system is further elaborated.

Influenced by Lombard effect, the synthetic noisy speech training data commonly used in speech enhancement algorithm based on deep learning is different from the pronunciation under actual noise, which leads to the limited application performance of the algorithm in the actual environment. This paper summarizes the data compensation strategy proposed by researchers for speech enhancement algorithm under Lombard effect. However, these compensation strategies mainly focus on the front-end data processing part of speech enhancement, which improves the complexity of speech enhancement system.

In order to enable fishermen to better obtain information about the distance of the net from the bottom of the sea and the distance between the upper and lower net outlines, this paper proposes a wireless trawl monitoring system fishing net depth sensor with MSP430FR5994 as the main processor, which has wireless communication capability and overcomes the inconvenience of cable operation. The wireless trawl monitoring system fishing net depth sensor can collect the depth information of fishing net underwater and transmit the information back to the deck computer on the fishing vessel through wireless communication. This allows the fishermen on board to obtain information about the underwater position of the nets. The paper specifically describes the design of the transceiver algorithm and the design of the fishing net depth sensor.

We report a distributed fiber optic parametric amplifier (FOPA). The effects of pumping and fiber parameters on the gain characteristics of the distributed FOPA are investigated. Combined with the simulation results, the, it indicates the necessity of low attenuation coefficient fiber in distributed fiber parametric amplification system, the importance of low dispersion slope for gain spectral width and the limited role of pump optical power for effective amplification distance enhancement of the system.

Reconfigurable reflect-arrays (RRA) have received considerable interest recently. Although various RRA have been reported, most of them have relatively narrow bandwidth. Considering the importance of bandwidth in various wireless systems, it is highly desirable to design an ultra-wideband RRA with a simple configuration. This paper presents a 1-bit RRA with tightly coupled elements, which achieves ultra-wideband and promising beam scanning performance. In this work, firstly a wideband 1-bit RRA element consisting of two dielectric substrates perpendicular to each other is designed. Microstrip dipoles are printed on one substrate, while the other substrate is covered with copper and serves as the ground. In addition, the element design adopts the tightly coupled method, which results in ultra-wide bandwidth. The simulation shows the phases of the two states maintain a relatively stable difference within 180 ± 20° in the frequency range of 3-4.27 GHz and the relative bandwidth is 36.5%. Based on the broadband RRA element, a 30 × 30 RRA was designed and simulated. The simulation results show that the proposed RRA achieves a 1 dB gain bandwidth of 25% and promising beam scanning performance.

Digital array radar has many advantages, such as large gain, high reliability, extended dynamic range and airspace anti-interference. However, due to the limited development of digital array transceiver components, there are still some difficulties to realize the full digital array radar. This paper proposes a digital array radar applied to the fighter, which realizes the frequency transformation, filtering and sampling at the sub-array level. The signal is transmitted to the beam synthesis module through optical fiber, and separately generate the dots and navigation tracks. The key points of delay, transmission and dynamic range in digital array system design are presented. Finally, the test and calibration methods of digital array channels and the opportunities and challenges of digital array system design are discussed. The radar system scheme of sub-array-level digitization takes into account many factors, such as applicability, anti-interference and cost.It meets the tactical requirements of the current stage.

A low-power, long-range chain buoy monitoring network based on LoRa technology is designed for marine environment monitoring with characteristics of continuity and wide area. Firstly, the overall architecture of the marine environment monitoring network is constructed, two communication mechanisms are designed, namely timed reporting and downlink control, and are designed from both hardware and software perspectives. By analyzing the different parameters of LoRa, the best compromise was determined in terms of communication distance, power consumption and number of network nodes. Experiments show that the transmission performance of the buoy network is reliable and stable in LoRa chain networking mode, the transmitting current of a single node in networking state is less than 150mA, the packet loss rate is less than 1% in normal mode, and the distance between nodes can reach 1000m, which can meet the functional requirements of marine monitoring buoys.

The design of this paper is a 12bit SAR ADC,in order to optimize its performance and be able to achieve high accuracy, there are certain requirements for certain modules of the ADC. The design uses the bottom plate sampling, access to the common mode level Vcm on the upper pole plate of the DAC capacitor array is accessed by a higher precision bootstrap switch to reduce the non-linearity brought by the Vcm level access switch. By performing FFT simulation on the sampling points, it can be used at a sampling rate of 50Ms/s when the input signal frequency is 0.8301MHz and ENOB is 15.64bit. The capacitor array uses a combination of segmentation and splitting to greatly reduce the number of total capacitors, achieving only 188 unit capacitors, a 97.7% reduction in number compared to the conventional structure, for the entire ADC digital circuit The power consumption as well as the area of the ADC digital circuit has been significantly improved. The comparator uses a dynamic latching comparator to reduce power consumption while reducing the equivalent input noise of the comparator. The logic circuit uses dynamic SAR logic to control the analog-to-digital converter's successive comparisons. Sampling and analysis of the entire SAR ADC output achieves a valid bit count of 11.93 bits at a low frequency input signal of 0.1953MHz, enabling the conversion of a 12bit SAR ADC.

Taking the typical DAC binary code structure "R-2R" type DAC structure as an example, as the number of bits increases, the resistance structure also increases, resulting in excessive layout and power consumption. In addition, the analog switches in the R-2R resistor network are out of sync and cause voltage spikes in the output signal, which affects the accuracy of the digital-to-analog converter. In order to improve the accuracy of digital-to-analog converters, reduce chip area and reduce power consumption, a 16-bit high-low two-stage DAC is designed based on the "R-2R" DAC using 0.18- μm CMOS technology, which is a high-bit thermometer code network structure and a low-bit binary code network structure, respectively, and uses a Class AB amplifier to amplify the signal. Spectre simulations show that the DAC consumes 180uA of current when the supply voltage and reference voltage are 3.3V, and can drive a capacitive load of 10pF.

As the environment deteriorates, energy conservation and emission reduction have become the theme of the development of the engine industry. At the same time, vehicle emission regulations have become increasingly strict, and the actual road performance under law enforcement has gradually attracted people's attention. Therefore, it is of great significance to research and develop the remote monitoring system of vehicles. Mobile communication, CAN bus and Internet network communication technology were used in this paper to research and develop a set of engine remote monitoring system to realize remote real-time monitoring, data storage and data query of vehicle engines. The data transmission mode of mobile communication/Internet network communication was adopted by the system and a communication link between the vehicle terminal and the remote monitoring center was established. The test results show that the system can realize remote monitoring of the engine stably with delay no more than 1.6s and the packet loss rate no more than 0.7%.

Traffic light recognition is essential to intelligent vehicle information perception, and its accuracy directly affects traffic safety. Based on sorting out and analyzing the existing related research on traditional machine learning and deep learning in traffic light recognition, the basic principles of two standard algorithms in meta-learning are introduced in detail. Compared with traditional machine learning, using the MAML algorithm and Reptile algorithms in meta-learning to recognize traffic lights by selecting the WPI dataset and programming in PYTHON. The simulation results show that the recognition accuracy based on the meta-learning algorithm is higher than that based on the traditional machine learning algorithm; In the meta-learning algorithm, the Reptile algorithm outperforms the MAML algorithm.

In a microgrid, paralleling multiple bidirectional interface converters can effectively improve the power exchange capability on both sides of the microgrid. However, the power adaptive control problem of paralleling multiple bidirectional interface converters significantly increases the difficulty of microgrid control. The access to the energy storage system can buffer the power fluctuations of the microgrid and reduce the difficulty of power control. In order to ensure the stable operation of the microgrid and improve the power adaptive control accuracy, a hybrid energy storage multi-microgrid power adaptive control method based on edge computing is proposed. The structure of hybrid energy storage multi-microgrid is analyzed, and an automatic management platform for hybrid energy storage multi-microgrid is constructed based on edge computing technology. On this basis, the DC sub-network bus voltage regulation is introduced to adjust the frequency of the reference output voltage of the interface converter to complete the adaptive control of the power exchange on both sides of the AC-DC hybrid microgrid. It is considered to connect the energy storage system in the hybrid microgrid, and realize the power adaptive control of the hybrid energy storage multi-microgrid through power exchange control and autonomous power frequency division adaptive control. The experimental results show that the hybrid energy storage multi-microgrid power control effect of the proposed method is better, and the active power control error is small. It can effectively ensure the stable operation of the hybrid energy storage multi-microgrid and improve the power adaptive control accuracy.

Because of the great bandwidth and high data rate, THz communication has become the most attractive technology trend of the next generation wireless communication. However, the high frequency is a two-edge sword which leads to the electromagnetic signal sensitive to the environmental conditions. In the paper, we used the Fries transfer formula and ITUR P.676-9 to study the free space and atmosphere propagation characteristics of THz communication respectively. To investigate and estimate the attenuation of THz communication system established in our country, we further analyzed the main types of environmental conditions and calculate the attenuation of THz electromagnetic wave under different conditions which pave the way for the widespread applications of THz communication in the future.

With the rapidly development of intelligent connected vehicle, Security problem is becoming a core concern of vehicle owners. Current vehicle-ground communication related researches mainly focus on wide-beam antenna, which may result in data leakage for its wide signal coverage of on-board terminal. In this paper, a novel kind of beam scanning array which can be used for security enhancement of intelligent connected vehicles is proposed. This new kind of array is composed of a beam controller which based on the Butler matrix and a four-cell patch, which can not only reduce the power consumption of on-board terminal but also can enhance the security performance of V2X DATA link. This new array is characterized with lightweight, low profile, and low cost. Good performance of the on-board array can be observed in the simulation results.

Under the new technology and application of the power satellite Internet of Things, satellite communication will inevitably face more security risks. This paper studies the delay problem of satellite communication, analyzes the security risk caused by the delay, and proposes a route for the realization of satellite channel encryption technology. Low-latency, high-efficiency IPK identification public key technology is used for power satellite IoT applications. By comparing with the traditional PKI certificate authentication technology, the advantages of IPK technology in satellite channel encryption technology are established, and the environment is built to test the IPK signature verification time. By contrast with communication delay, it is negligible. Based on IPK technology, satellite communication data can be encrypted and transmitted, with low-latency and high-efficiency secure computing, which has no impacts on satellite communication delay, and can fully meet the needs of power satellite IoT applications.

In the paper, the penetration testing software aircrack-ng tool group was used to attack the wireless network, and the wireless network password of WPA/WPA2 encryption mode was cracked in the Kali-Linux virtual experimental environment. After enumerating the operation methods of 26 uppercase and lowercase letters or 9 numbers to the n-power by using computers and password dictionaries, the WiFi password can be quickly obtained with the current high-speed development of computer hardware. This method has the advantages of simplicity, speed, stability, and high security.

In this paper, we study the leader-following consensus problem of general linear multi-agent systems under distributed adaptive event-triggered control. A distributed controller with estimated states is designed, and an adaptive event-triggered communication protocol with auxiliary variables is proposed for each agent to adjust its triggering threshold. Under this control strategy, the system only needs to use the estimated states at the moment of triggering. Compared with the traditional static threshold, the existence of dynamic threshold reduces the number of triggers. Also, an estimator is designed between triggering moments to reduce the state deviation. The next state of each agent depends on local information about itself and its neighbors rather than global information. In addition, it is shown that the multi-agent systems can reach consensus without Zeno behavior. Finally, the effectiveness and feasibility of the proposed method are verified by numerical simulations.

Feed-forward timing recovery scheme has the advantage of fast estimation without acquisition time, but it is rarely used in resource-constrained spaceborne applications because of the computational complexity required to achieve highperformance estimation. In view of the limited resources and weak receiving power of the inter-satellite optical communication system, a low-complexity and parallelized feedforward timing recovery scheme is presented in this paper. Low-pass filtering based on Kalman filter is introduced to estimate the timing offset accurately. Phase jump detector is added to avoid the timing phase slip at 0/π that traditional feedforward timing recovery faces in low SNR. The corresponding experiment results show that the proposed scheme can achieve stable estimation and correction of clock bias under the condition of low signal-to-noise ratio with low implementation complexity.

AI education uses and continues to use AI and cognitive science technology to try to understand the essence of learning and teaching, thus establishing a system to help students master new skills or understand new concepts. Facing the huge and complex education database, how to use ai technology to help teachers monitor students' classroom behavior and non-classroom behavior in real time without affecting students' daily learning and life, so as to improve the learning efficiency and reduce the effect of failing classes, has attracted the attention and consideration of scholars and experts at home and abroad. This paper proposes a behavior state classification system for college students based on human physiological information. The system uses intelligent bracelets to collect students' physiological information data, conduct large-scale data preprocessing and feature extraction, and construct a multi-classifier model based on combination strategy to realize the classification of college students' learning, entertainment, and sleep. The experimental results show that the recognition accuracy of the system reaches 95.43%.

The underwater imaging environment is relatively complex. The light in the water has blurred image edges, low overall contrast, and the objects in the water are difficult to recognize. This paper proposes a method based on depth learning and physical model to enhance the quality of underwater image. The enhanced image is obtained by building the module to calculate the important parameters of the model combined with the input. The experimental results show that the algorithm in this paper has faster processing speed and better imaging effect than traditional methods, and can correct the color deviation of underwater images.

The traditional calculation method of 5G low-power safety communication module intelligent distribution terminal feeder has high cost and poor load balancing control ability. This paper proposes a feeder automation technology for intelligent power distribution terminals based on a 5G low-power secure communication module, the distributed power load parameter analysis model of 5G low-power safety communication module feeder is constructed, and the constraint parameters of 5G low-power safety communication module feeder are analyzed by the lumped parameter line simulation method. Through the difference of traveling waves at both ends of the line, the dynamic characteristic parameters of 5G low-power safety communication module intelligent power distribution terminal are fused, the dynamic characteristic values of 5G low-power safety communication module intelligent power distribution terminal are extracted, and the dynamic constraint parameters such as transmission delay and wave impedance are optimized and analyzed to realize the feeder of 5G low-power safety communication module intelligent power distribution terminal. The simulation results show that the load balance of the feeder of the intelligent distribution terminal of 5G low-power safety communication module is good, and the output stability and power gain of the intelligent distribution terminal of 5G low-power safety communication module are high.

In this paper, the authors investigate two-way hybrid satellite-terrestrial relaying networks, where a satellite communicates with a terrestrial user through a terrestrial half-duplex amplify-and-forward (AF) relay, and propose a novel semi-blind AF strategy, which utilizes the statistical channel state information (CSI) pertaining to the satellite-relay link and the instantaneous CSI pertaining to the user-relay link, avoiding numerous CSI training pilots of the satellite-to-relay link and possessing low implementation complexity, thus improving the energy efficiency of the satellite. Exact closed-form outage expressions for the proposed strategy are derived, which can provide the important performance reference. Simulation results confirm the validity of theoretical analysis and demonstrate that the power allocation between the satellite and the user and placing the relay close to the user, which is a reasonable configuration for practical systems, can efficiently guarantee the outage performance of the proposed strategy.

This paper discusses the research of privacy protection based on network traffic data. Firstly, the concept and collection method of network traffic data are introduced, and the method of collecting network traffic data by intelligent identification algorithm is proposed, so as to improve the accuracy and efficiency of data collection. Then, aiming at the privacy protection of network traffic data, a privacy protection scheme based on desensitization algorithm is proposed. The scheme uses chaotic cryptography, homomorphic encryption and differential privacy to ensure the privacy and security of data. Finally, the effectiveness and practicability of the scheme are illustrated by the conclusion, which provides a new solution for the privacy protection of network traffic data.

With the development of "Internet+Education", universities are facing the leap from digital campus to smart campus. However, data silos, incomplete data, data leakage and dirty data are common problems in universities, which hinder the construction of smart campus. In order to break through barriers and achieve secure data usage, this paper proposes a data governance framework and a multi-layer protection mechanism. The data governance framework divides all elements involved in the data life cycle into four subjects, and ensures the effectiveness and feasibility of data governance through their mutual cooperation. The multi-layer protection mechanism is designed to ensure the data security as much as possible by segregating the data in layers based on operation attributes. After practice, the level of campus data governance under the guidance of this paper has been greatly improved.

Based on NaI (Tl) scintillation crystals is currently the main technology for marine radioactivity monitoring in the world. However, the marine radioactive gamma spectrum sensor based on this technology generally has the disadvantage of a large output data volume. When the sensor is equipped with buoys and other marine automatic monitoring platforms to achieve long-term, continuous and automatic monitoring, the seawater gamma energy obtained by continuous measurement Remote transmission of spectral data becomes a difficult problem. In this paper , the gamma energy spectrum analysis process is transplanted from the host computer to the sensor end to avoid errors caused in the process of large-capacity data transmission. Using STM32 on the sensor end for early energy spectrum analysis can realize real-time measurement data of seawater gamma energy spectrum The automatic analysis of radioactive pollution is of great significance to the monitoring, early warning and emergency response of marine radioactive pollution.

In our work, we came up with an algorithm for user pairing in a non-perfect SIC (successive interference cancellation) downlink NOMA (Non-orthogonal multiple access) system under a heterogeneous network model. The minimum threshold condition for user pairing is first derived demonstrating the effect of non-perfect SIC on NOMA user's rate. Then, the user scheduling under the heterogeneous network model is completed by using the neighborhood search for optimization, and the system is achieved, and the rate is maximized. Finally, it is verified through simulation experiments that the method can obtain the global optimal solution and has a smaller complexity, which is more suitable for the case of large-scale users. While the transmission rate requirements are met effectively for weak users and yielding a superior performance compared to the previous user pairing strategy.

Compared with the Internet, the IoT has more potential security issues and the traffic is exposed to greater security risks. The number of IoT devices is large, and the generalization performance and accuracy of current traffic attack detection algorithms are not satisfactory. In order to improve the efficiency of traffic detection, the Cross validation feature selection random forest algorithm (CVFSRF) is used to select the effective features for different types of IoT devices, taking into account the characteristics of IoT devices themselves. information for different types of IoT devices to train the detection model. The traffic detection uses a semi-supervised learning strategy to improve the detection accuracy by optimizing the type and number of individual learners through integrated learning. After conducting simulation experiments, the average detection accuracy of the algorithm is 99.52% and the detection time is 1.26s, which can efficiently and accurately detect traffic attacks on different IoT devices to ensure device security.

With the rapid development of wireless network technology, modern land and sky have formed a huge communication network. However, in the marine environment, network communication is still at a low level. With the economic globalization, the demand for the exploration of marine resources is increasing, and the country is also increasing the research on underwater acoustic communication and data transmission technology. The underwater acoustic network transmits data through acoustic signals, and the speed of sound is usually 1500m/s , which is about 5 orders of magnitude lower than the propagation speed of radio waves (3×10 8 m/s). In the underwater acoustic channel, the signal is also affected by background noise, temperature, salinity and Doppler effect, which makes the utilization of channel resources low, resulting in long delay, low signal-to-noise ratio and low throughput of underwater acoustic communication. Therefore, it is very important to design a reliable, efficient, and low-latency underwater acoustic network communication method.

Spatial information network itself is a comprehensive information network with multiple functions, which plays an important role in information acquisition, attack support, navigation and positioning, etc. In order to describe the function of spatial information network in detail, the combat ring theory is introduced to model the nodes, inter-satellite links and earth-satellite links of the network. At the same time, due to its complex and dynamic nature, Different from conventional combat network, the time accumulation graph model is introduced to further describe the network model, so as to truly reflect the support and attack capability of spatial information network under the combat background.

In order to tackle the issues of co-channel interference from cellular users and limited data rates in D2D communication for power equipment, the authors have proposed a D2D communication rate-based transmission power optimization method, and formulated an optimization problem based on D2D communication rate. The proposed method employs the Harris Hawks Optimization (HHO) algorithm to maximize the sum rate as the objective function while taking D2D communication rate symmetry and cellular communication quality as constraints. By optimizing the transmission power of power equipment and using sum rate as the influencing factor, the method selects the spectrum that is shared by both cellular users and power equipment. The simulation results indicate that the proposed method can significantly enhance the sum rate of the communication system when compared to the spectrum random access scheme.

Blockchain, as a type of distributed ledger technology, is well-suited for the distribution, sharing, certification, and tracing of information. Therefore, DApps implemented on blockchain have been utilized in various industries, where businesses utilize DApps to achieve reliable information flow and certification across enterprises or within an enterprise. However, DApps are limited by the constraints of the blockchain architecture, which results in issues such as poor performance, limited scalability, difficult maintenance and management, and high development costs. As a result, it is difficult for DApps to replace traditional database models in enterprises. In order to address this issue, we conducted an analysis of the shortcomings of DApps and proposed a novel blockchain service framework based on ordered Hash tree architecture, which provides a consistent and reliable solution for data management in DApps. Our work, through analysis, is capable of resolving or improving the challenges faced by DApps and holds practical applications value.

As the most important means of underwater information perception for submarines, the sonar system is an important guarantee for submarines to complete combat missions. In view of the demand for the development of integrated sonar, this paper starts from the modular open architecture and COTS technology, active and passive, multi-band, multifunctional integrated acoustic array, multi-array fusion processing technology, sonar performance evaluation and auxiliary decision-making , intelligent sonar and other aspects to conduct research on the current development status of integrated acoustic array technology , and analyze the development trend and technology gap.

With the widespread application of the fifth-generation mobile communication system, mobile data traffic has seen rapid growth, bringing new challenges to traditional cellular networks. Ultra-dense networks (UDN) are one of the key technologies used in 5G communication to address user capacity and high-speed transmission. How to effectively solve the problems of inter-site interference, spectrum resource scarcity, and high energy consumption in UDN has become a major research topic, and resource allocation technology is essential in this regard. This article summarizes the spectrum resource allocation methods and applications in UDN through literature review. Firstly, the applicable scenarios, models, and various indicators of resource allocation in UDN are summarized and analyzed. Secondly, this paper systematically sorts and classifies recent research on resource allocation methods for UDNs, analyzing their applicability, advantages, and disadvantages. Additionally, the paper discusses the future development of spectrum resource allocation methods for UDNs.

In response to the current lack of effective methods to obtain the real-time field data in the process of urban management remote command and dispatch, and operation maintenance diagnosis, this paper utilizes WebRTC technology, HTML5 technology, WebSocket technology, NAT traversal technology, JSEP technology and ROAP technology, studies and applies WebRTC audio and video communication system based on the Spring Cloud & Node.js mix microservice platform framework, and redeveloped in JavaScript language. By integrating in smart city informatization applications, the system achieves real-time audio and video communication between commander, remote diagnosis experts, and site operators at any time and in any area, lets commander and experts intuitively to master the real scene on site in real-time, and provides remote command and dispatch, and on-site emergency response advice without being limited by time and space, that makes urban management, operation, maintenance, and command and dispatch more safe, more convenient, and more efficient.