Proceedings of the 4th International Conference on Frontiers of Electronics, Information and Computation Technologies (ICFEICT 2024)

Airborne early warning radar uses pulse Doppler (PD) system, which has good low-altitude detection performance, but its Doppler blind zone (DBZ) problem cannot be ignored. In the process of target tracking, the blind zone is easy to cause target track loss. An interacting multi-model blind-zone particle filtering (IMM-BZPF) algorithm is proposed for continuous target tracking in the condition of DBZ. In this algorithm, the prior information of the DBZ is incorporated into the IMM-BZPF, and the blind-zone particle filtering is performed on each motion model in the model set. The simulation results show that the proposed algorithm has high state estimation accuracy for maneuvering targets in the blind zone, solving the continuous tracking problem of maneuvering targets in the DBZ.

Aircraft attitude vibration of airborne early warning (AEW) radar can affect the detection performance and accuracy of targets. Considering that AEW radar based on a 3-plane array is affected by aircraft attitude vibration, A mathematical model for aircraft rolling and pitching has been established. Under the conditions of mono-pulse sum-difference, the influence of two types of vibrations on the accuracy of angle and height measurement was analyzed. The accuracy of height finding under various vibration angles is determined by conducting simulation experiments. The results of the simulations indicate a noticeable decrease in height finding accuracy due to the presence of vibration; when the vibration angle is greater than a certain angle, radar height information cannot be used due to the large mean square error; the rolling of an aircraft has a significant impact on the high accuracy of oblique photos, and the overhead view of an aircraft has a significant impact on the high accuracy of the forward array.

This paper designs a driving control method of electromagnetic metering pump, including the following calculation of the initial pulse number of each step, active degaussing initial current, active degaussing initial time, and the initial duty cycle of the degaussing process according to the system Settings and hardware parameters. This paper realizes the drive control of electromagnetic metering pump and the active degaussing function of electromagnetic coil, which has the characteristics of short degaussing time to meet the high-speed drive. This paper uses MATLAB to simulate and verify and apply in the actual circuit. The drive control method can easily configure the drive parameters of the controller, effectively expand the maximum stroke and maximum pump pressure range, and reduce the design requirements of the pump body heat dissipation structure.

In this paper, we focus on the problem of target detection with interference in Gaussian background. For achieving better performance under the condition of small independent identically distributed (IID) training samples number, the receiving data is reduced in dimension and the oblique projection method is used to suppress the interference. Then the adaptive detector is obtained by Wald criterion. Finally, the mathematical expressions of the detection probability and the false alarm probability of the detector are given. Simulation results show that the proposed detector achieve better detection performance with insufficient number of training samples.

In this paper, a three point passive location model based on the characteristics of the circular trajectory is established. The circle track is believed to be analyzed, and the three geometric relationships of the track on the circle are generated through the number theory knowledge, the mathematical relationship is established, the constraint relationship between the deviated target position is established, the actual position and target position are simulated, the position restoration and position relationship are studied, and a complete and clear model and simulation realization are given.

Consistence is a key metric for evaluating quality and performance of lithium battery packs, and grouping is a crucial means for improving consistence and overall performance of battery modules and packs. We introduce a novel framework that combines Knowledge Fusion-based Transformer (KFT) with an improved DPC clustering algorithm. The KFT serves as a data reconstruction model to extract features from multiple sources. In this model, we construct feature extractors based on expert knowledge, extracting features from both the original input samples and the reconstructed outputs of KFT. This achieves the solidification of knowledge, incorporating it into the data features. Finally, grouping is performed using the improved DPC clustering algorithm. After 250 charge-discharge cycles, the average State of Health (SOH) of the module is 92.98%, and the average inconsistence score of the pack is 0.0144, which outperforms the baselines, demonstrating the effectiveness.

In the real-time damage monitoring of structures under complex conditions, the damage source is easily affected by various external dynamic factors, and the monitoring information processing needs to continuously and accurately analyze the damage source signals containing a lot of noise information. The monitoring signal of acoustic emission technology is a typical non-stationary dynamic data set, and the received signal and the source signal are typically time-varying unstable. This study proposes a time-varying convolution BSS algorithm for non-stationary signals that may separate dynamic time-varying convolution mixed signals. Firstly, with the use of the variable Decibel Bayesian (VB) inference approach based on the Gaussian process (GP) prior, the non-stationary source is isolated from the time-varying convolution signal frame by frame. Secondly, VB learning is used to retrieve the source signal and mixed matrix that contains parameter information. To facilitate VB inference, the acquired parameters and hyperparameters are propagated to multiple frames as prior information, and the posterior distribution is obtained by combining the likelihood function. Lastly, an estimate of the source signal is established. The acoustic emission damage signal modeling experiment verifies the algorithm's effectiveness. This study provides a new theoretical approach to real-time continuous damage signal analysis in complex environments.

The towed radar active decoy (TRAD) is a serious threat to the combat effectiveness of radar-guided missiles. In order to improve the anti-TRAD ability of radar seekers, this paper proposes a target tracking method for polarization MIMO radar seekers based on polarization invariants. The method is based on the differences in polarization invariants between the jamming signal transmitted by TRAD and the carrier aircraft echo signals, and achieves identification and tracking for the carrier aircraft through dual-gate tracking. Simulation experiments confirm the feasibility and effectiveness of the method.

A novel method for generating pseudo-measured data tailored for inverse synthetic aperture radar (ISAR) learning-based imaging is proposed in this paper. This method initially performs semantic segmentation on images containing target categories from publicly available datasets to extract the geometric contour of the target. Subsequently, the geometric contour is meshed and mapped to a Cartesian coordinate system through a gridding process. A random terrain generation algorithm is then employed to randomly generate scattering blocks within the target contour, completing the data generation process. This approach not only simulates the basic shape of the target but also generates a random scattering distribution within the geometric contour that closely resembles real measurement data. As a result, it provides richer and more realistic image data for data-driven ISAR imaging methods. Experimental results indicate that the pseudo-measured datasets produced using this method, under the SRCNN model, yield superior imaging results in terms of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) compared to those obtained using publicly available datasets.

This paper presents an innovative approach for estimating human vital signs parameters. Firstly, the signal model for human vital signs is introduced. Then, the proposed GWO-VMD method which aims to improve the decomposition effect is used to extract the respiratory and heartbeat components. Next, a multi-channel statistical approach is used to obtain more stable and accurate results. Finally, the experimental results confirm the superiority and effectiveness of the proposed method.

With the wide application of simulation system in the field of military test and training, a variety of simulation application platforms have been developed. The simulation models developed based on these simulation platforms are difficult to reuse between different simulation application platforms due to their different technical architectures and operating mechanisms. In view of the problems faced by the reuse of simulation models, this paper summarizes and analyzes the current simulation model reuse methods, proposes a method to realize the reuse of simulation models by using object model modeling and service-oriented encapsulation technology, and based on this method, a service-oriented encapsulation framework for simulation models is encoded, and finally realizes the reuse on the VMS simulation application platform through the service-based packaging of simulation models such as tanks and infantry vehicles in the SSG simulation application platform, and the verification results show that the proposed method can support the flexible reuse of simulation models.

In order to solve the problem that the dynamic measurement of non-cooperative targets was difficult to carry out, the real-time simulation method for dynamic polarization scattering characteristics of moving target was studied in this paper. Firstly, the formulas to calculate the azimuth and elevation angles of the line of sight of radar in the target body coordinate system were derived. Secondly, the polarized scattering model of a single scattering point is introduced. Lastly, the implementation steps for eliminating obstructed points are given. The simulation results showed that the dynamic polarization scattering characteristics of moving targets are sensitive to the line of sight of radar. The validity and accuracy of the real-time simulation method were verified.

The images of HGIS bushing terminal blocks in substations often contain rich details, such as different materials of bushings, complex wiring structures, and possible background interferences. These details make contour extraction more complex. In order to improve the accuracy of extracting the contour of substation HGIS bushing terminal, an attention mechanism based method for extracting the contour of substation HGIS bushing terminal is proposed. Using edge guided operator template matching technology, combined with sliding window and nonlinear grayscale transformation algorithm, preprocess the HGIS bushing terminal image of the substation, enhance the edge information of the image, and reduce the impact of lighting and noise. In order to accurately extract the contour of wiring terminals, a deep learning network architecture based on attention mechanism was designed. The network gradually enriches feature information through an encoder, and the decoder restores high-quality contour clear images. In addition, the convolutional attention module (CBAM) and attention mechanism sub network in the network can accurately focus on the contour area of the terminal block, suppress non target features, and thus achieve accurate extraction of the terminal block contour. The experimental results show that the proposed method can accurately extract the contour of the HGIS bushing connection terminals in substations, providing strong support for subsequent image analysis and recognition tasks.

The integration of Space-air-ground power internet of things (SAGPloT) and multi-tier computing has become a key technology to deal with frequent extreme weather disasters and ensure the safe and stable operation of power communication systems. However, the design of SAG-PIoT involves more strict low energy consumption requirements than the traditional SAG networks. In this work, we consider a nonorthogonal multiple access (NOMA) and multi-tier computing assisted SAG-PloT, and investigate the weighted energy minimization problem from the energy-efficiency perspective. Moreover, we decouple the problem into two subproblems and propose a joint optimal power control, computation resource allocation and unmanned aerial vehicle (UAV) trajectory (JOPCT) algorithm. Numerical results show that the proposed algorithm in this work can significantly reduce system energy consumption and efficiently plan UAV trajectories to serve more PIoT devices than the benchmark algorithms. The provided results can offer useful information for design of future space-air-ground power internet of things networks.

In response to the increasing need for stable power supply and reliable communication services in remote regions worldwide, Low Earth Orbit (LEO) satellites offer a promising solution owing to the difficulty of deploying traditional power communication infrastructure in remote areas and their vulnerability to natural disasters. Considering the demand of high mobility of LEO satellites that require frequent handover to maintain service continuity, one of the main technical challenges for LEO satellite networks is how to manage satellite handover to ensure the continuity of communication services. Therefore, it becomes crucial to study optimization techniques for satellite handover, and We introduce an optimization approach for handover that integrates Graph Neural Networks (GNN) with Deep Reinforcement Learning (DRL). The simulation results show that our proposed handover method (MPNN-DQN) outperforms the DRL-based approach in reducing handover frequency and communication delay.

In 6G, high data rate services are expected to be provided for marine users. However, it’s challenging to design an efficient maritime wireless communication system due to the limited bandwidth and harsh marine environments. In this paper, a reconfigurable intelligent surface (RIS) assisted millimeter-wave (mm-Wave) multiple-input multiple-output (MIMO) system is proposed for maritime wireless communications. Specifically, the RIS is carefully designed according to the maritime wireless channel conditions, where singular value decomposition based alternative optimization is considered. Then, in order to improve the system performance, a receive beamforming scheme is proposed by considering a weighted adaptive strategy for iteratively mitigating the multiuser interference. With the aid of our proposed algorithm, a robust multiuser interference mitigation can be achieved by offering a versatile solution for multiuser interference issues in the RIS-assisted mm-Wave MIMO system. Finally, simulation results are provided, showing that an improved system performance can be attained by the proposed RIS assisted mm-Wave MIMO system for maritime wireless communications.

To address the issues of low detection accuracy and the high number of model parameters in current printed circuit board (PCB) surface defect detection algorithms, a YOLOv5-based algorithm model, namely, YOLOv5_HM is proposed in this paper. Firstly, a lightweight neck network, High-level Screening-feature Fusion Pyramid (HS_FPN), is designed to improve the model's feature representation capabilities. Secondly, a lightweight attention mechanism, the Mixed Local Channel Attention (MLCA) module, is introduced. Thirdly, by removing the large target detection layers, the model's detection head is optimized, thereby increasing the model's ability to recognize small objects. Experiments results show that the YOLOv5_HM model achieves a 2.6% improvement in mean average precision (mAP), a 32.3% reduction in model parameters, and a 3.9 reduction in GFLOPs compared to the YOLOv5 model, demonstrating the effectiveness of the proposed method.

With the evolution of space-air-ground integrated networks (SAGIN), the satellite and unmanned aerial vehicle (UAV) synergy in sensing application exhibits significant potential. Moreover, the freshness of status information in sensing application is crucial. And age of Information (AoI) can potently measure the freshness of status information. Accordingly, it is imperative to research the AoI in satellite-UAV synergy real-time sensing systems. Initially, the system model is built up. Subsequently, the system model is converted to a finite-states continuous-time Markov chain. Then, the moment generating functions (MGF) of the AoI is derived by employing the stochastic hybrid system (SHS) approach. Next, the AoI performance under full frequency reuse (FFR) and frequency division multiple access (FDMA) is analyzed and compared in terms of transmit power. The results show that FFR demonstrates better AoI performance than FDMA under limited communication resources.

In vehicular ad hoc networks (VANETs), content caching at edge devices can facilitate direct content delivery without fetching content from the remote server. The existing cache strategy regards all vehicles on the road as cache nodes with identical characteristics, without considering the differences among vehicle types. Buses on the road have larger cache space and more stable driving speeds and paths. By fully utilizing these bus features, cache performance can be enhanced. Therefore, we propose a hierarchical cooperative edge caching strategy based on Double Deep Q-Network (DDQN). In this scheme, buses with a large communication range, stable driving speed and paths are used, along with RSUs, form a high-level cache backbone network. This network provides content caching and delivery services for private cars at the lower level, which have a high privacy and a considerable degree of mobility randomness. At the same time, we design a cache replacement strategy based on Double Deep Q Network (DDQN) model to optimize the content access delay. Simulation results show that compared with the traditional benchmark scheme, the proposed scheme demonstrates a substantial enhancement in cache hit ratio and reduction in content access delay.

Nondestructive and high-throughput screening of 3D scanning method in the early detection of scoliosis has played a major role. Gaussian curvature and mean curvature methods are often used in current research to express the 3D point cloud information of the human body. Meanwhile, Gaussian curvature is often used by scholars to detect the characteristic regions of the human back, such as: the spine-ridge, scapula, C7 and other locations. However, Gaussian curvature does not perform well enough in the task of identifying the spine-ridge area in the shape of a strip and the lumbar fossa and buttocks in the shape of a round pit. Therefore, based on the fact that the difference between the maximum curvature and the minimum curvature values at the spine-ridge is larger than the difference between the other locations on the back, this paper proposes a new method for calculating the curvature. This curvature calculation method is composed of a dynamic Gaussian curvature value that combines the quotient of maximum curvature and minimum curvature. The values at the spine-ridge under this curvature method are larger than those at other sites. This curvature calculation method enhances the contrast at the spine-ridge in the curvature map, making it convenient for spine-ridge localization. The results show that this new 3D curvature calculation method improves the energy gradient index by 512.688%.

Improving the accuracy of photovoltaic power generation prediction is of great importance to ensure safe dispatch and stable operation of the power system. In this research, an improved hybrid method is adopted which called SA-EEMD-BiLSTM. It is proposed as a solution to the challenge of short-term photovoltaic power prediction. Which employs cutting-edge techniques from machine learning, Embracing self attention(SA) mechanism, ensemble empirical mode decomposition (EEMD) algorithm, and bidirectional long-short term memory (BiLSTM) network. In this approach, the initial photovoltaic power sequence is initially broken down into multiple subsequences using the EEMD algorithm, The goal is to break down the intricate issue into more manageable sub-problems. Subsequently, the sub-sequences are reconstructed using an enhanced SA mechanism designed to uncover the connections between the sub-sequences. Ultimately, the reconstituted sequence set serves as the input for the BiLSTM model, from which prediction results for the entire issue are derived. Compared with the traditional method, SA-EEMD-BiLSTM shows better prediction effect.

Insulators are an important component of power transmission lines. Using computer vision to identify the condition of insulators can improve maintenance efficiency, ensuring the stability of the line, and reducing the likelihood of fires. However, some insulators on power transmission lines are located in mountainous areas, where the complex terrain leads to variable shooting angles and significant differences in target sizes in images, making detection difficult. Additionally, visual algorithms need to be deployed on edge devices for mountainous use, requiring lighter-weight algorithms. To solve this issue, we obtained and annotated 1600 mountain insulator images as the research dataset. Then we propose the mountain insulator defect detection model LightBi-YOLO, which is an improvement based on YOLOv5. We replaced the original upsampling operation with the CARAFE lightweight upsampling operator and applied the improved BiFPN network to replace the original FPN network. Experimental results show that the LightBi-YOLO model achieves an average detection accuracy of 98.9%, representing 14% and 1.2% improvements over the Faster R-CNN model and the original YOLOv5 model. At the same time, The FLOPs of the model is 16.1, which exceeds the computational speed of the original model, making it more suitable for deployment in edge devices.

In recent years, with the swift advancement of lithium battery technology and annual increases in energy and power density, ensuring their safe, stable operation and accurately forecasting their Remaining Useful Life (RUL) have become crucial. This paper introduces a method that combines Fixed-Budget Kernel Recursive Least Squares (FB-KRLS) and Unscented Kalman Filtering (UKF) for estimating the RUL of lithium batteries, termed the UKF-FB-KRLS method. The combination of UKF and FB-KRLS enhances the nonlinear modeling capability of the model and can better characterize the internal features of the prediction model. In addition, the fitting ability of the model is greatly improved. To assess the predictive capability of the model, lithium battery data sourced from NASA was used for validation and comparison with other prediction models. The results show that the model attains a Mean Absolute Percentage Error (MAPE) of 0.26%, a Root Mean Square Error (RMSE) of 0.004, and a Mean Absolute Error (MAE) of 0.0037. These metrics underscore the model’s effectiveness in improving the precision of Remaining Useful Life (RUL) estimates for lithium batteries.

In response to the lack of specific descriptions of cargo stowage positions within general cargo ship holds and the inability of most loading software to calculate the actual center of gravity of the cargo hold after loading, this paper proposes the integration of Web technologies into the loading interface to visually display the stowage positions of general cargo, calculate the true center of gravity of the cargo hold after loading, and provide real-time display of the ship’s stability post-loading to ensure safe navigation. Utilizing the Spring Boot technology framework, Vue technology framework, and MySQL database, a visual loading system has been designed and deployed on relevant servers for user use. The system enhances the visualization of cargo loading in the hold, enabling crew members to visually load general cargo through the system, thereby ensuring transport safety and providing a theoretical basis and support for intelligent cargo management.

As the continuous emergence of new applications, such as face recognition and automatic driving, wireless devices are faced with problems, for instance, limited computing resources and excessive energy consumption. Thus, this study presents a joint task offloading and computation algorithm for multi-carrier and multi-relay mobile edge computing (MEC) systems to resolve aforementioned problems. First, we build a multi-carrier and multi-relay MEC system. Next, we calculate the delay constraints according to the given tasks, optimize resource allocation. To optimize the total energy consumed of relays and users, the optimization of resource allocation is expressed as a mixed integer programming problem. Further, because difficulty in solving the issue, we use continuous relaxation and algebraic transformation to convert the problem to equivalent problem. Finally, we solve the problem utilizing the interior point method, which realizing the highly efficient resource allocation. Simulation results demonstrated that the presented joint collaborative task offloading and computing program decreases energy consumed by 26.67% compared with the relay-only assisted task computing program.

The reconstruction error-based anomaly detection refers to constructing a data reconstruction model to determine whether a sample is an anomaly based on the reconstruction error on the test set. We propose a network architecture consisting of three channels, including random dropping, causal, and dilated convolution modules. The random dropping module is employed to stochastically discard sample information, compelling the network to rely on local context for current information reconstruction. The casual convolution module captures temporal dependencies in the data, and the dilated convolution module expands the convolutional receptive field. Together, these modules enhance the model’s ability to understand and reconstruct complex temporal patterns in 1D real-time signals. The proposed architecture can learn more comprehensive features through 3 scales, providing greater robustness. Validation on lithium-ion cell discharge voltage data demonstrates that our approach outperforms the baseline in terms of performance.

Time series analysis has various applications in different fields, such as traffic management, weather forecasting, and crime prevention. Currently, deep learning-based models, particularly those employing self-attention mechanisms like transformer methods, are achieving remarkable results for time series prediction. However, the current models are not sophisticated enough to accurately model time series due to the non-stationarity of time series data and challenges in merging information. To address these issues, this study proposes a novel approach: the Frequency Fusion Network (FFN). FFN learns the mapping from the frequency domain to the time domain, utilizing frequency convolution to merge information from different patterned time series while preserving their statistical characteristics. Experimental results demonstrate that FFN consistently outperforms existing methods across all datasets, and offers a fresh perspective and effective methodology for improving time series forecasting accuracy, especially in handling non-stationary data.

This letter researches the resource allocation issue in an intelligent reflecting surface (IRS)-assisted cooperative cognitive covert communication system, where IRS relay efficiently secures the covert transmissions of secondary users from potential eavesdroppers. Taking practical considerations into account, it is assumed that while the IRS only acquires channel distribution information (CDI), it remains unaware of eavesdropper’s specific detection threshold. In this scenario, we introduce a transferred generative adversarial network based resource allocation algorithm (TGAN-RA), which comprises of a source domain generator, a target domain generator, and a discriminator. The proposed TGAN-RA has extracted and transferred resource allocation feature of secondary users not transmitting covert message, and then transformed the whole covert communication process into an interactive game between the legitimate users and the eavesdropping. Numerical results indicate that even under non-ideal conditions with only known CDI and unknown eavesdropper’s detection thresholds, the proposed TGAN-RA algorithm can effectively attain nearly optimal resource allocation for covert communication while ensuring rapid convergence.

Digital receiver is an important component in the radar analog array channel. Its influence on the slope, signal spectrum and noise spectrum of the linear Frequency Modulation(LFM) signal are modeled and simulated in the paper. Firstly, the time domain model of LFM signal in descending and ascending channels is introduced respectively, and the change pattern of the corresponding FM slope is given. Then, the signal spectrum and the noise spectrum in the frequency domain are derived and the frequency spectrum is simulated. Combined with the characterization of two scenarios such as uniform distribution and normal distribution, the conclusion that the phase noise between spectral lines and the FM bandwidth are almost irrelevant is validated. The correlation analysis in the paper gives the relatively clear signal and noise characteristics of narrowband LFM signals.

With the increasing number of intelligent networked devices in cities, the control centers of these devices are under increasing communication and computation pressure. Vehicular Ad-hoc Networks (VANETs) routing protocols not only need to address the challenge of frequent changes in network topology but also need to adapt to routing without the assistance of control centers. To this end, this paper proposes a distributed V2V routing algorithm for VANETs based on block Q-learning, which realizes the final routing by connecting the best routing in each block. In each block, the routing relay selection problem is modeled as a Markov decision process and solved using Q-learning. When realizing cross-block routing, we propose an improved Q-value updating formula to integrate the current block routing and neighboring block routing by considering the influence of neighboring block best routing. Simulation results show that the algorithm performs better in terms of average end-to-end delay, hop count, and packet delivery rate than other algorithms.

Recently, the Internet of Things system (IoT) in the power distribution station area has gradually developed towards intelligence. However, due to the existence of numerous low-voltage end-sensing units in the power distribution Internet of Things, these devices have limited computing resources and weak protection, which poses great security risks to the entire intelligent power distribution IoT. Therefore, this scheme studies a remote attestation mechanism for power distribution IoT terminals based on trusted measurement. This mechanism measures the trust of the power distribution station area terminals in multiple dimensions, including static environment and dynamic behavior. Then, these trust values are integrated into a comprehensive trusted measurement through information entropy. The simulation results show that this scheme can truly reflect the trusted status of terminals and accurately and effectively detect malicious nodes.

In order to ease the problem of business processing speed of large Internet nodes, the business processing logic and architecture of the current mainstream Clos [1] structure of the Internet are first described, and then the business path is modeled to establish a matrix model. The existing ring algorithm is improved by using the bipartite graph coloring principle, the Bidirectional exchange scheduling algorithm and the same exchange scheduling algorithms are designed to search businesses from the rows and columns of the matrix, so as to quickly achieve business load balancing. Finally, the algorithm was loaded onto FPGA to demonstrate the effectiveness of both algorithms in improving business processing speed.

Aiming at the problem of dangerous behavior monitoring, a dangerous behavior intelligent monitoring system based on UWB wireless carrier communication technology was designed and developed. The system uses UWB positioning technology to complete real-time positioning of target locations by arranging UWB positioning base stations at home and wearing UWB positioning tags for users. It uses the YOLOv5s target detection algorithm combined with open source flame and smoke image data sets, MSCOCO data sets, and Google Open Image data set, etc., complete the monitoring of dangerous goods in safe areas. Using the AlphaPose human posture estimation algorithm, the Home Action Genome Dataset is selected as the training and test data set, which can monitor dangerous behaviors such as users climbing through windows or falling down. The system can issue alarms in a timely manner and transmit monitoring data in real time through connections with relevant equipment to ensure safety in all aspects.

Electric vehicles generate a large amount of data during operation, such as vehicle travelling routes, charging and discharging patterns, road condition information, etc., which are of great significance for grid planning and construction, charging and discharging facility management, and autonomous driving. However, electric vehicle data involves personal privacy and security issues, and traditional vehicle data security sharing methods can hardly meet the demand of decryption key distribution and one-to-many sharing in complex dynamic environments. To address the above challenges, this paper improves the traditional one-to-many data security sharing architecture to construct a new lightweight data security sharing framework. The method combines broadcast encryption, proxy re-encryption and anonymous authentication techniques, and improves the traditional encryption method, and ultimately realizes one-to-many secure data sharing between restricted vehicles while the edge computing vehicles in the system can directly share data with nearby vehicles through anonymous authentication, thus alleviating the backbone bandwidth pressure and improving the efficiency of the overall data sharing in the system. After theoretical proof and experimental validation, the efficiency of one-to-many data sharing in vehicular networking is significantly improved, and the resource utilization and data security of data sharing are enhanced. The proposed method is of great significance for data sharing among vehicles and can enhance the performance and efficiency of the overall Telematics system.

Considering the current issues encountered in the deployment of 5G private networks, including high costs, imperfect traffic diversion mechanism, and insecure user access control scheme, this paper presents a 5G campus private network model based on Educational Metropolitan Area Networks (EMAN). The proposed solution comprises three aspects: Firstly, a private User Plane Function (UPF), whether physical or virtual, is deployed and connected with the EMAN via dedicated communication links. All schools in the same region or city can build their own 5G campus private networks quickly and economically just by reusing the existing EMAN interconnection lines and establishing Generic Routing Encapsulation (GRE) tunnels between the UPF and the boundary devices of their respectively campus networks. Secondly, a traffic offloading scheme based on private data network name (DNN) and uplink classifier (ULCL) is adopted to realize imperceptible access to campus Intranet and Internet in 4G/5G networks and roaming scenarios. Thirdly, a network access control scheme based on converged authentication gateway is proposed, which effectively ensure secure access control, authorization and comprehensive lifecycle management for 5G private network users. The feasibility and effectiveness of the proposed solution have been validated through its implementation within a provincial EMAN.

To meet the low-latency routing requirements of a large number of emerging applications in ultra-reliable communication environments, this paper introduces a deep reinforcement learning (DRL)-based low-latency high-reliability intelligent routing scheme for software-defined networking (SDN). We formulate a minimum decision latency model to describe the optimal routing problem based on a comprehensive overview of network resources and centralized control of network devices from SDN, where dynamic differentiated routing requirements are considered. A DRL-based routing algorithm is presented to achieve ultra-reliable low-latency routes by integrating the ultra-reliable routing requirement module responsible for the unqualified routes filter. Experiment results show that the proposed algorithm outperforms in routing reliable requirements and decision latency compared to other popular algorithms, especially in large-scale networks and high-dynamic network scenarios.

A novel pilot-assisted channel estimation model, Matrix-DenseNet, is introduced, which has a unique matrix-like structure consisting of five rows and six columns. Dense connectivity is incorporated within each row to enhance feature propagation and reduce parameter count. Additionally, deep sampling paths and feature feedback paths are set up across columns, creating a deep sampling feedback structure that further improves the extraction of multi-resolution features from the initial CSI tensor. Simulation results demonstrate that the proposed Matrix-DenseNet significantly improves the normalized mean square error (NMSE) and bit error rate (BER) performance of OFDM systems in high-speed environments.

Rate splitting multiple access (RSMA) is deemed to be a multiple access technique of the next generation, which is also adopted in visible light communication (VLC) to achieve massive connection and boost data rate. However, how to use RSMA in the multicarrier VLC system remains an open problem. To solve it, an RSMA scheme is conceived for multicarrier VLC system, in which asymmetrically clipped optical orthogonal frequency division multiplexing (ACO-OFDM) is employed. In the proposed scheme, optical power constraints imposed on the precoding matrix of RSMA are first deduced to meet the lighting requirements for the ACO-OFDM-based VLC system. Furthermore, given the objective of the max-min rate, joint optimization algorithm of the precoding matrix and rate allocation is designed under the optical power constraints to improve the user rate, as well as guarantee the communication fairness. Simulation results have shown that the proposed scheme significantly outperforms the conventional space division multiple access (SDMA) in terms of the spectral efficiency.

Approximately 280 million people worldwide suffer from depression, with over 700,000 suicides annually attributed to depression. Early diagnosis allows depression patients to receive timely treatment and intervention, alleviating symptoms, reducing the risk of deterioration, and lowering medical costs. However, current clinical diagnostic methods for depression suffer from subjective bias and low accuracy. Therefore, continuous research into more accurate and simple detection methods for depression using artificial intelligence is of great clinical significance. Inspired by existing methods, this paper proposes an innovative automatic depression identification method that combines electroencephalogram (EEG) signals with artificial intelligence technology. Firstly, the phase lag index (PLI) of EEG signals is computed to obtain their functional connectivity networks. Then, the elements within the PLI matrix are select based on the altered Kendall's Rank Correlation Coefficient and convergence of accuracy (AKRC-C). Finally, the selected multidimensional features are input into an RF classifier for automatic classification. This method achieves a classification accuracy of 95.03% for distinguishing depression patients from healthy controls, which is superior to the existing methods. This indicates that the proposed depression detection model in this paper can achieve intelligent and rapid depression detection, providing an efficient, accurate, and diverse solution for clinical depression detection.

Low Earth Orbit (LEO) Satellite communication, with its strengths for wide networks coverages, strong flexibility, and good communication performance, is widely used in communication technologies such as emergency communication. However, due to geographical factors, there may be an uneven distribution of services for Internet of Things (IoT), resulting in congestion and networks load imbalance in certain links. To address this issue, a Traffic-Aware Routing Selection algorithm for LEO IoT is proposed. Firstly, a Convolutional Neural Network-Bidirectional Long Short Term Memory-Attention is utilized to predict the traffic of satellite. Then, the predictive traffic values are incorporated as observations into a Partially Observable Markov Decision Process and input into the Multi-Agent Deep Deterministic Policy Gradient algorithm model for reinforcement learning. Lastly, the effectiveness of the proposed algorithm is confirmed. It is noted that, compared with other algorithms, the proposed algorithm achieved a reduction of 11% to 53% in terms of maximum link utilization as well as a successful decrease in packet loss, thereby improving networks performance.

Sidelink Synchronization Signal Block (S-SSB) is used for establishing synchronization between devices before communication which is crucial for downlink synchronization in sidelink. In this article, the evolution process of sidelink technology in different versions is summarized, then, the signal and transmission design of S-SSB is given, including the structure and resource mapping of S-SSB, as well as the generation of S-SSB's time-frequency position and sequence. Further, the difference between S-SSB and normal SSB is summarized. Afterward, this paper also provides the receiving base band algorithm which considers the timing offset (TO) and frequency offset (FO). Finally, a link-level simulator platform is designed to evaluate the performance of sidelink synchronization signal detection and the block error rate of the PSBCH, an analysis of the error rate of PSBCH resource blocks and the detection performance of the synchronization signal under different frequency offsets. Additionally, it also analyzes the detection performance of the synchronization signal when using a single-symbol synchronization signal.

Sidelink communication technology is an innovative device-to-device communication method in 5G networks. It is primarily used for vehicle-to-everything (V2X), proximity service, public safety, and so on. 3GPP has been dedicated to developing the technical standards for sidelink. This paper mainly introduces the applications and developments of sidelink technology, and summarizes the detail design of the physical sidelink control channel (PSCCH). In addition, based on the application scenarios and communication requirements of sidelink and traditional wireless communication, the design differences between PSCCH and physical downlink control channel (PDCCH) at the physical layer are illustrated. It also explains the information, application scenarios and functions carried by the sidelink control information 1 (SCI1) transmitted on the PSCCH and the sidelink control information 2 (SCI2) transmitted on the physical sidelink shared channel (PSSCH). Finally, the PSCCH reception algorithm is proposed, and the performance simulation curves of SCI1 and SCI2 under specific parameters are provided, and their performance differences are analyzed.

A dynamic resource allocation problem for jointly optimizing access point selection and task offloading is proposed for a mobile edge computing system in an unmanned aerial vehicle (UAV)-assisted cell-free (CF) networks for the purpose of delay reduction in an urban scenario. On the one hand, given that various resources are coupled in the optimization problem, exhibiting non-convex traits and thus cannot be directly separated. On the other hand, to expedite algorithm convergence while ensuring user data security, we propose a method that combines the federated learning (FL) framework with deep reinforcement learning (DRL). Specifically, we introduce a federated deep Q network (F-DQN) algorithm to execute the dynamic resource allocation strategy for the proposed problem. Simulation results show that the proposed algorithm has better convergence performance than traditional Q-learning and deep Q network (DQN), and the proposed strategy has lower system delay with a maximum gain of 60.5% compared to other baseline strategies.

The resilience of military communication networks against attacks and their ability to provide stable communication services directly impacts the outcome of military conflicts. Thus, their inherent robustness against attacks is crucial. This paper addresses the mapping problem of military communication network structures under random attacks and proposes a heuristic military communication network mapping optimization algorithm that takes attacks into account. First, a corresponding network structure model is established by considering the topological characteristics of military communication networks, modeling the military communication network as a dual-layer network consisting of physical and virtual layers. Second, with the goal of enhancing network robustness, an objective function is designed based on the surplus and balance of physical network link bandwidth resources, and a military communication network mapping optimization algorithm based on particle swarm optimization algorithm is proposed. The mapping effects under different algorithms are compared and analyzed through simulations, validating the effectiveness of the proposed algorithm, which has certain reference significance for further research on the military communication network mapping problem.

With the rapid development of the power Internet of Things (IoT) system, it has also brought about a more complex protocol adaptation process, posing more challenges to the power system. In this context, feature matching technology under the background of machine learning has increasingly become an important component in the field of protocol adaptation. The IoT protocol adaptation technology based on feature matching can automatically extract features from different network protocols and achieve optimal matching of features, thereby improving the accuracy of protocol adaptation in the entire IoT system. This paper proposes a feature-matching-based power IoT protocol adaptation framework. The framework processes and extracts the original features of IoT protocols through a fine-tuned Transformer model, and uses the Support Vector Machine (SVM) algorithm to match the core features with the protocols, enabling the final adaptation of the protocols with a predefined protocol feature library, greatly enhancing the accuracy and flexibility of power IoT protocol adaptation. The experimental results demonstrate that the accuracy has been achieved up to 89.9% on the PAWS-X dataset and 96.9% on the IoT-23 dataset, indicating a significant improvement in both the accuracy and efficiency of the power IoT protocol adaptation under this framework.

With the rapid development of Internet services, bandwidth costs have become a major part of operating expenses. To save on bandwidth costs, an increasing number of Internet video companies are choosing to use PCDN for content distribution. However, due to a lack of effective control technologies, Peer-to-Peer Content Delivery Networks (PCDN) often severely disrupts network order, leading to a negative user experience. This article proposes a home-based distributed CDN system that integrates operator CDN and network capabilities. It adopts a two-level architecture, innovates terminal multi-connection grouping technology solutions, reduces the construction costs of operator networks and CDNs while ensuring user access quality, and achieves orderly exploitation of home terminal capacity resources.

Conventionally, estimating the transmitter’s position relies on two-step localization methods. Compared to conventional two-step localization methods, direct position determination (DPD) is a technique with superior performance. In most localization scenarios, shortwave and ultrashort wave signals are mostly used. But in reality, the propagation of shortwave signals is usually affected by ionospheric reflections, and the signal strength of the ultrashort wave signal will decay rapidly with the increase of distance. Therefore, finding a method that can cooperate with the two kinds of signals is the key to improving the localization accuracy of the DPD method. This study considers the shortwave signals propagated by ionospheric reflections, and the ultrashort wave signals propagates through line-of-sight. Furthermore, this paper proposes a DPD method that cooperates with the 2-dimensional DOAs (Direction of Arrival) of shortwave signals and the DOA and FOA (Frequency of Arrival) of ultrashort wave signals. To achieve decoupled positioning of multiple transmitters, this paper uses the idea of Weighted subspace data fusion (SDF). The simulation results show the proposed DPD method has good performance.

The traditional Multiple Signal Classification (MUSIC) super-resolution time delay estimation algorithm has the characteristics of high resolution and can effectively solve the problem of multipath time delay estimation, but the estimation accuracy is poor at low signal-to-noise ratio (SNR). Noncircular signal is a common signal in modern communication systems. Using the second-order noncircular property of noncircular signal can increase the available information and improve the performance of parameter estimation algorithm. In this paper, we introduce the noncircular property of the signal into the MUSIC time delay estimation algorithm and propose a super-resolution time delay estimation algorithm joint signal noncircular characteristic. The new algorithm uses both the covariance matrix and the conjugate covariance matrix of the array output to improve the accuracy of the time delay estimation. Simulation results show that the new algorithm has the characteristics of steep spectral peak and better estimation accuracy.

In the pursuit of fully realizing the vision of 5G New Radio (NR), which aims to accommodate a diverse array of devices, services and deployment scenarios, the introduction of sidelink represents a significant milestone. Sidelink communication is integrated to bolster the ability of NR for facilitating device-to-device (D2D) communication, and thus bringing in a host of enhancements, such as latency reduction, spectrum efficiency improvement, and reliability enhancement. This strategic evolution aligns with the overarching objective of fortifying 5G connectivity to meet the demands of varied and dynamic usage scenarios. This paper firstly introduces the development history and research status of NR sidelink. Then, an in-depth design of PSSCH (physical sidelink shared channel) is summarized, from the perspective of data multiplexing, resource allocation, DMRS configuration, etc. In order to evaluate the link level performance, a baseband algorithm for PSSCH is designed, and corresponding simulation results are provided under different conditions.

With the increasing demand for intelligent prosthetics, intelligent perception and human-computer interaction, flexible pressure sensors have rapidly developed as the core device of wearable electronic devices. However, flexible pressure sensor with high sensitivity, wide detection range and low cost is still a challenge. In this work, we reported the carbon-based flexible pressure sensor enabled by multigradient architectures with a high sensitivity of 6.83 kPa−1 at low pressure and 0.72 kPa−1 at high pressure. Besides, the minimum detection limit of the sensor can achieve 4 Pa. We made an intelligent glove with five sensors to detect the pulse of human body and the signal changes when grasping different objects. These applications indicate great potential of the intelligent glove in pulse diagnosis and object recognition fields.

In the information age, rail transit equipment is constantly innovative, and high-speed trains have better performance. Advanced network operation technology, such as automatic train control and real-time monitoring system, needs supporting information visualization technology to transform a large amount of information generated during train operation into easy-to-understand visual information, so as to improve the safety and efficiency of train operation and improve the level of passenger service. Information visualization technology is very important in the rail transit equipment system. Therefore, this paper makes a comprehensive analysis of the visualization technology that can be applied in the rail transit equipment, puts forward the visualization elements applicable to the rail transit field, prepares the visualization chart library of the information of rail transit equipment and carries out the information visualization design of the rail transit equipment system. Hope enough for the relevant personnel to provide basic reference suggestions.