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Articles in press have been peer-reviewed and accepted, which are not yet assigned to volumes /issues, but are citable by Digital Object Identifier (DOI).
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Screen-Shooting Resilient Watermarking Scheme Combining Invertible Neural Network and Inverse Gradient Attention
LI Xiehua, LOU Qin, YANG Junxue, LIAO Xin
 doi: 10.11999/JEIT230953
[Abstract](0) [FullText HTML](0) [PDF 2937KB](0)
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With the growing use of smart devices, the ease of sharing digital media content has been enhanced. Concerns have been raised about unauthorized access, particularly via screen shooting. In this paper, a novel end-to-end watermarking framework is proposed, employing invertible neural networks and inverse gradient attention, to tackle the copyright infringement challenges related to screen content leakage. A single invertible neural network is employed by the proposed method for watermark embedding and extraction, ensuring information integrity during network propagation. Additionally, robustness and visual quality are enhanced by an inverse gradient attention module, which emphasizes pixel values and embeds the watermark in imperceptible areas for better invisibility and model resilience. Model parameters are optimized using the Learnable Perceptual Image Patch Similarity (LPIPS) loss function, minimizing perception differences in watermarked images. The superiority of this approach over existing learning-based screen-shooting resilient watermarking methods in terms of robustness and visual quality is demonstrated by experimental results.
A Review of the Artificial Intelligence-based Image Classification of Fishes in the Global Oceans
ZHOU Peng, LI Chang-Yong, BU Yu-Xin, ZHOU Zhinuo, WANG Chun-Sheng, SHEN Hong-Bin, PAN Xiaoyong
[Abstract](6) [FullText HTML](5) [PDF 1016KB](2)
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Understanding the species composition, abundance and temporal and spatial distribution of fish on a global scale will help their biodiversity conservation. Underwater image acquisition is one of the main means to survey fish species diversity, but image data analysis is time-consuming and labor-intensive. Since 2015, a series of progress has been made in updating the datasets of marine fish images and optimizing the algorithm of deep learning models, but the performance of fine-grained classification is still insufficient, and the production practice application of research results is relatively weak. Therefore, the need for automated fish image classification in marine investigations is firstly studied. Then a comprehensive introduction to fish image datasets and deep learning algorithm applications is provided, and the main challenges and the corresponding solutions are analyzed. Finally, the importance of automated classification of marine fish images for related image information processing research is discussed, and its prospects in the field of marine monitoring are summarized.
Polarized Beam Online Reconfiguration Technique For Flexible Deformation Antennas
CHEN Zhikun, CUI Jinhe, WANG Wei, CHEN Zhibin, GUO Yunfei
 doi: 10.11999/JEIT240070
[Abstract](12) [FullText HTML](6) [PDF 2540KB](0)
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In response to the challenges posed by the deformation of flexible polarized array antennas, which results in difficulties in beam reconstruction and compromised beam performance, polarization beam online reconstruction technique based on flexible deformation polarized antennas is proposed in this paper. Firstly, the deformation state of the array is modeled based on a wing model, and real-time deformation data is obtained using modal analysis to reconstruct the antenna array model online. Secondly, the element response of vector array antenna is utilized to construct a flexible array antenna signal model in three-dimensional space. Finally, a deep integration of the Cyclic Algorithm (CA) and Second-Order Cone Programming (SOCP) is employed to solve the dynamic optimization problem of this optimal polarization beam reconstruction. Simulation results demonstrate that within a certain range of deformation and under different arc and angle requirements from environmental loads, the proposed method can achieve online antenna array reconstruction and real-time optimal polarization beam reconstruction based on the dynamic antenna array model. The directional gain, beamwidth, and polarization matching design all meet the requirements for practical engineering applications.
Joint Multi-UAV Trajectory Design for Power Line Inspection
GAO Yunfei, HU Yulin, LIU Mingliu, HUANG Yuxi, SUN Peng
 doi: 10.11999/JEIT231199
[Abstract](8) [FullText HTML](8) [PDF 3615KB](1)
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Unmanned Aerial Vehicles (UAV) technology holds significant importance and offers extensive potential for application in the field of inspection. Taking into account the limited endurance of the UAV, it needs to fly from the nest to the designated inspection area, complete the inspection of the transmission tower, and then return to the nest safely before the battery is exhausted. For large-scale inspection scenarios aiming, a multi-UAV inspection method is proposed to minimize inspection time. In detail, the k-means++ algorithm is used to optimize UAVs task allocation and the modified simulated annealing algorithm is utilized to optimize the inspection trajectory to improve inspection efficiency. Finally, the tower pole distribution data from a simulated real-world environment, the proposed algorithm is employed to assign UAVs tasks and design trajectories. The simulation results confirm that the proposed algorithm can significantly reduce the total inspection time through multi-UAV task allocation and trajectory design.
Spiking Sequence Label-Based Spatio-Temporal Back-Propagation Algorithm for Training Deep Spiking Neural Networks
WANG Zihua, YE Ying, LIU Hongyun, XU Yan, FAN Yubo, WANG Weidong
 doi: 10.11999/JEIT230705
[Abstract](12) [FullText HTML](7) [PDF 1032KB](2)
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Spiking Neural Networks (SNN) have a signal processing mode close to the cerebral cortex, which is considered to be an important approach to realize brain-inspired computing. However, the lack of effective supervised learning algorithms for deep spiking neural networks has been a great challenge for spiking sequence label-based brain-inspired computing tasks. A supervised learning algorithm for training deep spiking neural network is proposed in this paper. It is an error backpropagation algorithm that uses surrogate gradient to solve the problem of non-differentiable spike generation function, and define the postsynaptic potential and membrane potential reversal iteration factors represent the spatial and temporal dependencies of pulsed neurons, respectively. It differs from existing learning algorithms based on firing rate encoding, fully reflects analytically the temporal dynamic properties of the spiking neuron. Therefore, the algorithm proposed in this paper is well-suited for application to tasks that require longer time sequences rather than spiking firing rates, such as behavior control. Proposed algorithm is validated on the static image datasets CIFAR10, and the neuromorphic dataset NMNIST. It shows good performance on all these datasets, which helps to further investigate spike-based brain-inspired computation.
Action Recognition Network Combining Spatio-Temporal Adaptive Graph Convolution and Transformer
HAN Zongwang, YANG Han, WU Shiqing, CHEN Long
 doi: 10.11999/JEIT230551
[Abstract](11) [FullText HTML](4) [PDF 2773KB](1)
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In a human-centered smart factory, perceiving and understanding workers’ behavior is crucial, as different job categories are often associated with work time and tasks. In this paper, the accuracy of the model's recognition is improved by combining two approaches, namely adaptive graphs and Transformers, to focus more on the spatiotemporal information of the skeletal structure. Firstly, an adaptive graph method is employed to capture the connectivity relationships beyond the human body skeleton. Furthermore, the Transformer framework is utilized to capture the dynamic temporal variations of the worker's skeleton. To evaluate the model's performance, six typical worker action datasets are created for intelligent production line assembly tasks and validated. The results indicate that the model proposed in this article has a Top-1 accuracy comparable to mainstream action recognition models. Finally, the proposed model is compared with several mainstream methods on the publicly available NTU-RGBD and Skeleton-Kinetics datasets, and the experimental results demonstrate the robustness of the model proposed in this paper.
Radio Environment Map Construction Method for Complex Scenes Based on Inverse Obstacle Distance Weighted
TAO Shifei, WU Yujiang, LUO Jia, DING Hao, WANG Yuanhe
 doi: 10.11999/JEIT231374
[Abstract](37) [FullText HTML](16) [PDF 4405KB](11)
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Addressing the issues of inadequate performance in constructing Radio Environment Maps (REMs) in complex scenarios due to non-penetrable obstacles for electromagnetic waves, and the arbitrary selection of interpolation neighborhoods imposed by Inverse Distance Weighted (IDW), a Voronoi-based Inverse Obstacle Distance Weighted algorithm (VIODW) is proposed in this paper. This algorithm adaptively defines interpolation neighborhoods for each interpolation point by creating Voronoi diagrams incorporating obstacles for numerical computation. Then, using the ANY-Angle (ANYA) Algorithm to calculate the obstacle distance between the interpolation point and each monitoring station within the interpolation neighborhood. Finally, by calculating the weighted mean with the inverse power of the obstacle distance as the weight, the value at the point is obtained, achieving high-precision construction of REMs in complex scenarios. Both theoretical analysis and simulation results demonstrate that this method offers excellent construction accuracy and can accurately model the power distribution of electromagnetic waves in complex scenarios. Hence, it provides an effective approach for high-precision REM construction in complex scenarios.
Research on Multi-User Detection Algorithm for Non-Orthogonal Multiple Access Short Message Based on Low Complexity Adder Network
WANG Ji, LI Zilong, XIAO Jian, LI Huanzhe, XIE Wenwu, YU Chao
 doi: 10.11999/JEIT231186
[Abstract](10) [FullText HTML](5) [PDF 3219KB](3)
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A joint constellation trace diagram and deep learning-based blind modulation detection scheme is proposed for Non-Orthogonal Multiple Access (NOMA) systems, which can avoid the required expensive signaling overhead in successive interference cancellation algorithms, especially for NOMA-based short packet transmission. Considering the high computational complexity and energy consumption for communication equipment in the deployment of neural network, the original convolutional network is replaced by the adder network. The modulation detection accuracy, computing delay and energy consumption are fully compared for two kinds of network architectures. Meanwhile, time-domain oversampling technology is used to improve the recognition rate under low signal-to-noise ratio. Finally, the influence of power allocation and data packet length on detection performance is analyzed and verified.
A Method for Radio Frequency Interference Space Angle Sparse Bayesian Estimating in Synthetic Aperture Microwave Radiometer
ZHANG Juan, ZHUANG Lehui, LI Yinan, LI Hong, DOU Haofeng
 doi: 10.11999/JEIT231367
[Abstract](14) [FullText HTML](11) [PDF 3356KB](1)
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A sparse Bayesian estimation for spatial Radio Frequency Interference (RFI) of synthetic aperture microwave radiometers is proposed in this paper. Firstly, an interferometry measurement model of the visibility function for synthetic aperture microwave radiometers is established. The observed data are expressed as the product of the observation matrix of the aperture synthesis antenna baseline correlation steering vector and the brightness temperature of the field of view. Due to the orthogonality of the observation matrix and the sparsity of the RFI spatial angle distribution, the transformation coefficients of brightness temperature in the support domain are sparse. Under the Sparse Bayesian Learning (SBL) framework, brightness temperature is sparsely reconstructed. Notably, this method can obtain high reconstruction performance without the prior information of sparsity and regularization parameters. The effectiveness of this method is verified through computer simulations.
Depression Intensity Recognition Based on Perceptually Locally-enhanced Global Depression Features and Fused Global-local Semantic Correlation Features on Faces
SUN Qiang, LI Zheng, HE Lang
 doi: 10.11999/JEIT231330
[Abstract](43) [FullText HTML](23) [PDF 5758KB](16)
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For automatic recognition of the depression intensity in patients, the existing deep learning based methods typically face two main challenges: (1) It is difficult for deep models to effectively capture the global context information relevant to the level of depression intensity from facial expressions, and (2) the semantic consistency between the global semantic information and the local one associated with depression intensity is often ignored. One new deep neural network for recognizing the severity of depressive symptoms, by combining the Perceptually Locally-Enhanced Global Depression Features and the Fused Global-Local Semantic Correlation Features (PLEGDF-FGLSCF), is proposed in this paper. Firstly, the PLEGDF module for the extraction of global depression features with local perceptual enhancement, is designed to extract the semantic correlations among local facial regions, to promote the interactions between depression-relevant information in different local regions, and thus to enhance the expressiveness of the global depression features driven by the local ones. Secondly, in order to achieve full integration of global and local semantic features related to depression severity, the FGLSCF module is proposed, aiming to capture the correlation of global and local semantic information and thus to ensure the semantic consistency in describing the depression intensity by means of global and local semantic features. Finally, on the AVEC2013 and AVEC2014 datasets, the PLEGDF-FGLSCF model achieved recognition results in terms of the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE) with the values of 7.75/5.96 and 7.49/5.99, respectively, demonstrating its superiority to most existing benchmark methods, verifying the rationality and effectiveness of our approach.
Research on Opportunistic Localization with 5G Signals in Co-channel Interference Environments
SUN Qian, DING Tianyu, JIAN Xin, LI Yibing, YU Fei
 doi: 10.11999/JEIT231423
[Abstract](19) [FullText HTML](12) [PDF 3280KB](7)
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In response to the challenge of ensuring positioning accuracy in environments where the Global Navigation Satellite System (GNSS) is denied, a positioning scheme based on opportunistic New Radio (NR) signals is devised and an Interference Cancellation Subspace Pursuit (ICSP) algorithm is proposed in this paper. This algorithm aims to resolve the issue of inadequate precision in the extraction of positioning observations due to co-channel interference within Ultra-Dense Networks (UDNs) and Heterogeneous Networks (HetNets). The effectiveness of the ICSP algorithm in optimizing the performance of 5G opportunistic signal receivers and enhancing positioning accuracy in complex network environments has been validated through simulation experiments and semi-physical simulations utilizing the Universal Software Radio Peripheral (USRP).
A Cloud Server Anomaly Detection Model Based on Time Series Decomposition and Spatiotemporal Information Extraction
TANG Lun, ZHAO Yuchen, XUE Chengcheng, CHEN Qianbin
 doi: 10.11999/JEIT230679
[Abstract](40) [FullText HTML](12) [PDF 2995KB](20)
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Anomaly detection is an important task to maintain cloud data center performance. A large number of cloud servers are running in cloud data centers to implement various cloud computing functions. Since the performance of cloud data centers depends on the normal operation of cloud services, it is crucial to detect and analyze anomalies in cloud servers. To this end, a cloud server anomaly detection model based on time series decomposition and spatiotemporal information extraction-Multi-Channel Bidirectional Wasserstein Generative Adversarial Networks with Graph-Time Network (MCBiWGAN-GTN) is proposed in this paper. Firstly, the Bidirectional Wasserstein GAN with Graph-Time Network (BiWGAN-GTN) algorithm is proposed. This algorithm is built upon the Bidirectional Wasserstein GAN with Gradient Penalty (BiWGAN-GP) algorithm. In this modification, the generator and encoder are replaced by a spatiotemporal information extraction module— Graph-Time Network (GTN) composed of Graph Convolutional Networks (GCN) and Temporal Convolutional Networks (TCN). This modification aims to extract spatiotemporal information from the data, enhancing the capabilities of the algorithm. Secondly, the semi-supervised BiWGAN-GTN algorithm is proposed to identify anomalies in multi-dimensional time series to avoid the risk of abnormal data intrusion during the training process and enhance model robustness. Finally, the MCBiWGAN-GTN is designed to achieve the goal of reducing data complexity and improving model learning efficiency. The Complete Ensemble Empirical Mode Decomposition with Adaptive Noise algorithm (CEEMDAN) is used to decompose the time series data, and then different components are sent to the BiWGAN-GTN algorithm under the corresponding channel for training. The effectiveness and stability of the proposed model are verified on two real-world cloud data center datasets, Clearwater and MBD, using three evaluation metrics: precision, recall and F1 score. Experimental results show that the performance of MCBiWGAN-GTN on these two datasets is stable and better than the compared methods.
Robust Secure Resource Allocation Algorithm for Multiple Input Single Output Symbiotic Radio Based on Reconfigurable Intelligent Surface Assistance
WU Cuixian, ZHOU Chunyu, XU Yongjun, CHEN Qianbin
 doi: 10.11999/JEIT230426
[Abstract](91) [FullText HTML](50) [PDF 2833KB](11)
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To resolve the channel uncertainties, wireless information leakage and low communication quality caused by obstacles, a robust secure resource allocation algorithm for a Reconfigurable Intelligent Surface (RIS) aided Multiple Input Single Output (MISO) Symbiotic Radio (SR) network is proposed. Considering the constraint of the secure rate of the primary user, the constraint of the minimum rate of the secondary user and the constraint of the minimum harvested energy of the RIS, a resource allocation problem based on the bounded channel uncertainties is formulated by jointly optimizing the active beamforming vector and the passive beamforming vector. Then, the parameter perturbation included non-convex problem is transformed into a deterministic convex optimization problem via the semidefinite relaxation, S-procedure and the variable substitution methods, and a semidefinite relaxation based robust resource allocation algorithm is proposed. Numerical results verify that the proposed algorithm has better convergence and robustness compared with existing algorithms.
Backscatter-NOMA Enabled Hybrid Multicast-Unicast Cooperative Transmission Scheme
KUO Yonghong, XUE Yanwen, LÜ Lu, HE Bingtao, CHEN Jian
 doi: 10.11999/JEIT230672
[Abstract](13) [FullText HTML](8) [PDF 1513KB](1)
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In order to address the low spectral efficiency and inefficient link utilization problem in cooperative relay communication system, a Backscatter-NOMA enabled hybrid multicast-unicast cooperative transmission scheme is proposed for the scenario of coexistence of multicast and unicast services. A multicast user is opportunistically selected as a cooperative node, which used a part of the power of the received signal for its own decoding, and backscatter the residual power to enhance the reception quality of other users. To improve system performance, the minimum achievable rate for unicast users is maximized by jointly optimizing the base station power allocation coefficients, cooperative user backscatter coefficient and cooperative node selection variable, while guaranteeing the quality of service for multicast. To solve the above highly non-convex joint optimization problem, a cooperative user selection criterion was designed and an iterative algorithm was proposed to obtain the optimal solution to the original problem. The simulation results verify the fast convergence of the proposed iterative algorithm, which can improve the minimum achievable rate of unicast users by 11.5% compared to the non-cooperative transmission scheme, and effectively ensure the quality of multi-service.
A Fusion Network for Infrared and Visible Images Based on Pre-trained Fixed Parameters and Deep Feature Modulation
XU Shaoping, ZHOU Changfei, XIAO Jian, TAO Wuyong, DAI TianYu
 doi: 10.11999/JEIT231283
[Abstract](19) [FullText HTML](7) [PDF 3293KB](4)
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To better leverage complementary image information from infrared and visible light images and generate fused images that align with human perception characteristics, a two-stage training strategy is proposed to obtain a novel infrared-visible image fusion Network based on pre-trained fixed Parameters and Deep feature modulation (PDNet). Specifically, in the self-supervised pre-training stage, a substantial dataset of clear natural images is employed as both inputs and outputs for the UNet backbone network, and pre-training is accomplished with autoencoder technology. As such, the resulting encoder module can proficiently extract multi-scale depth features from the input image, while the decoder module can faithfully reconstruct it into an output image with minimal deviation from the input. In the unsupervised fusion training stage, the pre-trained encoder and decoder module parameters remain fixed, and a fusion module featuring a Transformer structure is introduced between them. Within the Transformer structure, the multi-head self-attention mechanism allocates deep feature weights, extracted by the encoder from both infrared and visible light images, in a rational manner. This process fuses and modulates the deep image features at various scales into the manifold space of deep features of clear natural image, thereby ensuring the visual perception quality of the fused image after reconstruction by the decoder. Extensive experimental results demonstrate that, in comparison to current mainstream fusion models (algorithms), the proposed PDNet model exhibits substantial advantages across various objective evaluation metrics. Furthermore, in subjective visual evaluations, it aligns more closely with human visual perception characteristics.
Cover
Cover
2024, 46(3).  
[Abstract](92) [PDF 5201KB](25)
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Contents
Contents
2024, 46(3): 1-4.  
[Abstract](69) [FullText HTML](13) [PDF 278KB](16)
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Overviews
A Survey of Key Issues in Edge Intelligent Computing Under Cloud-Edge-Terminal Architecture: Computing Optimization and Computing Offloading
DONG Yumin, ZHANG Jing, XIE Changzuo, LI Ziyang
2024, 46(3): 765-776.   doi: 10.11999/JEIT230390
[Abstract](648) [FullText HTML](344) [PDF 2152KB](311)
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With the rapid increase in the number of network access devices and the volume of network access data currently, the shortcomings of the centralized computing architecture represented by cloud computing are increasingly exposed. Edge computing, that is making computing as close to the data source as possible to reduce data transmission time and network delay, has become the focus of academia and industry as a supplement to cloud computing. An instance architecture widely used in edge computing: Cloud-Edge-Terminal architecture, and a typical application of edge computing: edge intelligent computing is focused on in this paper. Two key issues of edge intelligent computing under Cloud-Edge-Terminal architecture: computing optimization and computing offloading is analyzed. First, the research focus of edge intelligent computing is analyzed, and the application and research status of intelligent computing optimization under Cloud-Edge-Terminal architecture is combed. Then the research ideas and current situation of computing offloading under Cloud-Edge-Terminal architecture is discussed. Finally, the challenges and research trends of edge intelligent computing under Cloud-Edge-Terminal architecture is summarized.
A New Paradigm for Intelligent Traffic Perception: A Traffic Sign Detection Architecture for the Metaverse
WANG Junfan, CHEN Yi, GAO Mingyu, HE Zhiwei, DONG Zhekang, MIAO Qiheng
2024, 46(3): 777-789.   doi: 10.11999/JEIT230357
[Abstract](280) [FullText HTML](65) [PDF 8364KB](54)
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Traffic sign detection plays an important role in the safe and stable operation of intelligent transportation systems and intelligent driving. Unbalanced data distribution and monotonous scene will lead to poor model performance, but building a complete real traffic scene dataset requires expensive time and labor costs. Based on this, a new metaverse-oriented traffic sign detection paradigm is proposed to alleviate the dependence of existing methods on real data. Firstly, by establishing the scene mapping and model mapping between the metaverse and the physical world, the efficient operation of the detection algorithm between the virtual and real worlds is realized. As a virtualized digital world, Metaverse can complete custom scene construction based on the physical world, and provide massive and diverse virtual scene data for the model. At the same time, knowledge distillation and the mean teacher model is combined in this paper to establish a model mapping to deal with the problem of data differences between the metaverse and the physical world. Secondly, in order to further improve the adaptability of the training model under the Metaverse to the real driving environment, a heuristic attention mechanism is designed to improve the generalization ability of the detection model by locating and learning features. The proposed architecture is experimentally verified on the CURE-TSD, KITTI, Virtual KITTI (VKITTI) datasets. Experimental results show that the proposed metaverse-oriented traffic sign detector has excellent detection results in the physical world without relying on a large number of real scenes, and the detection accuracy reaches 89.7%, which is higher than other detection methods of recent years.
Research Progress of ElectroEncephaloGraphy-Near-InfRared Spectroscopy Combined Analysis in Brain-Computer Interface
ZHANG Lixin, ZHOU Hongzhan, WANG Dong, MENG Jiayuan, XU Minpeng, MING Dong
2024, 46(3): 790-797.   doi: 10.11999/JEIT230257
[Abstract](395) [FullText HTML](194) [PDF 525KB](76)
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Brain-Computer Interface (BCI) can convert the brain activity related to the subject's intention into external device control instructions, which have high application potential in treating neurological diseases, motor rehabilitation, and other aspects. Considering that the materialization of BCI needs to obtain meaningful signals from the human brain, ElectroEncephaloGraphy (EEG) and Near-InfRared Spectroscopy (NIRS) has become important signal acquisition methods for BCI because they are non-invasive, convenient to wear, and relatively cheap. EEG reflects neural electrical activity and is widely applied in BCI systems with high real-time response requirements; NIRS mainly reflects the level of hemodynamics and is mainly utilized in research with precise localization of active brain regions, such as identifying neurophysiological status. Compared with the single-mode BCI system, the BCI system based on EEG-NIRS combined analysis has attracted interest and research in physiological state detection, motor imagination, etc., because of its richer signal characteristics. This review begins with the application of EEG-NIRS combined data analysis in BCI, summarizes the current development on the data and feature fusion level, and looks forward to the research prospects of EEG-NIRS signal processing methods.
Wireless Communication,Internet of Things and Digital Signal Processing
Reconfigurable Intelligent Surfaces-aided Physical Layer Secure Transmission in Non-Orthogonal Multi-Access Systems Against Full-duplex Active Eavesdropping
KUO Yonghong, CAO Lin, LÜ Lu, HE Bingtao, CHEN Jian
2024, 46(3): 798-807.   doi: 10.11999/JEIT230296
[Abstract](148) [FullText HTML](23) [PDF 989KB](48)
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Considering the multi-user Non-Orthogonal Multi-Access(NOMA) system under the full-duplex attack of passive eaves-dropping and active interference at the same time, a robust beamforming scheme based on Reconfigurable Intelligent Surfaces (RIS) is proposed to realize physical layer secure communication. Under the condition that only the channel state information of the eavesdropper is known, the system transmission interruption probability and the confidential interruption probability are taken as the constraints and the system confidentiality rate is maximized by jointly optimizing the base station transmission beamforming, RIS phase shift matrix, transmission rate and redundancy rate. In order to solve the above multivariate coupling non-convex optimization problem, an effective alternating optimization algorithm is proposed to obtain the suboptimal solution of the joint optimization problem. The simulation results show that the proposed scheme can achieve a higher confidentiality rate, and when the number of RIS reflective elements is increased, the system confidentiality performance gets better.
Robust Beamforming Algorithm for Terahertz Communication Systems Aided by Reconfigurable Intelligent Surfaces
YUAN Yiming, XU Yongjun, ZHOU Jihua
2024, 46(3): 808-816.   doi: 10.11999/JEIT230160
[Abstract](259) [FullText HTML](63) [PDF 3194KB](67)
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Terahertz communication, as one of the key technologies for 6G, is considered an effective means of addressing the scarcity of spectrum resources and improving system capacity. However, due to high path loss and the molecule absorption, terahertz is easily blocked by obstacles leading to communication interruptions. To address this problem, Reconfigurable Intelligent Surface (RIS) is introduced into terahertz communication systems and the impact of channel uncertainty on transmission stability is considered to establish a multi-user energy-efficiency maximization beamforming model based on user quality of service constraints, base station transmit power constraints and RIS discrete phase shift constraints. The original nonconvex optimization problem is solved by transforming it into a convex optimization problem using Dinkelbach, continuous convex approximation, S-procedure, semi-positive definite relaxation, phase mapping and block coordinate descent. Simulation results show that the proposed algorithm improves the energy efficiency by 15.4% and reduces the outage probability by 15.48% compared with the traditional non-robust beamforming algorithm.
Combined Positioning of High-Speed Train Based on Improved Adaptive IMM Algorithm
WANG Xiaomin, LEI Xiao, ZHANG Yadong
2024, 46(3): 817-825.   doi: 10.11999/JEIT230251
[Abstract](190) [FullText HTML](171) [PDF 3924KB](38)
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A high accuracy combined positioning method for high-speed trains based on the Improved Adaptive Interacting Multiple Model (IMM) is proposed for the high-precision positioning problem of trains. Firstly, a combined positioning scheme of four sensors, namely, satellite receiver, wheel speed sensor, speed radar and single-axis gyroscope, is designed according to the train positioning requirements and the characteristics of each sensor. Next, to address the issue that the IMM fusion filtering algorithm has improper fixed parameter settings due to inaccurate a priori information, the Sage-Husa adaptive filtering and the Transition Probability Matrix (TPM) adaptive update set are introduced to become the adaptive IMM algorithm. To solve the lag problem of multi-model switching, the likelihood function value is set as the judgment flag by using the feature that sub-model likelihood function value can quickly respond to the model change trend, and the judgment window is introduced to correct the TPM matrix elements, which effectively improves the model switching speed. Finally, based on the improved adaptive IMM algorithm, the fusion filtering of four sensor positioning information is carried out to realize the high-precision combined positioning of high-speed trains. Simulation results show that the enhanced algorithm improves the positioning accuracy by 1.6%~14.7% compared with other adaptive IMM algorithms, and it can effectively reduce the peak positional error by increasing the switching speed between models, and it also has a better anti-noise performance.
Orthogonal Time Sequency Multiplexing Waveform Framework Based on Multi-dimensional Extension and Its Performance Analysis
WANG Zhenduo, TAN Zhengfeng, SUN Rongchen
2024, 46(3): 826-834.   doi: 10.11999/JEIT230248
[Abstract](359) [FullText HTML](206) [PDF 5260KB](67)
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Orthogonal Time Sequency Multiplexing (OTSM) is a low-complexity modulation method suitable for high-speed mobile scenarios. However, a single waveform design method is difficult to meet diverse application requirements and performance demands. Therefore, on basis of Weighted FRactional Fourier Transform (WFRFT), a Weighted FRactional Walsh-Hadamard Transform (WFRWHT) is proposed and an integrated WFRFT-WFRWHT-OTSM waveform framework based on multidimensional extensions is put forward. Through the flexible configuration of two-dimensional parameters, this framework can be degraded to different waveforms such as OTSM, orthogonal time-frequency-space, hybrid carrier, orthogonal frequency division multiplexing and single carrier. In addition, Bit Error Rate (BER) and Peak-to-Average Power Ratio (PAPR) performances of the integrated WFRFT-WFRWHT-OTSM framework over delay-Doppler channels are studied with Gauss-Seidel (GS) iteration equalization. Simulation results show that the proposed framework achieves better BER and PAPR performances through changing the order of WFRFT and WFRWHT over different delay-Doppler channels.
3D Trajectory and Power Optimization Method Based on Full Spectrum Sharing
PEI Errong, CHEN Xinhu, CHEN Qimei, SUN Yuanxin, LI Wei
2024, 46(3): 835-847.   doi: 10.11999/JEIT230261
[Abstract](641) [FullText HTML](156) [PDF 6971KB](77)
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Due to the extreme shortage of licensed spectrum in cellular systems currently, unlicensed spectrum is thus recommended for cellular systems. The Unmanned Aerial Vehicle(UAV) flight trajectory and power control have a significant impact on spectrum utilization efficiency. However, Three-Dimensional(3D) flight trajectory and power optimization methods based on full spectrum sharing have rarely been unstudied. Therefore, a full spectrum sharing method is first proposed in this paper, where the UAV can use the unlicensed spectrum by controlling the transmission power of uplink cellular users and Device to Device (D2D) users without affecting the data transmission of WiFi devices; at the same time, the UAV can use the licensed spectrum without affecting other downlink cellular users. And then, based on the proposed full spectrum sharing method, a joint optimization problem of 3D flight trajectory and transmission power is constructed under the energy constraint of the UAV battery. In order to solve the proposed complex non-convex optimization problem with multi-variable coupling, the successive convex approximation technique and block coordinate descent method are used to transform the original problem into two convex optimization subproblems i.e. 3D trajectory optimization and power control and solve them iteratively. A large of simulation results show that the proposed spectrum sharing method based on 3D trajectory and power optimization significantly improves the spectrum utilization efficiency.
Routing Optimization of Ultra Violet Light Communication Unmanned Aerial Vehicle Formation Based on JAYA Algorithm
HAO Rui, WANG Jianping, CHEN Danyang, LU Huimin
2024, 46(3): 848-857.   doi: 10.11999/JEIT230206
[Abstract](129) [FullText HTML](49) [PDF 3008KB](21)
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Due to its high flexibility, good safety and all-weather work, ultraviolet light communication is considered to be a potential communication solution for the emergency communication Unmanned Aerial Vehicle (UAV) formation. Based on the Low Energy Adaptive Clustering Hierarchy (LEACH) algorithm, and combined with JAYA intelligent optimization algorithm, a novel routing optimization algorithm Rcomp JAYA LEACH (RJLEACH) is proposed to improve the effective operation time of the ultraviolet light communication UAV formation. The algorithm is applied to optimize the formation routing of ultraviolet light communication UAVs with different structures, and the results obtained by other algorithms are compared and analyzed. The results show that RJLEACH algorithm reduces the residual energy variance between UAV nodes in the cluster head election stage, and the search for the optimal route reduces the energy consumption of inter-cluster communication. Finally, the time of the first node’s death and half nodes’ death in the network are prolonged by 31.8% and 13.8%, respectively compared with the classic LEACH algorithm, and the energy utilization rate is significantly improved, which can gain valuable time for tasks such as disaster relief and emergency communication.
Optimization of User Pairing, Beamforming and Phase-shifting for Intelligent Reflecting Surface Assisted Non-Orthogonal Multiple Access Systems
LEI Weijia, YU Shunhong, LEI Hongjiang, TANG Hong
2024, 46(3): 858-866.   doi: 10.11999/JEIT230329
[Abstract](260) [FullText HTML](161) [PDF 1541KB](64)
Abstract:
The joint optimization of user pairing, beamforming and phase shifting in a Non-Orthogonal Multiple Access(NOMA) network assisted by Intelligent Reflecting Surface(IRS) is studied in this paper. In the system, each pair is assigned with one beam and the intra-group successive interference cancellation in the signal detection is adopted. A user pairing strategy independent of transmit beamforming and IRS phase shift is proposed, so the user pairing is separated from the joint optimization and the difficulty and complexity in the solving of the optimization is significantly reduced. Then, the transmit beamforming, power allocation and IRS phase shift are jointly optimized to minimize the total transmission power of the base station. The original optimization problem is non-convex. By using the methods such as slack variables, successive convex approximation, semidefinite relaxation and alternative optimization, the original problem is transformed into a convex problem and solved. Simulation results show that the performance of the proposed scheme is better than that of the scheme that one user is assigned with one beam when the number of the transmit antennas is less than that of the users, and is very close to the scheme that one user is assigned with one beam when the number of antennas is more than that of the users, while the computational complexity of the proposed scheme is lower. Compared with the schemes that employ different user pairing strategy, random IRS phase shift, maximum ratio transmission beamforming, or that without IRS, the proposed scheme is always superior in performance.
Residual Sliding Window Decoding Algorithm for Spatially-coupled Low-density Parity-check Codes
ZHOU Hua, LI Zijie
2024, 46(3): 867-874.   doi: 10.11999/JEIT230288
[Abstract](280) [FullText HTML](117) [PDF 3518KB](33)
Abstract:
A dynamic Residual Sliding Window Decoding (RSWD) algorithm is proposed in this paper to reduce the high bit error rate caused by error propagation in the Sliding Window Decoding (SWD) algorithm for Spatially-Coupled Low-Density Parity-Check (SC-LDPC) codes. By calculating the residuals of edge information before and after updates, RSWD algorithm dynamically updates the edge of the lowest reliability (or equivalently maximum residual value) with priority, resulting in reduction in the frequency of ineffective edge updating and improvement in the convergence speed of sliding window decoding. The simulation results show that compared to the traditional SWD algorithm, the proposed RSWD algorithm successfully decreases the number of error bit at various positions in the window and shows significant effect on suppressing error propagation; in the high Signal-to-Noise Ratio (SNR) region or in the case of small number of decoding iterations, RSWD presents superior bit error rate performance to SWD; Applying dynamic residuals to both Message Reuse(MR) and Window Extension(WE) window decoding algorithms can also obtain similar observations and improve the decoding performance.
A Robust Resource Allocation Algorithm for Intelligent Reflecting Surface-assisted Anti-jamming Secure Communication Systems
XI Bing, FENG Yanbo, DENG Bingguang, ZHANG Zhizhong
2024, 46(3): 875-885.   doi: 10.11999/JEIT230343
[Abstract](229) [FullText HTML](105) [PDF 2559KB](57)
Abstract:
To address issues such as reduced communication quality and poor security caused by malicious jamming attacks, eavesdropping, and imperfect channel state information, a robust resource allocation algorithm for Intelligent Reflecting Surface(IRS)-assisted anti-jamming secure communication system is proposed in this paper. Firstly, based on the minimum security rate constraint, maximum transmit power constraint, and IRS phase shift constraint of legitimate users, a robust resource allocation problem is constructed by jointly optimizing the beamforming vectors of the base station, the covariance matrix of artificial noise, and the phase shift matrix of IRS, in the presence of imperfect channel state information of illegal nodes and unknown beamforming vectors of jammers. Secondly, to solve the problem, the original optimisation problem is transformed into an tractable convex optimisation problem using alternating optimisation, the Cauchy-Schwarz inequality, successive convex approximations and Taylor series expansions. The simulation results demonstrate that the proposed algorithm can effectively enhance system security, reduce power consumption, and improve anti-jamming resilience, compared to traditional algorithms. Furthermore, within a certain range of channel errors, it can decrease the probability of confidential interruption by approximately 35%, indicating its strong robustness.
1-bit Precoding Algorithm for Massive MIMO OFDM Downlink Systems with Deep Learning
ZHOU Chenhao, WEN Liyuan, QIAN Hua, KANG Kai
2024, 46(3): 886-894.   doi: 10.11999/JEIT230239
[Abstract](201) [FullText HTML](66) [PDF 3981KB](35)
Abstract:
The base station of a massive Multiple-Input Multiple-Output (MIMO) system is equipped with hundreds of antennas, enhancing the spectral efficiency of the system and increasing the system costs. To address this problem, our research group proposed a Convergence-Guaranteed Multi-Carrier one-bit precoding (CG-MC1bit) iterative algorithm suitable for Orthogonal Frequency-Division Multiplexing (OFDM) downlink massive MIMO systems, which can ensure superior system performance. However, the corresponding computational complexity is high, hindering the practical application of the algorithm in real-time systems. To address this issue, we propose a model-driven, unfolding neural network, which is based on the CG-MC1bit iterative algorithm and introduces trainable parameters to replace high-complexity operations in forward propagation. In particular, we unfold the iterative algorithm into a neural network and introduce trainable parameters to replace high-complexity operations in forward propagation. Simulation results reveal that this method can automatically update parameters. In addition, compared with the traditional precoding algorithms, the proposed method has a higher convergence speed and lower computational complexity.
Jamming Pattern Open Set Recognition Based on Hyperspherical Triplet Coding
GAO Yulong, WANG Guoqiang, WANG Gang
2024, 46(3): 895-905.   doi: 10.11999/JEIT230145
[Abstract](142) [FullText HTML](77) [PDF 2234KB](39)
Abstract:
Jamming pattern recognition is an indispensable part of modern military communication countermeasure. With the emergence of various new malicious jamming patterns in complex electromagnetic environment, the judgment of unknown jamming has become more and more important. Therefore, the jamming pattern recognition algorithm is required to maintain the high-precision recognition of the known jamming, and can also complete the judgment of the unknown jamming to eliminate the influence of the unknown malicious jamming. Based on this, the jamming pattern recognition problem in the presence of unknown jamming as an open set recognition problem is modeled in this paper, and a jamming pattern open set recognition method based on hyperspherical triple coding is proposed. The proposed method uses hyperspherical triples to reduce the dimension of the input time-frequency image to improve the recognition accuracy, and then uses the meta-recognition classifier to accurately complete the open set recognition of the jamming pattern. The simulation results show that the algorithm can efficiently complete the jamming pattern recognition task in open space when the jamming-to-signal ratio is greater than –2 dB.
High Accuracy Carrier Frequency Estimation of Multi-band Communication Signals Based on Undersampling
HUANG Xiangdong, SONG Jinshui, LI Yanping
2024, 46(3): 906-913.   doi: 10.11999/JEIT230297
[Abstract](98) [FullText HTML](19) [PDF 1846KB](22)
Abstract:
To essentially overcome the 3 deficiencies of the mainstream Modulation Wideband Converter (MWC )-based undersampling frequency estimator (i.e., over-consumption of undersampling channels, low accuracy of carrier frequency estimation, high sparsity of the source distribution), this paper proposes the phase-difference corrector based on coprime spectral analysis for the carrier frequency estimation of multi-band communication signals. Specifically, by substituting the multi-path MWC undersampling with the 2-path coprime undersampling, the consumption of undersampling channels is greatly reduced; Further, by developing the mapping relationship between the panoramic spectrum peak indices and the IDFT index pairs of coprime analyzers, the phase difference of the adjacent snapshots’ IDFT outputs corresponding to these index pairs can be analytically extracted, thus achieving much higher estimation accuracy compared to the mainstream MWC method. Meanwhile, by means of incorporating the minimum-sized half-decomposition based all-phase filter design into the prototype filter design, a two-path paralleled coprime spectral analyzer can be constructed, which thoroughly gets rid of the dependency of the high sparsity of the source distribution. Numerical results show that, compared to the mainstream MWC method, the proposed spectral corrector’s estimation error is no more than 1/20 of the former, while only consuming less than half of the sample amount.
Beamforming Design for Reconfigurable Intelligent Surface Enhanced Full-duplex Ambient Backscatter Communication Networks
ZHANG Xiaoxi, XU Yongjun, WU Cuixian, HUANG Chongwen
2024, 46(3): 914-924.   doi: 10.11999/JEIT230356
[Abstract](288) [FullText HTML](79) [PDF 3551KB](67)
Abstract:
Current conventional ambient backscatter communication suffers from double fading, obstacle blockage, and limited network capacity. Reconfigurable Intelligent Surface (RIS) as a key candidate technology of 6G has been concerned due to the improvement of signal transmission quality and the enhancement of the transmission performance of communication systems. Based on the above advantages, RIS and full-duplex techniques are introduced into the ambient backscatter communication system, and the beamforming algorithm is designed in an RIS-enhanced full-duplex ambient backscatter communication network with hardware impairments and discrete phase-shift constraints. Firstly, a beamforming optimization problem is formulated to minimize the total transmit power by considering the minimum harvested energy and the quality-of-service constraint of the backscatter nodes, the maximum transmit power constraint of the power station, and the phase shift constraint of the RIS. Then, the original non-convex problem is transformed into a tractable convex optimization problem by using alternating optimization methods, semi-definite relaxation methods, variable substitution, and semi-definite programming. Finally, simulation results show that the proposed algorithm decreases the average power consumption by 7.8% compared to the conventional beamforming method.
Directional Reception Low Collision Probability MAC Protocol for Underwater Acoustic Networks
ZHENG Maochun, HAN Xiao, GE Wei, SUN Yao, YIN Jingwei
2024, 46(3): 925-933.   doi: 10.11999/JEIT230153
[Abstract](91) [FullText HTML](23) [PDF 3167KB](20)
Abstract:
In the omni-directional underwater acoustic communication network, large propagation delay and high packet collision rate seriously affect the network performance. Compared with the omni-directional reception, the sound pressure and vibration velocity of the acoustic vector sensor can form a unilateral directivity through the linear weighted combination, so as to realize the directional reception of signals in a certain direction, and then effectively improve the network spatial multiplexing rate. The network outage probability in the directional reception mode of single vector sensor is analyzed, and its feasibility is also verified. Then, a Directional Reception Low Collision Probability Media Access Control (DRLCP-MAC) protocol is proposed, which uses a directional reception beam handshake mechanism to establish a stable data transmission link, and constructs multiple pairs of parallel communication links through state transition strategy, so as to reduce virtual carrier monitoring range and improve spatial reuse of the network. Simulation results show that compared with Multiple Access Collision Avoidance for Underwater (MACA-U) protocol and Slotted Floor Acquisition Multiple Access (Slotted-FAMA) protocol, the channel access cost of DRLCP-MAC is reduced by 50% and 60%, the network throughput is increased by about 60% and 400%, and the end-to-end delay is reduced by about 50% and 85%.
Optimization of Computation Offloading for UAV-Assisted Intelligent Transportation Systems Considering Age of Information
ZHONG Weifeng, HUANG Xumin, KANG Jiawen, XIE Shengli
2024, 46(3): 934-943.   doi: 10.11999/JEIT230459
[Abstract](348) [FullText HTML](201) [PDF 1567KB](72)
Abstract:
The intelligent transportation system that combines Unmanned Aerial Vehicle (UAV) based traffic monitoring and Mobile Edge Computing (MEC) technologies is considered. In order to ensure the timeliness of data and reduce energy consumption in the system, a UAV computation offloading optimization method considering Age of Information (AoI) is proposed. Firstly, the UAV-assisted MEC system model is established to allow the MEC server to cache commonly used applications and provide UAVs with computation offloading, which supports the UAVs to perform traffic monitoring tasks. By jointly optimizing UAV task offloading decisions, UAV uplink and downlink communication bandwidth allocation, and computing resource allocation of offloaded tasks, the total energy consumption of all UAVs and the MEC server is minimized while satisfying constraints of AoI and resource capacities. Secondly, the system energy consumption minimizing problem is a mixed-integer non-convex optimization problem. Discretization and linearization methods are adopted to quickly obtain an approximately optimal solution to the problem. A discrete point generation algorithm is designed to adjust the approximation error. Finally, simulation results show that even for large non-convex problems, the proposed method can quickly obtain approximately optimal solutions and can satisfy constraints of AoI in different task scenarios, minimizing the system energy consumption as much as possible. The simulation results verify the effectiveness of the proposed method.
Electromagnetic Field and Electromagnetic Wave Technology
Instantaneous Length Estimation of Ships through Wideband Composite Bistatic Radar
AI Xiaofeng, QIU Mengqi, HU Yihang, XU Zhiming, ZHAO Feng
2024, 46(3): 944-951.   doi: 10.11999/JEIT230088
[Abstract](158) [FullText HTML](98) [PDF 3840KB](22)
Abstract:
The length feature plays an important role in the ship target identification. A new algorithm for ship length estimation based on the joint observation of wideband composite bistatic radar is presented, which utilize the mono-/bi-static High-Resolution Range Profiles (HRRPs) and the bistatic spatial geometry relation to estimate the actual length of the ship target with only one-time measurement. Then the estimation errors under different conditions and geometrical constructions are analyzed through Monte Carlo simulations. Finally, the proposed method is validated through typical scene simulation experiments. The results show that when the error of HRRP length is less than 5%, the estimation error of the actual ship length is less than 5%, which provides a new idea for feature extraction and identification of ship targets.
Efficient Parameters Estimation of Multi-target Based on Space-Time Cascaded Monopulse
SHEN Mingwei, ZHANG Yongshu, LI Jianni, WU Di, ZHU Daiyin
2024, 46(3): 952-959.   doi: 10.11999/JEIT230347
[Abstract](125) [FullText HTML](51) [PDF 3122KB](21)
Abstract:
An ACM-ML (Amplitude Comparison Monopulse-Maximum Likelihood) algorithm that employs a two-dimensional relaxation iterative search for range and velocity inevitably leads to low computational speeds and large amount calculation. Focusing on the problems mentioned above, a method for efficient Multi-target parameter estimation based on a Space-Time Cascaded MonoPulse algorithm (M-STCMP) is proposed in the paper. The algorithm introduces the monopulse technique to the pulse domain for target velocity computing with temporal monopulse, which results in a one-dimensional search for range and significantly reducing the computational burden of a two-dimensional iterative search within the ACM-ML algorithm. Because the temporal monopulse cannot simultaneously match multiple targets of varying velocities across main beams, the M-STCMP algorithm is improved by estimating the velocity in each Doppler cell with the Doppler information of received signals. To suppress energy leakage between targets, estimations produced in the main beams are cascaded and iterated for each target, that results in greater accuracy. Theoretical analysis and simulation verify the effectiveness of the proposed algorithm.
A Dual-band Broadband Circular Polarized Antenna for Radio Frequency Identification Reader
WANG Lili, GAO Zhiyong, DU Zhonghong, XU Yani
2024, 46(3): 960-966.   doi: 10.11999/JEIT230321
[Abstract](193) [FullText HTML](61) [PDF 6630KB](54)
Abstract:
A compact dual-band wideband circularly polarized antenna for Radio Frequency IDentification (RFID) reader is designed. The antenna consists of a curved rectangular patch, an L-shaped patch, and a triangular floor, which is fed through a microstrip line. The two radiation patches control the high and low frequency bands independently, and their axial ratio bandwidth can also be adjusted independently. The triangular floor can change the transverse current and the longitudinal current, so as to change the ratio of the transverse current and the longitudinal current to achieve circular polarization performance. The antenna dimensions are 0.92\begin{document}$ {\lambda _0} $\end{document}×0.92\begin{document}$ {\lambda _0} $\end{document}×0.0064\begin{document}$ {\lambda _0} $\end{document}(\begin{document}$ {\lambda _0} $\end{document} is the free space wavelength at 2.40 GHz). The measurement results show that the proposed antenna achieves an impedance bandwidth of 49% (0.77~1.27 GHz) and an axial ratio bandwith of 46% (0.84~1.34 GHz) in the Ultra High Frequency (UHF) band, and achieves 47.5% (1.54~2.50 GHz) impedance bandwidth and 24.2% (1.96~2.50 GHz) axial ratio bandwidth in Wireless Local Area Networks (WLAN) band. It can completely cover UHF and WLAN two frequency bands and has good radiation characteristics. Compared with other dual-frequency circular-polarized antennas, the antenna has compact structure and simple design, avoids the use of complex feed network, and has a wide bandwidth of 3 dB axial ratio.
Image and Intelligent Information Processing
High-precision Gesture Recognition Based on DenseNet and Convolutional Block Attention Module
ZHAO Yaqin, SONG Yuqing, WU Han, HE Shengyang, LIU Puqiu, WU Longwen
2024, 46(3): 967-976.   doi: 10.11999/JEIT230165
[Abstract](423) [FullText HTML](191) [PDF 4831KB](146)
Abstract:
Non-contact gesture recognition is a new type of human-computer interaction method with broad application prospects. It can be used in Augmented Reality (AR)/Virtual Reality (VR), smart homes, smart medical, etc., and has recently become a research hotspot. Motivated by the need for more precise micro-motion gesture recognition using mm-wave radar in recent years, a novel micro-motion gesture recognition method based on MIMO millimeter wave radar is proposed in this paper. The MilliMeter Wave CAScaded (MMWCAS) radar cascaded with four AWR1243 radar boards is used to collect gesture echoes. Time-frequency analysis is performed on gesture echoes, and the hand target is detected based on the Range-Doppler (RD) map and 3D point cloud. Through data pre-processing, the Range-Time Map (RTM), Doppler-Time Map (DTM), Azimuth-Time Map (ATM) and Elevation-Time Map (ETM) of the gestures are extracted to more comprehensively characterize the motion of the hand gesture. The mixed Feature-Time Maps (FTM) are formed and adopted for the recognition of 12 types of micro-motion gestures. An innovative gesture recognition network based on DenseNet and Convolutional Block Attention Module (CBAM) is designed, and the mixed FTM is used as the input of the network. Experimental results show that the recognition accuracy reaches 99.03%, achieving high-accuracy gesture recognition. It is discovered that the network focuses on the first half of the gesture movement, which improves the recognition accuracy.
Few-shot Image Classification Based on Task-Aware Relation Network
GUO Lihua, WANG Guangfei
2024, 46(3): 977-985.   doi: 10.11999/JEIT230162
[Abstract](278) [FullText HTML](184) [PDF 3321KB](57)
Abstract:
Considering that Relation Network (RN) ignores the global task correlation information, a Few-Shot Learning(FSL)method based on a Task-Aware Relation Network (TARN) for fully using global task correlation information is proposed in this paper. Method class prototype based on global task relationship is created using the Fuzzy C-Mean (FCM) clustering algorithm, and a Task Correlation Attention mechanism (TCA) is designed to improve the one-vs-one evaluation metric in RN for fusing the global task relationship into features. Compared with RN, in the Mini-ImageNet dataset, the classification accuracy of 5-way 1-shot and 5-way 5-shot settings is increased by 8.15% and 7.0% respectively. While in the Tiered-ImageNet dataset, the classification accuracy of 5-way 1-shot and 5-way 5-shot settings is increased by 7.81 and 6.7% respectively. Compared with the position-awareness relation network, in Mini-ImageNet, the classification accuracy of 5-way 1-shot settings is still increased by 1.24%. Compared with other few-shot image classification methods, TARN also achieves the best performance in these two datasets. The combination of the relation network and task correlation can effectively improve the few-shot image classification accuracy.
Few Shot Domain Adaptation Tongue Color Classification in Traditional Chinese Medicine via Two-stage Meta-learning
ZHUO Li, ZHANG Lei, JIA Tongyao, LI Xiaoguang, ZHANG Hui
2024, 46(3): 986-994.   doi: 10.11999/JEIT230249
[Abstract](189) [FullText HTML](114) [PDF 3034KB](34)
Abstract:
Tongue color is one of the most concerning diagnostic features of tongue diagnosis in Traditional Chinese Medicine (TCM). In practical applications, the performance of the model trained from the tongue data acquired by one device is dramatically degraded when applied to other devices due to the data distribution discrepancy. Therefore, in this paper, a few shot domain adaptation tongue color classification method with two-stage meta-learning is proposed. Firstly, a two-stage meta-learning training strategy is proposed to extract domain invariant features from labeled samples in the source domain, and then, the meta-trained network model is fine-tuned using a few labeled data in the target domain, so that the model can quickly adapt to the new sample characteristics in the target domain, improving the generalization ability of the tongue color classification model and avoid overfitting problem. Next, a progressive pseudo label generation strategy is proposed, which uses the meta-trained model to predict the unlabeled samples in the target domain. The prediction results with high confidence are selected and treated as pseudo labels. So high-quality pseudo labels can be gradually generated. Finally, these high-quality pseudo labels are used to train the model, together with the labeled data in the target domain. The tongue color classification model can be obtained. Considering the noisy pseudo labels, the contrast regularization function is adopted, which can effectively suppress the negative impact of noisy samples in the training process and improve the tongue color classification accuracy in the target domain. The experimental results on two self-established TCM tongue color classification datasets show that the classification accuracy of tongue color in the target domain reaches 91.3% when only 20 labeled samples are given in the target domain, which is only 2.05% lower than that of the supervised classification model in the target domain.
Tracklet Generation Method by Submodular Optimization for Multi-Object Tracking
SUN Jin, DU Guanming
2024, 46(3): 995-1004.   doi: 10.11999/JEIT230208
[Abstract](171) [FullText HTML](90) [PDF 8855KB](72)
Abstract:
As the basis of many intelligent visual tasks, Multi-Object Tracking (MOT) is a challenging problem in computer vision. Occlusion is a main factor affecting the tracking accuracy. To solve the occlusion problem, in this paper, the strategy of tracking-by-detection is adopted to obtain complete trajectories of targets based on associating tracklets. Meanwhile, to improve the tracking robustness, the tracklet generation problem is transformed into the facility location problem in operations research area and further a submodular optimization based tracklet generation method is proposed. In this method, two complementary features including Histogram of Oriented Gradient (HOG)and Color Name (CN) are integrated to describe the target appearance, and a weighting coefficient is also designed by motion information to improve the matching accuracy. At length, a submodular maximization algorithm with constraints is developed to achieve the global data association by selecting the targets to form the tracklets. By comparative experiments on the benchmark datasets, the proposed method can solve the occlusion problem effectively with guaranteed performance.
Multi-Head Attention Time Domain Audiovisual Speech Separation Based on Dual-Path Recurrent Network and Conv-TasNet
LAN Chaofeng, JIANG Pengwei, CHEN Huan, ZHAO Shilong, GUO Xiaoxia, HAN Yulan, HAN Chuang
2024, 46(3): 1005-1012.   doi: 10.11999/JEIT230260
[Abstract](264) [FullText HTML](143) [PDF 1431KB](46)
Abstract:
The current audiovisual speech separation model is essentially the simple splicing of video and audio features without fully considering the interrelationship of each modality, resulting in the underutilization of visual information and unsatisfactory separation effects. The article adequately considers the interconnection between visual features and audio features, adopts a multi-headed attention mechanism, and combines the Convolutional Time-domain audio separation Network (Conv-TasNet) and Dual-Path Recurrent Neural Network (DPRNN), the Multi-Head Attention Time Domain AudioVisual Speech Separation (MHATD-AVSS) model is proposed. The audio encoder and the visual encoder are used to obtain the audio features and the lip features of the video, and the multi-head attention mechanism is used to cross-modality fuse the audio features with the visual features to obtain the audiovisual fusion features, which are passed through the DPRNN separation network to obtain the separated speech of different speakers. The Perceptual Evaluation of Speech Quality (PESQ), Short-Time Objective Intelligibility (STOI), and Signal-to-Noise Ratio (SNR) evaluation metrics are used for experimental testing in the VoxCeleb2 dataset. The research shows that when separating the mixed speech of two, three, or four speakers, the SDR improvement of the method in this paper is above 1.87 dB and up to 2.29 dB compared with the traditional separation network. In summary, this article shows that the method can consider the phase information of the audio signal, better use the correlation between visual information and audio information, extract more accurate audio and video characteristics, and obtain better separation effects.
Ultrasound Image Lesion Detection Algorithm Optimized by Feature Feedback Mechanism
DING Jianrui, WANG Lingtao, TANG Fenghe, NING Chunping
2024, 46(3): 1013-1021.   doi: 10.11999/JEIT230385
[Abstract](173) [FullText HTML](84) [PDF 5710KB](35)
Abstract:
A lesion detection method in ultrasound images based on feature feedback mechanism is proposed to realize real-time accurate localization and detection of ultrasound lesions. The proposed method consists of two parts: feature extraction network based on feature feedback mechanism and adaptive detection head based on divide-and-conquer strategy. The feature feedback network fully learns the global context information and local low-level semantic details of ultrasound images through feedback feature selection and weighted fusion calculation to improve the recognition ability of local lesion features. The adaptive detection head performs divide-and-conquer preprocessing on the multi-level features extracted by the feature feedback network. By combining physiological prior knowledge and feature convolution, adaptive modeling of lesion shape and scale features is performed on features at all levels to enhance the detection effect of the detection head on lesions of different sizes under multi-level features. The proposed method is tested on the thyroid ultrasound image dataset, and 70.3% AP, 99.0% AP50 and 88.4% AP75 are obtained. Experimental results show that the proposed algorithm can achieve more accurate real-time detection and positioning of ultrasound image lesions in comparison with mainstream detection algorithm.
Multi-channel Enhanced Graph Convolutional Network for Aspect-based Sentiment Analysis
HAN Hu, FAN Yating, XU Xuefeng
2024, 46(3): 1022-1032.   doi: 10.11999/JEIT230353
[Abstract](229) [FullText HTML](106) [PDF 4029KB](59)
Abstract:
In traditional single-channel-based feature extraction approaches, features are captured solely based on dependency, while semantic similarity and dependency types between words are ignored. Although some success has been achieved through the graph convolutional network-based approach for sentiment analysis, aggregating both semantic information and syntactic structure features remains challenging, and the gradual loss of semantic features throughout the training process affects the final sentiment classification effect. To prevent the model from misinterpreting relevant sentiment words due to the absence of prior knowledge, the inclusion of external knowledge is recommended to enrich the text. Presently, how to utilize Graph Neural Networks(GNN) to fuse syntactic and semantic features still deserves further research. A multi-channel enhanced graph convolutional network model is proposed in this paper to address the above issues. First, graph convolution operations on syntactic graphs enhanced with sentiment knowledge and dependency types are performed to obtain two syntax-based representations, which are fused with the semantic representations learned through multi-head attention and graph convolution, so that the multi-channel features can be learned complementarily. It is observed from the experimental results that both the accuracy and macro F1 of our model surpass those of the benchmark model on five publicly available datasets. These findings indicate the importance of dependency types and affective knowledge to enhance syntactic graphs and highlight the effectiveness of combining semantic information with syntactic structure.
Image Super-Resolution Algorithms Based on Deep Feature Differentiation Network
CHENG Deqiang, YUAN Hang, QIAN Jiansheng, KOU Qiqi, JIANG He
2024, 46(3): 1033-1042.   doi: 10.11999/JEIT230179
[Abstract](238) [FullText HTML](94) [PDF 6582KB](51)
Abstract:
Traditional deep neural networks usually stack deep features in a way such as skip connection, which is easy to cause information redundancy. To improve the utilization of deep feature information, a Deep Feature Differentiation Network (DFDN) is proposed and applied to single image super-resolution. First, multi-scale deep feature differentiation information is extracted and fused by Mutual-Projected Fusion Block (MPFB) to reduce the contextual information loss. Second, a differential feature attention module is proposed to further learn the differences of deep features while expanding the perception field. Third, the modules are connected in a recursive form to increase the network depth and realize feature reuse. The DIV2K dataset is used as the training dataset, and the pre-trained model is tested with four benchmark datasets, and the results are obtained by comparing the reconstructed images with popular algorithms. Extensive experiments show that the algorithm proposed in this study learns richer texture information than existing algorithms and achieves the best rankings in both subjective visualization and quantitative evaluation metrics, which again proves its robustness and superiority.
A Domain Adaptive Method with Orthogonal Constraint for Predicting the Remaining Useful Life of Rolling Bearings under Cross Working Conditions
HAN Yan, LIN Zhichao, HUANG Qingqing, XIANG Min, WEN Rui, ZHANG Yan
2024, 46(3): 1043-1050.   doi: 10.11999/JEIT230274
[Abstract](156) [FullText HTML](88) [PDF 7246KB](26)
Abstract:
To address the problems that blurred decision boundaries and low identifiability of features in the rolling bearing Remaining Useful Life (RUL) prediction under cross working conditions, a domain adaptive method with Maximum Classifier Discrepancy network with Orthogonal Constraints (MCD_OC) is proposed. Firstly, the fast Fourier transform is applied to transform the raw vibration signal into the frequency domain signal and input it to the model. Then, Convolutional Neural Network (CNN) and Gate Recurrent Unit (GRU) are used to extract the depth spatiotemporal features of the bearing signal, the source and target domain feature is aligned using the maximum classifier discrepancy, and the orthogonal constraint is applied to constrain target domain features to increase the identifiability between features of unlabeled target domain feature. Finally, comparative experiments are conducted on the prediction of cross working condition RUL predict based on the bearing life dataset to evaluate the method in this work, and the optimal results are obtained in multiple experiments.
Weakly Supervised Object Real-time Detection Based on High-resolution Class Activation Mapping Algorithm
SUN Hui, SHI Yulong, ZHANG Jianyi, WANG Rui, WANG Yuyue
2024, 46(3): 1051-1059.   doi: 10.11999/JEIT230268
[Abstract](240) [FullText HTML](153) [PDF 7240KB](45)
Abstract:
Thanks to the development of deep learning technology, object detection techniques have gained wide attention in various vision tasks. However, obtaining bounding box annotations for objects requires high time and labor costs, which hinders the application of object detection technology in practical scenarios. Therefore, a weakly supervised real-time object detection method based on high resolution class activation mapping algorithm is proposed, using only image class labels to reduce the dependence of network on object instance labels. It subdivides object detection into two subtasks: weakly supervised object localization and real-time object detection. In weakly supervised object localization task, a novel High Resolution Class Activation Mapping(HR-CAM) algorithm based on contrastive layer-wise relevance propagation theory is designed. It can obtain high quality class activation maps and generate pseudo detection annotation box. In real-time detection task, Single Shot multibox Detector(SSD) network as object detector is selected and an Object-Aware Loss function(OA-Loss) based on the class activation maps is designed. It can jointly supervise the training process of the SSD network with generated pseudo detection annotation box, to improve the networks' detection performance for objects. The experimental results show that the method proposed in this paper can achieve accurate and efficient object detection on the CUB200 and TJAB52 datasets, verifying the effectiveness and superiority of this method.
Blockchain Smart Contract Classification Method Based on Double Siamese Neural Network
GUO Jiashu, WANG Qi, LI Zeya, WU Mengde, ZHANG Hongxia
2024, 46(3): 1060-1068.   doi: 10.11999/JEIT230185
[Abstract](187) [FullText HTML](96) [PDF 1624KB](33)
Abstract:
At present, methods for classifying blockchain smart contracts using deep learning methods are becoming increasingly popular. However, methods based on deep learning often require a large amount of sample label data for supervised model training to achieve high classification performance. A blockchain smart contract classification method based on a two-level twin neural network in a small sample scenario is proposed to address the problem that currently available smart contract datasets have uneven data categories and insufficient labeled data volumes, which can lead to difficulty in model training and poor classification performance. Firstly, by analyzing the characteristics of smart contract data, a two-level twin neural network model that can capture the characteristics of longer contract data is constructed; Then, based on this model, a training strategy and classification method for smart contracts in small sample scenarios are designed. Finally, experimental results show that the classification performance of the proposed method in this paper is superior to the most advanced smart contract classification methods in small sample scenarios, with a classification accuracy of 94.7% and an F1 value of 94.6%. At the same time, this method requires less tag data, requiring only about 20% data from other methods of the same type.
Gait Emotion Recognition Based on a Multi-scale Partitioning Directed Spatio-temporal Graph
ZHANG Jiabo, GAO Jie, HUANG Zhongyu, XU Guanghui
2024, 46(3): 1069-1078.   doi: 10.11999/JEIT230175
[Abstract](193) [FullText HTML](104) [PDF 1962KB](22)
Abstract:
To enhance the precision of gait emotion recognition by effectively capturing the dependencies between nodes at multiple scales, long distances, and temporal and spatial positions, a novel method comprising three parts is proposed in this paper. Firstly, a partitioned directed spatio-temporal graph construction method is proposed. It connects all frame nodes in a directed manner based on their regions. Secondly, a multi-scale partition aggregation and fusion method is proposed. This method updates the graph nodes using graph deep learning and fuses similar node features. Lastly, a Multi-scale Partition Directed Adaptive Spatio-Temporal Graph Convolutional Neural network (MPDAST-GCN) is proposed. It constructs a graph in the temporal dimension to obtain the features of distant frame nodes and learns the feature data adaptively on each frame. The MPDAST-GCN classifies input data into four emotion types: happy, sad, angry, and normal. Experimental results on the Emotion-Gait dataset demonstrate that the proposed method outperforms state-of-the-art methods by 6% in terms of accuracy.
Design of Transformer Accelerator with Regular Compression Model and Flexible Architecture
JIANG Xiaobo, DENG Hanke, MO Zhijie, LI Hongyuan
2024, 46(3): 1079-1088.   doi: 10.11999/JEIT230188
[Abstract](317) [FullText HTML](195) [PDF 4181KB](72)
Abstract:
The Transformer model based on attention mechanism demonstrates superior performance. The complexity of the Transformer model includes both quantity and structural complexity, where the structural complexity leads to a mismatch between irregular models and regular hardware, reducing the efficiency of mapping the model to the hardware. Current accelerator research mainly focuses on addressing the complexity in terms of model quantity, but there is limited research on how to tackle the complexity in model structure. A regularized compressed model is proposd to reduce the structural complexity of the model, improving the matching between the model and the hardware, and increasing the efficiency of mapping the model to the hardware. A hardware-friendly model compression method is introduced, which utilizes a rule-based pruning scheme for weight with offset diagonals and simplifies the hardware quantization inference logic.An efficient and flexible hardware architecture is also present, including a pulsatile operation array with weight fixed at the block level, as well as a quasi-distributed storage architecture. This architecture enables efficient mapping of algorithms to the operation array, while achieving high data storage efficiency and reducing data movement. Experimental results show that the proposed approach achieves a compression rate of 93.75% with minimal performance loss. The accelerator implemented on an FPGA can efficiently handle the compressed Transformer model, resulting in energy efficiency improvements of 12.45 times compared to Central Processing Unit (CPU) and 4.17 times compared to Graphics Processing Unit (GPU).n energy efficiency improvements of 12.45 times compared to Central Processing Unit (CPU) and 4.17 times compared to Graphics Processing Unit (GPU).
Unmanned Aerial Vehicle Detection and Recognition Method Based on Multi-dimensional Signal Feature
NIE Wei, DAI Qifei, YANG Xiaolong, WANG Ping, ZHOU Mu, ZHOU Chao
2024, 46(3): 1089-1099.   doi: 10.11999/JEIT230302
[Abstract](387) [FullText HTML](159) [PDF 3275KB](125)
Abstract:
Nowadays, Unmanned Aerial Vehicles (UAVs) are widely used in military and civilian fields. While UAVs bring convenience, they also bring huge security risks. The detection and identification technology for UAVs has gradually become a research hotspot. The traditional UAV detection method mainly detects UAVs by obtaining radar echoes, UAV sound signals and photoelectric signals. However, such methods are often susceptible to environmental influences and have certain limitations, and cannot accurately locate and identify UAVs. A UAV identification method based on multi-dimensional signal features is proposed in this paper. Firstly, UAV signals from the received wireless signals through the adaptive triangular threshold method are detected and screened, and at the same time the Channel Status Information (CSI) of the acquired wireless signals is analyzed. Then, the Orthogonal Matching Pursuit (OMP) algorithm is used for parameter estimation to obtain the position information of the UAV to locate the UAV. Finally, the box dimension and Radial Integral Bispectrum (RIB) in UAV signals are extracted to classify and identify UAVs. Through experiments, the method's three-dimensional positioning accuracy for UAVs is less than 1 m, and the classification and recognition accuracy for UAVs can reach up to 100%.
Human Activities Recognition Based on Two-stream NonLocal Spatial Temporal Residual Convolution Neural Network
QIAN Huimin, CHEN Shi, HUANGFU Xiaoying
2024, 46(3): 1100-1108.   doi: 10.11999/JEIT230168
[Abstract](297) [FullText HTML](89) [PDF 2296KB](55)
Abstract:
Three-Dimensional Convolution Neural Network (3D CNN) and two-stream Convolution Neural Network (two-stream CNN) are commonly-used for human activities recognition, and each has its own advantages. A human activities recognition model with low complexity and high recognition accuracy is proposed by combining the two architectures. Specifically, a Two-stream NonLocal Spatial Temporal Residual Convolution Neural Network based onchannel Pruning (TPNLST-ResCNN) is proposed in this paper. And Spatial Temporal Residual Convolution Neural Networks (ST-ResCNN) are used both in the temporal stream subnetwork and the spatial stream subnetwork. The final recognition results are acquired by fusing the recognition results of the two subnetworks under a mean fusion algorithm. Furthermore, in order to reduce the complexity of the network, a channel pruning scheme for ST-ResCNN is presented to achieve model compression. In order to enable the compressed network to learn the long-distance spatiotemporal dependencies of human activity changes better and improve the recognition accuracy of the network, a nonlocal block is introduced before the first residual spatial temporal convolution block of the pruned network. The experimental results show that the recognition accuracies of the proposed human activity recognition model are 98.33% and 74.63% on the public dataset UCF101 and HMDB51, respectively. Compared with the existed algorithms, the proposed model in this paper has fewer parameters and higher recognition accuracy.
Cryption and Network Information Security
Dynamic Quantum Secret Sharing Scheme Based on Nonlocal Orthogonal Product States
SONG Xiuli, LI Chuang
2024, 46(3): 1109-1118.   doi: 10.11999/JEIT230193
[Abstract](197) [FullText HTML](75) [PDF 1172KB](44)
Abstract:
Current Quantum Secret Sharing(QSS) has the drawbacks of high consumption of resource preparation and the security is not stronger. To overcome the above drawbacks, a verifiable quantum secret sharing scheme based on orthogonal product states is proposed, where multiple participants can dynamically join or leave the secret sharing. In the proposed scheme, the particle pairs of product states are divided into two sequences, the first sequence is transmitted among participants, and the previous participant performs the unitary operator to aggregate the shares on it and then transmits it to the next participant; for the other sequence, the last participant(verifier) performs the Oracle operator on the received particles. Afterward, the verifier uses global measurements on the particle pairs to obtain the quadratic residues of the secrets. Finally, learning from the idea of non-single mapping between ciphertext and plaintext in Rabin cipher, the verifier jointly with Alice verifies the correctness of the measurement results and identifies the secrets from the results. Security analysis shows that the proposed scheme can resist common external and internal attacks, and that the verification process is strongly secure. Since the nonlocal orthogonal product states are transmitted separately in two sequences, the security of the secret reconstruction process is enhanced. Performance analysis shows that the proposed scheme has low quantum resource consumption using orthogonal product state as information carriers, and extends the dimension of orthogonal product basis from low dimension to d dimension, and the number of participants can be dynamically increased or decreased, so it provides better flexibility and generality.
A Selective Defense Strategy for Federated Learning Against Attacks
CHEN Zhuo, JIANG Hui, ZHOU Yang
2024, 46(3): 1119-1127.   doi: 10.11999/JEIT230137
[Abstract](247) [FullText HTML](105) [PDF 2431KB](56)
Abstract:
Federated Learning (FL) performs model training based on local training on clients and continuous model parameters interaction between terminals and server, which effectively solving data leakage and privacy risks in centralized machine learning models. However, since multiple malicious terminals participating in FL can achieve adversarial attacks by inputting small perturbations in the process of local learning, and then lead to incorrect results output by the global model. An effective federated defense strategy – SelectiveFL is proposed in this paper. This strategy first establishes a selective federated defense framework, and then updates the uploaded local model on the server on the basis of extracting attack characteristics through adversarial training at the terminals. At the same time, selective aggregation is carried out according to the attack characteristics, and finally multiple adaptive defense models can be obtained. Finally, the proposed defense method is evaluated on several representative benchmark datasets. The experimental results show that compared with the existing research work, the accuracy of the model can be improved by 2% to 11%.
Satellite Image Encryption Algorithm Based on Chaos Theory and DNA Dynamic Coding
XIAO Song, CHEN Zhe, YANG Yatao, MA Yingjie, YANG Teng
2024, 46(3): 1128-1137.   doi: 10.11999/JEIT230203
[Abstract](217) [FullText HTML](102) [PDF 10285KB](53)
Abstract:
Considering the information security problems involved in the transmission and storage of satellite images, a new satellite image encryption algorithm based on chaos theory and DNA dynamic coding is proposed. Firstly, an improved infinite folding chaotic map is proposed, which broadens the chaotic interval of the original infinite folding chaotic map. Then, combined with the improved Chebyshev chaotic map and SHA-256 hash algorithm, the key stream of the encryption algorithm is generated to improve the plaintext sensitivity of the algorithm. Then, the state value of the chaotic system is used to encode the pixels after Hilbert local scrambling to realize DNA dynamic coding, which solves the weakness of being vulnerable to violent attacks caused by fewer DNA coding rules. Finally, the chaotic sequence is used to complete further chaotic encryption, to completely confuse the original pixel information, increase the randomness and complexity of the encryption algorithm, and obtain the ciphertext image. The experimental results show that the algorithm has a better encryption effect and the ability to deal with various attacks.
Circuit and System Design
Dynamic Characteristics of Fractional-order Photosensitive Neuron and Its Coupling Synchronization
YANG Ningning, MENG Shiyue, WU Chaojun
2024, 46(3): 1138-1146.   doi: 10.11999/JEIT230283
[Abstract](246) [FullText HTML](134) [PDF 10311KB](30)
Abstract:
Neurons are the basic unit of the nervous system, and the accuracy of neuron models affects the analysis and understanding of their essential properties. In this paper, a fractional-order photosensitive FitzHugh-Nagumo (FHN) neuron circuit constructed by fractional-order capacitor and inductor is investigated. The dynamics of the fractional-order photosensitive neuron model are analyzed using bifurcation diagrams, phase portraits, and time series diagrams. It was found that the activity of the fractional-order photosensitive neuron increased as the fractional-order decreased. When different system parameters are selected, the neuron system transitions between periodic and chaotic discharge states. The system can induce different discharge modes, such as periodic discharge states, chaotic discharge states, and spike discharge states. In addition, two fractional-order photosensitive neurons were connected using electrical synaptic coupling. Phase synchronization and complete synchronization between the fractional-order photosensitive neuron systems can be achieved by adjusting the coupling strength. Finally, the modulation effect of an external light signal on neuronal excitability was verified by dSPACE.
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