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IET International radar Conference 2018, IRC 2018

作者： Wang, Yanhua Bi, Xuejie Chen, Wei Li, Yang Chen, Qiao Long, Teng Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing China Beijing Racobit Radar Technology Research Institute Co. Ltd. Beijing China

High-resolution range profile (HRRP) has received intensive attention in the radar automatic target recognition filed. Here, deep forest is applied to the recognition of HRRP. The deep forest is a deep learning method, which is a cascade of ensemble learners. In each layer, there are various ensemble learners. The input of each layer is the combination of the previous layer output and the original input data. The number of cascade levels can be adaptively determined such that the model complexity can be automatically set. Experiments based on measured data show that deep forest is feasible and achieves highly competitive performance for HRRP recognition. © 2019 Institution of Engineering and Technology. All rights reserved.

关键词： Deep learning

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Beamforming Design and Association Scheme for Multi-RIS Multi-User mmWave Systems Through Graph Neural Networks

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IEEE Transactions on Wireless Communications 2025年

作者： Liu, Mengbing Huang, Chongwen Alhammadi, Ahmed Renzo, Marco Di Debbah, Merouane Yuen, Chau Nanyang Technological University School of Electrical and Electronics Engineering 639798 Singapore Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Zhejiang Provincial Key Laboratory of Multi-Modal Communication Networks and Intelligent Information Processing Hangzhou310027 China Technology Innovation Institute Masdar City Abu Dhabi United Arab Emirates Université Paris-Saclay CNRS Laboratoire des Signaux et Systèmes CentraleSupélec 3 Rue Joliot-Curie Gif-sur-Yvette91192 France King’s College London Centre for Telecommunications Research – Department of Engineering LondonWC2R 2LS United Kingdom Khalifa University KU6G. Research Center Department of Computer and Information Engineering Abu Dhabi127788 United Arab Emirates University Paris-Saclay CentraleSupelec Gif-sur-Yvette91192 France

Reconfigurable intelligent surface (RIS) is emerging as a promising technology for next-generation wireless communication networks, offering a variety of merits such as the ability to tailor the communication environment. Moreover, deploying multiple RISs helps mitigate severe signal blocking between the base station (BS) and users, providing a practical and efficient solution to enhance the service coverage. However, fully reaping the potential of a multi-RIS aided communication system requires solving a non-convex optimization problem. This challenge motivates the adoption of learning-based methods for determining the optimal policy. In this paper, we introduce a novel heterogeneous graph neural network (GNN) to effectively leverage the graph topology of a wireless communication environment. Specifically, we design an association scheme that selects a suitable RIS for each user. Then, we maximize the weighted sum rate (WSR) of all the users by iteratively optimizing the RIS association scheme, and beamforming designs until the considered heterogeneous GNN converges. Based on the proposed approach, each user is associated with the best RIS, which is shown to significantly improve the system capacity in multi-RIS multi-user millimeter wave (mmWave) communications. Specifically, simulation results demonstrate that the proposed heterogeneous GNN closely approaches the performance of the high-complexity alternating optimization (AO) algorithm in the considered multi-RIS aided communication system, and it outperforms other benchmark schemes. Moreover, the performance improvement achieved through the RIS association scheme is shown to be of the order of 30%. © 2002-2012 IEEE.

关键词： Beamforming

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Interpretable classification of epileptic EEG signals using ALIF decomposition and attention-augmented cascaded deep neural networks

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Applied Soft Computing 2025年 180卷

作者： Zeng, Wei Zhang, Minglin Shan, Liangmin Chen, Yang Li, Zuoyong Du, Shaoyi School of Physics and Mechanical and Electrical Engineering Longyan University Longyan364012 China School of Mechanical Engineering and Automation Fuzhou University Fuzhou350116 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an710049 China

Epilepsy is a chronic neurological disorder characterized by recurrent seizures. Accurate diagnosis and effective monitoring require the precise classification of electroencephalogram (EEG) signals. In this study, we introduce a novel approach that combines Adaptive Local Iterative Filtering (ALIF) for signal decomposition with an attention-enhanced cascaded deep neural network (CDNN) architecture. The ALIF algorithm decomposes EEG signals into intrinsic mode functions (IMFs) that capture inherent oscillatory components. These IMFs are processed by the CDNN, which operates in two stages: a feature extraction module and a classification module. In the feature extraction stage, a SEblock channel attention mechanism dynamically prioritizes significant features within the IMFs. The classification stage employs a hybrid CNN-LSTM architecture that effectively captures both spatial and temporal dependencies. To enhance interpretability, the SHapley Additive exPlanations (SHAP) framework is incorporated to provide insights into the model's decision-making process, while Gradient-weighted Class Activation Mapping (Grad-CAM) visualizes the most discriminative regions in the input data. Rigorously validated using 10-fold cross-validation on the Bonn and EEG Epilepsy databases, the proposed methodology achieved an exceptional classification accuracy of 100%, with sensitivity, specificity, and F1-scores exceeding 99% across various scenarios. The integration of SHAP and Grad-CAM not only elucidates the model's decision processes but also contributes to a more interpretable and reliable system for epileptic EEG signal classification. This synergistic combination of advanced signal processing, deep learning, and interpretability techniques holds significant potential to enhance epilepsy diagnosis and strengthen trust in clinical decision support systems. © 2025

关键词： Deep neural networks

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Modal Analysis of Optical Waveguide using a Novel Symplectic Compact FDTD Schemes

Modal Analysis of Optical Waveguide using a Novel Symplectic...

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International Conference on Numerical Electromagnetic Modeling and Optimization for RF, Microwave, and Terahertz Applications (NEMO)

作者： Xiaojing Kuang Kong Meng Qi Qi Xin Zeng Zhixiang Huang Mingshen Chen The Anhui Province key laboratory of simulation & design for electronic information system Hefei Normal University Hefei China Digital circuit design east china institute of photo-electronic Suzhou China Key laboratory of intelligent & computing & signal processing Anhui University Hefei China School of Electronic Engineering Huainan Normal University Huainan China

ISBN: (数字)9781728169668

ISBN: (纸本)9781728169675

For modal analysis of longitudinally invariant optical devices, the compact-finite-difference time-domain is an efficient algorithm for alleviating the large amount of storage space and computing time under the 3D complex structure simulation. However, this algorithm needs to scale down the time step to mitigate the numerical dispersion errors while simulating optical waveguide working at high frequency. Here, a novel high-order symplectic compact FDTD scheme is proposed, which is attributed to the theory combination of the fast-spatial scheme and stable temporal scheme. The research results not only verify the effectiveness by numerical experiment on a planar dielectric waveguide with complex cross section, but also provide efficient theoretical guidance for the design and optimization of optical devices at the nanometer scale.

关键词： Electromagnetic waveguides Optical waveguide theory Adaptive optics Numerical models Time-domain analysis Optical waveguides Finite difference methods

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radar HRRP recognition based on CNN

Radar HRRP recognition based on CNN

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IET International radar Conference 2018, IRC 2018

作者： Song, Jia Wang, Yanhua Chen, Wei Li, Yang Wang, Junfu Radar Research Laboratory School of Information and Electronics Beijing Institute of Technology Beijing China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing China Beijing Racobit Electronic Information Technology Co. Ltd. Beijing China

In this study, ground target recognition based on one-dimensional convolutional neural network (CNN) is studied by exploiting the targets' high-resolution range profiles (HRRPs). Contrary to conventional methods which need feature extraction artificially, CNN can automatically discover features for classification. The authors propose a multi-channel CNN architecture that can be applied on diverse forms of HRRP such as amplitude, complex, spectrum etc. Experimental results demonstrate the superiorities of the proposed method over conventional methods based on handcrafted features and single-channel CNN in terms of recognition accuracy. Visualisation of the 'deep features' shows higher separability than handcrafted features, thus providing an insight into its effectiveness in exploiting the intrinsic structures. © 2019 Institution of Engineering and Technology. All rights reserved.

关键词： radar target recognition

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Polarised HRRP scattering centre estimation via atomic norm minimisation

Polarised HRRP scattering centre estimation via atomic norm ...

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IET International radar Conference 2018, IRC 2018

作者： Liu, Haibo Xi, Ying Wang, Yanhua Li, Yang Zhao, Tong Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Racobit Radar Technology Research Institute Co. Ltd. Beijing100081 China

The combination of polarisation and high-resolution technology is a promising research direction for radar automatic target recognition. Fusing the polarisation information into the scattering centre model is able to refine the scattering structural information. This study proposes a fully-polarised radar high range resolution profile (HRRP) scattering centre estimation method based on a multiple measurement vector atomic norm minimisation model. The estimation is performed in a continuous domain, thus avoids the basis mismatch problem. The experimental results demonstrate the superiorities of the proposed method in terms of accuracy and robustness. © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/)

关键词： Polarization

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FPGA-based reconfigurable matrix inversion implementation for inverse filtering of multichannel SAR imaging

FPGA-based reconfigurable matrix inversion implementation fo...

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IET International radar Conference 2018, IRC 2018

作者： Li, HuiXing Feng, YangKai Hu, ShanQing Li, BingYi Xie, YiZhuang Wu, MengChao Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing100081 China Shanghai Institute of Satellite Engineering Shanghai China

In multi-channel synthetic aperture radar (SAR), the azimuth non-uniform sampling tends to result in a large number of virtual point targets, which are not expected. Inverse filter algorithm provides a new idea for solving this problem. This way can be abstracted as a matrix inversion in essence, which becomes the key factor that affects the real-time and accuracy of multi-channel pre-processing. This study presents the implementation of matrix inversion method on field programmable gate array (FPGA), based on lower and upper triangular matrix (LU) decomposition algorithm. In this process, the efficient parallelism of FPGA and the rich floating-point intellectual property (IP) cores are fully utilised to speed up the process of inverting the matrix with a data type of 32-bit single-precision floating-point. In this design, the parallelism of the algorithm was fully considered and a hierarchical iterative processing strategy was adopted to realise the reconfigurable storage and computing unit both. At the same time, in order to achieve the balance of resources and efficiency, a reusable structure was proposed also, using the pipeline technology and appropriate data scheduling. Finally, Modelsim platform is used to observe the simulation results, and the performance can be detected combined with MATLAB platform. At last, the computational accuracy is up to 10−7, and the speedup ratio can reach about 1.461 × 103 © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/)

关键词： MATLAB

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Self-supervised Representation Learning from Videos for Facial Action Unit Detection

Self-supervised Representation Learning from Videos for Faci...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition

作者： Yong Li Jiabei Zeng Shiguang Shan Xilin Chen Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology

ISBN: (纸本)9781728132945

In this paper, we aim to learn discriminative representation for facial action unit (AU) detection from large amount of videos without manual annotations. Inspired by the fact that facial actions are the movements of facial muscles, we depict the movements as the transformation between two face images in different frames and use it as the self-supervisory signal to learn the representations. However, under the uncontrolled condition, the transformation is caused by both facial actions and head motions. To remove the influence by head motions, we propose a Twin-Cycle Autoencoder (TCAE) that can disentangle the facial action related movements and the head motion related ones. Specifically, TCAE is trained to respectively change the facial actions and head poses of the source face to those of the target face. Our experiments validate TCAE's capability of decoupling the movements. Experimental results also demonstrate that the learned representation is discriminative for AU detection, where TCAE outperforms or is comparable with the state-of-the-art self-supervised learning methods and supervised AU detection methods.

关键词： Face Videotapes Head motions Gold African Union ASTRONOMICAL UNIT Movements Facial Muscles Driving head Action

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Airfield detection based on JPEG2000 compressed domain

Airfield detection based on JPEG2000 compressed domain

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IET International radar Conference 2018, IRC 2018

作者： Duan, Chenhui Zhao, Baojun Tang, Linbo Li, Cheng Li, Chen Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing100081 China Information Security Laboratory School of Information North China University of Technology Beijing100093 China

With the rapid increase of optical remote sensing (RS) images, on-orbit specific target detection has been facing more and more challenges: limited processing environment, wireless bandwidth, space, power and storage. To resolve this issue, visual characteristics in the compressed domain are exploited to represent and recognise specific objects. In this study, a new saliency method and an airfield detection framework in the JPEG2000 compressed domain is proposed. First, the authors exploit the header information in raw JPEG2000 code streams to generate structural saliency map, and a great deal of nonstructural regions in RS images are eliminated. Second, airfield candidates are extracted from low frequency wavelet sub-bands using line segment detection and the parallel density model. Finally, the geometrical properties of airfield candidates are represented by the speed-up robust features descriptor, and the support vector machine classifier is utilised to gain the final detection results. The proposed framework is evaluated on self-made dataset. Compared with the existing relevant state-of-the-art approaches, the proposed method reduces the processing time by 60% while guaranteeing the detection rate. © 2019 Institution of Engineering and Technology. All rights reserved.

关键词： Optical remote sensing

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Real-time Remote Sensing Video Compression for On-orbit Heterogeneous Platform

Real-time Remote Sensing Video Compression for On-orbit Hete...

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2019 IEEE International Conference on signal, information and Data processing, ICSIDP 2019

作者： Duan, Chenhui Tang, Linbo Li, Chen Wu, Chen Li, Cheng Zhao, Baojun Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing100081 China School of Computer Science North China University of Technology Beijing100144 China

ISBN: (纸本)9781728123455

With the rapid advancement of optical remote sensing (RS) satellites, RS video has replaced RS image as the main way to obtain information in orbit environment. However, the storage and transmission of on-orbit RS video faces enormous challenges: the contradiction between limited power, space and bandwidth and massive video data volume is becoming increasingly acute. To solve this problem, this paper proposes an effective implementation of spaceborne real-time RS video compression based on CPU+GPU heterogeneous framework. First, we propose a new rate control method based on HEVC for on-orbit RS video compression, which allocates less bitrate for low complexity code blocks (CBs) and more bitrate for high complexity CBs, which can improve the accuracy of rate control algorithm while ensuring compression quality. Then this paper makes full use of the CPU + GPU heterogeneous framework to speed up the whole compression processing. The experimental results show that the proposed compression framework realizes 200x real-time compression of 1080p spaceborne RS video with a PSNR greater than 38.7 dB. © 2019 IEEE.

关键词： Orbits

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