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5th IEEE International Conference on Electronic information and Communication technology, ICEICT 2022

作者： Chen, Yan Liu, Xin Li, Yingsong Huang, Zhixiang The Fifth Research Institute of Miit Guangzhou511370 China College of Information and Communication Engineering Harbin Engineering University Harbin150001 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China

ISBN: (数字)9781665472111

ISBN: (纸本)9781665472111

A super-broadband rectifier with expended input power range(IPR) for wireless power transfer (WPT) and radio frequency energy harvesting (RFEH) is proposed and analyzed. Meanwhile, using a simple impedance compression network consisting of quadruple microstrip lines (QMLICN) with the topology of the dickson charge pump, high power conversion efficiency (PCE) and super-broadband impedance matching are achieved. In addition, the IPRs of the rectifier are also extended. The simulation results indicate that an IPR of 12 - 25 dBm with a frequency range of 0.4 - 2.5 GHz (fractional bandwidth over 144%), the PEC exceeds 50% are realized, while at an input power of 19 dBm, the rectifier reaches a peak PCE of 74.2%. The rectifier covers the GSM 900, GSM 1800, 3G 2100 and WIFI 2400 bands, which design would have significant potential applications for the development of microwave power transmission systems. © 2022 IEEE.

关键词： Energy transfer

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AutoSketch: automatic sketch-oriented compiler for query-driven network telemetry 24

AutoSketch: automatic sketch-oriented compiler for query-dri...

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Proceedings of the 21st USENIX Symposium on Networked Systems Design and Implementation

作者： Haifeng Sun Qun Huang Jinbo Sun Wei Wang Jiaheng Li Fuliang Li Yungang Bao Xin Yao Gong Zhang National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University Institute of Computing Technology CAS Northeastern University China Huawei Theory Department

ISBN: (纸本)9781939133397

Recent network telemetry witnesses tremendous progress in two directions: query-driven telemetry that targets expressiveness as the primary goal, and sketch-based algorithms that address resource-accuracy trade-offs. In this paper, we propose AutoSketch that aims to integrate the advantages of both classes. In a nutshell, AutoSketch automatically compiles high-level operators into sketch instances that can be readily deployed with low resource usage and incur limited accuracy drop. However, there remains a gap between the expressiveness of high-level operators and the underlying realization of sketch algorithms. AutoSketch bridges this gap in three aspects. First, AutoSketch extends its interface derived from existing query-driven telemetry such that users can specify the desired telemetry accuracy. The specified accuracy intent will be utilized to guide the compiling procedure. Second, AutoSketch leverages various techniques, such as syntax analysis and performance estimation, to construct efficient sketch instances. Finally, AutoSketch automatically searches for the most suitable parameter configurations that fulfill the accuracy intent with minimum resource usage. Our experiments demonstrate that AutoSketch can achieve high expressiveness, high accuracy, and low resource usage compared to state-of-the-art telemetry solutions.

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GRIDLESS DOA ESTIMATION FOR AUTOMOTIVE RADARS WITH VARIOUS ARRAY GEOMETRIES: THE NON-VANDERMONDE ATOMIC SOFT THRESHOLDING APPROACH

GRIDLESS DOA ESTIMATION FOR AUTOMOTIVE RADARS WITH VARIOUS A...

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IET International Radar Conference 2023, IRC 2023

作者： Gao, Silin Wang, Muhan Zhang, Zhe Zhang, Bingchen Wu, Yirong Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou215123 China Suzhou Aerospace Information Research Institute Suzhou215123 China

ISBN: (纸本)9781839539954

This paper is centered on gridless direction of arrival (DoA) estimation for single-snapshot data collected by non-uniform linear arrays (NLAs) in the context of automotive applications. While recent single-snapshot DoA estimation algorithms rooted in grid-based compressed sensing (CS) offer super-resolution capabilities, they are notably sensitive to discrepancies between the assumed sparsity basis and the actual basis. Furthermore, the existing atomic soft thresholding (AST) algorithm, a promising gridless sparse recovery technique within the Toeplitz model, is constrained to uniform linear arrays (ULAs) exhibiting Vandermonde structure in their array manifolds. However, in automotive scenarios, it becomes imperative to efficiently apply gridless DoA estimation to NLAs featuring non-Vandermonde array manifolds. In this paper, we introduce the non-Vandermonde atomic soft thresholding (NVAST) algorithm and employ it for DoA estimation in NLAs. The novel algorithm establishes a link between Vandermonde and non-Vandermonde atoms by decomposing non-Vandermonde vectors and addressing them using semi-definite programming (SDP). Simulation and measurement experiments conducted with automotive radars underscore the remarkable performance of our proposed method. © The Institution of Engineering & technology 2023.

关键词： Automotive radar

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Dual Encoders for Diffusion-based Image Inpainting

Dual Encoders for Diffusion-based Image Inpainting

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Dezhi Zheng Kaijun Deng Jinbao Wang Linlin Shen Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Current diffusion-based inpainting models struggle to preserve unmasked regions or generate highly coherent content. Additionally, it is hard for them to generate meaningful content for 3D inpainting. To tackle these challenges, we design a plug-and-play branch that runs through the entire generation process to enhance existing models. Specifically, we utilize dual encoders - a Convolutional Neural Network (CNN) encoder and the pre-trained Variational AutoEncoder (VAE) encoder, to encode masked images. The latent code and the feature map from the dual encoders are fed to diffusion models simultaneously. In addition, we apply Zero-padded initialization to solve the problem of mode collapse caused by this branch. Experiments on BrushBench and EditBench demonstrate that models with our plug-and-play branch can improve the coherence of inpainting, and our model achieves new state-of-the-art results.

关键词： Visualization Three-dimensional displays Codes Autoencoders Coherence Signal processing Diffusion models Decoding Convolutional neural networks Speech processing

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AN AUTOFOCUS NETWORK FOR MULTI-CHANNEL PHASE ERRORS WITH APPLICATION TO TOMOSAR IMAGING

AN AUTOFOCUS NETWORK FOR MULTI-CHANNEL PHASE ERRORS WITH APP...

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IET International Radar Conference 2023, IRC 2023

作者： Wang, Muhan Gao, Silin Zhang, Zhe Qiu, Xiaolan Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou215123 China Suzhou Aerospace Information Research Institute Suzhou215123 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781839539954

Synthetic aperture radar (SAR) tomography (TomoSAR) has garnered significant attention due to its capability for three-dimensional reconstruction. Compressed sensing (CS) methods are widely employed to address the TomoSAR inversion challenge. Nevertheless, practical applications reveal phase errors among different channels, resulting in defocusing and blurring when relying solely on CS for 3D reconstruction. Current state-of-the-art autofocus techniques suffer from prohibitive computational complexity, limiting their applicability to large-scale 3D imaging. In pursuit of efficient TomoSAR 3-D autofocusing, we proposed ASAMP-Net, an innovative deep unfolding network. Operating within a two-step framework, each layer comprises two stages: phase error estimation and iterative scattering coefficient reconstruction using the sparse adaptive matching pursuit (SAMP) algorithm. Additionally, phase error estimation is obtained through mathematical derivation, while challenges associated with fixed sparsity and limited efficiency in conventional methods are mitigated through deep learning techniques. Simulation experiments and real data validation affirm the effectiveness and superiority of the proposed method. © The Institution of Engineering & technology 2023.

关键词： Radar imaging

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Low-rank matrix recovery with total generalized variation for defending adversarial examples

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Frontiers of information technology & Electronic Engineering 2024年第3期25卷 432-445页

作者： Wen LI Hengyou WANG Lianzhi HUO Qiang HE Linlin CHEN Zhiquan HE Wing W.Y.Ng School of Science Beijing University of Civil Engineering and ArchitectureBeijing 100044China School of Computer Science and Engineering South China University of TechnologyGuangzhou 510006China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen 518060China Institute of Big Data Modeling and Technology Beijing University of Civil Engineering and ArchitectureBeijing 100044China

Low-rank matrix decomposition with first-order total variation(TV)regularization exhibits excellent performance in exploration of image *** advantage of its excellent performance in image denoising,we apply it to improve the robustness of deep neural ***,although TV regularization can improve the robustness of the model,it reduces the accuracy of normal samples due to its *** our work,we develop a new low-rank matrix recovery model,called LRTGV,which incorporates total generalized variation(TGV)regularization into the reweighted low-rank matrix recovery *** the proposed model,TGV is used to better reconstruct texture information without *** reweighted nuclear norm and Li-norm can enhance the global structure ***,the proposed LRTGV can destroy the structure of adversarial noise while re-enhancing the global structure and local texture of the *** solve the challenging optimal model issue,we propose an algorithm based on the alternating direction method of *** results show that the proposed algorithm has a certain defense capability against black-box attacks,and outperforms state-of-the-art low-rank matrix recovery methods in image restoration.

关键词： Total generalized variation Low-rank matrix Alternating direction method of multipliers Adversarial example

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Uncertainty-boosted Robust Video Activity Anticipation

arXiv

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arXiv 2024年

作者： Qi, Zhaobo Wang, Shuhui Zhang, Weigang Huang, Qingming The School of Computer Science and Technology Harbin Institute of Technology Weihai264209 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Peng Cheng Laboratory Shenzhen518066 China The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China

Video activity anticipation aims to predict what will happen in the future, embracing a broad application prospect ranging from robot vision and autonomous driving. Despite the recent progress, the data uncertainty issue, reflected as the content evolution process and dynamic correlation in event labels, has been somehow ignored. This reduces the model generalization ability and deep understanding on video content, leading to serious error accumulation and degraded performance. In this paper, we address the uncertainty learning problem and propose an uncertainty-boosted robust video activity anticipation framework, which generates uncertainty values to indicate the credibility of the anticipation results. The uncertainty value is used to derive a temperature parameter in the softmax function to modulate the predicted target activity distribution. To guarantee the distribution adjustment, we construct a reasonable target activity label representation by incorporating the activity evolution from the temporal class correlation and the semantic relationship. Moreover, we quantify the uncertainty into relative values by comparing the uncertainty among sample pairs and their temporal-lengths. This relative strategy provides a more accessible way in uncertainty modeling than quantifying the absolute uncertainty values on the whole dataset. Experiments on multiple backbones and benchmarks show our framework achieves promising performance and better robustness/interpretability. Source codes are available at https://***/qzhb/UbRV2A. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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Object Detection Algorithm Based on Coordinate Attention and Context Feature Enhancement 22

Object Detection Algorithm Based on Coordinate Attention and...

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Proceedings of the 2022 11th International Conference on computing and Pattern Recognition

作者： Lingzhi Liu Baohua Qiang Yuanchun Wang Xianyi Yang Jubo Tian Shihao Zhang Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology China Hebei Key Laboratory of Intelligent Information Perception and Processing The 54th Research Institute of CETC China

ISBN: (纸本)9781450397056

In recent years, object detection has been widely used in various fields such as face detection, remote sensing image detection and pedestrian detection. Due to the complex environment in the actual scene, we need to fully obtain the feature information in the image to improve the accuracy of object detection. This paper proposes an object detection algorithm based on coordinate attention and contextual feature enhancement. We design a multi-scale attention feature pyramid network, which first uses multi-branch atrous convolution to capture multi-scale context information, and then fuses the coordinate attention mechanism to embed location information into channel attention, and finally uses a bidirectional feature pyramid structure to effectively fuse high-level features and low-level features. We also adopt the GIoU loss function to further improve the accuracy of object detection. The experimental results show that the proposed method has certain advantages compared with other detection algorithms in the PASCAL VOC datasets.

关键词： Feature pyramid network Object detection Coordinate attention Atrous convolution

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Semantic Maximum Relevance and Modal Alignment for Cross-Modal Retrieval 22

Semantic Maximum Relevance and Modal Alignment for Cross-Mod...

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Proceedings of the 2022 11th International Conference on computing and Pattern Recognition

作者： Pingping Sun Baohua Qiang Zhiguang Liu Xianyi Yang Guangyong Xi Weigang Liu Ruidong Chen Shihao Zhang Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology China Hebei Key Laboratory of Intelligent Information Perception and Processing The 54th Research Institute of CETC China

ISBN: (纸本)9781450397056

With the increasing abundance of multimedia data resources, researches on mining the relationship between different modalities to achieve refined cross-modal retrieval are gradually emerging. In this paper, we propose a novel Semantic Maximum Relevance and Modal Alignment (SMR-MA) for Cross-Modal Retrieval, which utilizes the pre-trained model with abundant image text information to extract the features of each image text, and further promotes the modal information interaction between the same semantic categories through the modal alignment module and the multi-layer perceptron with shared weights. In addition, multi-modal embedding is distributed to the normalized hypersphere, and angular edge penalty is applied between feature embedding and weight in angular space to maximize the classification boundary, thus increasing both intra-class similarity and inter-class difference. Comprehensive analysis experiments on three benchmark datasets demonstrate that the proposed method has superior performance in cross-modal retrieval tasks and is significantly superior to the state-of-the-art cross-modal retrieval methods.

关键词： Semantic Relevance CLIP Cross-Modal Retrieval Modal Alignment

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Current Problems and Solutions of Industrial Control Network Intrusion Detection: A Brief Survey

Current Problems and Solutions of Industrial Control Network...

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2022 IEEE International Conference on Real-Time computing and Robotics, RCAR 2022

作者： Jiang, Tianyu Zhang, Chen Wang, Rui Chai, Senchun Institute of Automation Chinese Academy of Sciences and University of Science and Technology Beijing China Beijing Institute of Technology China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems China Institute of Intelligent Information Processing and Control Beijing Institute of Technology China

ISBN: (数字)9781665469838

ISBN: (纸本)9781665469838

With the advancement of industrial intelligence, the Industrial Internet has been widely used in energy, manufacturing and other important industries. In recent years, security incidents have occurred frequently in industrial control network, which means that maintaining industrial control network security has become more and more important. Intrusion detection technology can actively detect abnormal behavior in the network, and is an important means to ensure the security of industrial control networks. Presently, the intrusion detection technology of industrial control network has problems such as extreme imbalance of classes and redundant interference of traffic characteristics. This paper analyzes these problems and clarifies the existing solutions for different problems in principle. © 2022 IEEE.

关键词： Network security

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