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arXiv 2020年

作者： Roth, Wolfgang Schindler, Günther Klein, Bernhard Peharz, Robert Tschiatschek, Sebastian Fröning, Holger Pernkopf, Franz Ghahramani, Zoubin Graz University of Technology Laboratory of Signal Processing and Speech Communication Austria Institute of Computer Engineering Heidelberg University Germany Institute of Theoretical Computer Science Graz University of Technology Austria Faculty of Computer Science University of Vienna Austria University of Cambridge Cambridge United Kingdom

While machine learning is traditionally a resource intensive task, embedded systems, autonomous navigation, and the vision of the Internet of Things fuel the interest in resourceefficient approaches. These approaches aim for a carefully chosen trade-off between performance and resource consumption in terms of computation and energy. The development of such approaches is among the major challenges in current machine learning research and key to ensure a smooth transition of machine learning technology from a scientific environment with virtually unlimited computing resources into everyday's applications. In this article, we provide an overview of the current state of the art of machine learning techniques facilitating these real-world requirements. In particular, we focus on resource-efficient inference based on deep neural networks (DNNs), the predominant machine learning models of the past decade. We give a comprehensive overview of the vast literature that can be mainly split into three non-mutually exclusive categories: (i) quantized neural networks, (ii) network pruning, and (iii) structural efficiency. These techniques can be applied during training or as post-processing, and they are widely used to reduce the computational demands in terms of memory footprint, inference speed, and energy efficiency. We also briefly discuss different concepts of embedded hardware for DNNs and their compatibility with machine learning techniques as well as potential for energy and latency reduction. We substantiate our discussion with experiments on well-known benchmark data sets using compression techniques (quantization, pruning) for a set of resource-constrained embedded systems, such as CPUs, GPUs and FPGAs. The obtained results highlight the difficulty of finding good trade-offs between resource efficiency and prediction quality. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Clutter suppression for airborne FDA-MIMO radar using multi-waveform adaptive processing and auxiliary channel STAP

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signal processing 2019年 154卷 280-293页

作者： Cai Wen Mingliang Tao Jinye Peng Jianxin Wu Tong Wang School of Information Science and Technology Northwest University Xi'an 710127 China School of Electronics and Information Northwestern Polytechnical University Xi'an 710072 China National Laboratory of Radar Signal Processing Xidian University Xi’ an 710071 China

Range dependent clutter suppression is a challenging problem in non-side-looking airborne radar, especially in the presence of range ambiguity. To deal with this problem, this paper proposes a two-stage adaptive clutter suppression method. It utilizes the degrees of freedom in range, angle and time domains provided by a frequency diverse array multiple-input multiple-output (FDA-MIMO) radar. In the first stage, a novel multi-waveform based adaptive beamforming in joint transmit-receive (Tx–Rx) domain is proposed for range-ambiguous clutter suppression. Analysis on second-order statistic property shows that the clutter data obtained from multiple waveforms with disjoint frequency bands are (approximately) independent and identically distributed (IID) in joint Tx–Rx spatial domain. By exploiting this characteristic, a target-free Tx–Rx covariance matrix is estimated by the IID data, which are extracted from carefully designed secondary waveforms. Therefore, a near-optimal Tx–Rx response is achieved, and range-ambiguous clutter can be suppressed effectively. In the second stage, a new auxiliary channel based space-time adaptive processing approach incorporated with range dependence compensation is devised for residual clutter suppression. It takes full advantage of the prior knowledge of clutter spectrum. Thus, excellent clutter suppression performance can be obtained by the proposed approach. Simulation results demonstrate the effectiveness of the proposed method.

关键词： Clutter suppression Space-time adaptive processing (STAP) Multiple-input multiple-output (MIMO) radar Frequency diverse array (FDA) Waveform diversity

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Learning Parallax Attention for Stereo Image Super-Resolution

Learning Parallax Attention for Stereo Image Super-Resolutio...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition

作者： Longguang Wang Yingqian Wang Zhengfa Liang Zaiping Lin Jungang Yang Wei An Yulan Guo College of Electronic Science and Technology National University of Defense Technology National Key Laboratory of Science and Technology on Blind Signal Processing

ISBN: (纸本)9781728132945

Stereo image pairs can be used to improve the performance of super-resolution (SR) since additional information is provided from a second viewpoint. However, it is challenging to incorporate this information for SR since disparities between stereo images vary significantly. In this paper, we propose a parallax-attention stereo super-resolution network (PASSRnet) to integrate the information from a stereo image pair for SR. Specifically, we introduce a parallax-attention mechanism with a global receptive field along the epipolar line to handle different stereo images with large disparity variations. We also propose a new and the largest dataset for stereo image SR (namely, Flickr1024). Extensive experiments demonstrate that the parallax-attention mechanism can capture correspondence between stereo images to improve SR performance with a small computational and memory cost. Comparative results show that our PASSRnet achieves the state-of-the-art performance on the Middle bury, KITTI 2012 and KITTI 2015 datasets.

关键词： stereo imaging SAVANNAH RIVER OPERATIONS OFFICE Strontium images super-resolution Imagery (Psychotherapy) Image parallax information

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Cross-Modality Coherent Registration of Whole Mouse Brains

Research Square

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Research Square 2021年

作者： Peng, Hanchuan Qu, Lei Li, Yuanyuan Xie, Peng Liu, Lijuan Wang, Yimin Wu, Jun Liu, Yu Wang, Tao Li, Longfei Guo, Kaixuan Wan, Wan Ouyang, Lei Xiong, Feng Zheng, Yefeng Xu, Fang Gong, Hui Dong, Hongwei Luo, Qingming Bi, Guo-Qiang Hawrylycz, Michael Zeng, Hongkui Allen Institute for Brain Science United States Key Laboratory of Intelligent Computation & Signal Processing Ministry of Education Anhui University China SEU-ALLEN Joint Center Institute for Brain Intelligence Southeast University Bangladesh Institute for Brain and Intelligence Southeast University Bangladesh Tencent Jarvis Lab China CAS Key Laboratory of Brain Connectome and Manipulation China Britton Chance Center for Biomedical Photonics Wuhan National Laboratory for Optoelectronics MoE Key Laboratory for Biomedical Photonics Huazhong University of Science and Technology China Center for Integrative Connectomics Department of Neurobiology David Geffen School of Medicine UCLA United States Hainan University China Hefei National Laboratory for Physical Sciences at the Microscale School of Life Sciences University of Science and Technology of China China

Recent whole brain mapping projects are collecting large-scale 3D images using powerful and informative modalities, such as STPT, fMOST, VISoR, or MRI. Registration of these multi-dimensional whole-brain images onto a standard atlas is essential for characterizing neuron types and constructing brain wiring diagrams. However, cross-modality image registration is challenging due to intrinsic variations of brain anatomy and artifacts resulted from different sample preparation methods and imaging modalities. We introduced a cross-modality registration method, called mBrainAligner, which uses coherent landmark mapping as well as deep neural networks to align whole mouse brain images to the standard Allen Common Coordinate Framework atlas. We also built a single cell resolution atlas using the fMOST modality, and used our method to generate whole brain map of 3D full single neuron morphology and neuron cell types. © 2021, CC BY.

关键词： Brain mapping

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Ship wake region detection by using multi-feature recombination and area-based morphological analysis in ATI-SAR systems

Ship wake region detection by using multi-feature recombinat...

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IET International radar Conference 2018, IRC 2018

作者： Tian, Min Yang, Zhiwei Mao, Zhijie Liao, Guisheng National Laboratory of Radar Signal Processing Xidian University Xi'an Shaanxi China Collaborative Innovation Center of Information Sensing and Understanding Xidian University Xi'an Shaanxi China College of Information and Communication National University of Defense Technology Xi'an Shaanxi China

For moving ship detection in synthetic aperture radar (SAR) images, ship wakes on the sea surface may be detectable, thus leading to interferences in reconnaissance. To address this issue, a wake region detection approach by using multi-feature recombination and area-based morphological analysis is proposed with along-track interferogram-SAR systems. This approach aims to detect the near-ship wake region that is nearby a ship's hull and between two kelvin cusp lines. A novel test is constructed by recombining magnitude, phase, and the local variance of phase in the complex multi-look interferometric SAR image to detect the potential pixels of the ship wakes. Then, with a clustering algorithm based on the proposed test and the pixel spatial distance, the potential wake regions are acquired, and the real ones having sufficiently large areas can be accurately determined. Finally, experimental results on the TerraSAR-X dataset validate the effectiveness of the proposed method. © 2019 Institution of Engineering and technology. All rights reserved.

关键词： Synthetic aperture radar

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Correction to: Multi-step inertial Krasnosel’skiǐ–Mann iteration with new inertial parameters arrays

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Journal of Fixed Point Theory and Applications 2021年第4期23卷 1-4页

作者： Dong, Qiao-Li Li, Xiao-Huan Cho, Yeol Je Rassias, Themistocles M. Tianjin Key Laboratory for Advanced Signal Processing and College of Science Civil Aviation University of China Tianjin People’s Republic of China School of Mathematics and Statistics Shandong University of Technology Shandong People’s Republic of China Department of Mathematics Education Gyeongsang National University Jinju Korea Center for General Education China Medical University Taichung Taiwan Department of Mathematics National Technical University of Athens Athens Greece

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Enhancing Device-Free Gesture Recognition Capability of Mobile Communication signals

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IEEE Transactions on Communications 2025年

作者： Wu, Jingmiao He, Weijie Sun, Kai Huang, Wei Zhang, Haijun Leung, Victor C. M. Inner Mongolia University School of Electronic Information Engineering Inner Mongolia Key Laboratory of Intelligent Communication and Sensing and Signal Processing Hohhot010021 China University of Science and Technology at Beijing Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services Beijing100083 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Device-free gesture recognition using mobile communication signals is a convenient and efficient technology with broad application prospects in smart homes and human-computer interaction. It utilizes the effect of gestures on surrounding signals to achieve gesture recognition. The cell-specific reference signals (CRS) information can be used to achieve the task in close-range training scenarios. However, when gestures are performed at long-range or in non-training scenarios, the recognition performance will significantly degrade. To enhance device-free gesture recognition capability in arbitrary scenarios, we propose the signal quality enhancement algorithm and the gesture spectrogram construction method to solve this problem. Specifically, we superimpose the CRS information from multiple carriers to improve the gesture signal-to-noise ratio and increase the gesture sensing range. Then, we extract the gesture dynamic components from the CRS information and construct gesture spectrograms to represent scenario-independent gesture motion patterns. Using the gesture spectrogram features, we design a deep network to accomplish the gesture recognition task. We built a prototype system on a software-defined radio platform. Experimental results show that our proposed method can effectively increase the gesture sensing range from 30m² to 228m² and achieve an average recognition accuracy of 82.5% for five types of gestures in arbitrary scenarios. © 1972-2012 IEEE.

关键词： Gesture recognition

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processing of Millimeter Wave Airborne Interferometric SAR Data

Processing of Millimeter Wave Airborne Interferometric SAR D...

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

作者： Tian, Feng Wang, Yuekun Wang, Zhibin Liao, Zhiqiang Suo, Zhiyong Li, Zhenfang Yan, Wei Hu, Jiwei National Key Laboratory of Radar Signal Processing Xidian University Xi'an China Nanjing Research Institute of Electronics Technology Nanjing China Beijing Institute of Spacecraft System Engineering Beijing China Sichuan Aerospace Electronic Equipment Research Institute Sichuan China China Academy of Space Technology Xi'An Brance Xi'an China

ISBN: (纸本)9781728123455

Compared to other wave bands, interferometric synthetic aperture radar (InSAR) system employed millimeter wave equipment is capable of providing finer spatial resolution and higher measurement accuracy with smaller volume and lighter weight. However, the shorter wave length, which is more sensitive to the range error, also brings some problems in SAR/InSAR data processing. In this paper, a complete processing chain is investigated to generate high-precision digital elevation model (DEM) starting from the millimeter wave radar raw data. Experimental results obtained by the proposed approach are presented and discussed with the real measured airborne millimeter wave SAR data. © 2019 IEEE.

关键词： Millimeter waves

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基于强弱对流耦合提高蒸发器性能以及梯级应用探索（英文）

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science Bulletin 2025年第8期 1264-1274页

作者：李政通张嘉汉张静燕鹏杨涛杨超林鹏孟陶陶安盟周英棠徐兴涛 The National Key Laboratory of Water Disaster Prevention Yangtze Institute for Conservation and DevelopmentState Key Laboratory of Hydrology-Water Resources and Hydraulic EngineeringHohai University Electronic-Photonic Smart Sensing Device R&D Team Inner Mongolia Key Laboratory of Intelligent Communication and Sensing and Signal ProcessingSchool of Electronic Information EngineeringInner Mongolia University Ningbo Key Laboratory of Green Petrochemical Carbon Emission Reduction Technology and Equipment Zhejiang Institute of Tianjin University Department of Mechanical Engineering The University of Tokyo Marine Science and Technology College Zhejiang Ocean University College of Material Science and Engineering Nanjing Forestry University

Modulating the macro/nanoarchitecture of evaporators to effectively harness diverse renewable energy sources is of paramount importance for optimizing the performance of solar-driven interfacial *** by the geometric structure of a windmill,we designed an innovative solar evaporator that expertly harnesses both strong and weak *** the purification of heavy metal wastewater,the maximum evaporation rate can reach 4.95 kg m-2h-1under one sun irradiation by introducing an ultralow wind flow (0.1 m s-1),yielding an evaporation rate that is twice that of traditional ***,the gradual deposition of inorganic salt sediments on the evaporator surface is clearly *** address this issue,we present several innovative proof-of-concept cascade treatments that significantly extend the evaporator’s operational *** innovative design and exceptional performance of this solar evaporator open avenues for advancements in sustainable water treatment,energy generation,and environmental remediation.

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Enhanced gear fault diagnosis via heterodyne downconversion: Theoretical verification and optimized ultrasonic signal acquisition

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Measurement 2025年 256卷

作者： Jingchao Zhang Shaoqun Zhang Yixue Dong Chen Li Guoqian Jiang Yingwei Li Xiaoli Li School of Information Science and Engineering Yanshan University Qinhuangdao 066004 China Hebei Key Laboratory of Information Transmission and Signal Processing Qinhuangdao 066004 China Intelligent Control System R&D Department Computer Application Technology Research Institute China Ordnance Industry Beijing 100089 China School of Electrical Engineering Yanshan University Qinhuangdao 066004 China School of Automation Science and Engineering South China University of Technology Guangzhou 510641 China Pazhou Lab Guangzhou 510330 China

Ultrasonic fault diagnosis has emerged as a promising technique for gear fault detection, owing to its capability to capture modulated high-frequency transients induced by incipient or localized defects. However, its practical application is constrained by the requirement for extremely high sampling rates. Heterodyne downconversion offers a potential solution by translating ultrasonic spectral components to lower frequencies, though its effectiveness in preserving diagnostic features remains insufficiently validated. This study conducts a theoretical analysis of the influence of heterodyne downconversion on ultrasonic signal characteristics and proposes an optimized heterodyne-based ultrasonic acquisition system with enhanced charge amplification and frequency conversion circuits. Experimental evaluations using a gear fault test platform demonstrate that the downconverted signals preserve the envelope spectral features of the original ultrasonic signals. Furthermore, comparative analyses indicate that the proposed method achieves superior fault detection sensitivity compared to conventional vibration-based techniques, particularly under high rotational speeds. These findings validate the feasibility and diagnostic advantages of the proposed heterodyne-based approach for efficient and accurate ultrasonic condition monitoring.

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