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2017 IEEE 第2届先进信息技术,电子与自动化控制国际会议(IAEAC 2017)

作者： Guiliang Wang Yuanling Huang Science and Technology on Blind Signal Processing Laboratory

Radio frequency fingerprinting(RFF) is used to uniquely identify individual radios by exploiting the radio frequency characteristics. Often attributing to the phase ambiguity, the features of an emitt

ISBN: (纸本)9781467389808

关键词： Special emitter identification Radio frequency fingerprinting Discriminant analysis Feature extraction Pattern recognition Feature extraction discriminant analysis Pattern recognition emitters phase ambiguity Clustering algorithms sample covariance radiofrequency

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Network completion via deep metric learning

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Journal of Physics: Conference Series 2020年第1期1656卷

作者： Qiang Wei School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu 611731 China National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu 610041 China

Completing networks is often a necessary step when dealing with problems arising from applications in incomplete network data mining. This paper investigates the network completion problem with node attributes. We proposed a new method called DeepMetricNC by exploiting the correlation between node attributes and the underlying network structure. In DeepMetricNC, the correlation is modeled as a nonlinear mapping from node attributes to the probability of edge existence. To obtain the mapping, deep metric learning is applied with batch training and random negative sampling. DeepMetricNC has linear training time complexity and can adapt to large-scale network completion tasks. Experiments of real networks show that DeepMetricNC completes network structures better than other methods, and is more suitable when the portion of the observed part is small.

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MOF-based high-entropy-alloy evaporator featuring enhanced interband transitions for efficient solar steam and green electricity generation

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Chemical Engineering Journal 2025年 517卷

作者： Dai, Chenyang Zhang, Jia-Han He, Cheng-Yu Dai, Rongrong Pan, Likun Yamauchi, Yusuke Zhang, Jing Li, Zhengtong Xu, Xingtao Marine Science and Technology College Zhejiang Ocean University Zhoushan316022 China Electronic-Photonic Smart Sensing Device R&D Team Inner Mongolia Key Laboratory of Intelligent Communication and Sensing and Signal Processing School of Electronic Information Engineering Inner Mongolia University Hohhot010021 China Research Center of Resource Chemistry and Energy Materials State Key Laboratory of Solid Lubrication Lanzhou Institute of Chemical Physics Chinese Academy of Sciences Lanzhou China Shanghai Key Laboratory of Magnetic Resonance School of Physics and Electronic Science East China Normal University Shanghai200026 China Department of Materials Process Engineering of Engineering Nagoya University Nagoya464-8603 Japan The University of Queensland BrisbaneQLD4072 Australia Department of Chemical and Biomolecular Engineering Yonsei University 50 Yonsei-ro Seodaemun-gu Seoul03722 Korea Republic of Ningbo Key Laboratory of Green Petrochemical Carbon Emission Reduction Technology and Equipment Zhejiang Institute of Tianjin University Zhejiang Ningbo315201 China State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering Yangtze Institute for Conservation and Development Hohai University Nanjing210098 China

Photothermal materials exhibiting broadband optical absorption and high energy efficiency are highly sought after in the field of solar-driven evaporation. Herein, we present a novel photothermal material derived from polymetallic metal–organic frameworks (MOFs) through a d-d interband transition (d-d IBT) engineering strategy. This approach enables comprehensive occupation of electronic states within ±4 eV relative to the Fermi level through 3d transition metal coordination, achieving an exceptional average solar absorption efficiency of over 97 % across the full solar spectrum (250–2500 nm). This breakthrough effectively overcomes the inherent wide bandgap limitation of MOFs that traditionally restricts their full-spectrum solar utilization. Leveraging its remarkable broadband sunlight harvesting capacity and ultrafast localized heating performance, this evaporator engineered with high-entropy alloy nanoparticles, achieves an evaporation efficiency surpassing 95% under one sun irradiation while maintaining a high evaporation rate of 2.80 kg m−2 h−1. Moreover, this evaporator demonstrates exceptional desalination efficiency even with high-salinity seawater (20 wt%), while the waste heat generated during operation can be strategically repurposed to produce green electricity, enhancing its sustainability profile. Thus, the FeCoNiMnCuAlZn high-entropy alloy nanoparticle-based photothermal evaporator offers innovative solutions for addressing the scarcity of clean water resources and the need for green energy. © 2025

关键词： Zinc alloys

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Low Altitude, Slow Speed and Small Size Object Detection Improvement in Noise Conditions Based on Mixed Training

Low Altitude, Slow Speed and Small Size Object Detection Imp...

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2018 3rd International Conference on Communication,Image and signal processing（CCISP 2018）

作者： J.F.Que H.F.Peng J.Y.Xiong National Key Laboratory of Science and Technology on Blind Signal Processing

The low altitude, slow speed and small size object which we call LSS-object for short, such as small UAV(unmanned aerial vehicles) have become a hot issue of air defense security, which is difficult to detect and identify accurately from the image. In this paper, aiming at the problem of LSS-object detection under noise environment, the detection method based on deep learning is proposed. Firstly, a standard training dataset consisting 5 classes of typical objects is constructed. Then, the standard dataset is augmented with noise of different intensity. Finally, YOLO v3 algorithm is used to form a LSS-object detection system which can adapt to environment noise. The training and detection experiments were carried out on the GPU server. After only using the noise-free dataset for training, the mAP(mean Average Precision) of the noise-free test set detection reached 81.07%, but the mAP decreased to 20.68% when the noise variance was *** adopting the mixed training strategy of the dataset with noise variance of 0.01 and noise-free data, the mAP for the test set detection with noise variance of 0.03 was increased to 70.61%, and the mAP still reached 79.85% in noise-free test set detection. The experiment results show that the mixed training strategy can greatly improve the accuracy in the noisy images detection while maintaining a higher accuracy in noise-free images.

关键词： image LSS Low Altitude Slow Speed and Small Size Object Detection Improvement in Noise Conditions Based on Mixed Training Object

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Direct Location for Multiple Passive radars without and with Reference

Direct Location for Multiple Passive Radars without and with...

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2018 3rd International Conference on Communication,Image and signal processing（CCISP 2018）

作者： K.K.Song Y.X.Yang H.F.Peng National Key Laboratory of Science and Technology on Blind Signal Processing

The problem of stationary target location by multiple passive radar sensors that using unknown and non-cooperative opportunity illuminator is considered. Traditional twostep approach which estimating the time difference of arrival(TDOA) and angle of arrival(AOA) firstly and locating using those parameters secondly. We explore the direct location with multiple passive radar sensors without estimating the intermediate parameters. As the reference path from the transmitter to receivers may be blocked in practice, we discuss the both cases that location without and with reference path. Two maximum likelihood algorithms of direct location are proposed for multiple passive radar sensors without and with reference. Monte Carlo simulations indicate that the direct location algorithm is superior to two-step approach with TDOA and AOA at low SNR for multiple passive radar sensors.

关键词： AOA Direct Location for Multiple Passive radars without and with Reference

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Monaural speech enhancement using deep multi-branch residual network with 1-D causal dilated convolutions

arXiv

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arXiv 2019年

作者： Zhang, Qiquan Nicolson, Aaron Wang, Mingjiang Paliwal, Kuldip K. Wang, Chenxu School of Electronic & Information Engineering Harbin Institute of Technology Shenzhen518055 China Signal Processing Laboratory Griffith University BrisbaneQLD4111 School of Information Science & Engineering Harbin Institute of Technology Weihai264209 China

Deep learning has achieved substantial improvement on single-channel speech enhancement tasks. However, the performance of multi-layer perceptions (MLPs)-based methods is limited by its ability to capture the long-term effective history information. Long short-term memory (LSTM) model is limited by its high computational complexity and the exploding/vanish gradients problem. In this paper, we propose a novel convolution neural network (CNN) architecture, called multi-branch residual network (MB-ResNet), for monaural speech enhancement. The MB-ResNet exploits split-transform-aggregate design, which is expected to obtain strong representational power at a low computational complexity. The combination of causal dilated one dimensional CNN and residual connection significantly expands receptive fields, which allows for capturing extremely long-term temporal contextual information. Our extensive experimental investigation suggest that the MB-ResNets outperform the residual long short-term memory networks (ResLSTMs) and the CNN networks that employ residual or dense aggregations in terms of speech intelligibility and quality, while providing superior parameter efficiency. Furthermore, our experimental results demonstrate that our proposed MB-ResNet model is able to outperform multiple state-of-the-art deep learning-based speech enhancement methods in terms of five widely used objective metrics. Copyright © 2019, The Authors. All rights reserved.

关键词： Convolution

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NTIRE 2020 Challenge on Image and Video Deblurring

arXiv

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arXiv 2020年

作者： Seungjun, Nah Sanghyun, Son Radu, Timofte Kyoung Mu, Lee Tseng, Yu Xu, Yu-Syuan Chiang, Cheng-Ming Tsai, Yi-Min Brehm, Stephan Scherer, Sebastian Xu, Dejia Chu, Yihao Sun, Qingyan Jiang, Jiaqin Duan, Lunhao Yao, Jian Purohit, Kuldeep Suin, Maitreya Rajagopalan, A.N. Ito, Yuichi Hrishikesh, P.S. Puthussery, Densen Akhil, K.A. Jiji, C.V. Kim, Guisik Deepa, P.L. Xiong, Zhiwei Huang, Jie Liu, Dong Kim, Sangmin Nam, Hyungjoon Kim, Jisu Jeong, Jechang Huang, Shihua Fan, Yuchen Yu, Jiahui Yu, Haichao Huang, Thomas S. Zhou, Ya Li, Xin Liu, Sen Chen, Zhibo Dutta, Saikat Das, Sourya Dipta Garg, Shivam Sprague, Daniel Patel, Bhrij Huck, Thomas Department of ECE ASRI SNU Korea Republic of Computer Vision Lab ETH Zurich Switzerland MediaTek Inc University of Augsburg Chair for Multimedia Computing and Computer Vision Lab Germany Peking University China Beijing University of Posts and Telecommunications China Beijing Jiaotong University China Wuhan University China Indian Institute of Technology Madras India Vermilion College of Engineering Trivandrum India CVML Chung-Ang University Korea Republic of APJ Abdul Kalam Technological University India University of Science and Technology of China China Image Communication Signal Processing Laboratory Hanyang University Korea Republic of Southern University of Science and Technology China University of Illinois at Urbana-Champaign United States CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China China IIT Madra Jadavpur University India University of Texas Austin United States Duke University Computer Science Department United States

Motion blur is one of the most common degradation artifacts in dynamic scene photography. This paper reviews the NTIRE 2020 Challenge on Image and Video Deblurring. In this challenge, we present the evaluation results from 3 competition tracks as well as the proposed solutions. Track 1 aims to develop single-image deblurring methods focusing on restoration quality. On Track 2, the image deblurring methods are executed on a mobile platform to find the balance of the running speed and the restoration accuracy. Track 3 targets developing video deblurring methods that exploit the temporal relation between input frames. In each competition, there were 163, 135, and 102 registered participants and in the final testing phase, 9, 4, and 7 teams competed. The winning methods demonstrate the state-of-the-art performance on image and video deblurring tasks. Copyright © 2020, The Authors. All rights reserved.

关键词： Image enhancement

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Power Equalization Scheme for Multi-User Radiative Focusing Wireless Power Transfer

Power Equalization Scheme for Multi-User Radiative Focusing ...

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International Conference on Microwave and Millimeter Wave technology Proceedings

作者： Jinlong Bao Deshuang Zhao Weiping Cao Bing Li Bing-Zhong Wang Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Guilin Institute of Applied Physics University of Electronic Science and Technology of China Chengdu School of Electrical Engineering Southwest JiaoTong University Chengdu

Radiative focusing wireless power transfer (RFWPT) has attracted an increasing attention due to its potential of powering multi-users wirelessly without power cables at a long distance. However, it is still challenging to realize efficient equalized power transfer among users. In this paper, we present an efficient power equalization scheme for multi-user RFWPT system. First, we build a multi-user RFWPT model based on the time reversal focusing theory. Second, we derive the efficiency formula and the conditions for efficient multi-user wireless power transfer, and then present a receiving power equalization scheme for multi-users with the deduced efficiency formula. Finally, we numerically verify the power equalization scheme by full-wave electromagnetic simulations.

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Optimal Multimodel Representation by Laguerre Filters Applied to a Communicating Two Tank System

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Journal of Systems science & Complexity 2018年第3期31卷 621-646页

作者： SAMEH Adaily ABDELKADER Mbarek TAREK Garna JOSE Ragot Laboratory of Automatic Control Signal and Image Processing National Engineering School of Monastir University of Monastir 5019 Tunisia Higher Institute of Applied Science and Technology of Sousse University of Sousse 4003 Tunisia Center of Research on Automatic of Nancy CNRS 2 Avenue de la for de Haye 54516 Vandoeuvre Cedex France.

This paper presents the development of a new nonlinear representation by exploiting the multimodel approach and the new linear representation ARX-Laguerre for each operating region. The resulting multimodel, entitled ARX-Laguerre multimodel, is characterized by the parameter number reduction with a recursive representation. However, a significant reduction of this multimodel is subject to an optimal choice of Laguerre poles characterizing each local linear model ARX-Laguerre. Therefore, the authors propose an optimization algorithm to estimate, from input/output measurements, the optimal values of Laguerre poles. The ARX-Laguerre multimodel as well as the proposed optimization algorithm are tested on a continuous stirred tank reactor system （CSTR）. Moreover, the authors take into account a practical validation on an experimental communicating two tank system （CTTS）.

关键词： ARX-Laguerre model Laguerre poles multimodel approach optimization

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Learning compact target-oriented feature representations for visual tracking

arXiv

引用

arXiv 2019年

作者： Li, Chenglong Huang, Yan Wang, Liang Tang, Jin Lin, Liang Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Institute of Physical Science and Information Technology Anhui University Center for Research on Intelligent Perception and Computing NLPR CASIA School of Data and Computer Science Sun Yat-Sen University

Many state-of-the-art trackers usually resort to the pre-trained convolutional neural network (CNN) model for correlation filtering, in which deep features could usually be redundant, noisy and less discriminative for some certain instances, and the tracking performance might thus be affected. To handle this problem, we propose a novel approach, which takes both advantages of good generalization of generative models and excellent discrimination of discriminative models, for visual tracking. In particular, we learn compact, discriminative and target-oriented feature representations using the Laplacian coding algorithm that exploits the dependence among the input local features in a discriminative correlation filter framework. The feature representations and the correlation filter are jointly learnt to enhance to each other via a fast solver which only has very slight computational burden on the tracking speed. Extensive experiments on three benchmark datasets demonstrate that this proposed framework clearly outperforms baseline trackers with a modest impact on the frame rate, and performs comparably against the state-of-the-art methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Target tracking

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