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IET International radar Conference 2018, IRC 2018

作者： Li, Long Jiang, Jie Liu, Zheng Xi'an Research Institute of Navigation Technology Xi'an China National Laboratory of Radar Signal Processing Xidian University Xi'an China

The radar high-resolution range profile (HRRP) is of great significance for target recognition in the ground compound environment. As two essential processes of HRRP target recognition, discrimination and classification performance improvement are studied in this study. A novel target recognition method is designed, denoted by multiple support vector (SV) method. In the training phase, a multiple correlate SV model is constructed, which separates the multi-target feature space into several central-coherence distributed regions. Each region has been described by the modified SV method, and the category determination is based on the description of multiple regions. This method can overcome the issues of both outlier discrimination and targets classification. Due to the hierarchical process in the testing phase, this method needs less memory and calculation to fit the practical radar target recognition task. Finally, the experiment based on the measured data verifies the excellent performance of this method. © 2019 Institution of Engineering and technology. All rights reserved.

关键词： radar target recognition

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Mainlobe interference cancellation based on PCA + ECA in passive bistatic radar

Mainlobe interference cancellation based on PCA + ECA in pas...

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IET International radar Conference 2018, IRC 2018

作者： Gao, Bo Guo, Shuai Wang, Jue Wang, Jun Lou, Baofang Yan, Yifei Xi'an Research Institute of Navigation Technology Xi'an China National Laboratory of Radar Signal Processing Xidian University Xi'an China

The mainlobe interference is a noticeable issue in the passive bistatic radar. As the direction of arrival (DOA) of the interference is close to the DOA of the target echo, the interference hardly be cancelled by the adaptive spatial filtering. After clutter cancellation, the authors assume the mainlobe interference is the principal component of the array element signal. This study proposes the method based on the principal component analysis (PCA) and the extensive cancellation algorithm (ECA) to cancel the mainlobe interference in the echoes. The PCA is applied to extract the interference component from the array element signals, and the ECA is applied to cancel the interference with the extracted interference component as the reference. Finally, the simulation result proves the feasibility of the method proposed by this paper. © 2019 Institution of Engineering and technology. All rights reserved.

关键词： Principal component analysis

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Principled design of translation, scale, and rotation invariant variation operators for metaheuristics

arXiv

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arXiv 2021年

作者： Tian, Ye Zhang, Xingyi He, Cheng Tan, Kay Chen Jin, Yaochu Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Anhui University Hefei230601 China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei230601 China Guangdong Provincial Key Laboratory of Braininspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Department of Computing The Hong Kong Polytechnic University Hong Kong Department of Computer Science University of Surrey Guildford SurreyGU2 7XH United Kingdom

In the past three decades, a large number of metaheuristics have been proposed and shown high performance in solving complex optimization problems. While most variation operators in existing metaheuristics are empirically designed, this paper aims to design new operators automatically, which are expected to be search space independent and thus exhibit robust performance on different problems. For this purpose, this work first investigates the influence of translation invariance, scale invariance, and rotation invariance on the search behavior and performance of some representative operators. Then, we deduce the generic form of translation, scale, and rotation invariant operators. Afterwards, a principled approach is proposed for the automated design of operators, which searches for highperformance operators based on the deduced generic form. The experimental results demonstrate that the operators generated by the proposed approach outperform state-of-theart ones on a variety of problems with complex landscapes and up to 1000 decision variables. © 2021, CC BY.

关键词： Heuristic algorithms

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Robust Deep Learning Framework for Predicting Respiratory Anomalies and Diseases 42

Robust Deep Learning Framework for Predicting Respiratory An...

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42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020

作者： Pham, Lam McLoughlin, Ian Phan, Huy Tran, Minh Nguyen, Truc Palaniappan, Ramaswamy University of Kent School of Computing Medway Kent United Kingdom University of Science and Technology of China National Engineering Laboratory for Speech and Language Information Processing Hefei China Queen Mary University School of Electronic Engineering and Computer Science London United Kingdom University of Oxford Nuffield Department of Anaesthesia United Kingdom Graz University of Technology Signal Processing and Speech Communication Lab Austria

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119908

This paper presents a robust deep learning framework developed to detect respiratory diseases from recordings of respiratory sounds. The complete detection process firstly involves front end feature extraction where recordings are transformed into spectrograms that convey both spectral and temporal information. Then a back-end deep learning model classifies the features into classes of respiratory disease or anomaly. Experiments, conducted over the ICBHI benchmark dataset of respiratory sounds, evaluate the ability of the framework to classify sounds. Two main contributions are made in this paper. Firstly, we provide an extensive analysis of how factors such as respiratory cycle length, time resolution, and network architecture, affect final prediction accuracy. Secondly, a novel deep learning based framework is proposed for detection of respiratory diseases and shown to perform extremely well compared to state of the art methods. © 2020 IEEE.

关键词： Network architecture

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Enhanced Gaussian Process Regression for Diagnosing Wind Energy Conversion Systems

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IFAC-PapersOnLine 2022年第6期55卷 673-678页

作者： Majdi Mansouri Radhia Fezai Mohamed Trabelsi Hajji Mansour Hazem Nounou Mohamed Nounou Department of Mathematics and Sciences Prince Sultan University Riyadh 11586 Saudi Arabia Research Laboratory of Automation Signal Processing and Image National Engineering School of Monastir 5019 Tunisia Electronic and Communications Engineering Department Kuwait College of Science and Technology P. O. Box 27235 Kuwait Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Tunisia Chemical Engineering Program Texas A&M University at Qatar Doha Qatar

Fault detection and diagnosis techniques are increasingly important to ensure robust and resource efficient operation of Wind Energy Conversion (WEC) systems. In this context, this paper presents a Reduced Enhanced Gaussian Process Regression (REGPR)-based Random Forest (RF) technique (REGPR-RF) to identify and diagnose faults occurring in a nonlinear WEC systems. The proposed technique uses REGPR technique for features extraction and selection from raw sensor data. Then, these selected features are fed to RF classifier to reliably detect and classify faults. The use of REGPR to learn features avoid the dimension problems and improves the classification performance significantly with a small number of training data. The results obtained by REGPR-RF are compared to those obtained with other conventional classifiers (Support Vector Machines (SVM), Naive Bayes (NB), ...). The results show that the developed REGPR-RF technique achieve higher accuracy (99.99%) with small data sets.

关键词： Gaussian Process Regression (GPR) Noise-signal-Dependence Random Forest (RF) Feature Extraction Selection Fault Detection Diagnosis (FDD) Wind Energy Conversion (WEC) Systems

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DRL-M4MR: An Intelligent Multicast Routing Approach Based on DQN Deep Reinforcement Learning in SDN

arXiv

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arXiv 2022年

作者： Zhao, Chenwei Ye, Miao Xue, Xingsi Lv, Jianhui Jiang, Qiuxiang Wang, Yong School of Information and Communication Guilin University of Electronic Technology Guilin541004 China Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Guilin University of Electronic Technology Guilin541004 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fujian Fuzhou350118 China Peng Cheng Lab. Guangdong Shenzhen518038 China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China

Traditional multicast routing methods have some problems in constructing a multicast tree, such as limited access to network state information, poor adaptability to dynamic and complex changes in the network, and inflexible data forwarding. To address these defects, the optimal multicast routing problem in software-defined networking (SDN) is tailored as a multi-objective optimization problem, and an intelligent multicast routing algorithm DRL-M4MR based on the deep Q network (DQN) deep reinforcement learning (DRL) method is designed to construct a multicast tree in SDN. First, the multicast tree state matrix, link bandwidth matrix, link delay matrix, and link packet loss rate matrix are designed as the state space of the DRL agent by combining the global view and control of the SDN. Second, the action space of the agent is all the links in the network, and the action selection strategy is designed to add the links to the current multicast tree under four cases. Third, single-step and final reward function forms are designed to guide the intelligence to make decisions to construct the optimal multicast tree. The experimental results show that, compared with existing algorithms, the multicast tree construct by DRL-M4MR can obtain better bandwidth, delay, and packet loss rate performance after training, and it can make more intelligent multicast routing decisions in a dynamic network environment. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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Spatial-Resampling Wideband Compressive Beamforming

Spatial-Resampling Wideband Compressive Beamforming

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OCEANS

作者： Zhen Zhu Cheng Chi Chunhua Zhang University of Chinese Academy of Sciences Beijing China Chinese Academy of Sciences The Institute of Acoustics Beijing China Key Laboratory of Science and Technology on Advanced Underwater Acoustic Signal Processing Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665427883

Compressive beamforming has been successfully applied to the estimation of the direction of arrival (DOA) of array signals, and has higher angular resolution than traditional high-resolution beamforming methods. However, most of the existing compressive beamforming methods are based on narrow signal models. Wideband signal processing using these existing compressive beamforming methods is to divide the frequency band into several narrow-bands and add up the beamforming results of each narrow-band. However, for sonar application, signals usually consist of continuous spectrum and line spectrum, and the line spectrum is usually more than 10dB higher than the continuous spectrum. Due to the large difference of signal-to-noise ratio (SNR) of each narrow-band, different regularization parameters should be used, otherwise it is difficult to get an ideal result, which makes compressive beamforming highly complicated. In this paper, a compressive beamforming method based on spatial resampling for uniform linear arrays is proposed. The signals are converted into narrow-band signals by spatial resampling technique, and compressive beamforming is then performed to estimate the DOA of the sound source. Experimental results show the superiority of the proposed method, which avoids the problem of using different parameters in the existing compressive beamforming methods, and the resolution is comparable to the existing methods using different parameters for wideband models. The spatial-resampling compressive beamforming has a better robustness when the regularization parameter is fixed, and exhibits lower levels of background interference than the existing methods.

关键词： Direction-of-arrival estimation Array signal processing Sonar applications Interference Robustness Broadband communication Spatial resolution

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Tag Pollution Detection in Web Videos via Cross-Modal Relevance Estimation

Tag Pollution Detection in Web Videos via Cross-Modal Releva...

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International Workshop on Quality of Service

作者： Yixin Chen Xinye Lin Keshi Ge Wenbo He Dongsheng Li College of Computer National University of Defense Technology National Key Laboratory of Science and Technology on Blind Signal Processing McMaster University

ISBN: (数字)9781728168876

ISBN: (纸本)9781728168883

In the era of big data, web videos are known for their astronomical volume and great difficulties to be understood by computers. Therefore it is challenging to detect and curb tag pollution on social networking platforms. Intuitively, the pollution in the tags of a video can be identified by exploiting the tags of visually similar videos. From this intuition, we develop a semi-supervised approach to estimate the relevance between Internet videos and user-provided labels accurately and detect polluted labels accordingly. To further enhance the accuracy of relevance estimation and pollution detection, we introduce three multi-view multi-label models, which employ the coherence and differences between various similarity relations of videos. Compared with two quintessential multi-view fusion models, the proposed models consistently outperform or achieve comparable performance.

关键词： Videos Visualization Pollution Neural networks Social network services Correlation Unsolicited electronic mail

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Off-Grid Compressive Time-Delay Estimation for Passive Sonars

Off-Grid Compressive Time-Delay Estimation for Passive Sonar...

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OCEANS

作者： Feilong Ding Cheng Chi Shuo Wang Yu Li Haining Huang University of Chinese Academy of Sciences Beijing China Chinese Academy of Sciences The Institute of Acoustics Beijing China Key Laboratory of Science and Technology on Advanced Underwater Acoustic Signal Processing Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665427883

In passive sonars, the time delay estimation methods based on phase information are robust and accurate. However, these methods face a serious decrease in the accuracy of time delay estimation when phase wrapping arises. This paper proposes an off-grid time delay estimation (OGTDE) method suitable for passive sonars. This proposed method provides high-precision time delay estimation results, while avoiding the phase wrapping problem. The simulations show that the accuracy of time delay estimation of our proposed method is superior to that of the reference method under the same conditions.

关键词： Delay effects Simulation Oceans Estimation Sonar Bandwidth Delays

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A New Approach for Optimization Selection of Spaceborne SAR Beam Position Parameters 6

A New Approach for Optimization Selection of Spaceborne SAR ...

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6th Asia-Pacific Conference on Synthetic Aperture radar, APSAR 2019

作者： Du, Mingang Sun, Guang-Cai Cao, Yandong Xing, Mengdao Guo, Liang Yang, Jun National Laboratory of Radar Signal Processing Xidian University Xi'an China School of Physics and Optoelectronic Engineering Xidian University Xi'an China College of Geomatics Xi'An University of Science and Technology Xi'an China

ISBN: (纸本)9781728129129

In this paper, a new optimized selection method of beam position parameters for spaceborne synthetic aperture radar(SAR) is proposed. Aiming at the difficulty in selecting pulse repeatition frequency (PRF) and observation band width in spaceborne SAR beam position design, this paper deduces the iterative relationship between adjacent beam positions in detail from the spaceborne SAR beam geometry, and proposes an automatic optimization and selection method for beam position parameters. The proposed method meets the principles of PRF restriction and maximum observation band width. The PRF and observation band width of one beam position can be optimized and selected at the same time, and the next beam position parameters can be automatically iteratively selected according to the current selection result. Simulation results validate the effectiveness of the proposed method. © 2019 IEEE.

关键词： Iterative methods

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