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Learning transferable representations by topic guided graph adversarial network

signal processing 2026年 238卷

作者： Zhengjue Wang Zhihui Xin Chiyu Chen Hao Zhang Yuhao Huang Yunsong Li Hongwei Liu Bo Chen State Key Laboratory of Integrated Service Networks School of Telecommunications Engineering Xidian University Xi’an 710071 China Yunnan Key Laboratory of Opto-electronic Information Technology School of Physics and Electronic Information Yunnan Normal University Kunming 650500 China National Laboratory of Radar Signal Processing Xidian University Xi’an 710071 China

To reduce the need for costly labeled data in a target domain, it is desirable to learn transferable representations from a related source domain, where it is feasible to obtain labeled data. Facing distribution shift between different domains, one often aligns the marginal feature distributions by performing feature-level adversarial learning. Despite the recent success, existing approaches often ignore the consistency between feature and label, which is a challenging problem since there is no supervision in the target domain. Hence, we are motivated to transfer the discriminative information from the source to the target domain to realize better domain alignment. To this end, we propose a topic-guided graph adversarial network (TGAN), composed of a graph constructor, a graph feature extractor, a domain discriminator, and a classifier. Specifically, to learn a graph describing the relational structure among samples from different domains, we propose a semantic disentangled topic model to extract domain-shared and domain-specific topics, so that the graph edges can be defined by the sample similarities in a domain-shared semantic space. Then, TGAN aggregates the discriminative characteristics of source nodes and propagates them to the target nodes by attentive message passing through the graph, with the final node embeddings used for adversarial learning between source and target domains. TGAN has achieved the state-of-the-art performance on sentiment classification and clinical risk prediction tasks. Moreover, the discovered domain-invariant discriminative topics show interpretable meanings, which is beneficial to understanding the prediction results, especially for biomedical researches.

关键词： Transfer learning Adversarial learning Topic modeling Disentangled representation Graph neural network

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Optimal spectrum allocation for a cooperative radar-communication system

Optimal spectrum allocation for a cooperative radar-communic...

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IET International radar Conference (IET IRC 2020)

作者： Y. Chen G. Liao Y. Zeng Z. Yang Y. Liu S. Zhu Xidian University National Laboratory of Radar Signal Processing Xi’an 710071 China Shaanxi Normal University School of Computer Science Xi’an 710062 China

A radar waveform with spectral notches is designed to share spectrum for the coexistence of radar and communications. To suppress high range sidelobes caused by the discontinuous spectrum, a novel waveform design for cooperative radar-communication system is presented. In this method, the distribution of working band for communications is first used as the degree of freedom (DOF), and thus the waveform optimization is equivalent to search the optimal spectrum allocation scheme. For this reason, we formulate an objective function of minimizing peak sidelobe level (PSL) with respect to the power spectrum density (PSD) of radar waveform under a prescribed range resolution constraint. To address this problem, a global optimization procedure, namely firefly algorithm (FA) is utilized to obtain the desired PSD configuration. Moreover, a PSD-to-waveform implementation based on PSD fitting is employed to convert the PSD to the transmit waveform. Simulation results show that the proposed method can prevent the cooperative radarcommunication system from mutual interference while ensuring the radar signal with low autocorrelation sidelobes and a fine range resolution.

关键词： radar interference radar signal processing optimisation correlation methods cooperative communication radar resolution resource allocation

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Automatic Modulation Classification of Frequency-Hopping signals Using High-Dimensional Phase Diagrams

Automatic Modulation Classification of Frequency-Hopping Sig...

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IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

作者： Qibo Chen Kun Yan Hsiao-Chun Wu Shih Yu Chang Xiao Yan Yiyan Wu Haonan Chang Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Guilin University of Electronic Technology Guilin China Department of Information and Telecommunication Guilin University of Electronic Technology Guilin Guangxi China School of Electrical Engineering and Computer Science Louisiana State University Baton Rouge LA USA Department of Applied Data Science San Jose State University San Jose CA USA School of Aeronautics and Astronautics University of Electronic Science and Technology of China Chengdu P. R. China Communications Research Centre Canada Ottawa Canada Haonan Chang is with Xi'an University Xi'an P. R. China

Automatic modulation recognition for frequency-hopping (FH) signals remains very challenging to researchers due to the signals' time-varying spectral characteristics. In this work, a novel robust automatic modulation recognition scheme is investigated for FH signals using the phase-space topological features represented by the embedded phase diagrams. As such embedded phase diagrams are often high-dimensional, it is necessary to formulate the phase-space features as tensors. In the training process, the phase-space tensor features will be utilized to establish the regression models as linear encoders for the individual modulations. The aforementioned linear encoders are constructed using the support vector machine (SVM); the phase-space feature-tensors of the training signals of all modulations will be projected by their corresponding regression models (or linearly encoded) to produce the representative code-vectors, respectively. In the test stage, the phase-space feature-tensor produced from a test signal will be projected by each individual trained regression model (or linearly encoded) to generate the respective code-vectors. Then, the code-vectors resulting from the test stage will be compared with the representative code-vectors to find which modulation will lead to the smallest Euclidean distance in between and such a modulation will be picked as the modulation type of the test signal. Monte Carlo simulation results have demonstrated that the average recognition accuracy of our proposed new approach is more than 90% when the signal-to-noise ratio is no less than 0 dB for additive white Gaussian noise.

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Remarks on the Design of First Digital Computers in Japan - Contributions of Yasuo Komamiya 17th

Remarks on the Design of First Digital Computers in Japan - ...

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17th International Conference on Computer Aided Systems Theory, EUROCAST 2019

作者： Stanković, Radomir S. Sasao, Tsutomu Astola, Jaakko T. Yamada, Akihiko Mathematical Institute of SASA Belgrade Serbia Department of Computer Science Meiji University KawasakiKanagawa214-8571 Japan Department of Signal Processing Tampere University of Technology Tampere Finland Computer Systems and Media Laboratory Tokyo Japan

ISBN: (纸本)9783030450922

This paper presents some less known details about the work of Yasuo Komamiya in development of the first relay computers using the theory of computing networks that is based on the former work of Oohashi Kan-ichi and Mochiori Goto at the Electrotechnical laboratory (ETL) of Agency of Industrial science and technology, Tokyo, Japan. The work at ETL in the same direction was performed under guidance of Mochinori Goto. © 2020, Springer Nature Switzerland AG.

关键词： Digital computers

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radar high-resolution range profile recognition via multi-SV method

Radar high-resolution range profile recognition via multi-SV...

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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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