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6th Annual International Conference on Network and Information Systems for Computers, ICNISC 2020

作者： Wang, Keren Li, Weihao National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu China Information Engineering University Zhengzhou China

Wideband signal detection plays an important role in wireless communication systems. In recent years, deep learning (DL) has been introduced, and many trial efforts have been posed. In this paper, a fast deep-learning signal detection architecture is proposed for the short-wave band, which consists of the GPU-based Down Digital Converter (DDC), the DL-based signal detector, and the signal data viewer & recorder. Wideband signal data are taken directly as the input, and detected signals are automatically recorded as the output of the architecture. Experimental results suggest that our architecture is capable of detecting signals in the whole short-wave band in quasi-real-time, and specifically, with single GPU device. © 2020 Journal of Physics: Conference Series.

关键词： signal detection

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Integrated radar and Jammer Waveform Design Based on PNFM-NLFM

Integrated Radar and Jammer Waveform Design Based on PNFM-NL...

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IEEE International Conference on radar

作者： Zhuochen Chen Shengqi Zhu Yongjun Liu Haichuan Li Chilian Chen National Laboratory of Radar Signal Processing Xidian University Xi’an China Key Laboratory of Technology on Datalink The 20th Research Institute of CETC Xi'an China

ISBN: (纸本)9781665468893

To achieve the integration of radar and jammer in the waveform, we proposed a pseudo-code noise and nonlinear frequency modulation (PNFM-NLFM) waveform. The PNFM-NLFM waveform is a combination of pseudo-random-codes noise frequency modulation (PNFM) and nonlinear frequency modulation (NLFM). The detection performance is evaluated by ambiguity function, and we study the interference performance through pulse compression(PC) processing. Finally, the simulation results show that compared with the existing noise-linear frequency modulation (Noise-LFM) signal, the PNFM-NLFM has low autocorrelation side lobe while keeping interference performance.

关键词： Frequency modulation Simulation radar detection radar Interference Brain modeling Autocorrelation

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Novel Personalized Multimedia Recommendation Systems Using Tensor Singular-Value-Decomposition

Novel Personalized Multimedia Recommendation Systems Using T...

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2023 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB 2023

作者： Chang, Shih Yu Wu, Hsiao-Chun Yan, Kun Chen, Xinjia Wu, Yiyan San Jose State University Department of Applied Data Science San JoseCA95192 United States Louisiana State University School of Electrical Engineering and Computer Science Baton RougeLA70803 United States Guilin University of Electronic Technology Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Guilin541004 China Guilin University of Electronic Technology Department of Information and Telecommunication Guilin541004 China Northwestern State University of Louisiana Department of Engineering Technology NatchitochesLA71497 United States Communications Research Centre Canada OttawaONK2H 8S2 Canada

ISBN: (纸本)9798350321524

Nowadays, multimedia data are often produced by various sources, e.g., Internet of Things (IoT), social media, customer databases, etc. As tremendous multimedia data are produced every day, users or E-commerce customers cannot infer information from such data by themselves and therefore recommender systems have been developed to help users to select the products or services which better fit users' preferences or requirements. Nonetheless, there exists few works on the incorporation of side information or multiple attributes about items into the design of a more robust recommender system. In this work, we propose a novel approach based on the third-order tensor singular-value-decomposition (T3-SVD) to design new personalized multimedia recommender systems (PMRSs) for internet users. A PMRS can dynamically adjust its recommendation strategy subject to a particular user's on-line transaction behavior. To evaluate the effectiveness of our proposed PMRS based on T3-SVD, we compare the performances of our proposed new PMRS and two other existing tensor-based recommendation systems over realworld data in terms of normalized root-mean-square error (NRMSE). As a result, our proposed new PMRS greatly outperforms the other two existing systems. © 2023 IEEE.

关键词： Recommender systems

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Intelligent multicast routing method based on multi-agent deep reinforcement learning in SDWN

arXiv

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arXiv 2023年

作者： Hu, Hongwen Ye, Miao Zhao, Chenwei Jiang, Qiuxiang Wang, Yong Qiu, Hongbing Deng, Xiaofang School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Guilin University of Electronic Technology Guilin541004 China School of Information and Communication Guilin University of Electronic Technology Guilin541004 China

Multicast communication technology is widely applied in wireless environments with a high device density. Traditional wireless network architectures have difficulty flexibly obtaining and maintaining global network state information and cannot quickly respond to network state changes, thus affecting the throughput, delay, and other QoS requirements of existing multicasting solutions. Therefore, this paper proposes a new multicast routing method based on multiagent deep reinforcement learning (MADRL-MR) in a software-defined wireless networking (SDWN) environment. First, SDWN technology is adopted to flexibly configure the network and obtain network state information in the form of traffic matrices representing global network links information, such as link bandwidth, delay, and packet loss rate. Second, the multicast routing problem is divided into multiple subproblems, which are solved through multiagent cooperation. To enable each agent to accurately understand the current network state and the status of multicast tree construction, the state space of each agent is designed based on the traffic and multicast tree status matrices, and the set of AP nodes in the network is used as the action space. A novel single-hop action strategy is designed, along with a reward function based on the four states that may occur during tree construction: progress, invalid, loop, and termination. Finally, a decentralized training approach is combined with transfer learning to enable each agent to quickly adapt to dynamic network changes and accelerate convergence. Simulation experiments show that MADRL-MR outperforms existing algorithms in terms of throughput, delay, packet loss rate, etc., and can establish more intelligent multicast routes. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Critical Node Detection for Sequential Attacks in Complex Networks via Multi-objective Optimization 2

Critical Node Detection for Sequential Attacks in Complex Ne...

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2nd International Conference on Industrial Artificial Intelligence, IAI 2020

作者： Zhang, Lei Liu, Qing Xia, Jiajun Yang, Haipeng Zhang, Xingyi Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University China

ISBN: (纸本)9781728182162

The critical node detection for sequential attacks based on cascading failure model is an important way for analyzing network vulnerability, which has attracted the attention of many researchers in the field of complex network recently. However, most of the existing cascading critical node detection algorithms focus on designing effective attack strategies leading to the maximal damage to the network (i.e. attack effect), while ignoring the cost of attacks. To this end, we transform the cascading critical node detection for sequential attacks as a bi-objective optimization problem (named BCVNDSeq), where the attack cost and the attack effect are simultaneously optimized. In order to solve the transformed problem, we propose a multiobjective cascading critical node detection algorithm (named MO-BCVNDSeq), which can provide decision makers with a holistic view for analyzing the network vulnerability. In MO-BCVNDSeq, a local search strategy based on sequential matrix is proposed to accelerate the population convergence and an individual repairing strategy is also suggested to further improve the search efficiency. Finally, the experimental results on 6 real-world complex networks demonstrate the effectiveness of the proposed algorithm compared with several representative baselines. © 2020 IEEE.

关键词： Multiobjective optimization

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Constrained Energy Minimization (CEM) for Hyperspectral Target Detection: Theory and Generalizations

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IEEE Transactions on Geoscience and Remote Sensing 2024年 62卷

作者： Chang, Chein-I. Information and Technology College Dalian116026 China University of Maryland Baltimore County Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering BaltimoreMD21250 United States National Cheng Kung University Department of Electrical Engineering Tainan70101 Taiwan

Target detection is a fundamental task of hyperspectral imaging where constrained energy minimization (CEM) has been widely used for subpixel target detection techniques. Due to its effectiveness, CEM has been generalized to various versions, such as kernel CEM (KCEM), kernel target-constrained interference-minimized filter (KTCIMF), ensemble cascaded CEM (ECEM), and hierarchical CEM (HCEM). Unfortunately, these generalizations overlooked the key design rationale behind CEM. This article revisits CEM for hyperspectral target detection (HTD) and proves how and why it works mathematically. Specifically, several new CEM generalizations are derived and particularly noteworthy. By including spatial information in an iterative process, KCEM, ECEM, and HCEM can be generalized to iterative KCEM (IKCEM), iterative KTCIMF (IKTCIMF), iterative ECEM (IECEM), and iterative HCEM (IHCEM). Also, by utilizing an iterative random training sampling (IRTS) to generate the desired target signature to be detected, these algorithms are further generalized to IRTS KCEM (IRTS-KCEM), IRTS ECEM (IRTS-ECEM), and IRTS HCEM (IRTS-HCEM). A comprehensive analysis along with comparative study on these generalizations is conducted through extensive experiments to demonstrate the effectiveness of IKCEM, IHCEM, and IECEM. © 1980-2012 IEEE.

关键词： Iterative methods

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Moving Target Tracking Based on Shadow with Unsupervised Deep Track in Video-SAR

Moving Target Tracking Based on Shadow with Unsupervised Dee...

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IEEE International Conference on radar

作者： Hui Fang Yongjun Liu Guisheng Liao Haichuan Li Chilian Chen Xuchen Liu National Laboratory of Radar Signal Processing Xidian University Xi'an China Key Laboratory of Technology on Datalink The 20th Research Institute of CETC Xi'an China

ISBN: (纸本)9781665468893

The moving target is always defocused and shifted out of the scene in image sequences in video Synthetic Aperture radar (video-SAR), which makes moving target tracking be a formidable challenge. In this paper, a new moving target tracking method based on shadow with unsupervised deep track (UDT) is proposed. Our track network is built on the Siamese network framework and is trained on a series of unlabeled SAR image sequences with unsupervised learning way. Meanwhile, we use the trajectory detection technology to get more accurate tracking results. Finally., the processing results of the real data are provided to verify the effectiveness of the proposed method.

关键词： Training Target tracking radar tracking radar polarimetry Image sequences Trajectory Synthetic aperture radar

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Data Fusion Based on Temporal-Spatial Composite Sequential Three-Way Decision

SSRN

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SSRN 2022年

作者： Xu, Yi Qiu, Ziheng Sun, Xiaojun Department of Computer Science and Technology Anhui University Hefei230601 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230601 China

Three-way decision is the representative of trisecting-acting-outcome(TAO) model. Considering the temporality, in dynamic information systems, the objects may be inserted or deleted,attributes may be added or removed, and attributes values may be updated or modified. These variations can be reflected by the TAO model, which cares about consideration at different levels, acquisition of strategies, and handling of issues based on Strategies. Considering the spatiality, different levels, and different perspectives of granular structure are created by the sequential approach of three-way decision. In this paper, in order to fuse hybrid data, a sequential three-way decision is proposed by the combination of temporality and spatiality. Firstly, We present the frameworks of spatiality-based and temporality-based and focus on temporal-spatial three-way decision. Then we provide three operations to construct the composite POS structure by combining a family of approximations. Based on the composite POS structure, three different strategies for the fusion of dynamic hybrid data are adopted. Finally, we design the algorithm and implement a series of comparison experiments to verify our model is effective. © 2022, The Authors. All rights reserved.

关键词： Data fusion

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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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2023 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB 2023

作者： Chen, Qibo Yan, Kun Wu, Hsiao-Chun Chang, Shih Yu Yan, Xiao Wu, Yiyan Chang, Haonan Guilin University of Electronic Technology Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing GuilinGuangxi China Guilin University of Electronic Technology Department of Information and Telecommunication Guangxi Guilin541004 China Louisiana State University School of Electrical Engineering and Computer Science Baton RougeLA70803 United States San Jose State University Department of Applied Data Science San JoseCA95192 United States University of Electronic Science and Technology of China School of Aeronautics and Astronautics Chengdu China Communications Research Centre Canada OttawaK2H 8S2 Canada Haonan Chang Is with Xi'an University Xi'an China

ISBN: (纸本)9798350321524

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. © 2023 IEEE.

关键词： Support vector machines

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Remaining Useful Life Prediction of Bearing via a Double Attention-Based Deep Neural Network

Remaining Useful Life Prediction of Bearing via a Double Att...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Yongkang Liu Donghui Pan Haifeng Zhang Kai Zhong Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Mathematical Science Anhui University Hefei Anhui China Institutes of Physical Science and Information Technology Anhui University Hefei Anhui China

Predicting the remaining useful life (RUL) of bearings is important for the secure running and reducing maintenance costs of rotating machinery. In this article, a novel feature extraction and RUL prediction framework of bearing is proposed by utilizing time-frequency representations (TFRs) and deep neural network with double attention (DA-DNN). Firstly, TFRs are employed to extract the degradation features associated with RUL prediction, which can effectively cope with the non-stationary properties of bearing vibration data. The reduced dimensional TFRs are then used as the DA-DNN model input. Afterwards, DA-DNN is developed by employing the double attention structure to extract the key degradation features more efficiently. The dual-attention structure has ability to capture the degradationrelated features from two aspects of channel attention and spatial attention, which enables the network to focus on high RUL-relevant information. Finally, the experimental results demonstrate the effectiveness of DA-DNN, which shows higher performance in terms of prediction accuracy compared to other deep learning (DL) methods.

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