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6th IEEE International Conference on Computer and Communications, ICCC 2020

作者： Wei, Lanxin Wang, Siye Xu, Wenbo Li, Dong Yang, Kun Network and Wireless Communication Lab Beijing University of Posts and Telecommunications Beijing China National Key Laboratory of Science and Technology on Blind Signal Processing Shanghai China

ISBN: (纸本)9781728186351

Direction of arrival (DOA) estimation is a fundamental problem in signal processing. The super-resolution spatial spectrum estimation algorithm based on feature decomposition, such as multiple signal classification (MUSIC) and estimating signal parameter via rotational invariance techniques (ESPRIT), has a good performance in high signal-to-interference plus noise ratio (SINR) environment and single interference. However, strong interferences will make DOA estimation error. And the number of angles that can be estimated by these methods is limited by the freedom of the antenna. The existence of multiple interferences will affect accuracy of DOA estimation. To address this problem, in this paper, we propose a novel dual-channel synchronization architecture and a Piloted-Aided DOA estimation method. We improve the matching performance by dual channel synchronization. A new matched filter is designed by using the idea of filter truncation, which reduces the computational resource cost. And a new DOA estimation method is proposed, which can estimate the angle of target signal in the environment of low SINR and multiple interferences. Experiments were performed to verify the performance of the proposed design. Results show that this architecture can greatly reduce the computational complexity. And the method successfully gets the direction of target signal in low SINR environment. © 2020 IEEE.

关键词： Synchronization

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Inter-pulse carrier frequency agile design for cognitive radar

Inter-pulse carrier frequency agile design for cognitive rad...

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IET International Radar Conference (IRC 2023)

作者： Yin Li Tao Fan Xianxiang Yu Lan Lan Huai Ding Guolong Cui School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu People's Republic of China National Key Laboratory of Radar Signal Processing Xidian University Xi'an People's Republic of China The 29th Research Institute of China Electronics Technology Group Corporation Chengdu People's Republic of China

ISBN: (数字)9781837240982

This paper deals with the joint design of inter-pulse frequency and receive filter to reduce the Doppler sidelobe for cognitive Frequency Agile Radar (FAR) working in search and confirmation modes. At first, we introduce an inter-pulse coherent processing method that integrates phase compensation with Weighted Non-Uniform Discrete Fourier Transform (W-NUDFT). Then, a multi-parameter non-convex optimization model is established based on the issue of high Doppler sidelobes caused by inter-pulse non-uniform accumulation. Subsequently, we develop an alternating iterative optimization algorithm that combines Coordinate Descent with Alternating Direction Method of Multipliers (CD-ADMM) to tackle the resulting non-convex optimization problem. Finally, through numerical simulations, the effectiveness of the waveform via the application on weak targets detection accompanied by strong targets is demonstrated.

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Generalized Tensor-Aided Channel Estimation for Hardware Impaired Device Identification

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IEEE Transactions on Vehicular technology 2025年

作者： Wu, Qi Sui, Zeping Ngo, Hien Quoc Wan, Qun Matthaiou, Michail School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China National Key Laboratory on Blind Signal Processing Chengdu610041 China Queen’s University Belfast BelfastBT3 9DT United Kingdom School of Computer Science and Electronics Engineering University of Essex ColchesterCO4 3SQ United Kingdom Department of Electronic Engineering Kyung Hee University Gyeonggi-do Yongin-si17104 Korea Republic of

In this paper, we investigate the joint generalized channel estimation and device identification problem in Internet of Things (IoT) networks under multipath propagation. To fully utilize the received signal, we decompose the generalized channel into three components: transmitter hardware characteristics, path gains, and angles of arrival. By modeling the received signals as parallel factor (PARAFAC) tensors, we develop alternating least squares (ALS)-based algorithms to simultaneously estimate the generalized channels and identify the transmitters. Simulation results show that the proposed scheme outperforms both Khatri-Rao Factorization (KRF) and the conventional least squares (LS) method in terms of channel estimation accuracy and achieves performance close to the derived Cramér–Rao lower bound. © 2025 IEEE. All rights reserved,

关键词： Tensors

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Federated Multimedia Recommendation Systems with Privacy Protection

Federated Multimedia Recommendation Systems with Privacy Pro...

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

作者： Shih Yu Chang Hsiao-Chun Wu Kun Yan Scott Chih-Hao Huang Yiyan Wu Department of Applied Data Science San Jose State University San Jose CA USA School of Electrical Engineering and Computer Science Louisiana State University Baton Rouge LA USA Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Guilin University of Electronic Technology Guilin Guangxi China Department of Information and Telecommunication Guilin University of Electronic Technology Guilin Guangxi China Department of Electrical Engineering National Tsinghua University Hsinchu Taiwan

ISBN: (数字)9798350364262

ISBN: (纸本)9798350364279

In this big data era, distributed machine learning and global data pooling have become quite critical in practical implementation of any multimedia data-science technique. The emerging federated multimedia recommendation systems (FeMRSs) offer a promising approach to address the need of personalized recommendations subject to both users’ privacy and data security. However, there exists no quantitative method for assessing the discrepancy between the large original rating matrix stored on the server and the dimensionality-reduced rating matrix produced on each client’s equipment. Meanwhile, there lacks a systematic means to evaluate the differential privacy (DP), which is a critical security measure in many distributed systems, particularly when sensitive data are processed. In this work, we first introduce a novel personalized dimensionalityreduction algorithm utilizing the matrix sketching technique. This new algorithm effectively control the difference between the original rating matrix on the server and the dimensionality-reduced rating matrix on each client’s equipment. Moreover, a randomized DP matrix factorization algorithm is designed to be executed at each client’s equipment. The theoretical proof is also carried out to show how much DP can be attained by use of the aforementioned randomized DP matrix factorization algorithm. Finally, extensive numerical studies are presented to evaluate the effectiveness of our proposed novel algorithms using both simulated and real datasets for building the FeMRSs.

关键词： Dimensionality reduction Digital multimedia broadcasting Privacy Systematics Distributed databases Filtering algorithms Servers

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A Self-Adaptive Modified Whale Optimization Algorithm for Optimizing the Linear Antenna Array

A Self-Adaptive Modified Whale Optimization Algorithm for Op...

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International Conference on Measuring technology and Mechatronics Automation, ICMTMA

作者： Wentao Feng Kekang Song National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu China National Key Laboratory of Science and Technology on Blind Signal Processing Sichuan Chengdu China

ISBN: (数字)9781728170817

ISBN: (纸本)9781728170824

The whale optimization algorithm (WOA) is an effective algorithm for solving complex optimization problem. Its unique search mechanism results in better exploitation than exploration capability which means that it's easy to get trapped in the local optima. A self-adaptive modified whale optimization algorithm named of SAMWOA is proposed. A self-adaptive quasi-opposition method and novel population updating mechanism is integrated into the whale optimization algorithm to improve the solution accuracy. Finally, the proposed algorithm is utilized in optimizing the radiation pattern of a 16-element linear antenna array. The experimental results show that SAMWOA algorithm can reduce the sidelobe level obviously.

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A Fast Method for Shortest-Path Cover Identification in Large Complex Networks

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Computers, Materials & Continua 2020年第5期63卷 705-724页

作者： Qiang Wei Guangmin Hu Chao Shen Yunfei Yin School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu611731China National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu610041China MOE Key Lab for Intelligent Networks and Network Security Xi’an Jiaotong UniversityXi’an710049China The Electronic Engineering Department Universidad de SevillaSeville41004Spain School of Astronautics Harbin Institute of TechnologyHarbin150001China

Fast identifying the amount of information that can be gained by measuring a network via shortest-paths is one of the fundamental problem for networks exploration and ***,the existing methods are time-consuming for even moderate-scale *** this paper,we present a method for fast shortest-path cover identification in both exact and approximate scenarios based on the relationship between the identification and the shortest distance *** effectiveness of the proposed method is validated through synthetic and real-world *** experimental results show that our method is 105 times faster than the existing methods and can solve the shortest-path cover identification in a few seconds for large-scale networks with millions of nodes and edges.

关键词： Network discovery shortest-path cover shortest-path distance query large complex networks

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Speech language classification algorithm based on multiple-instance learning with negative sample cleaning 9

Speech language classification algorithm based on multiple-i...

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9th IEEE Joint International Information technology and Artificial Intelligence Conference, ITAIC 2020

作者： Zhang, Jianyi Xu, Wei Yao, Jiaqi Yan, Jikun Huang, Xin PLA Strategic Support Force Information Engineering University College of Information System Engineering Zhengzhou China National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu China

Speech language classification is an important direction in the field of machine learning. In order to further improve the effect of speech classification, this paper propose a new speech language classification algorithm based on multiple-instance learning with negative instance cleaning, namely NUMI-Biased SVM algorithm. Firstly, extract MFCC features from speech files, regarding each file as an instance bag, and every speech frame as an instance. Secondly, adopt PU learning algorithm to clean negative instances in positive bags to reduce their impact on classification. Finally, multiple-instance learning algorithm is used to classify the speech files. Experiments show that, compared with the SVM algorithm and multiple-instance SVM algorithm, NUMI-Biased-SVM has achieved significant improvement in classification performance. © 2020 IEEE.

关键词： Speech

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Suppression of mainlobe deceptive jammers using multi-domain coding

Suppression of mainlobe deceptive jammers using multi-domain...

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IET International Radar Conference (IRC 2023)

作者： Xiang Zhang Lan Lan Guisheng Liao Shengqi Zhu Xianxiang Yu Guolong Cui National Key Laboratory of Radar Signal Processing Xidian University No.2 South Taibai Road 710071 Xi’an People's Republic of China School of Electronic Engineering University of Electronic Science and Technology of China No. 2006 Xi Yuan Da Dao Gao Xin Xi Qu 611731 Chengdu People's Republic of China

ISBN: (数字)9781837240982

This paper deals with the problem of suppressing mainlobe deceptive jammers by using the Multi-Domain Coding (MDC). At the design stage, by coding in the spatial, slow-time and fast-time domains, the MDC radar is capable of separating the transmitted signals from various transmit elements and discriminating the true target from the mainlobe jammers in the joint transmit-receive spatial frequency domain. Furthermore, the mainlobe jammers can be eliminated by nulling at the equivalent transmit beampattern via an appropriate coding coefficient. Simulation results corroborate the effectiveness of the proposed MDC scheme for the mainlobe deceptive jammer suppression.

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Secure Optical Wireless Links with Dynamic Beam and Diversity Receiver 7

Secure Optical Wireless Links with Dynamic Beam and Diversit...

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7th IEEE International Conference on Network Intelligence and Digital Content, IC-NIDC 2021

作者： DIng, Jupeng Lin, Chih I Wang, Jintao Yang, Hui Wang, Lili School of Information Science and Engineering Xinjiang University Key Laboratory of Signal Detection and Processing in Xinjiang Uygur Autonomous Region China China Mobile Research Institute Beijing China Tsinghua University Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Beijing China Ludong University School of Information and Electrical Engineering Yantai China

ISBN: (纸本)9781665405829

Optical wireless communication (OWC) has emerged as one promising candidate to mitigate the frequency spectrum crisis. Due to the broadcast nature of OWC channel, physical layer security (PLS) techniques have been considered and explored to improve the transmission confidentiality of OWC links. However, almost all current schemes focus on scenarios including multiple transmitters and fail to match the scenarios with limited transmitters, even single transmitter. For addressing this issue, in this work, the dynamic optical beam based PLS enhancement scheme is proposed. Unlike conventional Lambertian beam based technique paradigm, the above scheme tentatively utilize the commercially available non-Lambertian beam to configure the secure OWC links. Numerical results show that, compared with about 1.10 bps/Hz average secrecy capacity (SC) of the conventional configuration, up to 1.93 bps/Hz average SC gain could be provided by the proposed dynamic beam scheme. Moreover, this potential gain will be increased to about 2.39 bps/Hz when the diversity combing is available for the legitimate user receiver. © 2021 IEEE.

关键词： Dynamics

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HSIGene: A Foundation Model for Hyperspectral Image Generation

arXiv

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arXiv 2024年

作者： Pang, Li Cao, Xiangyong Tang, Datao Xu, Shuang Bai, Xueru Zhou, Feng Meng, Deyu The School of Computer Science and Technology The Ministry of Education Key Lab for Intelligent Networks and Network Security Xi’an Jiaotong University Shaanxi Xi’an710049 China The School of Mathematics and Statistics Northwestern Polytechnical University Shaanxi Xi’an710021 China The National Laboratory of Radar Signal Processing Xidian University Xi’an710071 China The Key Laboratory of Electronic Information Countermeasure and Simulation of the Education Ministry of China Xidian University Xi’an710071 China The School of Mathematics and Statistics The Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University Shaanxi Xi’an710049 China Guangdong Guangzhou510555 China

Hyperspectral image (HSI) plays a vital role in various fields such as agriculture and environmental monitoring. However, due to the expensive acquisition cost, the number of hyperspectral images is limited, degenerating the performance of downstream tasks. Although some recent studies have attempted to employ diffusion models to synthesize HSIs, they still struggle with the scarcity of HSIs, affecting the reliability and diversity of the generated images. Some studies propose to incorporate multi-modal data to enhance spatial diversity, but spectral fidelity cannot be ensured. In addition, existing HSI synthesis models are typically uncontrollable or only support single-condition control, limiting their ability to generate accurate and reliable HSIs. To alleviate these issues, we propose HSIGene, a novel HSI generation foundation model which is based on latent diffusion and supports multi-condition control, allowing for more precise and reliable HSI generation. To enhance the spatial diversity of the training data while preserving spectral fidelity, we propose a new data augmentation method based on spatial super-resolution, in which HSIs are upscaled first, and thus abundant training patches could be obtained by cropping the high-resolution HSIs. In addition, to improve the perceptual quality of the augmented data, we introduce a novel two-stage HSI super-resolution framework, which first applies RGB bands super-resolution and then utilizes our proposed Rectangular Guided Attention Network (RGAN) for guided HSI super-resolution. Experiments demonstrate that the proposed model is capable of generating a vast quantity of realistic HSIs for downstream tasks such as denoising and super-resolution. The code and models are available at https://***/LiPang/HSIGene. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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