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5th IEEE International Conference on Electronics technology, ICET 2022

作者： Lu, Shihang Su, Zhengyu Liao, Kaisheng Li, Chao Xiao, Zhuoling Yan, Bo Lin, Shuisheng Wu, Bo University of Electronic Science and Technology of China Science and Technology on Electronic Information Control Laboratory National Key Laboratory of Science and Technology on Blind Signal Processing School of Information and Communication Engineering China

ISBN: (纸本)9781665485081

Receiver plays a vital role in various electronic countermeasure systems and has always been a hot topic to research. Based on the traditional receiver digital channelization structure, this paper further researches and derives the high-efficiency digital channelization structure based on polyphase discrete Fourier transform corresponding to various application scenarios. Then we design a 256-sub-channel digital channelization system and implement it on a 64-channel parallel digital down converter to verify the structure. The system design relies on system on chip as the platform, and we verify the feasibility of it on field programmable gate array. © 2022 IEEE.

关键词： System-on-chip

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Time Delay Estimation Using Windowed Differential agnitude of Cross Correlation and Its Hilbert Transform

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Procedia Engineering 2012年 29卷 2033-2038页

作者： Xinxin Ouyang Laiyuan Luo Jinyu Xiong Science and Technology On Blind Signal Processing Laboratory Chengdu China

A method is proposed for estimating time delay between MFSK signals received at two spatially separated sensors. onsidering the periodical characteristic of the peaks of cross correlation, the proposed method combines window echnology and differential magnitude between cross correlation and its Hilbert transform. The time argument at hich the differential magnitude achieves the peak in the range of window is the delay estimate. The method is ompared with several other methods. Simulation results show that the proposed method can get better performance. nder certain conditions the performance of proposed method can be better than CRLB by using priori information bout the transmitter and receivers.

关键词： MFSK time delay periodical peak window Hilbert transform

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A Predict Deterministic Finite Automaton for Practical Deep Packet Inspection

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Procedia Engineering 2012年 29卷 2156-2161页

作者： Qiang Wei Yunzhao Li Yanjie Chu Science and Technology On Blind Signal Processing Laboratory Chengdu China

Deep packet inspection has become extremely important due to network security. In deep packet inspection, the packet payload is compared against a set of patterns specified as regular expressions. Regular expressions are often implemented as deterministic finite automaton (DFA) for matching in linear time at high network link rates. We proposed a predict DFA which can accelerate the processing speed of DFA. A predict DFA uses additional information to predict several next transitions. We tested our proposal on Layer7 rule-set and validated it on real traffic traces, experiments show that our approach offers a significant performance improvement by accelerate rate factors from 1.6 to 2.8 over original DFA.

关键词： Deep packet inspection Regular expression DFA

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DOA Estimation for UCAs Based on Virtual Subarray Beamforming Technique 2

DOA Estimation for UCAs Based on Virtual Subarray Beamformin...

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2nd International Conference on Electronic Information technology and Computer Engineering, EITCE 2018

作者： Xu, Bo Huang, Zhigang Key Laborartory of Science and Technology for National Defence on Blind Signal Processing Chengdu612500 China

Direction-of-arrival (DOA) estimation is always a hotspot research in the fields of radar, sonar, communication and so on. And uniform circular arrays (UCAs) are more attractive in the context of DOA estimation since their symmetrical structures have potential to provide two directions coverage. This paper proposed a new DOA estimation method for UCAs via virtual subarray beamforming technique. The method would provide an acceptable DOA estimate even if the number of sources is great than the number of array elements. Also, the performance of the proposed method would hold good when the snapshot length or the signal-to-noise ratio (SNR) is small. Simulations show that the proposed technique offers significantly improved estimation resolution, capacity, and accuracy relative to the existing techniques. © The Authors, published by EDP sciences, 2018.

关键词： Direction of arrival

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Short-Time Variational Mode Decomposition

arXiv

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arXiv 2025年

作者： Jia, Hao Cao, Pengfei Liang, Tong Caiafa, Cesar F. Sun, Zhe Kushihashi, Yasuhiro Grau, Antoni Bolea, Yolanda Duan, Feng Solé-Casals, Jordi School of Medicine Nankai University Tianjin China Tianjin Key Laboratory of Interventional Brain-Computer Interface and Intelligent Rehabilitation Tianjin China Universitat Politècnica de Catalunya Barcelona Spain Mechanical Systems Engineering Nippon Institute of Technology Saitama Japan Villa Elisa Argentina Computational Bioengineering Laboratory Faculty of Health Data Science Juntendo University Tokyo Japan Data and Signal Processing Research Group University of Vic-Central University of Catalonia Vic Spain

Variational mode decomposition (VMD) and its extensions like Multivariate VMD (MVMD) decompose signals into ensembles of band-limited modes with narrow central frequencies. These methods utilize Fourier transformations to shift signals between time and frequency domains. However, since Fourier transformations span the entire time-domain signal, they are suboptimal for non-stationary time series. We introduce Short-Time Variational Mode Decomposition (STVMD), an innovative extension of the VMD algorithm that incorporates the Short-Time Fourier transform (STFT) to minimize the impact of local disturbances. STVMD segments signals into short time windows, converting these segments into the frequency domain. It then formulates a variational optimization problem to extract band-limited modes representing the windowed data. The optimization aims to minimize the sum of the bandwidths of these modes across the windowed data, extending the cost functions used in VMD and MVMD. Solutions are derived using the alternating direction method of multipliers, ensuring the extraction of modes with narrow bandwidths. STVMD is divided into dynamic and non-dynamic types, depending on whether the central frequencies vary with time. Our experiments show that non-dynamic STVMD is comparable to VMD with properly sized time windows, while dynamic STVMD better accommodates non-stationary signals, evidenced by reduced mode function errors and tracking of dynamic central frequencies. This effectiveness is validated by steady-state visual-evoked potentials in electroencephalogram signals. Copyright © 2025, The Authors. All rights reserved.

关键词： Cost functions

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Research on Tracking Method of Space Target Based on Interacting Multiple Models

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Procedia Engineering 2012年 29卷 2726-2731页

作者： Kuo Hu Huafeng Peng Wuqin Tong Science and Technology On Blind Signal Processing Laboratory Chengdu China

The existing methods of space target tracking cant track the maneuver space target accurately and steady. In order to solve this problem, a method for maneuver space target tracking based on IMM is proposed in this paper. After introducing the flow of Kalman Filtering, which is the basic of target tracking algorithm, and analysing Dynamic and Kinematic models relatively, the two models are combined by IMM algorithm. Simulation result demonstrate that This two algorithms combination not only improves tracking precision in tracking, but also solves the maneuver problem efficiently for space target.

关键词： Space target maneuver State model Interacting Multiple Model

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Feature-Consistent Cyclic Domain Adversarial Network for Unsupervised Transfer Diagnosis of the Autoimmune Disease Igg4-Rd by Raman Spectroscopy

SSRN

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SSRN 2025年

作者： Zhang, Ziyang Chen, Cheng Qin, Wentao Wu, Xue Liu, Yang Wu, Lijun Lv, Xiaoyi Chang, Chenjie Chen, Xinya Chen, Zhiyuan Xu, Qianxi Li, Zhuoyu Ma, Mengsi Chen, Chen Xinjiang University College of Software Urumqi830046 China Xinjiang Cloud Computing Application Laboratory Xinjiang University Karamay834099 China School of Computer Science and Technology Xinjiang University Urumqi830046 China Key Laboratory of signal detection and processing Xinjiang University Urumqi830046 China Department of Rheumatology＆Immunology People's Hospital of Xinjiang Uygur Autonomous Region Xinjiang Urumqi China Xinjiang Clinical Research Center for Rheumatoid arthritis Xinjiang Urumqi China

Abstracts IgG4-related disease (IgG4-RD) is a multi-organ immune disorder characterized by systemic involvement, diverse pathogenesis, and rarity, which complicates its diagnosis. Traditional medical diagnostic models, predominantly based on supervised learning in machine learning or deep learning frameworks, struggle to address the scarcity of labeled data due to the diagnostic challenges of IgG4-RD. To overcome this limitation, this study proposes a novel framework based on Feature-Consistent Cyclic Domain Adversarial Networks (FCCDAN). The framework first trains a classification model on the source domain for diagnosing source domain diseases under supervised conditions. Subsequently, leveraging the source domain model, the encoder is guided to extract semantic features, while feature consistency loss is integrated into both the generator and discriminator to align the semantic features of the target domain with those of the source domain. Finally, the aligned features are processed through a pre-trained linear mapping to achieve unsupervised classification and diagnosis in the target domain. The proposed model utilizes common autoimmune diseases as a knowledge source, enabling unsupervised transfer diagnosis for IgG4-RD, and demonstrates generalizability through experiments on other disease datasets, achieving an average diagnostic accuracy of 90.62%. This represents improvements of 4.14%, 9.82%, and 10.51% over baseline models, respectively. FCCDAN expands the applicability of Raman spectroscopy in disease diagnosis, maintaining high performance even in label-scarce environments. The overall research workflow also establishes a robust paradigm for the application and advancement of domain adaptation techniques in this context. © 2025, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Sparse Bayesian Learning for Distributed Passive Radar Imaging via Covariance Sparse Representation 6

Sparse Bayesian Learning for Distributed Passive Radar Imagi...

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

作者： Gong, Fukang Zhang, Shunsheng He, Qing Research Institute of Electronic Science and Technology University of Electronic Science and Technology of China Chengdu China National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu China

ISBN: (纸本)9781728129129

Due to low imaging cost and robustness, the distributed passive radars using multiple transmitters and multiple receivers to observe targets have become a hot research. In the case of low SNR, the imaging accuracy of the distributed passive radar imaging model via Orthogonal Matching Pursuit (OMP) sparse reconstruction is low. For this problem, a framework consisting of sparse representation of the received multi-snapshot radar signal covariance matrix, Sparse Bayesian Learning (SBL) based reconstruction algorithm has been built. At the end of paper, through the simulation experiment, the imaging results of the original data and covariance data at low SNR are compared, and the reconstruction errors under different SNR are used to verify the effectiveness of the proposed algorithm. © 2019 IEEE.

关键词： Covariance matrix

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Adaptive Target Birth Intensity Based on Scene Optimization 5

Adaptive Target Birth Intensity Based on Scene Optimization

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5th International Conference on Intelligent Control, Measurement and signal processing, ICMSP 2023

作者： Zhao, Jiuchao Zhan, Ronghui Cao, Jingmin Peng, Huafeng College of Electronic Science and Engineering National University of Defense Technology Changsha China National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu China

ISBN: (纸本)9798350336030

The effective multi-target detection and tracking has always been a research hotspot in the field of radar, and there are many factors that affect the performance of the algorithm for multi-target tracking. Aiming at the target birth issue, this paper proposes an adaptive target birth intensity method based on scene optimization. This method is mainly applied to the radar long-term continuous monitoring scenario, and the monitoring area is divided into the target-birth consideration area and non-target-birth consideration area by some prior information of the monitoring area and the target status. And then the target birth process in the target-birth consideration area is strengthened, while the clutter interference from the non-target-birth consideration area is eliminated, so as to improve the target tracking performance and processing efficiency. Finally, the effectiveness and robustness of the proposed algorithm are verified by simulation experiments and analysis. © 2023 IEEE.

关键词： Target tracking

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Tolerance Granulation Based Community Detection Algorithm

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Tsinghua science and technology 2015年第6期20卷 620-626页

作者： Shu Zhao Wang Ke Jie Chen Feng Liu Menghan Huang Yanping Zhang Jie Tang Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Center of Information Support & Assurance TechnologySchool of Computer Science and TechnologyAnhui University School of Computer Science and Technology Tsinghua University

Community structure is one of the most important features in real networks and reveals the internal organization of the vertices. Uncovering accurate community structure is effective for understanding and exploiting networks. Tolerance Granulation based Community Detection Algorithm(TGCDA) is proposed in this paper, which uses tolerance relation(namely tolerance granulation) to granulate a network hierarchically. Firstly, TGCDA relies on the tolerance relation among vertices to form an initial granule set. Then granules in this set which satisfied granulation coefficient are hierarchically merged by tolerance granulation operation. The process is finished till the granule set includes one granule. Finally, select a granule set with maximum granulation criterion to handle overlapping vertices among some granules. The overlapping vertices are merged into corresponding granules based on their degrees of affiliation to realize the community partition of complex networks. The final granules are regarded as communities so that the granulation for a network is actually the community partition of the *** on several datasets show our algorithm is effective and it can identify the community structure more accurately. On real world networks, TGCDA achieves Normalized Mutual Information(NMI) accuracy 17.55% higher than NFA averagely and on synthetic random networks, the NMI accuracy is also improved. For some networks which have a clear community structure, TGCDA is more effective and can detect more accurate community structure than other algorithms.

关键词： tolerance relation community tolerance granulation

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