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IEEE SENSORS LETTERS 2025年第3期9卷

作者： Ma, Peijun Yao, Cong Shi, Jiangyi Zhao, Gongzhi Ma, Mingyu Xidian Univ Sch MicroElect Xian 710000 Peoples R China

In recent years, with the development of brain science, the value of electroencephalography (EEG) data has become prominent. However, due to the characteristics of real-time transmission and large amounts of data, there is an urgent need for efficient and lightweight EEG compression algorithms. The existing EEG compression methods have many shortcomings, such as limited compression ratio (CR), poor reconstruction signal quality, and too large model scale, which cannot meet the developmental needs of portable wearable EEG detection devices. In this letter, a method of EEG signal compression based on 2-D rhythm feature maps is proposed. Through discrete wavelet transformation (DWT) extraction of the signal rhythm characteristics, the signal is compressed and reconstructed using encoding and reconstruction channels based on an autoencoder network. At the output end of the encoding channel, entropy coding is carried out to further compress the data volume. Through the discussion of several coding algorithms, JPEG2000 is selected as the local optimal coding algorithm. In addition, based on the idea of grouping convolution and void convolution kernel, a lightweight structure is designed to simplify the process of the proposed network and greatly reduce the number of model parameters. Experiments show that, compared with other similar algorithms, the percentage-root-mean-square distortion and mean squared error (MSE) of the proposed algorithm are 14.76% and 2.95%, respectively, at a relatively high CR (CR is about 16). And only 87.9-k parameters are used, which is more suitable for embedded scenarios and wearable devices.

关键词： Convolution Electroencephalography Feature extraction Transform coding Rhythm Kernel Autoencoders Brain modeling Sleep signal processing algorithms Sensor signal processing 2-D rhythm profile autoencoder electroencephalography (EEG) JPEG2000 lightweight signal compression

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Diffusion Augmented Complex Maximum Total Correntropy Algorithm for Power System Frequency Estimation

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IEEE signal processing LETTERS 2025年 32卷 1720-1724页

作者： Zhao, Haiquan Peng, Yi Chen, Jinsong Hu, Jinhui Southwest Jiaotong Univ Sch Elect Engn Chengdu 610031 Peoples R China

Currently, adaptive filtering algorithms have been widely applied in frequency estimation for power systems. However, research on diffusion tasks remains insufficient. Existing diffusion adaptive frequency estimation algorithms exhibit certain limitations in handling input noise and lack robustness against impulsive noise. Moreover, traditional adaptive filtering algorithms designed based on the strictly-linear (SL) model fail to effectively address frequency estimation challenges in unbalanced three-phase power systems. To address these issues, this letter proposes an improved diffusion augmented complex maximum total correntropy (DAMTCC) algorithm based on the widely linear (WL) model. The proposed algorithm not only significantly enhances the capability to handle input noise but also demonstrates superior robustness to impulsive noise. Furthermore, it successfully resolves the critical challenge of frequency estimation in unbalanced three-phase power systems, offering an efficient and reliable solution for diffusion power system frequency estimation. Finally, we analyze the stability of the algorithm and computer simulations verify the excellent performance of the algorithm.

关键词： Frequency estimation signal processing algorithms Noise Voltage Power system stability Adaptation models Vectors Robustness Training Standards WL model frequency estimation total least squares augmented diffusion

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TIME REVERSAL: algorithms FOR M-ARY TARGET CLASSIFICATION USING ARRAY signal processing

TIME REVERSAL: ALGORITHMS FOR M-ARY TARGET CLASSIFICATION US...

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IEEE Military Communications Conference (MILCOM 2008)

作者： Foroozan, Foroohar Asif, Amir York Univ Toronto ON M3J 2R7 Canada

ISBN: (纸本)9781424426768

An M-ary time reversal (TR) maximum likelihood classifier for a single pair of transmitting and receiving transducer element was derived in [1] for underwater acoustic target detection applications. This paper considers a more general TR setup consisting of a P-element transmitting array and an N element receiving array and derives the M-ary conventional and TR classifiers for the multielement case in an electromagnetic communication environment. We show that the TR algorithm provides a classification gain of over 3 dB at low signal to noise ratios as compared to the conventional classifiers.

关键词： signal processing algorithms Array signal processing Backscatter Transducers signal to noise ratio Radar detection Classification algorithms Antenna arrays Electromagnetic scattering Acoustic distortion

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Polarization-Agile Jamming Suppression for Dual-Polarized Digital Array Radars

IEEE TRANSACTIONS ON RADAR SYSTEMS

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IEEE TRANSACTIONS ON RADAR SYSTEMS 2025年 3卷 247-259页

作者： Wang, Zhigang He, Jin Shu, Ting Zhang, Ning Lu, Xiang Wang, Junfeng Truong, Trieu-Kien Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China Nanjing Marine Radar Inst Nanjing 211153 Peoples R China Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Key Lab Intelligent Sensing & Recognit Shanghai 200240 Peoples R China Nanjing Marine Radar Inst Nanjing 211153 Peoples R China

In the realm of modern radar electronic warfare, hostile jamming signals with time-variant polarization states pose a significant challenge to the performance of host radars. This article presents a signal-processing scheme specifically designed to suppress polarization-agile jamming signals in dual-polarized digital array radars (DARs). By innovatively modeling the polarization-agile jamming signal as two orthogonal linearly polarized signals sharing the same elevation-azimuth angle, a direction-cosine estimation and association algorithm tailored for such signals is derived. Furthermore, a spatial covariance matrix reconstruction (CMR) method that uniquely extracts the time-varying polarization parameters of each jamming signal is developed. Building upon this, a spatial-polarization CMR method is devised to effectively suppress all polarization-agile jamming signals. The key innovation lies in achieving adaptive polarization matching during the cancellation process, which sets this scheme apart from conventional radar signal-processing approaches. Simulation results underscore the superiority of the proposed scheme, demonstrating significant performance enhancements over commonly used methodologies.

关键词： Jamming Radar Vectors Radar antennas Antenna arrays Transmission line matrix methods Radar signal processing Array signal processing signal processing algorithms Estimation Covariance matrix reconstruction (CMR) digital array radar (DAR) dual-polarized radar jamming suppression polarization estimation radar electronic warfare

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Dual ${H}_{\infty}$ algorithms for signal processing— Application to Speech Enhancement

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IEEE Transactions on signal processing 2007年第11期55卷 5195-5208页

作者： David Labarre Eric Grivel Mohamed Najim Nicolai Christov Dynamiques Normales et Pathologiques UMR 5227MAC Equipe Noyaux Gris Centraux Université Victor Segalen Bordeaux 2 Bordeaux France LAPS UMR5218 IMS Equipe Signal et Image University of Bordeaux 1 Talence France Laboratoire dAutomatique Génie Informatique et Signal Université des Sciences et Technologies de Lille Villeneuve d'Ascq France

This paper deals with the joint signal and parameter estimation for linear state-space models. An efficient solution to this problem can be obtained by using a recursive instrumental variable technique based on two dual Kalman filters. In that case, the driving process and the observation noise in the state-space representation for each filter must be white with known variances. These conditions, however, are too strong to be always satisfied in real cases. To relax them, we propose a new approach based on two dual H infin filters. Once a new observation of the disturbed signal is available, the first H infin algorithm uses the latest estimated parameters to estimate the signal, while the second H infin algorithm uses the estimated signal to update the parameters. In addition, as the H infin filter behavior depends on the choice of various weights, we present a way to recursively tune them. This approach is then studied in the following cases: (1) consistent estimation of the AR parameters from noisy observations and (2) speech enhancement, where no a priori model of the additive noise is required for the proposed approach. In each case, a comparative study with existing methods is carried out to analyze the relevance of our solution.

关键词： Additive noise Parameter estimation signal processing algorithms Speech enhancement Filters Filtering signal processing Gaussian processes Maximum likelihood estimation Iterative algorithms

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Fast algorithms for digital signal processing

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Proceedings of the IEEE 1987年第7期75卷 973-974页

作者： A. Steinhardt MIT Lincoln Laboratory Lexington MA

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algorithms for statistical signal processing [Book Review]

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IEEE signal processing Magazine 2002年第2期19卷 94-95页

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Comparison of four recovery algorithms used in compressed sensing for ECG signal processing

Comparison of four recovery algorithms used in compressed se...

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Computers in Cardiology (CinC)

作者： Zhimin Zhang Shoushui Wei Dingwen Wei Liping Li Feng Liu Chengyu Liu School of Control Science and Engineering Shandong University Jinan China School of data science and soft Engineering Qingdao university Qingdao China School of Science and Technology Shandong University of Traditional Chinese Medicine Jinan China School of Information Technology and Electrical Engineering University of Queensland Queensland Australia

ISBN: (纸本)9781509008964

Compressed Sensing (CS) has been used in ECG signal compressing with the rapid development of real-time & dynamic ECG applications. signal reconstruction process is an essential step in CS-based ECG processing. Many recovery algorithms have been reported in the last decades. However, the comparative study on their reconstructing performances for CS-based ECG signal processing lacks, especially in real-time applications. This study aimed to investigate this issue and provide useful information. Four typical recovery algorithms, i.e., compressed sampling matching pursuit (CoSaMP), orthogonal matching pursuit (OMP), expectation-maximum-based block sparse Bayesian learning (BSBLEM) and bound-optimization-based block sparse Bayesian learning (BSBL BO) were compared. Two performance indices, i.e., the percentage of root-mean-square difference (PRD) and the reconstructing time (RT), were tested to observe their changes with the change of compression ratio (CR). The results showed that BSBL_BO and BSBL_EM methods had better performances than OMP and CoSaMP methods. More specifically, BSBL_BO reported the best PRD results while BSBL_EM achieved the best RT index.

关键词： Electrocardiography Matching pursuit algorithms Bayes methods signal processing algorithms Image reconstruction Compressed sensing Algorithm design and analysis

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Parallel algorithms For Multidimensional signal processing A Unified signal Algebra Approach

Parallel Algorithms For Multidimensional Signal Processing A...

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Asilomar Conference on signals, Systems & Computers

作者： C.R. Giardina College of Staten Island City College and Graduate Center City University of New York Staten Island NY USA

Operations an digital signals similar to those employed for Z transforms are used for producing parallel multidimensional algorithms. All operations are given in the time domain and are part of the Unified signal Algebra. algorithms are given using dataflow type diagrams and are presented for the parallel specification of convolution, triple convolution, Volterra convolution, and correlation.

关键词： Parallel algorithms Multidimensional signal processing signal processing Algebra Convolution signal processing algorithms Algorithm design and analysis Educational institutions Digital signal processing Delay effects

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Enhanced signal processing for conventional and unconventional PD measuring methods: Wavelet de-noising, automatic detection algorithms and averaging for arrival time-based PD location in transformers and power cables

Enhanced signal processing for conventional and unconvention...

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International Conference on Condition Monitoring and Diagnosis, CMD

作者： Sacha Markalous Thomas Strehl Christoph Herold Thomas Leibfried LDIC GmbH Kesselsdorf Germany Institute of Electricaal Energy Systems and High Voltage Technology University of Karlsruhe Karlsruhe Germany

ISBN: (纸本)9781424416219

Two major tasks of partial discharges (PD) measurements may be distinguished, (i) providing general evidence and the type of PD (detection) and (ii) the location of the PD. Dependent on the type of device under test the two issues have changing priority. For the on-line/on-site PD location in power transformers unconventional PD measuring methods like acoustic ultra-sonic measurements or electromagnetic measurements up to the UHF (ultra high frequencies) range are performed, while the Time Domain Reflectometry (TDR) of electric PD signals is a standard technique for the location of PD in power cables.

关键词： signal processing algorithms Noise reduction Detection algorithms Partial discharges Acoustic measurements Electromagnetic measurements Power transformers Power measurement Frequency measurement Electric variables measurement

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