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Nonlinear Schur-Type Audio signal Parameterization for Convolutional Networks

IEEE signal processing LETTERS 2025年 32卷 1665-1669页

作者： Biernacki, Pawel Libal, Urszula Wroclaw Univ Sci & Technol Dept Acoust Multimedia & Signal Proc PL-50370 Wroclaw Poland

This article introduces a novel signal parameterization approach, termed nonlinear Schur-type signal parameterization, designed to enhance machine learning tasks such as signal classification and recognition. Traditional linear parameterization methods often struggle with the complex, nonlinear nature of real-world data. The mathematical foundation of the proposed parameterization method is extraction of Schur coefficients. The presented method is scalable and can be adjusted to the signal nature. The nonlinear Schur parameterization produces a matrix of Schur coefficients in time, dedicated to be a 2D input of convolutional neural networks (CNN). The performed experiments for the audio signals from open access datasets show that the signal representation in the form of the Schur coefficients is very efficient for recognition performance. The results obtained by CNN show an improvement in the classification accuracy in comparison with solutions based on preprocessing in frequency domain as FFT or MFCC.

关键词： Vectors Decorrelation Convolutional neural networks signal processing algorithms Feature extraction Rotors Reflection coefficient Accuracy Training Prediction algorithms nonlinear parameterization reflection coefficients

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Bias Compensation for Kernel Least-Mean-Square algorithms

IEEE SENSORS LETTERS

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IEEE SENSORS LETTERS 2025年第4期9卷

作者： Chien, Ying-Ren Liu, Jin-Ling Lin, En-Ting Qian, Guobing Natl Taipei Univ Technol Dept Elect Engn Taipei 106344 Taiwan Natl Ilan Univ Dept Elect Engn I Lan City 260007 Taiwan Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China

This letter addresses the challenge of input noise in nonlinear system identification using kernel adaptive filtering (KAF) techniques. Conventional kernel least-mean-square (KLMS) algorithms are susceptible to input noise, which introduces bias into the estimated weights, degrading performance. To mitigate this issue, we propose a bias-compensated KLMS (BC-KLMS) algorithm. By employing a finite-order nonlinear regression model and leveraging Taylor series expansion, we analyze the bias terms generated by input noise and incorporate them into a modified cost function. The resulting BC-KLMS algorithm effectively reduces noise-induced bias, leading to improved accuracy in nonlinear system identification tasks. Simulation results demonstrate that BC-KLMS outperforms traditional KLMS methods, achieving substantial bias compensation even in low signal-to-noise ratio conditions. This approach enhances the robustness of KAFs in real-world applications where input noise is prevalent.

关键词： Noise Vectors Kernel Adaptive filters signal to noise ratio Nonlinear systems signal processing algorithms Adaptation models Noise measurement Cost function Sensor signal processing bias compensation finite-order nonlinear regression model kernel least-mean-square (KLMS)

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Design of Autoencoder Algorithm for Compression of Lightweight EEG signals Based on 2-D Rhythm Feature Maps

IEEE SENSORS LETTERS

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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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Beamforming Designs for Hybrid Relaying in mmWave Systems Based on Deep Unfolding

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IEEE signal processing LETTERS 2025年 32卷 2050-2054页

作者： Luo, Zhen Xie, Fu Zhang, Rongbin Liu, Hongqing Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing 400065 Peoples R China Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Shantou Univ Dept Elect & Informat Engn Shantou 515063 Peoples R China

In this paper, an efficient hybrid beamforming scheme is proposed for the relay node in mmWave relay systems based on deep unfolding. The optimal digital beamformer at the relay node is decomposed into an analog-digital-analog (A-D-A) hybrid structure, which can be formulated as a matrix factorization problem. The analog beamforming matrices and digital beamforming matrix in the A-D-A hybrid structure are efficiently solved by a deep unfolding neural network, which is unfolded based on the projected gradient descent algorithm. Numerical results verify that performance of the proposed scheme is close to the fully-digital solution, which prevails traditional model-based algorithms with a fast runtime.

关键词： Relays Matrix decomposition Millimeter wave communication Array signal processing signal processing algorithms Artificial neural networks Transmitting antennas Receiving antennas Optimization Training mmWave hybrid beamforming relay deep unfolding

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Two-Dimensional Successive Variational Mode Decomposition

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IEEE signal processing LETTERS 2025年 32卷 1865-1869页

作者： Faisal, Kazi Newaj Chandela, Ujjawal Sharma, Rishi Raj Mil Inst Sci & Technol Dept EECE Dhaka 1216 Bangladesh Def Inst Adv Technol Dept Elect Engn Pune 411025 India

This letter presents a 2D extension of the successive variational mode decomposition (2D-SVMD) method, which decomposes 2D signals into band-limited intrinsic mode functions without requiring prior knowledge of the number of modes. The proposed 2D-SVMD addresses the limitations of existing 2D variational mode decomposition (2D-VMD) and 2D variational mode decomposition with total variation (2D-TV-VMD) methods, which need initialization of the number of modes and can produce redundant or mixed modes if the number is not accurately specified. 2D-SVMD decomposes in a sequential manner which enhances the convergence rate of the decomposition process and also mitigates the extraction of extraneous modes, collectively resulting in reduced computational overhead. The proposed 2D-SVMD is also evaluated on synthetic and real-world images and compared to 2D-VMD and 2D-TV-VMD, demonstrating its superior performance in achieving natural decomposition of 2D signals.

关键词： signal processing algorithms Convergence Data mining Optimization Transforms Training Peptides Image reconstruction Artificial intelligence Two-dimensional displays 2D signal signal decomposition image decomposition 2D variational mode decomposition and successive variational mode decomposition

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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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A Sparsity-Agnostic SL0 Channel Estimation Approach for OTFS Systems

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IEEE COMMUNICATIONS LETTERS 2025年第5期29卷 1097-1101页

作者： Wang, Xin Zheng, Can Hu, Pengjiang Yang, Junan Kang, Chung G. Natl Univ Def Technol Coll Elect Engn Hefei 230000 Peoples R China Korea Univ Sch Elect Engn Seoul 02841 South Korea

Orthogonal time frequency space (OTFS) modulation has been shown to support reliable communication in high mobility scenarios. Due to the sparsity of the delay-Doppler (DD) domain, most existing algorithms utilize compressed sensing (CS) for OTFS channel estimation, while requiring a known channel sparsity. However, in cases that the sparsity is not readily accessible, the estimation accuracy of these CS algorithms decreases dramatically. In this letter, we propose a Smoothed l(0 )(SL0) algorithm free of prior knowledge about channel sparsity for OTFS channel estimation. We obtain a novel vector expression of the input-output relationship in the DD domain and formulate it as a sparse signal recovery problem. In the case of unknown sparsity, a low-complexity SL0 algorithm is introduced to solve the problem with a faster reconstruction speed. Simulation results show that the proposed algorithm has significant advantages in estimation accuracy and complexity compared to algorithms that also do not require channel sparsity.

关键词： Channel estimation signal processing algorithms Delays Doppler effect Symbols Matching pursuit algorithms Estimation Accuracy Vectors Time-frequency analysis OTFS channel estimation compressed sensing SL0

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Collision Resolution in RFID Systems Using Antenna Arrays and Mix Source Separation

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IEEE COMMUNICATIONS LETTERS 2025年第5期29卷 1122-1125页

作者： Siala, Mohamed Sellami, Noura Univ Carthage Higher Sch Commun Tunis SUP COM Mediatron Lab Tunis 2083 Tunisia Univ Sfax Natl Sch Engn Sfax ENIS LETI Lab Sfax 3038 Tunisia

In this letter, we propose an efficient mix source separation (MSS) algorithm for collision resolution in radio frequency identification (RFID) systems equipped with an antenna array at the reader. We first introduce an approach that exploits the zero constant modulus (ZCM) criterion to separate colliding tags through gradient descent, without using pilot symbols. We show that the ZCM characteristic, considered alone, in the design of the objective function can lead to significant ambiguities in the determination of the beamformers used in the recovery of tag messages. To address this limitation, we propose a more sophisticated approach, relying on a hybrid objective function, incorporating a new ambiguity-raising criterion in addition to the ZCM criterion.

关键词： Radiofrequency identification Linear programming Vectors Symbols signal processing algorithms Receiving antennas Fluctuations Encoding Blind source separation Antenna arrays Mix source separation (MSS) gradient descent algorithm RFID zero constant modulus criterion

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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...

引用

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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