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IEEE OPEN JOURNAL OF signal processing 2025年 6卷 369-384页

作者： Ghoorchian, Saeed Bilaj, Steven Maghsudi, Setareh Ruhr Univ Bochum Fac Elect Engn & Informat Technol D-44801 Bochum Germany Univ Sci & Technol Tehran Iran Univ Darmstadt Darmstadt Germany Tech Univ Berlin Berlin Germany

Sequential decision-making under uncertainty is often associated with long feedback delays. Such delays degrade the performance of the learning agent in identifying a subset of arms with the optimal collective reward in the long run. This problem becomes significantly challenging in a non-stationary environment with structural dependencies amongst the reward distributions associated with the arms. Therefore, besides adapting to delays and environmental changes, learning the causal relations alleviates the adverse effects of feedback delay on the decision-making process. We formalize the described setting as a non-stationary and delayed combinatorial semi-bandit problem with causally related rewards. We model the causal relations by a directed graph in a stationary structural equation model. The agent maximizes the long-term average payoff, defined as a linear function of the base arms' rewards. We develop a policy that learns the structural dependencies from delayed feedback and utilizes that to optimize the decision-making while adapting to drifts. We prove a regret bound for the performance of the proposed algorithm. Besides, we evaluate our method via numerical analysis using synthetic and real-world datasets to detect the regions that contribute the most to the spread of Covid-19 in Italy.

关键词： Delays Decision making Mathematical models Vectors Numerical analysis COVID-19 signal processing algorithms signal processing Uncertainty Synthetic data Combinatorial multi-armed bandit delayed feedback non-stationary environment online learning uncertainty

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ICASSP 2024 Speech signal Improvement Challenge

IEEE OPEN JOURNAL OF SIGNAL PROCESSING

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IEEE OPEN JOURNAL OF signal processing 2025年 6卷 238-246页

作者： Ristea, Nicolae-Catalin Naderi, Babak Saabas, Ando Cutler, Ross Braun, Sebastian Branets, Solomiya Microsoft Corp Redmond WA 98073 USA

The ICASSP 2024 Speech signal Improvement Challenge aims to advance research in enhancing speech signal quality within communication systems. The speech signal quality can be assessed using the SIG metric from ITU-T P.835 and still remains a top issue in audio communication and conferencing systems. For example, in the ICASSP 2023 Deep Noise Suppression Challenge, the improvement in the background and overall quality is impressive, while the speech signal enhancement was not statistically significant. To improve the speech signal the following speech impairment areas must be addressed: coloration, discontinuity, loudness, reverberation, and noise. To this end, we organized ICASSP 2024 Speech signal Improvement Challenge, which marks the second signal-focused challenge, built upon the success of the previous ICASSP 2023 Speech signal Improvement Challenge. A training and test set was provided for the challenge, and the winners were determined using an extended crowdsourced implementation of ITU-T P.804's listening phase and the word accuracy (WAcc) rate. The results show that significant improvement was made across all measured dimensions of speech quality.

关键词： Speech enhancement Reverberation Noise Measurement Distortion Quality assessment Noise reduction Background noise signal processing algorithms Correlation deep learning subjective testing speech quality assessment

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A Resource-Efficient Time-Domain-Based Algorithm to Estimate Respiration Rate From Single-Lead ECG signal

IEEE OPEN JOURNAL OF INSTRUMENTATION AND MEASUREMENT

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IEEE OPEN JOURNAL OF INSTRUMENTATION AND MEASUREMENT 2025年 4卷

作者： Krishnapriya, G. B. Ponnalagu, R. N. Goel, Sanket Birla Inst Technol & Sci Pilani Dept Elect & Elect Engn MEMS Microfluid & Nano Elect Lab Hyderabad Campus Hyderabad 500078 India Birla Inst Technol & Sci Pilani Dept Elect & Elect Engn Hyderabad Campus Hyderabad 500078 India

This study introduces a novel, computationally efficient time-domain (TD) algorithm for accurate breath rate (BR) estimation from single-lead ECG signals, designed for wearable devices. The proposed algorithm uses statistical TD parameters-mean, prominence, and distance (MPD)-to detect valid respiratory peaks in ECG-derived respiration (EDR) signals. The performance of the MPD algorithm was evaluated using two datasets: 1) a benchmark database containing ECG acquired during dynamic activities and 2) a real-time dataset comprising ECG signals from five subjects performing dynamic activities, including standing, jogging, and recovery. Comparative analysis against state-of-the-art TD methods, such as count-orig, zero-crossing detection, peak detection, and adaptive threshold techniques, demonstrates the superiority of MPD in both accuracy and computational efficiency. On the benchmark dataset, MPD achieved a mean absolute error (MAE) of 3.66 bpm and mean absolute percentage error (MAPE) of 23.69%, outperforming the Count-Orig method (MAE = 5.09 bpm, MAPE = 32.76%). For real-time data, MPD further demonstrated robust performance with an MAE of 1.53 bpm and MAPE of 7.25%. The algorithm's design simplicity, combined with its ability to handle spurious peaks and varying signal conditions, makes it particularly suitable for resource-constrained wearable applications. Its high accuracy, low computational demands, and adaptability across activity conditions underscore its potential for continuous, real-time respiratory monitoring in diverse scenarios.

关键词： Electrocardiography Accuracy Heuristic algorithms Estimation Monitoring Real-time systems Wearable devices signal processing algorithms Performance evaluation Biomedical monitoring Bio medical signal processing denoising ECG-derived respiration (EDR) rate ECG signal processing signal processing wearable devices

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Grating Lobe/Sidelobe Suppression Method for Near-Field Distributed MIMO Imaging Array

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IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2025年第3期73卷 1674-1687页

作者： He, Jiacheng Wang, Jun Yang, Bin Zhao, Ke Sun, Jinping Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Beihang Univ Hangzhou Innovat Inst Hangzhou 310000 Peoples R China

Distributed multiple-input-multiple-output(MIMO)arrays obtain a larger virtual aperture by combining subarrays at different locations. However, subarray spacing leads to sparse spatial sampling, introducing grating lobes in the imaging results. In this article, we propose a grating lobe suppression method utilizing array pattern migration and multiapodization (MA) for large-aperture distributed MIMO imaging arrays under near-field conditions. The MA processing includes applying multiple sets of compensation weights to achieve pattern migration of the imaging result and selecting the minimum value as the output. For further image quality improvement, we derive sum and difference images of the imaging results from sum and difference patterns of the array factor, and then propose a novel sidelobe suppression method utilizing weighted sum and difference (WSD) image cancellation. On this basis, we combine the two proposed methods with the backprojection (BP) algorithm through adaptive weighting techniques to eliminate the spatially variant characteristics of the array pattern in the near-field range, introducing a weighted BP (WBP) algorithm for near-field imaging. The effectiveness of the proposed methods has been verified through multiple simulations, measurements, and a publicly available dataset.

关键词： Gratings Imaging Radar imaging Apertures Vectors Radar Antennas and propagation signal processing algorithms Phased arrays MIMO radar Array pattern distributed multiple-input-multiple-output (MIMO) array grating lobe/sidelobe suppression multiapodization (MA) sum/difference images

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HRRP Synthesis and Imaging of Frequency Agile Waveform With Multidimensional Nonideal Factor Errors for High-Speed Targets

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IEEE SENSORS JOURNAL 2025年第8期25卷 13266-13280页

作者： Cui, Shuang Shao, Shuai Liu, Hongwei Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China

Acquiring high-resolution range profile (HRRP) image of noncooperative targets holds substantial significance in the field of radar automatic target recognition due to its higher range resolution and richer target information compared with narrowband signal. To overcome the high hardware requirements for signal generation and reception in transmission of wideband signals, the utilization of step-frequency agile waveform (FAW) is a favorable choice for HRRP synthesis. The high-speed motion of noncooperative targets and the instability of radar systems will lead to multidimensional nonideal factor errors such as motion errors, carrier frequency offset (CFO), and time-varying amplitude (TVA) in radar echoes, resulting in poor quality of HRRP images with traditional HRRP synthesis methods. To address the above problems, this article proposes an HRRP synthesis method of FAW with multidimensional nonideal factor errors for high-speed targets. In this technique, a fine-grained signal model with multidimensional errors is established, enabling the acquisition of high-precision HRRP through parameter estimation and compensation. Based on this, a joint optimization algorithm of sparrow search algorithm and simulated annealing algorithm (SSA-SAA) is proposed to solve the optimal parameter search. Moreover, a multicriterion fusion cost function is designed to enhance the robustness of parameter search compared with the single-criterion cost function. High-precision HRRP synthesis of FAW for high-speed targets is achieved by effectively eliminating the errors caused by nonideal factors. Furthermore, high-precision inverse synthetic aperture radar (ISAR) based on long-term observation of HRRP sequences is generated. Extensive experimental results based on both simulated and real data are provided to demonstrate the effectiveness and robustness of the proposed method.

关键词： Radar Radar imaging Imaging Wideband Interference Frequency synthesizers Time-frequency analysis signal resolution signal processing algorithms Frequency modulation Frequency agile waveform (FAW) high-resolution range profile (HRRP) synthesis multidimensional nonideal factor errors sparrow search algorithm and simulated annealing algorithm (SSA-SAA)

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Algorithm Unrolling Network With Learnable Sparse Regularization for Interpretable Mechanical Anomaly Detection

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2025年第5期21卷 3786-3795页

作者： Chen, Xiyue Wang, Shibin Wang, Shi-ao Ding, Baoqing Yan, Ruqiang Chen, Xuefeng Xi An Jiao Tong Univ Natl Key Lab Aerosp Power Syst & Plasma Technol Xian 710049 Peoples R China Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Peoples R China

Sparse representation-based interpretable algorithm unrolling is one of promising techniques for mechanical anomaly detection. In order to enhance the learning and representation capabilities of the algorithm unrolling model, this article proposes a learnable sparse regularization network (LSR-Net). Instead of imposing explicit regularization constraints on the encoding and dictionary during modeling, two subnetworks are designed to learn prior information: $\mathbf{Net}_{\mathbf{X}}$ and $\mathbf{Net}_{\mathbf{D}}$. The model is solved using the half quadratic splitting algorithm, and further unrolls the process of iterative computation into the form of a network. The architecture for encoding learning is structured as input convex neural networks, ensuring LSR-Net can learn meaningful encoding priors. Through the analysis of simulated and experimental data, it has been demonstrated that LSR-Net has strong feature extraction and noise resistance capabilities, and the design of its prior learning architecture is both reasonable and effective. In addition, the visualization of LSR-Net's overall reconstruction and the reconstruction of different dictionary atoms allows for both global and local interpretation of the learning results, thereby providing post hoc interpretability to LSR-Net. The visualization results confirm that LSR-Net is capable of learning features that align with mechanical vibration characteristics.

关键词： Convolution Encoding Feature extraction Anomaly detection Dictionaries Convolutional codes Analytical models Predictive models Neural networks signal processing algorithms Algorithm unrolling anomaly detection convolutional dictionary learning (CDL) interpretable neural network

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Self-Weighted Multi-View Fuzzy Clustering With Multiple Graph Learning

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IEEE signal processing LETTERS 2025年 32卷 1585-1589页

作者： Liu, Chaodie Chang, Cheng Nie, Feiping Henan Normal Univ Sch Comp & Informat Engn Xinxiang 453007 Peoples R China Henan Normal Univ Key Lab Artificial Intelligence & Personalized Lea Xinxiang 453007 Peoples R China Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect IOPEN Xian 710072 Peoples R China Minist Ind & Informat Technol Key Lab Intelligent Interact & Applicat Xian 710072 Peoples R China

Graph-based multi-view clustering has garnered considerable attention owing to its effectiveness. Nevertheless, despite the promising performance achieved by previous studies, several limitations remain to be addressed. Most graph-based models employ a two-stage strategy involving relaxation and discretization to derive clustering results, which may lead to deviation from the original problem. Moreover, graph-based methods do not adequately address the challenges of overlapping clusters or ambiguous cluster membership. Additionally, assigning appropriate weights based on the importance of each view is crucial. To address these problems, we propose a self-weighted multi-view fuzzy clustering algorithm that incorporates multiple graph learning. Specifically, we automatically allocate weights corresponding to each view to construct a fused similarity graph matrix. Subsequently, we approximate it as the scaled product of fuzzy membership matrices to directly derive clustering assignments. An iterative optimization algorithm is designed for solving the proposed model. Experiment evaluations conducted on benchmark datasets illustrate that the proposed method outperforms several leading multi-view clustering approaches.

关键词： Clustering algorithms Data mining Benchmark testing Linear programming Computational complexity Training signal processing algorithms Optimization Flowering plants Vectors Fuzzy clustering graph learning multi-view clustering self-weighted

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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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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 University School of Electrical Engineering Chengdu610031 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. © 1994-2012 IEEE.

关键词： Frequency estimation signal processing algorithms Noise Voltage Power system stability Adaptation models Vectors Robustness Training Standards

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