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A Novel Normalized Subband Adaptive Filter Algorithm Based on the Joint-Optimization Scheme

IEEE ACCESS 2022年 10卷 9868-9876页

作者： Shin, Jaewook Park, Bum Yong Lee, Won Il Yoo, Jinwoo Cho, Jaegeol Kumoh Natl Inst Technol Dept Elect Engn Gumi 39177 Gyeongbuk South Korea Kookmin Univ Dept Automobile & IT Convergence Seoul 02707 South Korea Soonchunhyang Univ Dept Med & Mechatron Engn Asan 31538 Chungnam South Korea

Herein, we propose a normalized subband adaptive filter (NSAF) algorithm that adjusts both the step size and regularization parameter. Based on the random-walk model, the proposed algorithm is derived by minimizing the mean-square deviation of the NSAF at each iteration to calculate the optimal parameters. We also propose a method for estimating the uncertainty in an unknown system. Consequently, the proposed algorithm improves performance in terms of tracking speed and misalignment. Simulation results show that the proposed NSAF outperforms existing algorithms in system identification scenarios.

关键词： signal processing algorithms Adaptive filters Covariance matrices System identification Convergence Licenses Indexes Adaptive filter normalized subband adaptive filter variable step size variable regularization parameter mean-square deviation

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Widely Linear Maximum Complex Correntropy Criterion Affine Projection Algorithm and Its Performance Analysis

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 3540-3550页

作者： Qiu, Chen Qian, Guobing Wang, Shiyuan Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China

Recently, affine projection algorithm has been extensively studied in the Gaussian noise environment. However, the performance of affine projection algorithm will deteriorate rapidly in the presence of impulsive noise and other non-Gaussian noise. To address this issue, this paper proposes a novel affine projection algorithm based on the complex Gaussian kernel function, called widely linear maximum complex correntropy criterion affine projection algorithm (WL-MCCC-APA). With the maximum complex correntropy criterion (MCCC), the robustness of affine projection algorithm is improved significantly in the non-Gaussian noise environment, especially in the presence of impulsive noise. Meanwhile, a variable step size WL-MCCC-APA (VSS-WL-MCCC-APA) method based on fixed-point theory is proposed to further balance the convergence rate and steady-state misalignment of the algorithm. Finally, the step size boundary analysis and steady-state analysis are presented for theoretical analysis. Simulation results verify the correctness of obtained theoretical results and the superiority of the proposed WL-MCCC-APA and VSS-WL -MCCC-APA.

关键词： signal processing algorithms Projection algorithms Optimized production technology Kernel Filtering algorithms Cost function Convergence Widely linear affine projection maximum complex correntropy criterion variable step size

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Deep Unrolled Recovery in Sparse Biological Imaging: Achieving fast, accurate results

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IEEE signal processing MAGAZINE 2022年第2期39卷 45-57页

作者： Ben Sahel, Yair Bryan, John P. Cleary, Brian Farhi, Samouil L. Eldar, Yonina C. Broad Inst MIT & Harvard Univ Cambridge MA 02142 USA Broad Inst MIT & Harvard Univ Opt Profiling Platform Cambridge MA 02142 USA Weizmann Inst Sci Dept Math & Comp Sci IL-7610001 Rehovot Israel MIT 77 Massachusetts Ave Cambridge MA 02139 USA Duke Univ Durham NC USA

Deep algorithm unrolling has emerged as a powerful, model-based approach to developing deep architectures that combine the interpretability of iterative algorithms with the performance gains of supervised deep learning, especially in cases of sparse optimization. This framework is well suited to applications in biological imaging, where physics-based models exist to describe the measurement process and the information to be recovered is often highly structured. Here we review the method of deep unrolling and show how it improves source localization in several biological imaging settings.

关键词： Location awareness Learning systems Biological system modeling Pipelines Imaging signal processing algorithms Performance gain

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A Robust Passive Islanding Detection Technique With Zero-Non-Detection Zone for Inverter-Interfaced Distributed Generation

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IEEE ACCESS 2022年 10卷 96296-96306页

作者： Pangedaiah, B. Reddy, P. L. Santosh Kumar Obulesu, Y. P. Kota, Venkata Reddy Alghaythi, Mamdouh L. Lakireddy Bali Reddy Coll Engn Elect & Elect Engn Dept Mylavaram 533003 Andhra Pradesh India Vellore Inst Technol Sch Elect Engn Vellore 632014 Tamil Nadu India JNTUK Elect & Elect Engn Dept Kakinada 533003 Andhra Pradesh India Jouf Univ Coll Engn Dept Elect Engn Sakaka 72388 Al Jouf Saudi Arabia

In this paper, a new and efficient passive islanding detection technique for a grid-connected hybrid distributed generating system has been proposed using Second Generation Wavelet Transform (SGWT) and Maximum Overlap Discrete Wavelet Transform (MODWT). In this technique, the ripple content of the voltage signal is measured at the Point of Common Coupling (PCC) and it is monitored through the decomposition of the signal by applying SGWT and MODWT technique up to suitable finer levels. This algorithm able to detects all kinds of islanding at nearly 0.3 sec, even with zero power mismatch. It is verified on a wide range of operational conditions and is also validated in various non-islanding conditions. Furthermore, the proposed technique is simple, there is no need for a classifier requirement, not depends on system parameters, zero non-detection zone (NDZ), and its operation is independent of the capacity and nature of distributed generation (DG) connected to the utility grid.

关键词： Islanding Discrete wavelet transforms signal processing algorithms Voltage measurement Renewable energy sources Classification algorithms Approximation algorithms Power grids Time-domain analysis Grid connected distributed generation maximum overlap discrete wavelet transform passive islanding detection second generation wavelet transform time domain analysis zero-non-detection zone

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Non-Contact Heart Rate Measurement From Facial Video Data Using a 2D-VMD Scheme

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IEEE SENSORS JOURNAL 2022年第11期22卷 11153-11161页

作者： Das, Mousumi Choudhary, Tilendra Bhuyan, M. K. Sharma, L. N. Indian Inst Technol Guwahati IITG Dept Elect & Elect Engn Gauhati 781039 India

Non-contact human body vital parameter measurements are getting more attention and have been extensively studied in a short span of time. One of such techniques is the heart rate (HR) estimation based on video recording of human face known as remote photoplethysmogram (rPPG). Usually, the recorded video gets contaminated due to illumination variation of ambient light, motion artifacts, and other environmental factors. Thus, extracting a reliable rPPG signal is a challenging task. In this paper, a novel spatial-temporal filtering method is proposed that utilizes 2D variational mode decomposition (2D-VMD) along with azimuthally averaged power spectrum density (AAPSD) and multimode kurtosis to extract a reliable rPPG signal. The robustness of the proposed algorithm is tested and validated using our own database and the publicly available standard dataset. The obtained experimental results are compared with the reference PPG signal measurements. Also, the proposed method is compared with the well-established independent component analysis (ICA)-based method. The performance results show that the non-contact HR estimated by the proposed method dramatically reduces the error and it proves our method to be superior than the existing method.

关键词： Heart rate Lighting Blood Sensors signal processing algorithms Light sources Image color analysis Spatial-temporal filtering 2D variational mode decomposition (2D-VMD) azimuthally averaged power spectrum density (AAPSD) remote photoplehysmogram (rPPG) non-contact HR

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Autovocoder: Fast Waveform Generation from a Learned Speech Representation Using Differentiable Digital signal processing

Autovocoder: Fast Waveform Generation from a Learned Speech ...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Jacob J Webber Cassia Valentini-Botinhao Evelyn Williams Gustav Eje Henter Simon King The Centre for Speech Technology Research University of Edinburgh United Kingdom Division of Speech Music and Hearing KTH Royal Institute of Technology Stockholm Sweden

Most state-of-the-art Text-to-Speech systems use the mel-spectrogram as an intermediate representation, to decompose the task into acoustic modelling and waveform generation.A mel-spectrogram is extracted from the waveform by a simple, fast DSP operation, but generating a high-quality waveform from a mel-spectrogram requires computationally expensive machine learning: a neural vocoder. Our proposed "autovocoder" reverses this arrangement. We use machine learning to obtain a representation that replaces the mel-spectrogram, and that can be inverted back to a waveform using simple, fast operations including a differentiable implementation of the inverse *** autovocoder generates a waveform 5 times faster than the DSP-based Griffin-Lim algorithm, and 14 times faster than the neural vocoder HiFi-GAN. We provide perceptual listening test results to confirm that the speech is of comparable quality to HiFi-GAN in the copy synthesis task.

关键词： Training Machine learning algorithms Speech coding Vocoders Synthesizers signal processing algorithms Machine learning

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Centralized and Distributed Robust State Estimation Over Sensor Networks Using Elliptical Distribution

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IEEE INTERNET OF THINGS JOURNAL 2022年第21期9卷 21825-21837页

作者： Wang, Guoqing Yang, Chunyu Ma, Lei Dai, Wei China Univ Min & Technol Engn Res Ctr Intelligent Control Underground Spac Minist Educ Xuzhou 221116 Jiangsu Peoples R China China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Jiangsu Peoples R China

We consider the robust state estimation over sensor networks with non-Gaussian noise, which is often encountered in many applications. Motivated by the fact that the elliptical distribution is the natural extension of the Gaussian distribution and includes a variety of non-Gaussian distributions with heavy-tailed characteristics, we here adopt the elliptical distribution to model the heavy-tailed process and measurement noise. The general state evolution model is used to replace the process equation and the elliptical distribution is denoted as a Gaussian mixture form. Based on that, the posterior estimation of the system state together with the parameters of the process and measurement noises can be inferred by the variational Bayes method. Moreover, the corresponding distributed estimation algorithm is then provided, which enables distributed implementation. The target tracking over sensor networks is used to show the estimation accuracy of the proposed algorithms.

关键词： State estimation signal processing algorithms Noise measurement Mathematical models Sea measurements Internet of Things Gaussian distribution Elliptical distribution robust state estimation sensor networks variational Bayes (VB)

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Successive Convex Approximation Based Off-Policy Optimization for Constrained Reinforcement Learning

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 1609-1624页

作者： Tian, Chang Liu, An Huang, Guan Luo, Wu Peking Univ Sch Elect State Key Lab Adv Opt Commun Syst & Networks Beijing 100871 Peoples R China Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Zhejiang Peoples R China

Constrained reinforcement learning (CRL), also termed as safe reinforcement learning, is a promising technique enabling the deployment of RL agent in real-world systems. In this paper, we propose a successive convex approximation based off-policy optimization (SCAOPO) algorithm to solve the general CRL problem, which is formulated as a constrained Markov decision process (CMDP) in context of the average cost. The SCAOPO is based on solving a sequence of convex objective/feasibility optimization problems obtained by replacing the objective and constraint functions in the original problem with convex surrogate functions. The proposed SCAOPO enables reuse of experiences from previous updates, thereby significantly reducing implementation cost when deployed in real-world engineering systems that need to online learn the environment. In spite of the time-varying state distribution and the stochastic bias incurred by off-policy learning, the SCAOPO with a feasible initial point can still provably converge to a Karush-Kuhn-Tucker (KKT) point of the original problem almost surely.

关键词： Convergence Costs signal processing algorithms Optimization Approximation algorithms Reinforcement learning Markov processes Constrained Safe reinforcement learning off-policy theoretical convergence

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Measuring Social Modulation of Gaze in Autism Spectrum Condition With Virtual Reality Interviews

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2022年 30卷 2373-2384页

作者： Artiran, Saygin Ravisankar, Raghav Luo, Sarah Chukoskie, Leanne Cosman, Pamela Univ Calif San Diego Dept Elect & Comp Engn La Jolla CA 92093 USA Univ Calif San Diego Comp Sci & Engn Dept La Jolla CA 92093 USA Univ Sydney Fac Engn Sydney NSW 2006 Australia Canyon Crest Acad San Diego CA 92130 USA Northeastern Univ Dept Phys Therapy Movement & Rehabil Sci Boston MA 02115 USA Northeastern Univ Art Design Dept Boston MA 02115 USA

Gaze behavior in dyadic conversations can indicate active listening and attention. However, gaze behavior that is different from the engagement expected during neurotypical social interaction cues may be interpreted as uninterested or inattentive, which can be problematic in both personal and professional situations. Neurodivergent individuals, such as those with autism spectrum conditions, often exhibit social communication differences broadly including via gaze behavior. This project aims to support situational social gaze practice through a virtual reality (VR) mock job interview practice using the HTC Vive Pro Eye VR headset. We show how gaze behavior varies in the mock job interview between neurodivergent and neurotypical participants. We also investigate the social modulation of gaze behavior based on conversational role (speaking and listening). Our three main contributions are: (i) a system for fully-automatic analysis of social modulation of gaze behavior using a portable VR headset with a novel realistic mock job interview, (ii) a signal processing pipeline, which employs Kalman filtering and spatial-temporal density-based clustering techniques, that can improve the accuracy of the headset's built-in eye-tracker, and (iii) being the first to investigate social modulation of gaze behavior among neurotypical/divergent individuals in the realm of immersive VR.

关键词： Behavioral sciences Interviews Modulation Headphones Autism Oral communication signal processing algorithms Gaze behavior job interview practice signal processing neurodivergence virtual reality social modulation

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Islanding Detection in Microgrid Compromising Missing Values Using NI Sensors

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IEEE SYSTEMS JOURNAL 2022年第2期16卷 1786-1795页

作者： Kumar, Dhruba Natl Inst Technol Patna Dept Elect Engn Patna 800005 Bihar India

Islanding detection has become an antiquated topic in microgrid, but during the shabby network condition, the conventional islanding detection methodologies become impractical. This article develops a new islanding detection technique considering interrupted communication during islanding detection. This article has experimented a new predictive model (PM)-based method for islanding detection in the laboratory-based multiple distributed generation (DG) system (system with more than one DG). Microphasor measurement units ($\mu $PMUs) are incorporated for measuring phasors from the real-time system. The measurements are analyzed by the artificial neural network (ANN) for initiating PM, which specifies the occurrence of islanding in terms of probability. The following two subalgorithms: evaluation and decoding are simultaneously working to estimate probability of the islanding during sound network condition. The posterior probability evaluation comes into action when communication is interrupted. The accuracy of the algorithm is tested under UL 1741 loading conditions. The performance of the algorithm is analyzed under the simultaneous manifestation of communication interruption and several islanding or nonislanding condition. Nondetection zone has been evaluated for different scenarios of missing frames. The algorithm has also been compared with existing methodologies.

关键词： Neural networks Microgrids Decoding signal processing algorithms Probabilistic logic Prediction algorithms Phasor measurement units Islanding detection microgrid missing data predictive model (PM) statistical method

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