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Data-Driven Sensor Selection Method Based on Proximal Optimization for High-Dimensional Data With Correlated Measurement Noise

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 5251-5264页

作者： Nagata, Takayuki Yamada, Keigo Nonomura, Taku Nakai, Kumi Saito, Yuji Ono, Shunsuke Tohoku Univ Dept Aerosp Engn Aoba Ku 6-6-01 Aramaki Sendai Miyagi 9808579 Japan Natl Inst Adv Ind Sci & Technol Dept Energy & Environm 1-2-1 Namiki Tsukuba Ibaraki 3058564 Japan Tokyo Inst Technol Dept Comp Sci Midori Ku 4259-G3-52 Nagatsuda Yokohama Kanagawa 2268503 Japan

The present paper proposes a data-driven sensor selection method for a high-dimensional nondynamical system with strongly correlated measurement noise. The proposed method is based on proximal optimization and determines sensor locations by minimizing the trace of the inverse of the Fisher information matrix under a block-sparsity hard constraint. The proposed method can avoid the difficulty of sensor selection with strongly correlated measurement noise, in which the possible sensor locations must be known in advance for calculating the precision matrix for selecting sensor locations. The problem can be efficiently solved by the alternating direction method of multipliers, and the computational complexity of the proposed method is proportional to the number of potential sensor locations when it is used in combination with a low-rank expression of the measurement noise model. The advantage of the proposed method over existing sensor selection methods is demonstrated through experiments using artificial and real datasets.

关键词： Noise measurement Covariance matrices Sea measurements Estimation Sparse matrices signal processing algorithms Matrix decomposition Alternating direction method of multipliers optimal design of experiment sensor selection sparse observation

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Continuous Time Digital signal processing and signal Reconstruction

Continuous Time Digital Signal Processing and Signal Reconst...

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IEEE Annual Computing and Communication Workshop and Conference (CCWC)

作者： Patrick Jungwirth W. Michael Crowe Advanced Computing Branch DEVCOM Army Research Lab Aberdeen MD Embedded Systems DEVCOM Aviation and Missile Center Redstone Arsenal AL

Continuous Time (CT) Systems offer several advantages over traditional digital signal processing (DSP); however, CT is not linear time invariant like DSP. Since discrete time and DSP are linear time invariant, signal reconstruction is applied after all signal processing steps have been completed. A DSP approach cannot be applied to continuous time systems. For CT systems, level crossing sampling signals are nonlinear. For linearity to hold in CT systems, signal reconstruction must be applied before any signal processing steps. In 2003, Tsividis showed an asynchronous zero order hold is the simplest signal reconstruction algorithm for CT. In 2008, Schell and Tsividis demonstrated an offline exact reconstruction algorithm with greater than 100 dB signal-to-noise and distortion ratio (SINAD). This paper focuses on CT level crossing signal reconstruction. For practical CT signal reconstruction, a low complexity approximation to offline signal reconstruction is preferred with higher signal-to-noise ratio than simple asynchronous zero order hold reconstruction. A Gaussian-sinc( ) reconstruction kernel is investigated as an approximation to offline reconstruction.

关键词： Nonlinear distortion signal processing algorithms Linearity Digital signal processing Reconstruction algorithms signal reconstruction Complexity theory

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Distributed Learning for Wireless Communications: Methods, Applications and Challenges

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IEEE JOURNAL OF SELECTED TOPICS IN signal processing 2022年第3期16卷 326-342页

作者： Qian, Liangxin Yang, Ping Xiao, Ming Dobre, Octavia A. Di Renzo, Marco Li, Jun Han, Zhu Yi, Qin Zhao, Jiarong Univ Elect Sci & Technol China Natl Key Lab Sci & Technol Commun Chengdu 611731 Sichuan Peoples R China Mem Univ Newfoundland St John NL A1B 3X9 Canada Royal Inst Technol S-11428 Stockholm Sweden Univ Paris Saclay Cent Supelec CNRS Lab Signaux & Syst F-91192 Gif Sur Yvette France Nanjing Univ Sci & Technol Nanjing 210094 Peoples R China Univ Houston Houston TX 77004 USA

With its privacy-preserving and decentralized features, distributed learning plays an irreplaceable role in the era of wireless networks with a plethora of smart terminals, an explosion of information volume and increasingly sensitive data privacy issues. There is a tremendous increase in the number of scholars investigating how distributed learning can be employed to emerging wireless network paradigms in the physical layer, media access control layer and network layer. Nonetheless, research on distributed learning for wireless communications is still in its infancy. In this paper, we review the contemporary technical applications of distributed learning for wireless communications. We first introduce the typical frameworks and algorithms for distributed learning. Examples of applications of distributed learning frameworks in emerging wireless network paradigms are then provided. Finally, main research directions and challenges of distributed learning for wireless communications are discussed.

关键词： Servers Distance learning Computer aided instruction Wireless communication Machine learning signal processing algorithms Computer architecture Distributed learning federated learning wireless communications

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Simulation of Wireless Video Multipath signal processing Model Based on Adaptive Filtering Algorithm

Simulation of Wireless Video Multipath Signal Processing Mod...

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Telecommunications, Electronics and Informatics (ICTEI), International Conference on

作者： Junyi Zheng The College of IOT Engineering Hohai University Changzhou Jiangsu China

ISBN: (数字)9798350342963

ISBN: (纸本)9798350342970

The research of adaptive filtering algorithm has always been a hot spot in the field of signal and information processing, and has been widely used in Speech processing, echo cancellation, channel equalization, system identification, spectral line enhancement, adaptive array processing, biomedical signal processing and many other fields. The amplitude control effect of multipath wireless video transmission is unstable, resulting in low bit rates for multipath single view transmission. In order to denoise and filter wireless video multipath signals, this paper proposes a variable step size LMS adaptive filtering algorithm for wireless video multipath signal processing to address the problem of wireless video signals being covered by noise. The algorithm is based on the feedback control function and combines the arctangent function and Exponential function together. The filtering performance of the algorithm is analyzed through MATLAB, and is applied to wireless video multi-path signal processing. The simulation results show that the algorithm has improved in Rate of convergence and steady-state error, and the error suppression ability has also improved in the filtering processing effect of wireless video multi-path signals, with good convergence performance and steady-state characteristics. The filtering effect has achieved satisfactory results.

关键词： Wireless communication Adaptive systems Filtering Noise signal processing algorithms signal processing Filtering algorithms

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Unscented Kalman Filter With General Complex-Valued signals

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IEEE signal processing LETTERS 2022年 29卷 2023-2027页

作者： Zhang, Xing Xia, Yili Li, Chunguo Yang, Luxi Nanjing Univ Posts & Telecommun Sch Commun & Informat Engn Nanjing 210049 Peoples R China Southeast Univ Sch Informat Sci & Engn Nanjing 210096 Peoples R China

For the estimation of real-valued Gaussian signals, the unscented Kalman filter (UKF) can provide a state estimate with second-order accuracy. However, when a general complex-valued system is considered, a direct extension of UKF from the real domain to the complex domain is inadequate, since the complementary covariance information associated with general improper complex-valued signals has been systematically ignored. To this end, in this work, we propose a general complex-valued unscented Kalman filter (GCUKF) algorithm which can be applied for both proper and improper signals. This is achieved by first proposing a novel sigma points selection scheme for the general complex-valued case, followed by a modified state update method to fully utilize both the innovation and its conjugate. A rigorous MSE analysis illustrates the superiority of the proposed state update method, and simulations support the analysis.

关键词： Estimation signal processing algorithms Kalman filters Technological innovation Approximation algorithms State-space methods Mathematical models State-space model general complex-valued unscented Kalman filter (GCUKF) noncircular

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Steady-State Mean-Square Error Analysis for Adaptive Joint Process Filter Using Pipelined Feedforward Second-Order Volterra Architecture

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2022年第3期69卷 1957-1961页

作者： Liu, Qianqian He, Yigang Hefei Univ Technol Sch Elect Engn & Automat Hefei 230009 Peoples R China

To lower the high computational burden of nonlinear filter in the practical applications, the previous paper has proposed the adaptive algorithm based on the joint process filter using pipelined feedforward second-order Volterra architecture (JPPSOV). Even though the JPPSOV adaptive filter has been common used for nonlinear systems identification, nonlinear active noise controller (ANC) and nonlinear acoustic echo cancellers (AEC), there is currently no literature that gives the steady state analysis of JPPSOV. Therefore, we analyze the steady state performance of the LMS adaptive algorithm of the JPPSOV filter for nonlinear system identification in this brief. Finally, the simulated results are in good agreement with the theoretical results.

关键词： Steady-state Pipelines Computer architecture signal processing algorithms Nonlinear systems Circuits and systems Adaptation models Volterra filter pipelined architecture steady-state analysis nonlinear filter

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Zero-Attracting Kernel Maximum Versoria Criterion Algorithm for Nonlinear Sparse System Identification

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IEEE signal processing LETTERS 2022年 29卷 1546-1550页

作者： Jain, Sandesh Majhi, Sudhan Indian Inst Sci Dept Elect Commun Engn Bangalore 560012 Karnataka India

Sparsity-induced kernel adaptive filters have emerged as a promising candidate for a nonlinear sparse system identification (SSI) problem. The existing zero-attracting kernel least mean square (ZA-KLMS) algorithm relies on minimum mean square error criterion, which considers only second order statistics of error, thereby resulting in suboptimal performance in the presence of non-Gaussian/impulsive distortions. In this letter, we propose a novel random Fourier features (RFF) based ZA kernel maximum Versoria criterion (ZA-KMVC) algorithm, and their variants, which are robust for nonlinear SSI in the presence of non-Gaussian distortions over both stationary and time-varying environments. Furthermore, the mean-square convergence analysis of the proposed RFF-ZA-KMVC algorithm is performed. It has been observed from the simulation results that the proposed algorithm delivers better convergence performance as compared to the existing state-of-art approaches.

关键词： signal processing algorithms Kernel Prediction algorithms Cost function Convergence Adaptive filters Steady-state KLMS maximum Versoria criterion non-Gaussian random Fourier features reproducing kernel Hilbert space sparsity-aware ZA-KLMS ZA-KMVC zero-attracting

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Fast Adaptive Local Subspace Learning With Regressive Regularization

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IEEE signal processing LETTERS 2022年 29卷 1759-1763页

作者： Chen, Qiang Zhao, Xiaowei Nie, Feiping Wang, Rong Li, Xuelong Northwestern Polytech Univ Sch Comp Sci Xian 710072 Peoples R China Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect iOPEN Xian 710072 Peoples R China Northwestern Polytech Univ Sch Comp SciMinist Ind & Informat Technol Sch Artificial Intelligence Opt & Elect iOPEN Key Lab Intelligent Interact & Applicat Xian 710072 Peoples R China Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect iOPEN Minist Ind & Informat Technol Xian 710072 Peoples R China Northwestern Polytech Univ Key Lab Intelligent Interact & Applicat Minist Ind & Informat Technol Xian 710072 Peoples R China

Linear Discriminant Analysis (LDA) has been widely used in supervised dimensionality reduction fields. However, LDA is usually weak in tackling data with Non-Gaussian distribution due to its incapability of extracting the intrinsic structure of data. In order to learn the intrinsic information more effectively, some dimensionality reduction methods incorporate the adaptive full-connected graph into the algorithm frame, but the defect is that the calculation of each pairwise distance is very time-consuming. In this letter, we propose a novel fast adaptive local subspace learning with regressive regularization model to solve the supervised dimensional reduction problem. Firstly, the adaptive anchor point graph is used to capture local structure information, which can greatly reduce computation complexity. Secondly, by using regressive regularization, the samples from different classes can be better separated in the projected space and the workload of selecting the optimal reduced dimension is easier. Moreover, entropy regularization is used to derive more appropriate weights. Finally, extensive experiments are conducted on real world data sets to verify the superiority of our model.

关键词： Adaptation models Dimensionality reduction signal processing algorithms Data models Optics Entropy Data mining Adaptive anchor point graph fast local subspace learning linear discriminant analysis regressive regularization

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Digital signal processing on 3-Axis Accelerometer

Digital Signal Processing on 3-Axis Accelerometer

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Intelligent Systems for Communication, IoT and Security (ICISCoIS), International Conference on

作者： Sreehari Krishnakumar Vignesh Karthikeyan R Manoharan V K Saritakumar N Department of ECE PSG College of Technology Coimbatore India PSG Space Centre PSG College of Technology Coimbatore India

This paper presents a method for processing 3-axis accelerometer signals using a Cortex M4F microcontroller. The accelerometer is a device that measures acceleration, which is the rate of change of velocity. It is often used in applications such as motion sensing and gesture recognition. The process begins by acquiring the accelerometer signals and transforming them using the Fast Fourier Transform (FFT). The FFT is an algorithm that decomposes a signal into its frequency components, which can be useful for identifying patterns and trends in the data. The transformed signals are then passed through a digital filter, which is a mathematical tool that can be used to remove or enhance specific frequencies from a signal. In this case, the filter is used to eliminate or reduce certain frequencies that are not of interest. The purpose of this process is not specified, but it is likely that the accelerometer and microcontroller are being used to perform tasks such as analyzing device movements or identifying specific gestures made by a user. Overall, this paper provides an insight into the use of a 3-axis accelerometer and Cortex M4F microcontroller for signal processing, with the application of the FFT and digital filter allowing for the extraction of useful information from the accelerometer signals that can potentially be used in a variety of applications.

关键词： Accelerometers Vibrations Microcontrollers Fast Fourier transforms signal processing algorithms Market research Information filters

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Wireless Area Positioning in RIS-Assisted mmWave Systems: Joint Passive and Active Beamforming Design

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IEEE signal processing LETTERS 2022年 29卷 1372-1376页

作者： Gao, Peng Lian, Lixiang Yu, Jinpei Shanghai Tech Univ Sch Informat Sci & Technol Shanghai 201210 Peoples R China Innovat Acad Microsatellites CAS Shanghai 201210 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

This letter investigates a reconfigurable intelligent surface (RIS) assisted mmWave system for wireless area positioning. We aim to optimize the worst-case localization performance in terms of squared position error bound within the target region by jointly optimizing beamforming vectors at RIS and user equipment (UE), which leads to a nonconvex-nonconcave minimax problem. To tackle the challenging minimax problem, we propose a two-step algorithm with good convergence performance. Specifically, a joint array gain and path loss search (JAPS) algorithm is proposed to effectively find the exact solution of inner nonconcave maximization problem and a difference of convex (DC)-based algorithm is proposed to update the beamforming vectors at RIS and UE to improve the worst localization performance. Numerical results show that the proposed algorithm achieves significant performance gains over the benchmark schemes.

关键词： Location awareness signal processing algorithms Array signal processing Uncertainty signal to noise ratio Optimization Wireless communication Reconfigurable intelligent surface minimax problem squared position error bound beamforming design

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