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Distributed Quantization-Aware RLS Learning With Bias Compensation and Coarsely Quantized signals

IEEE TRANSACTIONS ON signal processing 2022年 70卷 3441-3455页

作者： Danaee, Alireza de Lamare, Rodrigo C. Nascimento, Vitor Heloiz Pontifical Catholic Univ Rio de Janeiro Ctr Telecommun Studies BR-22541041 Rio De Janeiro RJ Brazil Univ York Dept Elect Engn York YO10 5DD N Yorkshire England Univ Sao Paulo Dept Elect Syst Engn BR-05508060 Sao Paulo Brazil

In this work, we present an energy-efficient distributed learning framework using coarsely quantized signals for Internet of Things (IoT) networks. In particular, we develop a distributed quantization-aware recursive least-squares (DQA-RLS) algorithm that can learn parameters in an energy-efficient fashion using signals quantized with few bits while requiring a low computational cost. Moreover, we develop a bias compensation strategy to further improve the performance of the proposed DQA-RLS algorithm. We carry out a statistical analysis of the proposed DQA-RLS algorithm and derive analytical expressions for predicting the mean-square deviation. A computational complexity evaluation and a study of the power consumption of the proposed and existing techniques are then presented. Numerical results assess the DQA-RLS algorithm against existing techniques for a distributed parameter estimation task in a scenario where IoT devices operate in peer-to-peer mode.

关键词： signal processing algorithms Internet of Things Energy efficiency Adaptive systems Sensors Power demand Peer-to-peer computing Distributed learning energy-efficient signal processing adaptive algorithms coarse quantization

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Environment Sensing Considering the Occlusion Effect: A Multi-View Approach

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 3598-3615页

作者： Tong, Xin Zhang, Zhaoyang Zhang, Yihan Yang, Zhaohui Huang, Chongwen Wong, Kai-Kit Debbah, Merouane Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Zhejiang Univ Key Lab Informat Proc Commun & Networking Zhejian Hangzhou 310027 Peoples R China Zhejiang Univ Key Lab Collaborat Sensing & Autonomous Unmanned Hangzhou 310015 Peoples R China Zhejiang Univ Int Joint Innovat Ctr Haining 314400 Peoples R China Zhejiang Lab Hangzhou 311121 Peoples R China UCL Dept Elect & Elect Engn London WC1E 6BT England Technol Innovat Inst Abu Dhabi 9639 U Arab Emirates Univ Paris Saclay Cent Supelec F-91192 Gif Sur Yvette France

In this paper, we consider the problem of sensing the environment within a wireless cellular framework. Specifically, multiple user equipments (UEs) send sounding signals to one or multiple base stations (BSs) and then a centralized processor retrieves the environmental information from all the channel information obtained at the BS(s). Taking into account the occlusion effect that is common in the wireless context, we make full use of the different views of the environment from different users and/or BS(s), and propose an effective sensing algorithm called GAMP-MVSVR (generalized-approximate-message-passing-based multi-view sparse vector reconstruction). In the proposed algorithm, a multi-layer factor graph is constructed to iteratively estimate the scattering coefficients of the cloud points and their occlusion relationship. In each iteration, the occlusion relationship between the cloud points of the sparse environment is recalculated according to a simple occlusion detection rule, and in turn, used to estimate the scattering coefficients of the cloud points. Our proposed algorithm can achieve improved sensing performance with multi-BS collaboration in addition to the multi-views from the UEs. The simulation results verify its convergence and effectiveness.

关键词： Sensors Wireless communication signal processing algorithms Robot sensing systems Wireless sensor networks Uplink Scattering Environment sensing integrated sensing and communication (ISAC) multi-view sensing occlusion effect

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The Harmonic Shift Algorithm for Efficient Multi-Pitch Detection

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE processing 2022年 30卷 548-561页

作者： Grinewitschus, Lukas Jung, Peter Univ Duisburg Essen Dept Commun Technol D-47057 Duisburg Germany

Existing approaches for multi-pitch detection have still been highly complex. In this paper, the harmonic shift algorithm (HSA) for multi-pitch detection is illustrated. The HSA is a noncoherent blind detection scheme, exploiting the constant-Q transform (CQT). The only requirement for the successful application of the HSA is that all the sources are harmonic sources. Due to this simplicity, the HSA is considered viable for both single-pitch and multi-pitch detection, making its attractive for the application in other areas of signal processing. When illustrating the performance capabilities of the HSA, we demonstrate that it outperforms the state of the art regarding the computational complexity providing detection precision, accuracy and a recall factor comparable with existing schemes.

关键词： Harmonic analysis Transforms Hidden Markov models Convolutional neural networks signal processing algorithms Frequency estimation Computational complexity Audio signal processing blind detection constant-Q transform harmonic shift algorithm multi-pitch detection noncoherent detection

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Detecting Central Nodes From Low-Rank Excited Graph signals via Structured Factor Analysis

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 2416-2430页

作者： He, Yiran Wai, Hoi-To Chinese Univ Hong Kong Dept SEEM Shatin Hong Kong Peoples R China

This paper treats a blind detection problem to identify the central nodes in a graph from filtered graph signals. Unlike prior works which impose strong restrictions on the data model, we only require the underlying graph filter to satisfy a low pass property with a generic low-rank excitation model. We treat two cases depending on the low pass graph filter's strength. When the graph filter is strong low pass, i.e., it has a frequency response that drops sharply at the high frequencies, we show that the principal component analysis (PCA) method detects central nodes with high accuracy. For general low pass graph filter, we show that the graph signals can be described by a structured factor model featuring the product between a low-rank plus sparse factor and an unstructured factor. We propose a two-stage decomposition algorithm to learn the structured factor model via a judicious combination of the non-negative matrix factorization and robust PCA algorithms. We analyze the identifiability conditions for the model which lead to accurate central nodes detection. Numerical experiments on synthetic and real data are provided to support our findings. We demonstrate significant performance gains over prior works.

关键词： Principal component analysis signal processing algorithms Matrix decomposition Sparse matrices Numerical models Social networking (online) Analytical models Graph signal processing low pass graph filter centrality estimation nonnegative matrix factorization

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Toward Generalization of sEMG-Based Pattern Recognition: A Novel Feature Extraction for Gesture Recognition

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2022年 71卷 1-12页

作者： Shen, Cheng Pei, Zhongcai Chen, Weihai Wang, Jianhua Zhang, Jianbin Chen, Zuobing Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China Beihang Univ Hangzhou Innovat Inst Hangzhou 310052 Zhejiang Peoples R China Beihang Univ Sch Mech Engn & Automat Beijing 100191 Peoples R China Zhejiang Univ Coll Med Affiliated Hosp 1 Dept Rehabil Med Hangzhou 310003 Zhejiang Peoples R China

Gesture recognition via surface electromyography (sEMG) has drawn significant attention in the field of human-computer interaction. An important factor limiting the performance of sEMG-based pattern recognition (PR) is the generalization ability which sEMG changes for the identical movements when conducted at various positions or by different persons. Thus, this study aims to explore the generalization of classifier to develop a stable classification model that does not require relearning, even if it is used by other people. We propose a new feature extraction method to diminish the influence of limb position on sEMG-based PR. Specifically, the sEMG features are extracted directly from time domain. This condition is accomplished by using Fourier transform properties, difference, and the sum of squares differences. The best offline cross-validation accuracy (CVA) results are 88.775% training data from the tenth subject and testing data from the fifth subject in the NinaPro dataset. The best online CVA is 99%, and the movement selection time is 47.036 +/- 1.028 ms. In comparison with the well-known sEMG feature, the CVA and the generalization of the proposed features improved substantially. These improvements aim to facilitate the practical implementation of myoelectric interfaces.

关键词： Feature extraction Time-domain analysis Electromyography Muscles Pattern recognition Speech recognition signal processing algorithms Generalizability gesture recognition pattern recognition (PR) signal processing surface electromyography (sEMG)

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Patch Tracking-Based Streaming Tensor Ring Completion for Visual Data Recovery

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2022年第12期32卷 8312-8326页

作者： He, Yicong Atia, George K. Univ Cent Florida Dept Elect & Comp Engn Orlando FL 32816 USA Univ Cent Florida Dept Comp Sci Orlando FL 32816 USA

Tensor completion aims to recover the missing entries of a partially observed tensor by exploiting its low-rank structure, and has been applied to visual data recovery. In applications where the data arrives sequentially such as streaming video completion, the missing entries of the tensor need to be dynamically recovered in a streaming fashion. Traditional streaming tensor completion algorithms treat the entire visual data as a tensor, which may not work satisfactorily when there is a big change in the tensor subspace along the temporal dimension, such as due to strong motion across the video frames. In this paper, we develop a novel patch tracking-based streaming tensor ring completion framework for visual data recovery. Given a newly incoming frame, small patches are tracked from the previous frame. Meanwhile, for each tracked patch, a patch tensor is constructed by stacking similar patches from the new frame. Patch tensors are then completed using a streaming tensor ring completion algorithm, and the incoming frame is recovered using the completed patch tensors. We propose a new patch tracking strategy that can accurately and efficiently track the patches with missing data. Further, a new streaming tensor ring completion algorithm is proposed which can efficiently and accurately update the latent core tensors and complete the missing entries of the patch tensors. Extensive experimental results demonstrate the superior performance of the proposed algorithms compared with both batch and streaming state-of-the-art tensor completion methods.

关键词： Tensors signal processing algorithms Electron tubes Visualization Tracking Streaming media Stacking Tensor completion tensor ring decomposition streaming video processing

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A Robust Algorithm Based on Link Label Propagation for Identifying Functional Modules From Protein-Protein Interaction Networks

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022年第3期19卷 1435-1448页

作者： Jiang, Hao Zhan, Fei Wang, Congtao Qiu, Jianfeng Su, Yansen Zheng, Chunhou Zhang, Xingyi Zeng, Xiangxiang Anhui Univ Sch Comp Sci & Technol Minist Educ Key Lab Intelligent Comp & Signal Proc Hefei 230601 Peoples R China Hunan Univ Sch Informat Sci & Engn Changsha 410082 Peoples R China

Identifying functional modules in protein-protein interaction (PPI) networks elucidates cellular organization and mechanism. Various methods have been proposed to identify the functional modules in PPI networks, but most of these methods do not consider the noisy links in PPI networks. They achieve a competitive performance on the PPI networks without noisy links, but the performance of these methods considerably deteriorates in the noisy PPI networks. Furthermore, the noisy links are inevitable in the PPI networks. In this paper, we propose a novel link-driven label propagation algorithm (LLPA) to identify functional modules in PPI networks. The LLPA first find link clusters in PPI networks, and then the functional modules are identified from the link clusters. Two strategies aimed to ensure the robustness of LLPA are proposed. One strategy involves the proposed LLPA updating the link labels in accordance with the designed weight of the link, which can reduce the incidence of noisy links. The other strategy involves the filtration of some noisy labels from the link clusters to further reduce the influence of noisy links. The performance evaluation on three real PPI networks shows that LLPA outperforms other eight state-of-the-art detection algorithms in terms of accuracy and robustness.

关键词： Noise measurement Proteins Robustness Clustering algorithms Detection algorithms signal processing algorithms Partitioning algorithms Functional modules identification link label propagation nosiy link protein-protein interaction network

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Shockwave signal Downsampling Rate Acquisition Based on Sparse Fourier Transform

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IEEE ACCESS 2022年 10卷 44076-44087页

作者： Xu, Bo Zhang, Zhi Xie, Zhenglin Han, Tailin Liu, Hong Liu, Xuan Ju, Mingchi Changchun Univ Sci & Technol Sch Elect & Informat Engn Changchun 130022 Peoples R China Changchun Univ Sci & Technol Sch Optoelect Engn Changchun 130022 Peoples R China Changchun Univ Sci & Technol Changchun 130022 Peoples R China

The wireless distributed transient shockwave testing system based on Nyquist sampling needs to keep high sampling rate and front-end data processing rate, which leads to the shortening of the life cycle of wireless nodes. In order to solve the above problems, the shockwave signal acquisition technology based on Sparse Fourier Transform is proposed in this paper. The specific method is to use the sparse characteristics of the frequency domain of the transient shockwave signal to collect the frequency domain by downsampling, reduce the amount of acquired data. The simulation results of the above experiments are compared with the simulation results of Compressed Sensing low sampling observation. The experimental results show that the time-domain acquisition accuracy error of the proposed framework is less than 10(-4) when the Nyquist sampling rate is less than 8 times, and the mean relative error in time domain is only 0.01%. And compared with the acquisition framework based on Compressed Sensing theory, the reconstructed mean absolute error of the proposed method is reduced by 88.66% in the case of the same downsampling multiple. Moreover, this method does not need to store the high-dimensional random observation matrix similar to Compressed Sensing, which overcomes the defect of hardware implementation difficulty.

关键词： Frequency-domain analysis signal processing algorithms Approximation algorithms Wireless sensor networks Wireless communication Transient analysis Sparse matrices Wireless nodes transient shockwave signal sparse fourier transform compressed sensing downsampling

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FedBCD: A Communication-Efficient Collaborative Learning Framework for Distributed Features

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 4277-4290页

作者： Liu, Yang Zhang, Xinwei Kang, Yan Li, Liping Chen, Tianjian Hong, Mingyi Yang, Qiang Tsinghua Univ Inst AI Ind Res Beijing 100084 Peoples R China Univ Minnesota Elect & Comp Engn Dept Minneapolis MN 55455 USA Webank Dept Artificial Intelligence Shenzhen 518063 Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China Hong Kong Univ Sci & Technol Comp Sci & Engn Dept Hong Kong Peoples R China

We introduce a novel federated learning framework allowing multiple parties having different sets of attributes about the same user to jointly build models without exposing their raw data or model parameters. Conventional federated learning approaches are inefficient for cross-silo problems because they require the exchange of messages for gradient updates at every iteration, and raise security concerns over sharing such messages during learning. We propose a Federated Stochastic Block Coordinate Descent (FedBCD) algorithm, allowing each party to conduct multiple local updates before each communication to effectively reduce communication overhead. Under a practical security model, we show that parties cannot infer others' exact raw data ("deep leakage") from collections of messages exchanged in our framework, regardless of the number of communication to be performed. Further, we provide convergence guarantees and empirical evaluations on a variety of tasks and datasets, demonstrating significant improvement inefficiency.

关键词： Stochastic processes Data models Collaborative work signal processing algorithms Distributed databases Security Data privacy Federated learning data privacy federated stochastic block coordinate descent cross-silo federated learning distributed features

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Joint DoA-Range Estimation Using Space-Frequency Virtual Difference Coarray

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 2576-2592页

作者： Mao, Zihuan Liu, Shengheng Zhang, Yimin D. Han, Leixin Huang, Yongming Southeast Univ Sch Informat Sci & Engn Nanjing 210096 Peoples R China Purple Mt Labs Nanjing 211111 Peoples R China Temple Univ Coll Engn Dept Elect & Comp Engn Philadelphia PA 19122 USA

In this paper, we address the problem of joint direction-of-arrival (DoA) and range estimation using frequency diverse coprime array (FDCA). By incorporating the coprime array structure and coprime frequency offsets, a two-dimensional space-frequency virtual difference coarray corresponding to uniform array and uniform frequency offset is considered to increase the number of degrees-of-freedom (DoFs). However, the reconstruction of the doubly-Toeplitz covariance matrix is computationally prohibitive. To solve this problem, we propose an interpolation algorithm based on decoupled atomic norm minimization (DANM), which converts the coarray signal to a simple matrix form. On this basis, a relaxation-based optimization problem is formulated to achieve joint DoA-range estimation with enhanced DoFs. The reconstructed coarray signal enables application of existing subspace-based spectral estimation methods. The proposed DANM problem is further reformulated as an equivalent rank-minimization problem which is solved by cyclic rank minimization. This approach avoids the approximation errors introduced in nuclear norm-based approach, thereby achieving superior root-mean-square error which is closer to the Cramer-Rao bound. The effectiveness of the proposed method is confirmed by theoretical analyses and numerical simulations.

关键词： Estimation Sensors Frequency diversity Minimization signal processing algorithms Sensor arrays Covariance matrices Coprime array direction-of-arrival estimation frequency diverse array atomic norm degree-of-freedom parameter estimation

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