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A Suboptimal Fusion Estimation Algorithm Weighted by Matrices Based on LMI and Machine Learning

IEEE SENSORS JOURNAL 2023年第17期23卷 20020-20026页

作者： Wang, Xin Li, Yuxi Hao, Gang Heilongjiang Univ Sch Elect Engn Harbin 150080 Peoples R China

This article is concerned with suboptimal fusion estimation weighted by matrices for multisensors linear systems with unknown cross-covariance. First, for reasonable correlation coefficients, the simplest constraints are derived by Shure complement theorem. The constraints can ensure the positive definiteness of the extended estimation error covariance matrix and the fusion estimation error covariance matrix weighted by matrices, and the consistency of the proposed fusion estimation. Then, based on the linear matrix inequality (LMI) algorithm combined with AutoML, a suboptimal fusion estimation weighted by matrices is proposed. The proposed suboptimal fusion algorithm provides an effective way for fusion estimation of linear distributed systems with unknown cross-covariance. Simulations analyses verify the effectiveness and correctness of the conclusion.

关键词： Estimation Covariance matrices Sensors Estimation error Linear matrix inequalities signal processing algorithms Matrix decomposition AutoML fusion weighted by matrices linear matrix inequalities (LMIs) unknown cross-covariance

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Global Memory and Local Continuity for Video Object Detection

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IEEE TRANSACTIONS ON MULTIMEDIA 2023年 25卷 3681-3693页

作者： Han, Liang Yin, Zhaozheng SUNY Stony Brook Dept Comp Sci Stony Brook NY 11794 USA SUNY Stony Brook Dept Biomed Informat Stony Brook NY 11794 USA

To deal with the challenges in video object detection (VOD), such as occlusion and motion blur, many state-of-the-art video object detectors adopt a feature aggregation module to encode the long-range contextual information to support the current frame. The main drawbacks of these detectors are three-folds: first, the frame-wise detection slows down the detection speed;second, the frame-wise detection usually ignores the local continuity of the objects in a video, resulting in temporal inconsistent detection;third, the feature aggregation module usually encodes temporal features either from a local video clip or a single video, without exploiting the features in other videos. In this work, we develop an online VOD algorithm, aiming at a balanced high-speed and high-accuracy, by exploiting the global memory and local continuity. In the algorithm, an effective and efficient global memory bank (GMB) is designed to deposit and update object class features, which enables us to exploit the support features in other videos to enhance object features in the current video frames. Besides, to further speed up the detection, we design an object tracker to perform object detection for non-key frames based on the detection results of the key frame by leveraging the local continuity property of the video. Considering the trade-off between detection accuracy and speed, the proposed framework achieves superior performance on the ImageNet VID dataset. Source codes will be released to the public via our GitHub website.

关键词： Feature extraction Object detection Detectors Proposals Target tracking signal processing algorithms Costs Video object detection global memory bank feature aggregation local continuity object tracker

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Latent Stochastic Differential Equations for Change Point Detection

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IEEE ACCESS 2023年 11卷 104700-104711页

作者： Ryzhikov, Artem Hushchyn, Mikhail Derkach, Denis Natl Res Univ Higher Sch Econ HSE Univ Comp Sci Dept Moscow 10100 Russia

Automated analysis of complex systems based on multiple readouts remains a challenge. Change point detection algorithms are aimed to locating abrupt changes in the time series behaviour of a process. In this paper, we present a novel change point detection algorithm based on Latent Neural Stochastic Differential Equations (SDE). Our method learns a non-linear deep learning transformation of the process into a latent space and estimates a SDE that describes its evolution over time. The algorithm uses the likelihood ratio of the learned stochastic processes in different timestamps to find change points of the process. We demonstrate the detection capabilities and performance of our algorithm on synthetic and real-world datasets. The proposed method outperforms the state-of-the-art algorithms on the majority of our experiments.

关键词： Time series analysis signal processing algorithms Deep learning Approximation algorithms Task analysis Monitoring Machine learning algorithms Anomaly detection Machine learning change point detection deep learning machine learning timeseries

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Partial Discharge Location Algorithm Based on Total Least-Squares With Matern Kernel in Cable Systems

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2023年第3期19卷 2421-2431页

作者： Lu, Lu Zhou, Kai Zhu, Guangya Yang, Xiaomin Chen, Badong Sichuan Univ Coll Elect & Informat Engn Chengdu 610065 Peoples R China Sichuan Univ High Voltage Lab Coll Elect Engn Chengdu 610065 Peoples R China Jiaotong Univ Inst Artificial Intelligence & Robot Xian 710049 Peoples R China

Partial discharge (PD) location techniques are a useful tool for condition monitoring of electrical apparatus in power systems. However, the noisy PD measurements may significantly degrade the performance of location algorithms. This article deals with the PD location problem by using adaptive filtering techniques. Heretofore, scarce literature focuses on addressing the PD location based on such method. A novel adaptive algorithm, termed as total least-squares (TLS)-Matern kernel (TLS-MK), is proposed. Benefiting from the merits of the Matern kernel, the TLS model can effectively suppress the noise from the direct and reflected waves of the PD source. Meanwhile, the TLS-MK algorithm is used to estimate the time difference, which is used in the PD location. Moreover, the convergence behavior of the TLS-MK algorithm is analyzed. Simulations and experiments show that the proposed algorithm can enhance the location accuracy as compared to state-of-the-art methods for various PD signals.

关键词： Partial discharges signal processing algorithms Sensors Kernel Estimation Informatics Noise reduction Adaptive filtering algorithm location matern kernel partial discharge (PD) total least-squares (TLS)

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RIS-Aided Cell-Free Massive MIMO System: Joint Design of Transmit Beamforming and Phase Shifts

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IEEE SYSTEMS JOURNAL 2023年第2期17卷 3093-3104页

作者： Jin, Si-Nian Yue, Dian-Wu Nguyen, Ha H. Dalian Maritime Univ Coll Informat Sci & Technol Dalian 116026 Peoples R China Southeast Univ State Key Lab Millimeter Waves Nanjing 210096 Peoples R China Univ Saskatchewan Dept Elect & Comp Engn Saskatoon SK S7N 5A9 Canada

This article studies a reconfigurable intelligent surface (RIS)-aided cell-free massive multiple-input multiple-output system and formulate the max-min fairness problem that maximizes the minimum achievable rate among all the users by jointly optimizing the transmit beamforming at access points and the phase shifts at RISs. To address such a challenging problem, we first study the special single-user scenario and propose an algorithm that can transform the optimization problem into a semidefinite program (SDP) or an integer linear program for the cases of continuous or discrete phase shifts, respectively. Then, in order to solve the optimization problem for the multiuser scenario with continuous phase shifts, we propose an alternating optimization algorithm, which can alternately transform the problem into a second-order-cone program and an SDP. Finally, for the multiuser scenario with discrete phase shifts, we design a zero-forcing-based successive refinement algorithm, which can find the suboptimal transmit beamforming and phase shifts by means of alternating optimization. Numerical results show that compared with the benchmark schemes of random phase shifts and without using the RIS, the proposed algorithms can significantly increase the minimum achievable rate. It is also demonstrated that, compared with the case of programming continuous phase shifts, using 2-bit discrete phase shifts can practically achieve the same performance.

关键词： Array signal processing Massive MIMO Optimization signal processing algorithms Transforms Linear programming Wireless communication Cell-free massive multiple-input multiple-output (MIMO) continuous phase shifts discrete phase shifts max-min fairness optimization reconfigurable intelligent surface (RIS)

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Gm-APD LiDAR Single-Source Data Self-Guided: Obtaining High-Resolution Depth Map

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2023年 16卷 4658-4669页

作者： Song, Boyi Sun, Jianfeng Yan, Yongji He, Rui Zhou, Xin Lu, Wei Harbin Inst Technol Inst Opto Elect Natl Key Lab Sci & Technol Tunable Laser Harbin 150001 Peoples R China

Geiger-mode avalanche photodiode (Gm-APD) array LiDAR has become the current research focus due to its sensitive response, high precision, and easy integration. However, due to the limitations of the fabrication process and manufacturing cost, the images collected by Gm-APD array LiDAR have very serious image low-resolution problems. Since the intensity map and depth map of the target can be obtained simultaneously by relying on single-source data from Gm-APD array LiDAR, we propose a Gm-APD LiDAR single-source data self-guided method. We propose to first perform Gm-APD LiDAR intensity map superresolution, and then use the processed intensity map with the corresponding depth map for guided superresolution. The advantage of this process is that instead of using high-resolution (HR) imaging devices from different domains, it relies only on single-source data from Gm-APD LiDAR to obtain HR depth map, thus eliminating the need for additional image registration work and providing wider applicability. We investigate the feasibility of the proposed single-source data processing method and evaluate our method on the real Gm-APD LiDAR single-source data with an average peak signal-to-noise ratio of 42.21, which better preserves the original distance information of the targets while providing visually sharper outputs.

关键词： Laser radar Imaging Superresolution Image registration Photonics Cameras signal processing algorithms Geiger-mode avalanche photodiode (Gm-APD) LiDAR noise suppression self-guided method single-source data

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Automatic Hybrid-Precision Quantization for MIMO Detectors

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IEEE TRANSACTIONS ON signal processing 2023年 71卷 1039-1052页

作者： Ge, Yingmeng Ji, Zhenhao Huang, Yongming Zhang, Zaichen You, Xiaohu Zhang, Chuan Southeast Univ Natl Mobile Commun Res Lab LEADS Nanjing 211189 Peoples R China Southeast Univ Frontiers Sci Ctr Mobile Informat Commun & Secur Nanjing 211189 Peoples R China Purple Mt Labs Nanjing 211189 Peoples R China

In the design of wireless systems, quantization plays a critical role in hardware, which directly affects both area efficiency and energy efficiency. Being an enabling technique, the wide applications of multiple-input multiple-output (MIMO) heavily rely on efficient implementations balancing both performance and complexity. However, most of the existing detectors quantize all variables in a uniform way, resulting in high redundancy and low flexibility. Requiring both expertise and efforts, an in-depth tailored quantization usually asks for prohibitive costs and is not considered by conventional MIMO detectors. In this paper, a general framework named the automatic hybrid-precision quantization (AHPQ) is proposed with two parts: integral part quantization (IPQ) determined by probability density function (PDF), and fractional part quantization (FPQ) by deep reinforcement learning (DRL). Being automatic, AHPQ demonstrates high efficiency in figuring out good quantizations for a set of algorithmic parameters. For the approximate message passing (AMP) detector, AHPQ achieves up to 57.8% lower average bitwidth than the unified quantization (UQ) one with almost no performance sacrifice. The feasibility of AHPQ has been verified by implementation with 65 nm CMOS technology. Compared with its UQ counterpart, AHPQ exhibits $2.97\times$ higher throughput-to-area ratio (TAR) with 19.3% lower energy dissipation. Moreover, by node compression and strength reduction, the AHPQ-guided detector outperforms the state-of-the-art (SOA) in both throughput (17.92 Gb/s) and energy efficiency (7.93 pJ/b). The proposed AHPQ framework is also applicable for other digital signal processing (DSP) algorithms.

关键词： Quantization (signal) Detectors MIMO communication signal processing algorithms Hardware Complexity theory Optimization Automatic quantization MIMO detection message passing DRL ASIC

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Adaptive Decomposition of Multicomponent signals and Estimation of Phase Synchronization

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IEEE TRANSACTIONS ON signal processing 2023年 71卷 1586-1598页

作者： Evangelidis, Apostolos Kugiumtzis, Dimitris Ctr Res & Technol Hellas CERTH ITI Informat Technol Inst Thessaloniki 57001 Greece Aristotle Univ Thessaloniki Dept Elect & Comp Engn Thessaloniki 54124 Greece

The decomposition of a nonlinear and non-stationary signal into intrinsic modes is challenging, and algorithms, such that of Empirical Mode Decomposition (EMD) used successfully in a broad range of applications, have limitations regarding their sensitivity to noise and sampling. To address these drawbacks a mode decomposition algorithm, which we name Maximal Spectral Overlap Wavelet Transform (MSO-WT), is proposed. MSO-WT relies on the adaptive decomposition of a signal into a series of components using wavelet packets, and the clustering of the components for the synthesis of the modes. The effectiveness of the algorithm is demonstrated using several synthetic signals. In addition, this algorithm is used for the development of a new phase synchronization index, named Multi-Component Mean Phase Coherence (MCMPC) generalizing the Mean Phase Coherence (MPC) index. A comparison between the two indices is performed by applying them for the detection of phase synchronization between two coupled Rossler and Mackey-Glass systems. Finally, the MCMPC index is used for the construction of a brain connectivity network from multichannel electroencephalogram data and the detection of an epileptic seizure.

关键词： signal processing algorithms Indexes Transforms Synchronization Clustering algorithms Wavelet packets Coherence Brain connectivity phase synchronization signal decomposition spectral clustering wavelet packet transform

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Distributed Random Reshuffling Over Networks

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IEEE TRANSACTIONS ON signal processing 2023年 71卷 1143-1158页

作者： Huang, Kun Li, Xiao Milzarek, Andre Pu, Shi Qiu, Junwen Chinese Univ Hong Kong Shenzhen Res Inst Big Data Sch Data Sci Shenzhen 518172 Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen 518129 Peoples R China Chinese Univ Hong Kong Sch Data Sci Shenzhen 518172 Peoples R China

In this paper, we consider distributed optimization problems where n agents, each possessing a local cost function, collaboratively minimize the average of the local cost functions over a connected network. To solve the problem, we propose a distributed random reshuffling (D-RR) algorithm that invokes the random reshuffling (RR) update in each agent. We show that D-RR inherits favorable characteristics of RR for both smooth strongly convex and smooth nonconvex objective functions. In particular, for smooth strongly convex objective functions, D-RR achieves O(1/T-2) rate of convergence (where T counts the epoch number) in terms of the squared distance between the iterate and the global minimizer. When the objective function is assumed to be smooth nonconvex, we show that D-RR drives the squared norm of the gradient to 0 at a rate of O(1/T-2/3). These convergence results match those of centralized RR (up to constant factors) and outperform the distributed stochastic gradient descent (DSGD) algorithm if we run a relatively large number of epochs. Finally, we conduct a set of numerical experiments to illustrate the efficiency of the proposed D-RR method on both strongly convex and nonconvex distributed optimization problems.

关键词： Convergence Linear programming signal processing algorithms Gradient methods Distributed databases Big Data Machine learning algorithms Distributed optimization random reshuffling stochastic gradient methods

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Multi-Frequency Joint Community Detection and Phase Synchronization

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IEEE TRANSACTIONS ON signal AND INFORMATION processing OVER NETWORKS 2023年 9卷 162-174页

作者： Wang, Lingda Zhao, Zhizhen Univ Illinois Coordinated Sci Lab Urbana IL 61801 USA Univ Illinois Dept Elect & Comp Engn Urbana IL 61801 USA

This paper studies the joint community detection and phase synchronization problem on the stochastic block model with relative phase, where each node is associated with an unknown phase angle. This problem, with a variety of real-world applications, aims to recover the cluster structure and associated phase angles simultaneously. We show this problem exhibits a "multi-frequency" structure by closely examining its maximum likelihood estimation (MLE) formulation, whereas existing methods are not originated from this perspective. To this end, two simple yet efficient algorithms that leverage the MLE formulation and benefit from the information across multiple frequencies are proposed. The former is a spectral method based on the novel multi-frequency column-pivoted QR factorization. The factorization applied to the top eigenvectors of the observation matrix provides key information about the cluster structure and associated phase angles. The second approach is an iterative multi-frequency generalized power method, where each iteration updates the estimation in a matrix-multiplication-then-projection manner. Numerical experiments show that our proposed algorithms significantly improve the ability of exactly recovering the cluster structure and the accuracy of the estimated phase angles, compared to state-of-the-art algorithms.

关键词： Maximum likelihood estimation Synchronization Iterative methods Stochastic processes signal processing algorithms Probabilistic logic Optimization Community detection phase synchronization spectral method column-pivoted QR factorization generalized power method

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