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A Convex Combination-Based Distributed Momentum Methods Over Directed Graphs

IEEE signal processing LETTERS 2025年 32卷 1835-1839页

作者： Huang, Siyuan Gao, Juan Dong, Qiao-Li Zhang, Cuijie Civil Aviat Univ China Coll Sci Tianjin 300300 Peoples R China Tianjin Univ Technol Inst Operat Res & Syst Engn Tianjin 300384 Peoples R China

In this article, we introduce a convex combination-based distributed momentum method (CDM) for solving distributed optimization to minimize a sum of smooth and strongly convex local objective functions over directed graphs. The proposed method integrates the convex combination, row- and column-stochastic weights, and the adapt-then-combination rule. By selecting different parameters, it can be reduced to other distributed momentum methods, such as the parametric distributed momentum. CDM converges to the optimal solution at a global R-linear rate for any smooth and strongly convex function when the step-size and momentum coefficient satisfy some bounded conditions. Numerical results for some distributed optimization problems demonstrate that CDM yields a performance that is superior to that of the state-of-the-art methods.

关键词： Convergence Vectors Optimization Directed graphs signal processing algorithms Linear programming Iterative methods Convex functions Visualization Training Convergence rate convex combination directed graphs distributed optimization machine learning

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A Neural-Enhanced Factor Graph-Based Algorithm for Robust Positioning in Obstructed LOS Situations

IEEE OPEN JOURNAL OF SIGNAL PROCESSING

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IEEE OPEN JOURNAL OF signal processing 2024年 5卷 29-38页

作者： Venus, Alexander Leitinger, Erik Tertinek, Stefan Witrisal, Klaus Graz Univ Technol A-8010 Graz Austria Christian Doppler Lab Locat A-8010 Graz Austria NXP Semicond A-8101 Gratkorn Austria

This paper presents a neural-enhanced probabilistic model and corresponding factor graph-based sum-product algorithm for robust localization and tracking in multipath-prone environments. The introduced hybrid probabilistic model consists of physics-based and data-driven measurement models capturing the information contained in both, the line-of-sight (LOS) component as well as in multipath components (NLOS components). The physics-based and data-driven models are embedded in a joint Bayesian framework allowing to derive from first principles a factor graph-based algorithm that fuses the information of these models. The proposed algorithm uses radio signal measurements from multiple base stations to robustly estimate the mobile agent's position together with all model parameters. It provides high localization accuracy by exploiting the position-related information of the LOS component via the physics-based model and robustness by exploiting the geometric imprint of multipath components independent of the propagation channel via the data-driven model. In a challenging numerical experiment involving obstructed LOS situations to all anchors, we show that the proposed sequential algorithm significantly outperforms state-of-the-art methods and attains the posterior Cramer-Rao lower bound even with training data limited to local regions.

关键词： signal processing algorithms Data models Feature extraction Location awareness Probabilistic logic Bayes methods Position measurement Obstructed line-of-sight non los nlos multipath sum product algorithm probabilistic data association belief propagation

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Manifold-Based Optimizations for RIS-Aided Massive MIMO Systems

IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY

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IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY 2024年 5卷 7913-7940页

作者： De Souza Junior, Wilson Guerra, David William Marques Filho, Jose Carlos Marinello Abrao, Taufik Hossain, Ekram State Univ Londrina UEL Dept Elect Engn BR-86057970 Londrina Brazil Fed Univ Pernambuco UFPE Dept Elect & Syst BR-50670901 Recife Brazil Fed Univ Technol Parana UTFPR Dept Elect Engn BR-86300000 Cornelio Procopio Brazil Univ Manitoba Dept Elect & Comp Engn Winnipeg MB R3T 5V6 Canada

Manifold optimization (MO) is a powerful mathematical framework that can be applied to optimize functions over complex geometric structures, which is particularly useful in advanced wireless communication systems, such as reconfigurable intelligent surface (RIS)-aided massive MIMO (mMIMO) and extra-large scale massive MIMO (XL-MIMO) systems. MO provides a structured approach to tackling complex optimization problems. By leveraging the geometric properties of the manifold, more efficient and effective solutions can be found compared to conventional optimization methods. This paper provides a tutorial on MO technique and provides some applications of MO in the context of wireless communications systems. In particular, to corroborate the effectiveness of MO methodology, we explore five application examples in RIS-aided mMIMO system, focusing on fairness, energy efficiency (EE) maximization, intra-cell pilot reuse interference mitigation, and grant-free (GF) random access (RA).

关键词： Optimization Manifolds Wireless communication Array signal processing signal processing algorithms Massive MIMO Complexity theory Resource management Reconfigurable intelligent surfaces NOMA Manifold optimization reconfigurable intelligent surfaces massive MIMO energy efficiency grant-free random access

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A New CFAR Detector Based on the EM Algorithm

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IEEE signal processing LETTERS 2025年 32卷 2035-2039页

作者： Orlando, Danilo Ricci, Giuseppe Univ Pisa Dipartimento Ingn Informaz I-56122 Pisa Italy Univ Salento Dipartimento Ingn Innovaz I-73100 Lecce Italy

This paper proposes a simple Constant False Alarm Rate (CFAR) approach relying on the expectation-maximization algorithm to deal with clutter edges. The possible signal backscattered from a target is modeled as an incoherent pulse train and the newly-introduced CFAR technique is assessed in conjunction with an energy detector. Natural competitors are the well-known Cell-Averaging (CA), Greatest Of (GO), and Smallest Of (SO) CFAR techniques. The performance analysis shows that the proposed approach outperforms the state-of-the-art competitors in terms of false alarms control while maintaining satisfying detection performance.

关键词： Detectors Clutter Vectors signal processing algorithms Program processors Probability density function Image edge detection Covariance matrices Radar detection Noise Cell-averaging constant false alarm rate (CA CFAR) clutter edges detection expectation-maximization algorithm greatest of (GO) CFAR radar smallest of (SO) CFAR

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Steady-State Performance Analysis of the Nearest Kronecker Product Decomposition Based LMS Adaptive Algorithm

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IEEE signal processing LETTERS 2025年 32卷 1995-1999页

作者： Li, Lei Zheng, Yunfei Guo, Zhongyuan Qian, Guobing Wang, Shiyuan Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China

Inorder to address issues, such as convergence rate, stability, and computational complexity caused by the identification of long length impulse response systems, an effective nearest Kronecker product (NKP) decomposition strategy has been introduced and extended to various adaptive filters in recent years. However, the theoretical performance of the NKP decomposition-based adaptive filtering algorithms has not been thoroughly analyzed in these studies. In this letter, we focus on analyzing the steady-state performance of the NKP-based least mean square (NKP-LMS) algorithm and presents the theoretical upper bound of the step-size. Finally, simulation results confirm the precision of the theoretical assessment of the NKP-LMS algorithm and highlight its benefits in low-rank system identification.

关键词： Vectors Adaptive filters Steady-state Finite impulse response filters Performance analysis Wiener filters Matrix decomposition Filtering algorithms signal processing algorithms System identification Adaptive filter nearest Kronecker product least mean square performance analysis

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Sparse Representation With Gaussian Atoms and Its Use in Anomaly Detection

IEEE OPEN JOURNAL OF SIGNAL PROCESSING

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IEEE OPEN JOURNAL OF signal processing 2024年 5卷 168-176页

作者： Ilie-Ablachim, Denis C. Baltoiu, Andra Dumitrescu, Bogdan Natl Univ Sci & Technol Politehn Bucharest Fac Automat Control & Comp Bucharest Romania

We propose sparse representation and dictionary learning algorithms for dictionaries whose atoms are characterized by Gaussian probability distributions around some central atoms. This extension of the space covered by the atoms permits a better characterization of localized features of the signals. The representations are computed by trading-off representation error and the probability of the actual atoms used in the representation. We propose two types of algorithms: a greedy one, similar in spirit with Orthogonal Matching Pursuit, and one based on L1-regularization. We apply our new algorithms to unsupervised anomaly detection, where the representation error is used as anomaly score. The flexibility of our approach appears to improve more the representations of the many normal signals than those of the few outliers, at least for an anomaly type called dependency, thus improving the detection quality. Comparison with both standard dictionary learning algorithms and established anomaly detection methods is favorable.

关键词： Anomaly detection signal processing algorithms Dictionaries Machine learning Gaussian distribution Encoding Heuristic algorithms Dictionary learning sparse representations anomaly detection normal distribution

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Approximate Pruned and Truncated Haar Discrete Wavelet Transform VLSI Hardware for Energy-Efficient ECG signal processing

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2021年第5期68卷 1814-1826页

作者： Seidel, Henrique Bestani Azevedo da Rosa, Morgana Macedo Paim, Guilherme Cesar da Costa, Eduardo Antonio Almeida, Sergio J. M. Bampi, Sergio Univ Catolica Pelotas UCPel Grad Program Elect Engn & Comp BR-96015560 Pelotas RS Brazil Fed Univ Rio Grande do Sul UFRGS Informat Inst Grad Program Microelect PGMICRO BR-90040060 Porto Alegre RS Brazil

The approximate computing paradigm emerged as a key alternative for trading off accuracy and energy efficiency. Error-tolerant applications, such as multimedia and signal processing, can process the information with lower-than-standard accuracy at the circuit level while still fulfilling a good and acceptable service quality at the application level. The automatic detection of R-peaks in an electrocardiogram (ECG) signal is the essential step preceding ECG processing and analysis. The Haar discrete wavelet transform (HDWT) is a low-complexity pre-processing filter suitable to detect ECG R-peaks in embedded systems like wearable devices, which are incredibly energy-constrained. This work presents an approximate HDWT hardware architecture for ECG processing at very high energy efficiency. Our best-proposal employing pruning within the approximate HDWT hardware architecture requires just seven additions. The use of a truncation technique to improve energy efficiency is also investigated herein by observing the evolution of the signal-to-noise ratio and the ultimate impact in the ECG peak-detection application. This research finds that our HDWT approximate hardware architecture proposal accepts higher truncation levels than the original HDWT. In summary: Our results show about 9 times energy reduction when combining our HDWT matrix approximation proposal with the pruning and the highest acceptable level of truncation while still maintaining the R-peak detection performance accuracy of 99.68% on average.

关键词： Electrocardiography Transforms Hardware Discrete wavelet transforms Computer architecture signal processing algorithms signal processing Approximate computing Haar discrete wavelet transform VLSI design energy efficiency ECG processing

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Simple One-Step Multi-View Clustering With Fast Similarity and Cluster Structure Learning

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IEEE signal processing LETTERS 2025年 32卷 1850-1854页

作者： Xia, Xianxian Huang, Dong Yang, Chen-Min He, Chaobo Wang, Chang-Dong South China Agr Univ Coll Math & Informat Guangzhou 510642 Peoples R China South China Normal Univ Sch Comp Sci Guangzhou 510635 Peoples R China Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Guangdong Key Lab Big Data Anal & Proc Guangzhou 510006 Peoples R China

Multi-view clustering (MVC) is essential for integrating heterogeneous data from multiple sources. However, many existing approaches are hindered by high computational complexity and the separate optimization of similarity and cluster structures. In light of these challenges, this paper presents a novel anchor-based MVC method termed simple one-step multi-view clustering with fast similarity and cluster structure learning (SONIC), which models adaptive anchor learning, multi-view similarity structure learning, and discrete cluster structure learning in a joint framework. In particular, we employ the anchor-based multi-view similarity learning to capture the consensus manifold structure latent in multiple views, thereby constructing a unified bipartite graph with adaptive anchor learning and view weighting. Then we impose a low-rank constraint on the bipartite graph structure to directly reveal the desired number of clusters without additional post-processing. An efficient alternating minimization algorithm is developed to optimize the model, resulting in a computational complexity that scales linearly with the number of samples. Extensive experiments on eight benchmark datasets demonstrate the superior performance of SONIC in both clustering quality and computational efficiency.

关键词： Optimization Bipartite graph Similarity learning Linear programming Adaptation models Vectors signal processing algorithms Laplace equations Clustering algorithms Training Consensus information data clustering lare-scale clustering linear time multi-view clustering

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Area-Delay-Power Efficient VLSI Architecture of FIR Filter for processing Seismic signal

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2021年第11期68卷 3451-3455页

作者： Bose, Sudipta De, Arijit Chakrabarti, Indrajit Indian Inst Technol Kharagpur Dept Elect & Elect Commun Engn Kharagpur 721302 W Bengal India

Low-complexity, high-speed and re-configurability are the primary requirements of the finite impulse response (FIR) filters employed for the processing of acquired seismic signal in real-time seismic-alert-system. The common sub-expression elimination (CSE) technique is employed widely to reduce the hardware complexity by minimizing the logic operators (LOs) and logic depths (LDs) in digital FIR filter. In this brief, a novel matrix grouped CSE (MCSE) algorithm has been proposed which outperforms the existing CSE algorithms in terms of LOs and LDs minimization. Moreover, a new half-unit biased (HUB) based rounding technique is incorporated in the proposed design to reduce the truncation error while maintaining low-complexity and the cut-set retiming technique is employed to reduce the critical-path-delay (CPD). Two hardware efficient FIR filter architectures (I and II) involving the proposed canonical signed digit (CSD) based MCSE algorithm, HUB rounding and cut-set retiming approach have been presented. Further, the architecture II represents a hardware efficient realization of a reconfigurable FIR filter. The hardware implementation of the architectures is performed on both FPGA and ASIC platforms. The hardware implementation of the proposed architecture I yields more than 25%, 49%, 38%, 36% and, 31% reduction in LOs, CPD, effective latency, area-delay-product (ADP) and, power-delay-product (PDP), respectively, over the state-of-the-art CSE based architectures. Whereas the reconfigurable architecture II exhibits nearly 48%, 12% and, 13% reduction in CPD, ADP and, PDP over the counterpart.

关键词： Finite impulse response filters Computer architecture Hardware Cascading style sheets signal processing algorithms Filtering algorithms Band-pass filters Finite impulse response (FIR) half-unit biased (HUB) canonical signed digit (CSD) common sub-expression elimination (CSE) seismic signal

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A New Iterative Weighted Least Squares Algorithm for 1-D SA Localization

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IEEE signal processing LETTERS 2025年 32卷 1740-1744页

作者： Zou, Yanbin Xiao, Yangpeng Wu, Wenbo Sun, Yimao Liu, Huaping Shantou Univ Dept Elect & Informat Engn Shantou 515063 Peoples R China Sichuan Univ Coll Comp Sci Chengdu 610065 Peoples R China Oregon State Univ Sch Elect Engn & Comp Sci Corvallis OR 97331 USA

Three-dimensional (3-D) target localization using one-dimensional (1-D) space angle (SA) measurements from linear arrays has recently gained significant attention. Each 1-D SA measurement defines a conical surface, and the intersection of multiple such surfaces determines the target's location in 3-D space. However, state-of-the-art methods for solving the 1-D SA localization problem are often either suboptimal or computationally intensive. In this paper, we propose a novel iterative weighted least squares (IWLS) algorithm to address the problem. To provide deeper insights, we present a geometric interpretation of the iterative process, highlighting its physical significance. Furthermore, we analyze the computational complexity of the proposed algorithm and compare it with existing methods. Simulation results demonstrate that the proposed algorithm not only achieves higher estimation accuracy but also requires less computational time compared to state-of-the-art approaches.

关键词： signal processing algorithms Location awareness Three-dimensional displays Vectors Sun Noise measurement Iterative algorithms Convergence Planar arrays Training One-dimensional space angle (1-D SA) iterative weighted least squares (IWLS)

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