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signal processing Plays a Key Role in Environmental Research Projects Keeping People and Ecosystems Alive and Healthy Is Perhaps the 21st Century's Biggest Challenge

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IEEE signal processing MAGAZINE 2021年第3期38卷 6-9页

作者： Edwards, John Gilbert Arizona USA

Despite the impressive technological strides made over the years, human lives still depend very much on the natural environment. Fortunately, technology can now be used to help address critical environmental concerns in air quality, soil condition, and weather events. In all of these areas and many others, signal processing is supporting the ability to provide immediate and long-term observations and insights.

关键词： signal processing algorithms Research and development Air quality Meteorology Environmental management Sensors Monitoring Optical fiber devices

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Impact of Varying Distance-Based Fingerprint Similarity Metrics on Affinity Propagation Clustering Performance in Received signal Strength-Based Fingerprint Databases

IEEE OPEN JOURNAL OF SIGNAL PROCESSING

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IEEE OPEN JOURNAL OF signal processing 2024年 5卷 1005-1014页

作者： Yaro, Abdulmalik Shehu Maly, Filip Maly, Karel Prazak, Pavel Univ Hradec Kralove Fac Informat & Management Dept Informat & Quantitat Methods Hradec Kralove 50003 Czech Republic Ahmadu Bello Univ Dept Elect & Telecommun Engn Zaria 810106 Nigeria

The affinity propagation clustering (APC) algorithm is popular for fingerprint database clustering because it can cluster without pre-defining the number of clusters. However, the clustering performance of the APC algorithm heavily depends on the chosen fingerprint similarity metric, with distance-based metrics being the most commonly used. Despite its popularity, the APC algorithm lacks comprehensive research on how distance-based metrics affect clustering performance. This emphasizes the need for a better understanding of how these metrics influence its clustering performance, particularly in fingerprint databases. This paper investigates the impact of various distance-based fingerprint similarity metrics on the clustering performance of the APC algorithm. It identifies the best fingerprint similarity metric for optimal clustering performance for a given fingerprint database. The analysis is conducted across five experimentally generated online fingerprint databases, utilizing seven distance-based metrics: Euclidean, squared Euclidean, Manhattan, Spearman, cosine, Canberra, and Chebyshev distances. Using the silhouette score as the performance metric, the simulation results indicate that structural characteristics of the fingerprint database, such as the distribution of fingerprint vectors, play a key role in selecting the best fingerprint similarity metric. However, Euclidean and Manhattan distances are generally the preferable choices for use as fingerprint similarity metrics for the APC algorithm across most fingerprint databases, regardless of their structural characteristics. It is recommended that other factors, such as computational intensity and the presence or absence of outliers, be considered alongside the structural characteristics of the fingerprint database when choosing the appropriate fingerprint similarity metric for maximum clustering performance.

关键词： Fingerprint recognition Measurement Clustering algorithms Databases Vectors signal processing algorithms Location awareness Affinity propagation clustering distance-based similarity metrics fingerprint clustering silhouette score

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Beamforming Designs for Hybrid Relaying in mmWave Systems Based on Deep Unfolding

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IEEE signal processing LETTERS 2025年 32卷 2050-2054页

作者： Luo, Zhen Xie, Fu Zhang, Rongbin Liu, Hongqing Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing 400065 Peoples R China Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Shantou Univ Dept Elect & Informat Engn Shantou 515063 Peoples R China

In this paper, an efficient hybrid beamforming scheme is proposed for the relay node in mmWave relay systems based on deep unfolding. The optimal digital beamformer at the relay node is decomposed into an analog-digital-analog (A-D-A) hybrid structure, which can be formulated as a matrix factorization problem. The analog beamforming matrices and digital beamforming matrix in the A-D-A hybrid structure are efficiently solved by a deep unfolding neural network, which is unfolded based on the projected gradient descent algorithm. Numerical results verify that performance of the proposed scheme is close to the fully-digital solution, which prevails traditional model-based algorithms with a fast runtime.

关键词： Relays Matrix decomposition Millimeter wave communication Array signal processing signal processing algorithms Artificial neural networks Transmitting antennas Receiving antennas Optimization Training mmWave hybrid beamforming relay deep unfolding

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Protecting the Future of Information: LOCO Coding With Error Detection for DNA Data Storage

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IEEE TRANSACTIONS ON MOLECULAR BIOLOGICAL AND MULTI-SCALE COMMUNICATIONS 2024年第2期10卷 317-333页

作者： Irimagzi, Canberk Uslan, Yusuf Hareedy, Ahmed Middle East Tech Univ Inst Appl Math TR-06800 Ankara Turkiye Middle East Tech Univ Dept Elect & Elect Engn TR-06800 Ankara Turkiye

From the information-theoretic perspective, DNA strands serve as a storage medium for 4-ary data over the alphabet {A, T, G, C} . DNA data storage promises formidable information density, long-term durability, and ease of replicability. However, information in this intriguing storage technology might be corrupted because of error-prone data sequences as well as insertion, deletion, and substitution errors. Experiments have revealed that DNA sequences with long homopolymers and/or with low GC-content are notably more subject to errors upon storage. In order to address this biochemical challenge, constrained codes are proposed for usage in DNA data storage systems, and they are studied in the literature accordingly. This paper investigates the utilization of the recently-introduced method for designing lexicographically-ordered constrained (LOCO) codes in DNA data storage to improve performance. LOCO codes offer capacity-achievability, low complexity, and ease of reconfigurability. This paper introduces novel constrained codes, namely DNA LOCO (D-LOCO) codes, over the alphabet {A, T, G, C} with limited runs of identical symbols. Due to their ordered structure, these codes come with an encoding-decoding rule we derive, which provides simple and affordable encoding-decoding algorithms. In terms of storage overhead, the proposed encoding-decoding algorithms outperform those in the existing literature. Our algorithms are based on small-size adders, and therefore they are readily reconfigurable. D-LOCO codes are intrinsically balanced, which allows us to achieve balanced AT- and GC-content over the entire DNA strand with minimal rate penalty. Moreover, we propose four schemes to bridge consecutive codewords, three of which guarantee single substitution error detection per codeword. We examine the probability of undetecting errors over a presumed symmetric DNA storage channel subject to substitution errors only. We also show that D-LOCO codes are capacity-achieving and that

关键词： Codes DNA Encoding Symbols Memory Table lookup signal processing algorithms Constrained codes low-complexity algorithms reconfigurable coding LOCO codes homopolymer run balancing error-detection DNA data storage

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Multi Target Localization With Block Orthogonal Least Squares for Multistatic MIMO Radars

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IEEE signal processing LETTERS 2025年 32卷 1990-1994页

作者： Willame, Martin Monnoyer, Gilles Yildirim, Hasan Can Horlin, Francois Louveaux, Jerome Univ Catholique Louvain UCLouvain B-1348 Ottignies Louvain La Neuv Belgium Univ Libre Bruxelles ULB B-1050 Brussels Belgium

Recently, there has been a growing interest in multistatic radar configurations to improve the localization of multiple targets. Theoretically, the maximum likelihood (ML) approach enables to fuse the information provided by each radar pair to localize the different targets. However, it involves a multi-dimensional search process whose complexity exponentially grows with the number of targets. Consequently, heuristic methods, notably including the block orthogonal matching pursuit (BOMP), have been used in the multistatic radar context to approach the ML estimation greedily. Interestingly, the more accurate block orthogonal least squares (BOLS) method has not been studied in this context because the performance improvement is usually low in regard to its computational complexity. In this work, we investigate the application of BOLS to an angle-based localization of multiple targets using a multistatic multiple-input and multiple-output (MIMO) radar. First, an efficient implementation of BOLS is presented reducing its computational complexity. Then, using Monte Carlo simulations, we show evidence of the significant advantage of this efficient implementation of BOLS over BOMP in this scenario featuring highly correlated signals. The impact of radar parameters on the localization root mean square error and on the computational complexity of both algorithms is studied.

关键词： Vectors Location awareness Radar Maximum likelihood estimation Channel estimation Matching pursuit algorithms MIMO radar Computational complexity signal processing algorithms Sensors BOLS BOMP data fusion maximum likelihood multistatic

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Exponential Hyperbolic Cosine Robust Adaptive Filters for Audio signal processing

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IEEE signal processing LETTERS 2021年 28卷 1410-1414页

作者： Kumar, Krishna Pandey, Rajlaxmi Bhattacharjee, Sankha Subhra George, Nithin V. Indian Inst Technol Dept Elect Engn Gandhinagar 382355 India

In recent years, correntropy-based algorithms which include maximum correntropy criterion (MCC), generalized MCC (GMCC), kernel MCC (KMCC) and hyperbolic cosine function-based algorithms such as hyperbolic cosine adaptive filter (HCAF), logarithmic HCAF (LHCAF), least lncosh (Llncosh) have been widely utilized in adaptive filtering due to their robustness towards non-Gaussian/impulsive background noises. However, the performance of such algorithms suffers from high steady-state misalignment. To minimize the steady-state misalignment along with having comparable computational complexity, an exponential hyperbolic cosine function (EHCF) based new robust norm is introduced and a corresponding EHCF based adaptive filter called exponential hyperbolic cosine adaptive filter (EHCAF) is developed in this letter. Further, computational complexity and bound on learning rate for stability of the proposed algorithm is also studied. A set of simulation studies has been carried out for system identification scenario to assess the performance of the proposed algorithm. Further, EHCAF algorithm has been extended and the filtered-x EHCAF (Fx-EHCAF) algorithm is proposed for robust room equalization.

关键词： signal processing algorithms Cost function Computational complexity Steady-state Noise measurement Finite impulse response filters Adaptation models Robust adaptive filters hyperbolic cosine functions impulsive noise exponential hyperbolic cosine function system identification room equalization

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Sparse Bayesian Learning Using Complex t-Prior for Beam-Domain Massive MIMO Channel Estimation

IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY

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IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY 2024年 5卷 5905-5920页

作者： Furuta, Kengo Takahashi, Takumi Ochiai, Hideki Osaka Univ Grad Sch Engn Suita 5650871 Japan

This paper proposes a novel beam-domain channel estimation (CE) algorithm via sparse Bayesian learning (SBL) using complex t-prior for massive multi-user multiple-input multiple-output (MIMO) systems. Due to the sidelobe leakage and insufficient observation resolution resulting from physical constraints, the equivalent channel after digital beamforming at the receiver has a structure with many small but non-zero elements, which cannot be modeled strictly as a sparse signal. To fully capture this pseudo-sparse structure characterized by the signal strength variations among elements, we design a novel SBL algorithm that incorporates a complex t-distribution using a hierarchical Bayesian model. By utilizing a high degree of adaptability of this heavy-tailed prior, it is possible to efficiently learn the signal strength, accounting for elements with non-zero but small values, which is verified by the regularization analysis based on an equivalent optimization problem. The efficacy of the proposed CE algorithm is confirmed by numerical simulations, which show that the proposed method not only significantly outperforms the state-of-the-art (SotA) sparse signal recovery (SSR)-based algorithms but also achieves the performance of a genie-aided scheme over a wide signal-to-noise ratio (SNR) range in both sub-6 GHz and millimeter-wave (mmWave) wireless communication scenarios.

关键词： Massive MIMO Bayes methods signal processing algorithms Channel estimation Millimeter wave communication Estimation Accuracy channel estimation sparse Bayesian learning hierarchical Bayesian model complex t-distribution beam-domain signal processing

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Efficient Nonnegative Tensor Decomposition Using Alternating Direction Proximal Method of Multipliers

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Chinese Journal of Electronics 2024年第5期33卷 1308-1316页

作者： Deqing WANG Guoqiang HU State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Key Laboratory of Marine Robotics Liaoning Province College of Artificial Intelligence Dalian Maritime University

Nonnegative CANDECOMP/PARAFAC（NCP） tensor decomposition is a powerful tool for multiway signal processing. The alternating direction method of multipliers（ADMM） optimization algorithm has become increasingly popular for solving tensor decomposition problems in the block coordinate descent framework. However,the ADMM-based NCP algorithm suffers from rank deficiency and slow convergence for some large-scale and highly sparse tensor data. The proximal algorithm is preferred to enhance optimization algorithms and improve convergence properties. In this study, we propose a novel NCP algorithm using the alternating direction proximal method of multipliers（ADPMM） that consists of the proximal algorithm. The proposed NCP algorithm can guarantee convergence and overcome the rank deficiency. Moreover, we implement the proposed NCP using an inexact scheme that alternatively optimizes the subproblems. Each subproblem is optimized by a finite number of inner iterations yielding fast computation speed. Our NCP algorithm is a hybrid of alternating optimization and ADPMM and is named *** experimental results on synthetic and real-world tensors demonstrate the effectiveness and efficiency of our proposed algorithm.

关键词： Tensors Neuroscience signal processing algorithms Linearization techniques signal processing Fluorescence Data processing

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Point Cloud Resampling via Hypergraph signal processing

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IEEE signal processing LETTERS 2021年 28卷 2117-2121页

作者： Deng, Qinwen Zhang, Songyang Ding, Zhi Univ Calif Davis Dept Elect & Comp Engn Davis CA 95616 USA

Three-dimensional (3D) point clouds are important data representations in visualization applications. The rapidly growing utility and popularity of point cloud processing strongly motivate a plethora of research activities on large-scale point cloud processing and feature extraction. In this work, we investigate point cloud resampling based on hypergraph signal processing (HGSP). We develop a novel method to extract sharp object features and reduce the data size of point cloud representation. By directly estimating hypergraph spectrum based on hypergraph stationary processing, we design a spectral kernel-based filter to capture high-dimensional interactions among point signal nodes and to better preserve object surface outlines. Experimental results validate the effectiveness of hypergraph in representing point clouds, and demonstrate the robustness of the proposed algorithm under noise.

关键词： Three-dimensional displays Kernel Surface reconstruction Tensors Feature extraction signal processing algorithms signal processing Compression hypergraph signal processing point cloud resampling virtual reality

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Robust Estimation of the Covariance Matrix From Data With Outliers

IEEE OPEN JOURNAL OF SIGNAL PROCESSING

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IEEE OPEN JOURNAL OF signal processing 2024年 5卷 1061-1072页

作者： Stoica, Petre Babu, Prabhu Varshney, Piyush Uppsala Univ Dept Informat Technol Div Syst & Control S-75237 Uppsala Sweden Indian Inst Technol Delhi Ctr Appl Res Elect CARE New Delhi 110016 India

The robust estimation of the covariance matrix is a frequent task in practical applications in which, more often than not, some data samples are outliers. There are several methods that can be used to robustly estimate a covariance matrix from corrupted data, a representative example of which is the minimum covariance determinant (MCD) method. In this paper we present a maximum conditional likelihood interpretation of MCD that provides a new motivation of as well as further insights into this method. To perform at its best MCD requires information on the number of outliers in the data, which usually is not available. We propose two new methods for covariance matrix estimation from data with outliers that do not suffer from this problem: TEST (multiple-hypothesis testing method) which uses the FDR (false discovery rate) to test a set of model hypotheses and hence estimate the number of outliers and their locations, and LIKE (penalized likelihood method) that solves the outlier estimation problem using a GIC (generalized information criterion) to penalize the complexity of a high-dimensional data model. We show by means of numerical simulations that the performances of TEST and LIKE are relatively similar to one another as well as to the performance of the oracle MCD (which uses the true number of outliers) and significantly better than the performance of MCD that uses an upper bound on the outlier number.

关键词： Covariance matrices Estimation Testing signal processing algorithms Minimization Upper bound signal processing Symbols Numerical simulation Numerical models Robust covariance matrix estimation outlier detection minimum covariance determinant false discovery rate

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