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Sparse Projection Matrix Approximation and Its Applications

IEEE signal processing LETTERS 2025年 32卷 351-355页

作者： Zhai, Zheng Wu, Mingxin Xu, Jialu Li, Xiaohui Beijing Normal Univ Fac Arts & Sci Dept Stat Zhuhai 519088 Peoples R China Yantai Univ Sch Math & Informat Sci Yantai 264032 Peoples R China

This letter introduces a sparse regularized projection matrix approximation (SPMA) model to recover cluster structures from affinity matrices. The model is formulated as a projection approximation problem with an entry-wise sparsity penalty to encourage sparse solutions. We propose two algorithms to solve this problem: one involves direct optimization on the Stiefel manifold using the Cayley transformation, while the other employs the Alternating Direction Method of Multipliers (ADMM). Numerical experiments on synthetic and real-world datasets demonstrate that our regularized projection matrix approximation approach significantly outperforms state-of-the-art methods in clustering accuracy and performance.

关键词： Sparse matrices Manifolds Approximation algorithms signal processing algorithms Optimization Convex functions Matrix decomposition Human activity recognition Eigenvalues and eigenfunctions Clustering algorithms ADMM clustering regularisation Stiefel manifold sparse projection matrix approximation

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A Hardware-Friendly Lightweight Partition Decision Algorithm for VVC Intra and Inter Coding

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IEEE signal processing LETTERS 2025年 32卷 941-945页

作者： Zan, Zhao Huang, Leilei Chen, Shushi Zeng, Xiaoyang Fan, Yibo Fudan Univ State Key Lab Integrated Chips & Syst Shanghai 201203 Peoples R China East China Normal Univ Inst Microelect Circuits & Syst Shanghai 200241 Peoples R China

The Versatile Video Coding (VVC) standard notably enhances encoding efficiency with the Quad-Tree plus Multi-Type Tree (QTMTT) partition structure. However, the complex QTMTT tool presents substantial challenges in both software and hardware implementation. To overcome those challenges, this paper introduces a hardware-friendly partition decision algorithm for VVC intra and inter coding. Firstly, we propose a lightweight backbone network to extract partition-aware features. Secondly, we employ a Quantisation Parameter (QP) fusion network to regulate the impact of QPs on the partition structure. Additionally, we apply a top-down threshold-driven post-processing algorithm, in which improbable partition types are removed to directly derive the unique partition structure. Experiments show that our method not only exceeds the previous state-of-the-art work in BD-BR performance, but also shows sufficient hardware-friendly characteristics. To the best of our knowledge, this work is among the earliest to comprehensively discuss and implement a hardware-friendly partition decision algorithm.

关键词： Partitioning algorithms Hardware Encoding signal processing algorithms Prediction algorithms Feature extraction Complexity theory Software algorithms Convolution Software VVC QTMTT partition decision complexity reduction hardware-friendly algorithm convolutional neural network intra coding inter coding

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Minimum Total Quaternion Error Entropy Filtering With Fiducial Points Against Asymmetric Noise

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IEEE signal processing LETTERS 2025年 32卷 306-310页

作者： Lin, Dongyuan Cai, Peng Chen, Xiaofeng Zheng, Yunfei Wang, Shiyuan Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Chongqing Jiaotong Univ Dept Math Chongqing 400074 Peoples R China

Quaternion adaptive filters (QAFs) are extensively used in processing three- or four-dimensional signals effectively. However, their performance can significantly deteriorate or even diverge when system inputs and outputs are contaminated by complex noises. Therefore, this letter addresses the issue of parameter estimation in the quaternion errors-in-variables (QEIV) in asymmetric noise. First, a novel robust criterion, called improved quaternion minimum error entropy criterion with fiducial points (IQMEEF), is constructed. Then, a minimum total quaternion error entropy algorithm with fiducial points (MTQEEF) is proposed by integrating the IQMEEF criterion with the total least squares (TLS) method, leveraging stochastic gradient and quaternion generalized Hamilton-real (GHR) calculus theory. Finally, simulations validate the superior performance of MTQEEF in the QEIV model under asymmetric noise environments.

关键词： Quaternions Noise Entropy signal processing algorithms Adaptation models Vectors Parameter estimation Kernel Filtering algorithms Calculus Robust quaternion adaptive filters quaternion error entropy criterion with fiducial points quaternion errors-in-variables model asymmetric noise

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Hypformer: A Fast Hypothesis-Driven Rescoring Speech Recognition Framework

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IEEE signal processing LETTERS 2025年 32卷 471-475页

作者： Zhuang, Xuyi Qian, Yukun Wang, Mingjiang Harbin Inst Technol Shenzhen Shenzhen 518000 Peoples R China

Recently, the performance of non-autoregressive ASR models has made significant progress but still lags behind hybrid CTC/attention systems. This paper introduces Hypformer, a fast hypothesis-driven rescoring speech recognition framework. Multiple hypothetical prefixes are realized by fast prefix generation algorithm. With two different rescoring methods, nar-ar rescoring and nar$<^>{2}$ rescoring, Hypformer can flexibly switch between autoregressive and non-autoregressive decoding modes to perform rescoring of hypothesis prefixes. Experiments on the standard Mandarin datasets AISHELL-1 and AISHELL-2 demonstrate that Hypformer outperforms the state-of-the-art Hybrid CTC/Attention systems in ASR performance while achieving a speedup of over six times. Experiments on the Mandarin sub-dialect dataset KeSpeech indicate that Hypformer achieves more accurate recognition by leveraging richer contextual information.

关键词： Decoding Training Iterative decoding Transformers Accuracy Standards Symbols Switches signal processing algorithms Probability distribution ASR E2E non-autoregressive hypothesis prefixes rescoring

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Improved Free-of-CPP ADMM-Based Iterative Decoding Algorithm of Binary LDPC Codes

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IEEE signal processing LETTERS 2025年 32卷 211-215页

作者： Bai, Jing An, Zedong Chi, Yuhao Song, Guanghui Yuen, Chau Shijiazhuang Tiedao Univ Sch Informat Sci & Technol Shijiazhuang 050043 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 487372 Singapore

Iterative decoding algorithms based on the alternating direction method of multipliers (ADMM) decoding of low density parity check (LDPC) codes has emerged as an alternating decoding method and bringed a boom of research on drawing upon mathematical optimization to LDPC decoding. Improving error-correcting performance is a key issue to enhance the superiority of ADMM decoding. In this letter, we investigate an efficient ADMM-based iterative decoder for binary LDPC codes. First, we build an mathematical programming equivalence of the maximum likelihood (ML) decoding problem by transforming parity-check constraints to multiple equivalent linear constraints and eliminating check-polytope projection (CPP). Then, an iterative algorithm based on ADMM technique is developed to solve this free-of-CPP (FCPP) equivalence and each ADMM update can be computed efficiently. Moreover, the proposed ADMM-FCPP decoding algorithm is analyzed to display a linear complexity to the length of the LDPC code at each iteration. Finally, simulation results demonstrate the superiority of the proposed decoder in error-correcting performance compared with the state-of-the-art ADMM-based decoders.

关键词： Maximum likelihood decoding Iterative decoding Convex functions Vectors signal processing algorithms Optimization Complexity theory Simulation Programming profession Low-density parity-check (LDPC) codes alternating direction method of multipliers (ADMM) check- polytope projection (CPP) mathematical programming mathematical programming

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Energy-Efficient Sensor Scheduling for State Estimation Over Homogeneous Multi-Hop Networks

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IEEE signal processing LETTERS 2025年 32卷 411-415页

作者： Li, Yao Song, Rui Zheng, Jianyong Guan, Xinping Shanghai Univ Sch Future Technol Shanghai 200444 Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China

In this letter, anestimation-oriented power-constrained sensor scheduling problem over multi-hop sensor networks is studied. Two different online scheduling schemes for multi-hop transmission, i.e., global-delay-based scheduling (GS) and covariance-based scheduling (CS) are proposed, respectively. We propose a stochastic triggering scheme to satisfy power constraints. A Markovian model is adopted to formulate the state transition relationship in the scheduling process. In order to calculate the switching threshold and selection probabilities, an algorithm for parameters determination is further properly designed to overcome the coupling property between hops. We have explicitly analyzed the performance of GS by figuring out the upper and lower bounds of cost. Moreover, the superiority and optimality of CS have been theoretically proved by using the optimal state distribution method. Numerical simulations and comparisons with existing methods have been conducted illustratively to verify the correctness and effectiveness of our proposed schemes, algorithms and results.

关键词： Spread spectrum communication Schedules Prediction algorithms Switches Processor scheduling Wireless sensor networks Lower bound signal processing algorithms Relays Costs Multi-hop networks sensor scheduling state estimation

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A Two-Level Weighted Low-Complexity Adaptive Beamforming Method

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IEEE signal processing LETTERS 2025年 32卷 1246-1250页

作者： Du, Yuxi Cui, Weijia Ba, Bin Chen, Yangyang Zhang, Long PLA Strateg Support Informat Engn Univ Zhengzhou 450001 Peoples R China

Aiming at the problem of high computational complexity of adaptive beamforming techniques in array radar systems, a two-level weighted low-complexity adaptive beamforming method is proposed in this letter. First, the uniform linear array is divided into subarrays and each subarray has the same elements. The desired signal received by each array element in the subarray is then superimposed by compensating for the delay using a first level weighting. Finally, the interference signals and noise are suppressed using a second level weighting to obtain the ideal output signal to interference plus noise ratio. Simulation results verify the effectiveness and reliability of the proposed method.

关键词： Interference Antenna arrays signal to noise ratio Array signal processing Noise Vectors Computational complexity signal processing algorithms Covariance matrices Receiving antennas Two-level weighted adaptive beamforming signal to interference plus noise ratio

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l0 Penalized Maximum Likelihood Estimation of Sparse Covariance Matrices

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IEEE signal processing LETTERS 2025年 32卷 66-70页

作者： Fatima, Ghania Stoica, Petre Babu, Prabhu Indian Inst Technol Delhi CARE New Delhi 110016 India Uppsala Univ Dept Informat Technol S-75105 Uppsala Sweden

In this letter we present a framework for estimating sparse covariance matrices, wherein we solve the l(0)-norm penalized maximum likelihood estimation problem using the extended Bayesian information criterion (EBIC), a high dimensional model selection rule. The framework combines choosing the sparsity pattern and estimating the covariance matrix in a single step, eliminating the need for any hyper-parameter tuning. Using the framework we propose a cyclic majorization-minimization based technique and apply it to synthetic data to evaluate its performance in terms of normalized root mean square error (NRMSE) and Kullback Leibler (KL) divergence.

关键词： Covariance matrices Maximum likelihood estimation Sparse matrices Minimization Tuning signal processing algorithms Optimization Bayes methods Wireless communication Taylor series Covariance matrix estimation EBIC l(0) penalized maximum likelihood

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FastPSA: A Fast Version of the Principal Skewness Analysis

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IEEE signal processing LETTERS 2025年 32卷 1226-1230页

作者： Gao, Jingyu Geng, Xiurui Ji, Luyan Chinese Acad Sci Aerosp Informat Res Inst Beijing 100094 Peoples R China Chinese Acad Sci Key Lab Technol Geospatial Informat Proc & Applica Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China

Recently, principal skewness analysis (PSA) has been introduced into the domain of feature extraction. It is equivalent to the skewness version of Fast independent component analysis (FastICA). Unlike FastICA, PSA does not require all sample points when searching for the projection directions, making it faster. However, for the data of dimension L, PSA needs to calculate the eigenvectors of L tensors, each of size LxLxL. When L is large, PSA still requires significant computational time to find all projection directions. In this letter, we find that the (m+1)th projection direction in PSA can be obtained by calculating the eigenvector of a tensor of size (L-m) x (L-m) x (L-m). Furthermore, we propose a fast version of PSA (FastPSA) that is mathematically equivalent to PSA. The experimental results demonstrate that FastPSA has lower computational complexity than PSA.

关键词： Tensors Principal component analysis Feature extraction Computational complexity Vectors signal processing algorithms Training Hyperspectral imaging Data mining Computational efficiency FastICA principal skewness analysis hyperspectral image

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A Robust Adaptive Beamforming Algorithm for Mismatch and Impulsive Noise Circumstance

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2025年 22卷

作者： Zhang, Xuejun Feng, Dazheng Wang, Zhonggen Gan, Fubao Anhui Univ Sci & Technol Sch Elect & Informat Huainan 232001 Peoples R China Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China

This letter provides a solution to a robust adaptive beamformer used in remote sensing systems under the effect of two factors: 1) the impulsive noise satisfying Gaussian mixture distribution and 2) unknown model mismatches, such as look direction errors and sensor location perturbations. First, a beamformer output-based cost function is designed to effectively suppress the influence of impulsive noise. Second, the weight vector of the beamformer is regularized through norm constraints to reduce the impact of model mismatches. Furthermore, an efficient iterative scheme is established to quickly minimize the designed cost function based on their properties, and a robust adaptive beamformer can be obtained. Finally, computer simulations are presented to demonstrate the robust performance and fast convergence of the novel algorithm in the presence of impulsive noise and model mismatches.

关键词： Noise Cost function Array signal processing Adaptation models Vectors Loading Interference signal to noise ratio Costs signal processing algorithms impulsive noise model mismatch regularization robust adaptive beamformer

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