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A Real-Time Subaperture Preprocessing for Multireceiver Wide-Beam SAS Imaging

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2025年 22卷

作者： Zhang, Jiafeng Cheng, Guangli Tang, Jinsong Wu, Haoran Naval Univ Engn Inst Elect Engn Wuhan 430000 Peoples R China

The multireceiver synthetic aperture sonar (SAS) data are usually converted to equivalent monostatic data through the displaced phase center approximation (DPCA) before the monostatic imaging. However, the DPCA error is azimuth-variant in the wide-beam case, resulting in the traditional algorithms compensating for the DPCA error in the extended Doppler domain of each receiver, which obviously increases the computational complexity. To solve the problem, this letter analyzes the space-variant characteristics of the DPCA error and discovers that the DPCA error exhibits significant receiver-variant and weak azimuth-variant characteristics. Based on this, a subaperture preprocessing is proposed to reduce computational complexity without sacrificing imaging accuracy. The proposed algorithm compensates for the DPCA error uniformly within the subaperture using the DPCA error at the center of the subaperture and then superposes all subapertures coherently to obtain equivalent monostatic data. The weak azimuth-variant characteristic ensures that the subapertures are very sparse, Additionally, the algorithm allows data recording and preprocessing to be synchronized, significantly reducing the imaging waiting time further. The simulated data and actual data experiments verify the effectiveness of the proposed algorithm.

关键词： Receivers Synthetic aperture sonar signal processing algorithms Imaging Doppler effect Azimuth Approximation algorithms Apertures Transmitters Geoscience and remote sensing Azimuth-variant characteristic displaced phase center approximation (DPCA) preprocessing real time subaperture synthetic aperture sonar (SAS) wide-beam

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Enhancing Nonintrusive Load Monitoring With Targeted Adaptive Networks

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Zhao, Maojiang Chen, Song Xiong, Zuqiang Bai, Zhemin Yang, Yu Xiamen Univ Dept Elect Sci Xiamen 361005 Peoples R China Huawei Technol Co Ltd Hangzhou 310012 Peoples R China

Nonintrusive load monitoring (NILM) technology is a cost-effective method that infers the operating status and working conditions of each system appliance by disaggregating the overall electricity load recorded by the electric meter. This technology has numerous potential applications in the smart grid, including energy management, demand response, and fault detection. However, its effectiveness highly depends on the development of accurate disaggregation algorithms. Recently, deep learning (DL) has significantly enhanced the accuracy and computational efficiency of NILM. Despite these advancements, the generalization ability of most DL algorithms is limited due to the scarcity of training data. To address this limitation, we propose a new method named targeted adaptive network for NILM (TAN-NILM). This method features a main disaggregation network and an auxiliary network, trained using a multitask learning strategy. The auxiliary network extracts and provides feature information on individual appliances, assisting the main network in targeting specific appliances and estimating their loads. The experimental results demonstrate that TAN-NILM exhibits superior disaggregation accuracy and generalization ability compared to state-of-the-art NILM algorithms.

关键词： signal processing algorithms Home appliances Feature extraction Training Accuracy Time-domain analysis Filtering Multitasking Load monitoring Adaptation models Auxiliary network mix source separation multitask learning nonintrusive load monitoring (NILM) self-attention

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A Low-Complexity and High-Throughput Hardware Design for Lempel-Ziv 4 Compression Algorithm

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2025年

作者： Chen, Tao Song, Suwen Wang, Zhongfeng Nanjing Univ Sch Elect Sci & Engn Nanjing 210023 Peoples R China Sun Yat Sen Univ Sch Integrated Circuits Shenzhen 518107 Peoples R China

The Lempel-Ziv (LZ) 4 compression algorithm, widely used in data transmission and storage, faces the challenge of high-speed implementation and increased complexity in the era of big data. Therefore, this paper proposes a single-core parallel architecture for LZ4 algorithm with high throughput and low complexity. Firstly, to enhance throughput, two innovative approaches are introduced from the perspective of parallelism and frequency with an acceptable compression ratio loss: each parallelization window is restricted to performing a single match, bridging the gap between actual and theoretical parallelism;the feedback loop in the circuit is broken by utilizing the spatial correlation between adjacent matches for higher frequency. Secondly, two optimization schemes are employed on resource-consuming modules to achieve low complexity. Multi-port hash tables using Live Value Table (LVT) are improved based on inherent data characteristics, significantly reducing the hardware resource consumption while ensuring excellent scalability on hash table depth and frequency. The match comparison operation is moved ahead, further reducing the logic resources by 64.36%. Finally, our design is implemented on FPGA and ASIC platforms. Experimental results on FPGA demonstrate that the proposed architecture achieves a throughput of 17.39 Gb/s, exhibiting a 2.86x improvement over the state-of-the-art, along with a 6.46x enhancement in area efficiency. Further optimizations including Canonic Signed Digit (CSD) coding and computational reuse on the ASIC platform result in a 45x improvement in area efficiency.

关键词： Hardware Throughput Parallel processing Complexity theory Field programmable gate arrays Compression algorithms Encoding signal processing algorithms Parallel architectures Logic Lossless data compression LZ4 algorithm FPGA ASIC high throughput low complexity hardware accelerator

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An Unbalanced Optimal Transport-Based Approach for Robust Dictionary Learning

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2025年 PP卷 PP页

作者： Wang, Shengjia Wang, Zhiguo Zhao, Xi-Le Shen, Xiaojing Sichuan Univ Coll Math Chengdu 610064 Sichuan Peoples R China Univ Elect Sci & Technol China Sch Math Sci Chengdu 610054 Peoples R China

Dictionary learning (DL) is a pivotal task in machine learning and signal processing, involving extracting representative features from a given dataset. However, conventional DL models are known to be highly sensitive to outliers. To circumvent this issue, we introduce a new and robust DL model based on unbalanced optimal transport (UOT). Compared to DL models based on conventional robust distances and the Wasserstein distance, our model not only captures and leverages the structural information within the data but also demonstrates strong resilience to outliers. By employing the structure of the proposed robust DL model, we develop a novel hybrid block coordinate descent (BCD) algorithm. The proposed algorithm maintains computational tractability by exploiting special block structures of the subproblems. In addition, we establish the convergence of our algorithm without the Lipschitz smooth condition. Through extensive experimentation, we validate our theoretical results and demonstrate the effectiveness of the proposed method on synthetic data, MNIST data, Olivetti faces dataset, and hyperspectral images (HSIs) datasets.

关键词： Robustness Vectors Convergence signal processing algorithms Computational modeling Training Noise Mathematical models Learning systems Feature extraction Block coordinate descent (BCD) Bregman distance robust dictionary learning (DL) unbalanced optimal transport (UOT)

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Modified PUMA/EPUMA Based on Forward and Backward Linear Prediction for DOA Estimation

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2025年 22卷

作者： Ma, Biyun Zhu, Fu Wang, Yide Zhu, Qingqing Liu, Jiaojiao South China Univ Technol Sch Elect & Informat Engn Guangzhou 510640 Peoples R China Minist Nat Resources Key Lab Marine Environm Survey Technol & Applicat Guangzhou 510300 Peoples R China Nantes Univ Inst Elect & Technol NumeR IETR CNRS UMR 6164 F-44306 Nantes France

The principal-singular-vector utilization for modal analysis (PUMA) and its modification (Mod-PUMA), which utilize forward linear prediction (FLP) to process the signal subspace, experience significant performance degradation if there are multiple coherent sources and such a performance degradation will be further aggravated in low-SNR regions, which is primarily attributed to the outliers arising from inaccurate estimations of the signal subspace. To address these issues, we propose an extension version of PUMA-related algorithms, called FBLP-Mod-PUMA/enhanced-PUMA (EPUMA). The proposed algorithms improve the threshold performance by refining the signal subspace through forward and backward linear prediction (FBLP), effectively mitigating subspace leakage when dealing with coherent sources. The number of resolvable coherent sources has been theoretically derived and simulation results are provided to show the performance of the proposed algorithms.

关键词： Covariance matrices Eigenvalues and eigenfunctions Vectors Prediction algorithms Direction-of-arrival estimation Smoothing methods Simulation signal to noise ratio signal processing algorithms Sensor arrays Coherent sources direction-of-arrival (DOA) estimation forward and backward linear prediction (FBLP) principal-singular-vector utilization for modal analysis (PUMA)/enhanced-PUMA (EPUMA)

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House Swallow Optimizer: A New Meta-Heuristic Algorithm and Its Application for DOA Estimation of Broadband signal in Vector Hydrophone Array

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IEEE Access 2025年 13卷 83717-83737页

作者： Jie Dong Lifu Wang Shuhui Qiao Peng Wang Xiuhui Tan School of Mathematics North University of China Taiyuan Shanxi China

A new meta-heuristic algorithm, termed the House Swallow Optimizer (HSO), is proposed. Inspired by the sophisticated behavioral repertoire of house swallows, this algorithm addresses the limitations of conventional optimization methods in solving complex, high-dimensional problems. The HSO systematically simulates 6 key survival strategies observed in house swallows—roosting, hovering, preying, nesting, breeding and seasonal migration—through a 5-phase mathematical model to achieve effective exploration-exploitation balance. To evaluate the algorithm’s performance, we conducted comparative studies using 23 standardized benchmark functions against 5 established meta-heuristics: BKA, SCA, SOA, DE, and PSO. The experimental outcomes prove that HSO has a strong ability to balance exploration and development, with out-performance over comparative algorithms. In order to verify the engineering application potential of the optimization algorithm proposed in this paper, aiming at the problems of large estimation error, high computational complexity and low accuracy in the DOA estimation of broadband signal of vector hydrophone array, the HSO algorithm is applied to the ML algorithm, and the DOA estimation of broadband signal is successfully carried out. Initially, the SST algorithm is employed for signal preprocessing, where broadband signals are transformed into narrowband equivalents through eigenvalue decomposition. This step facilitates the formulation of a ML-based DOA estimation model, which is subsequently optimized using the HSO algorithm. Simulation results demonstrate that the proposed HSO-ML method is superior to the ML implementation of other algorithms. Specifically, our approach achieves faster convergence speed, lower root mean square error, lower computational complexity and more robust performance.

关键词： signal processing algorithms Estimation Direction-of-arrival estimation Optimization Broadband communication Approximation algorithms Accuracy Maximum likelihood estimation Classification algorithms Array signal processing

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Subband Adaptive Filtering Based on the Squared Sine Error Criterion: Analysis and Applications

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language processing 2025年 33卷 1949-1959页

作者： Zhikai Gong Yingsong Li Xinqi Huang Liping Li Zhixiang Huang Rodrigo. C. de Lamare Yingying Zhu College of Information and Communication Engineering Harbin Engineering University Harbin China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China Centre for Telecommunications Research (CETUC) Pontifical Catholic University of Rio de Janeiro (PUC-Rio) Gávea Brazil School of Physics Engineering and Technology University of York York U.K.

In this work, we present a subband adaptive filtering (SAF) algorithm with good robustness for impulsive noise environments. The proposed squared sine normalized SAF (SS-NSAF) algorithm employs a squared sine function and is inspired by the idea of normalization. A statistical analysis of the proposed SS-NSAF algorithm is carried out under impulsive noise environments, which includes stability conditions for the convergence and analytical formulas to predict the mean-square deviation of the SS-NSAF algorithm. Numerical simulations for system-identification and echo cancellation applications are conducted to illustrate the performance of the SS-NSAF algorithm and competing techniques in impulsive noise environments.

关键词： Noise Prediction algorithms signal processing algorithms Filtering algorithms Nonlinear filters Maximum likelihood detection Convergence Vectors Mathematical models Information filters

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Robust direction-of-arrival estimation and signal separation method for integrated sensing and communication

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Journal of Communications and Networks 2025年第6期26卷 689-698页

作者： Ming Chen Liang Jin Zheng Wan Zheyuan Deng Bo Zhang Yajun Chen Kaizhi Huang PLA Strategic Support Force Information Engineering University Zhengzhou 450001 China Intelligent Gaming and Decision-making Laboratory PLA Academy of Military Science Beijing 100000 China School of International Relations National University of Defense Technology

In this study, we designed a single-channel direction-of-arrival (DOA) estimation and signal separation algorithm based on a grouping scheme for the time-varying metasurfaces (TVMs) for integrated sensing and communication systems. In this scheme, the harmonic effect of the TVM was used to transform the received single-channel signal into a multichannel signal through an orthogonal Fourier coefficient matrix. After achieving multi-channel DOA estimation, the corresponding weighted beam pointing was designed for signal separation. By applying a periodic modulation function on the TVM to modulate the incident signal, the signal was mapped to a multi-dimensional received space to recover the multi-channel received signal. Thus, conventional multi-channel algorithms could be used on the single-channel signal for DOA estimation. Next, we designed a sub-surface weighted beam pointing to maximize the received signal signal-to-interference-plus-noise ratio. Simulation results revealed that the proposed scheme of DOA estimation can exhibit performances comparable to that of the conventional multichannel antenna array. Moreover, the signal separation scheme designed based on this method was robust and could maintain good signal separation ability under a low signal-to-noise ratio.

关键词： Direction-of-arrival estimation Estimation Modulation Antenna arrays Source separation Frequency modulation signal processing algorithms Metasurfaces Integrated sensing and communication Harmonic analysis

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Intelligent Recognition of Voids Behind Tunnel Linings Using Deep Learning and Percussion Sound

Journal of Intelligent Construction

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Journal of Intelligent Construction 2025年第4期1卷 1-18页

作者： Xiaolei Zhang Xin Lin Wei Zhang Yong Feng Wei Lan Yuewu da Kan Hu Department of Geotechnical Engineering Key Laboratory of Geotechnical and Underground Engineering of the Ministry of Education Tongji University Shanghai China Wuxi Water Group Co. Ltd. Wuxi China Urban Mobility Institute Tongji University Shanghai China Shanghai Shenyuan Geotechnical Engineering Co. Ltd. Shanghai China

Voids behind tunnel linings are critical factors affecting tunnels' safety and durability. For automatic, rapid, and accurate detection of void defects behind tunnel linings, this paper proposes an intelligent recognition method of void detection based on deep learning (DL) and percussion method. Extensive indoor percussion experiments were first conducted to obtain a total of 77,925 percussion signals. Afterward, the mel-frequency cepstrum coefficients (MFCCs) are utilized for signal feature extraction, based on which a convolutional neural network (CNN) is developed for automatic void defect diagnosis. The void automated diagnosis tests are subsequently performed, and the impact of three key factors on the recognition results is investigated. The results show that the proposed CNN can accurately identify voids ranging from 0.10 to 0.30 m, with an average accuracy of 94.96% and an F1 score of 72.29%. The exploration of the slab thickness indicates that the proposed method is capable of detecting voids with an average accuracy of 94.37% and an F1 score of 74.55%, with the slab thicknesses ranging from 0.10 to 0.30 m. Furthermore, the boundary effects of concrete slabs are analyzed. Finally, an on-site validation is carried out, and the good agreements between the developed method and ultrasonic detection method indicate that the CNN-aided percussion method is feasible in practical tunnel lining void inspection tasks.

关键词： Deep learning Accuracy signal processing algorithms signal processing Feature extraction Distance measurement Safety Convolutional neural networks Slabs Mel frequency cepstral coefficient

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Phase Retrieval Using Deep Dual Alternating Direction Method of Multipliers Network With Deep Sparse Prior Knowledge

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language processing 2025年 33卷 557-569页

作者： Moogyeong Kim Wonzoo Chung Department of Artificial Intelligence Korea University Seoul South Korea

In this paper, a deep phase retrieval algorithm for speech signals based on the dual Alternating Direction Method of Multipliers (ADMM) incorporating a deep prior network that exploits the sparsity of speech signals is presented. The proposed network, named DADMM-net, unfolds the dual ADMM for the $\ell _{1}$ -regularized non-convex optimization problem of phase retrieval with several two-dimensional convolutional neural networks (2D-CNNs). In order to efficiently optimize the deep unfolding network for high-dimensional parameter vectors, a novel updating scheme referred to as soft coordinate descent (soft-CD) is proposed, where dual parameter updates are determined through interpolation between the current values and the updated values coordinate-wise with respect to the weights computed by deep networks in each layer. Numerical simulations on a publicly available dataset confirm the state-of-the-art performance of the proposed method in terms of perceptual evaluation of speech quality and short-time objective intelligibility with a significantly faster convergence speed compared to existing methods.

关键词： Optimization Convex functions Time-domain analysis Spectrogram signal processing algorithms Vectors Sparse approximation Approximation algorithms Knowledge engineering Time-frequency analysis

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