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Distributed Blind Source Separation Based on FastICA

IEEE signal processing LETTERS 2025年 32卷 881-885页

作者： Musluoglu, Cem Ates Bertrand, Alexander Katholieke Univ Leuven Stadius Ctr Dynam Syst Dept Elect Engn ESAT Signal Proc & Data Analyt B-3001 Leuven Belgium Leuven AI KU Leuven Inst AI B-3001 Leuven Belgium

With the emergence of wireless sensor networks (WSNs), many traditional signal processing tasks are required to be computed in a distributed fashion, without transmissions of the raw data to a centralized processing unit, due to the limited energy and bandwidth resources available to the sensors. In this paper, we propose a distributed independent component analysis (ICA) algorithm, which aims at identifying the original signal sources based on observations of their mixtures measured at various sensor nodes. One of the most commonly used ICA algorithms is known as FastICA, which requires a spatial pre-whitening operation in the first step of the algorithm. Such a pre-whitening across all nodes of a WSN is impossible in a bandwidth-constrained distributed setting as it requires to correlate each channel with each other channel in the WSN. We show that an explicit network-wide pre-whitening step can be circumvented by leveraging the properties of the so-called Distributed Adaptive signal Fusion (DASF) framework. Despite the lack of such a network-wide pre-whitening, we can still obtain the $Q$ least Gaussian independent components of the centralized ICA solution, where $Q$ scales linearly with the required communication load.

关键词： signal processing algorithms Wireless sensor networks Sensors Distributed databases Europe Artificial intelligence Sensor fusion Optimization Indexes Fitting Distributed optimization distributed spatial filtering independent component analysis

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Radar signal Sorting via Graph Convolutional Network and Semi-Supervised Learning

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IEEE signal processing LETTERS 2025年 32卷 421-425页

作者： Li, Ziying Fu, Xiongjun Dong, Jian Xie, Min Beijing Inst Technol Sch Integrated Circuits & Elect Beijing 100081 Peoples R China Beijing Inst Technol Tangshan Res Inst Tangshan 063015 Hebei Peoples R China

As a key technology in radar reconnaissance systems, radar signal sorting aims to separate multiple radar pulses from an interleaved pulse stream. Supervised signal sorting methods based on deep learning depend on a large volume of training data to optimize model parameters. However, acquiring labeled pulses in practice is challenging. In this letter, a semi-supervised learning (SSL) framework is proposed to address this issue. First, a Self-Organizing Map (SOM) is used to learn the spatial distribution of impulse features, and an anchor graph is constructed based on SOM nodes. A pseudo-label set is then generated using the SOM based on pulse discrepancy information. Finally, a three-layer Weighted Residual Graph Convolutional Network (WRGCN) is designed for signal sorting, with its parameters pre-trained on pseudo-labels and fine-tuned with a limited number of true labels. Experiments on a simulated radar pulse dataset demonstrate that this framework outperforms several existing methods for radar signal sorting with limited labeled pulses.

关键词： Radar Sorting Vectors Self-organizing feature maps Training Spaceborne radar Semisupervised learning Deep learning Training data signal processing algorithms Radar signal sorting graph convolutional network pseudo-labels semi-supervised learning

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Scene Inference Using Saliency Graphs With Trust-Theoretic Semantic Information Encoding

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IEEE signal processing LETTERS 2025年 32卷 256-260页

作者： Meena, Preeti Kumar, Himanshu Yadav, Sandeep Indian Inst Technol Jodhpur Discipline Elect Engn Jodhpur 342030 India

Scene inference refers to the identification of the scene from a given set of scene representations such as images. A saliency graph of a scene contains scene-defining objects along with semantic information between them in the graph representation. Existing methods consider the entire scene with uniform weighting to all semantic information for scene inference, resulting in a suboptimal performance. This letter presents an optimal edge weight estimation using the trust theoretic framework to encode semantic information effectively in saliency graphs. We have utilized the notion of converged global absolute trust in saliency scores of salient objects to compute the weighting of semantic information. Experimental results highlight the efficacy of the proposed method.

关键词： Peer-to-peer computing Semantics Estimation Symmetric matrices Image edge detection signal processing algorithms Inference algorithms Eigenvalues and eigenfunctions Visualization Accuracy Scene inference saliency graph semantic information trust graph P2P

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A Target Parameters Estimation Method for CP-OTFS-Based Terrestrial Radar System

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IEEE WIRELESS COMMUNICATIONS LETTERS 2025年第1期14卷 233-237页

作者： Miao, Qilong Liang, Jing Zhang, Ge Han, Jiachen Univ Elect Sci & Technol China Chengdu 610056 Peoples R China

Orthogonal time-frequency space (OTFS) modulation, which exhibits significant advantages in high-speed mobile communication scenarios, holds strong potential in the field of radar applications. The existing state-of-the-art target parameter estimation methods for cyclic prefix OTFS (CP-OTFS) based radar systems suffer from inaccuracies in target scattering coefficient (TSC) estimation under conditions of multiple targets, low bandwidth and short duration. In this letter, the equivalent matched filter (EMF) algorithm is designed. Compared to the matched filter (MF) algorithm, EMF intuitively explains the input-output relationship of CP-OTFS based radar, but it remains susceptible to interference from multiple targets. In order to mitigate this impact, the two-stage matched filter-based minimum mean squared error (TS-MF-MMSE) algorithm, a refined TSC estimation algorithm for terrestrial CP-OTFS based radar system is proposed. Simulation results demonstrate the superior performance of proposed algorithm in TSC estimation in comparison with EMF and the state-of-the-art.

关键词： Radar Estimation signal processing algorithms Symbols Matched filters Receivers OFDM Delays Wireless communication Spaceborne radar CP-OTFS target parameter estimation JRC minimum mean squared error

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TAML-Adapter: Enhancing Adapter Tuning Through Task-Agnostic Meta-Learning for Low-Resource Automatic Speech Recognition

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IEEE signal processing LETTERS 2025年 32卷 636-640页

作者： Liu, Yunpeng Yang, Xukui Zhang, Jiayi Xi, Yangli Qu, Dan Univ Informat Engn Sch Informat Syst Engn Dept Artificial Intelligence Zhengzhou 450000 Peoples R China Lab Adv Comp & Intelligence Engn Wuxi 214000 Peoples R China Zhengzhou Univ Zhengzhou 450000 Peoples R China

Parameter-efficient fine-tuning of pre-trained multilingual speech models can significantly enhance the speech recognition performance of target languages. However, traditional parameter-efficient fine-tuning methods, such as adapter tuning, often face challenges related to random initialization. This can lead to suboptimal performance when adapting to languages with limited resources. To address this issue, this letter introduces TAML-Adapter, which utilizes the Task-Agnostic Meta-Learning algorithm to initialize the parameters of the adapters before fine-tuning in target low-resource languages. Comprehensive experiments conducted on the Common Voice and Fleurs datasets highlight the superior performance of TAML-Adapter in five languages with limited resources. In addition, the TAML-Adapter demonstrates superior generalizability and extensibility compared to similar competing methods.

关键词： Adaptation models Metalearning Speech recognition Tuning Data models signal processing algorithms Multilingual Training Computational modeling Transformers Automatic speech recognition pre-trained multilingual speech models parameter-efficient fine-tuning Task-Agnostic Meta-Learning

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Underdetermined 2-D Angles Parameters Estimation for Incoherent Distributed Sources Based on an L-Shaped Array

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IEEE SENSORS JOURNAL 2024年第11期24卷 18479-18487页

作者： Jin, Longsheng Fang, Jiaxiong Liu, Weiyue Yu, Zhou Xu, Gaoming Zhu, Minghong Chen, Hua Ningbo Univ Fac Elect Engn & Comp Sci Ningbo 315211 Peoples R China Zhejiang Key Lab Mobile Network Applicat Technol Ningbo 315211 Peoples R China

This article proposes a localization algorithm based on an L-shaped array for estimating the 2-D directions-of-arrival (DOAs) of incoherently distributed (ID) sources in the underdetermined case. To begin with, the generalized array manifold (GAM) model is established based on the first-order Taylor series approximation. The raw received data are split into frames that can be processed through delay cross-correlation operation to construct the virtual received data. Then, the covariance matrix of the virtual data is calculated, followed by eigenvalue decomposition to obtain the signal and noise subspaces. The 2-D central DOAs estimation is achieved by two 1-D spectral searching, exploiting the generalized shift-invariance property and the reduced-rank principle. Finally, the 2-D central DOAs are matched by finding the minimum value of an optimization function, utilizing the property that the noise and signal subspaces are orthogonal to each other. Simulation results demonstrate that the proposed algorithm achieves high accuracy in the central DOAs estimation without requiring any prior information about the angular distribution.

关键词： Estimation Direction-of-arrival estimation Arrays signal processing algorithms Vectors Sensors Delays 2-D direction-of-arrival incoherently distributed (ID) sources temporal-spatial virtual array

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A Priori-Knowledge-Free Real-Valued Capon-Like Method and Implementation on FPGA

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2024年第12期71卷 6532-6543页

作者： Chen, Yili Fu, Zhe Li, Jianzhong Guangdong Univ Technol Sch Integrated Circuits Guangzhou 510000 Guangdong Peoples R China

Improving real-time computational efficiency is a major research direction in Direction-Of-Arrival (DOA) estimation. In this paper, a novel computationally efficient real-valued DOA estimator is presented, in which the estimation is performed without the need for EigenValue Decomposition (EVD) and therefore avoids estimating the source number in advance. Following the comparison between the traditional MUSIC algorithm and the Capon Method, we present a general form of DOA estimation, which reveals that the construction of the noise subspace in the traditional MUSIC algorithm derives from the activation function performed on the eigenvalues. Unlike the classic subspace-based algorithm, our proposed activation-like function eliminates the reliance on subspace decomposition, thereby removing the need for source number estimation and mitigating performance degradation caused by incorrect estimations. Moreover, existing real-valued DOA algorithms would estimate both the true DOAs and their corresponding mirror DOAs, and the space-shifting property is used to eliminate the mirror DOAs. In addition, the Field Programmable Gate Array (FPGA) implementation for our proposed real-valued algorithm is developed, showing a dramatic reduction of the hardware resource consumption and computation burden compared with the complex-valued MUSIC. Experiments illustrate that our proposed algorithm is computationally more efficient, and achieves higher estimation resolution compared to the existing methods.

关键词： Direction-of-arrival estimation Estimation Multiple signal classification Covariance matrices Field programmable gate arrays signal processing algorithms Computational efficiency Direction-of-arrival (DOA) estimation subspace method field programmable gate array real-valued computation

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Evolving Memristive Reservoir

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第10期35卷 13574-13588页

作者： Shi, Xinming Minku, Leandro L. Yao, Xin Southern Univ Sci & Technol SUSTech Dept Comp Sci & Engn Guangdong Prov Key Lab Brain Inspired Intelligent Shenzhen 518055 Peoples R China Univ Birmingham Sch Comp Sci Birmingham B15 2TT England

In light of the dynamic plasticity, nanosize, and energy efficiency of memristors, memristive reservoirs have attracted increasing attention in diverse fields of research recently. However, limited by deterministic hardware implementation, hardware reservoir adaptation is hard to realize. Existing evolutionary algorithms for evolving reservoirs are not designed for hardware implementation. They often ignore the circuit scalability and feasibility of the memristive reservoirs. In this work, based on the reconfigurable memristive units (RMUs), we first propose an evolvable memristive reservoir circuit that is capable of adaptive evolution for varying tasks, where the configuration signals of memristor are evolved directly avoiding the device variance of the memristors. Second, considering the feasibility and scalability of memristive circuits, we propose a scalable algorithm for evolving the proposed reconfigurable memristive reservoir circuit, where the reservoir circuit will not only be valid according to the circuit laws but also has the sparse topology, alleviating the scalability issue and ensuring the circuit feasibility during the evolution. Finally, we apply our proposed scalable algorithm to evolve the reconfigurable memristive reservoir circuits for a wave generation task, six prediction tasks, and one classification task. Through experiments, the feasibility and superiority of our proposed evolvable memristive reservoir circuit are demonstrated.

关键词： Reservoirs Memristors Hardware Task analysis signal processing algorithms Scalability Optimization Evolution algorithm evolvable hardware memristor neural networks reservoir computing

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Adaptive Binning Coincidence Test for Uniformity Testing

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 2421-2435页

作者： Salgia, Sudeep Wang, Xinyi Zhao, Qing Tong, Lang Cornell Univ Sch Elect & Comp Engn Ithaca NY 14850 USA

We consider the problem of uniformity testing of Lipschitz continuous distributions with bounded support. The alternative hypothesis is a composite set of Lipschitz continuous distributions whose l(1) distances from the uniform distribution are bounded by epsilon from below. We propose a sequential test that adapts to the unknown distribution under the alternative hypothesis. Referred to as the Adaptive Binning Coincidence (ABC) test, the proposed strategy adapts in two ways. First, it partitions the set of alternative distributions into layers based on their distances to the uniform distribution. It then sequentially eliminates the alternative distributions layer by layer in decreasing distance to the uniform, allowing it to take advantage of favorable situations of a distant alternative by terminating early. Second, it adapts, across layers of the alternative distributions, the resolution level of the discretization for computing the coincidence statistic. The farther away the layer is from the uniform, the coarser the discretization necessary for eliminating this layer or terminating altogether. It thus terminates the test both early (via the layered partition of the alternative set) and quickly (via adaptive discretization) to take advantage of favorable alternative distributions. The ABC test builds on an adaptive sequential test for discrete distributions, which is of independent interest.

关键词： Testing Complexity theory signal processing algorithms Symbols Random variables Partitioning algorithms Anomaly detection Uniformity testing adaptivity coincidence test

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UNO: Unlimited Sampling Meets One-Bit Quantization

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 997-1014页

作者： Eamaz, Arian Mishra, Kumar Vijay Yeganegi, Farhang Soltanalian, Mojtaba Univ Illinois Dept Elect & Comp Engn Chicago IL 60607 USA US DEVCOM Army Res Lab Adelphi MD 20783 USA

Recent results in one-bit sampling provide a framework for a relatively low-cost, low-power sampling, at a high rate by employing time-varying sampling threshold sequences. Another recent development in sampling theory is unlimited sampling, which is a high-resolution technique that relies on modulo ADCs to yield an unlimited dynamic range. In this paper, we leverage the appealing attributes of the two afore mentioned techniques to propose a novel unlimited one-bit(UNO) sampling approach. In this framework, the information on the distance between the input signal value and the threshold is stored and utilized to accurately reconstruct the one-bit sampled signal. We then utilize this information to accurately reconstruct the signal from its one-bit samples via the randomized Kaczmarz algorithm (RKA). In the presence of noise, we employ the recent plug-and-play (PnP) priors technique with alternating direction method of multipliers (ADMM) to exploit integration of state-of-the-art regularizers in the reconstruction process. Numerical experiments with RKA and PnP-ADMM-based reconstruction illustrate the effectiveness of our proposed UNO, including its superior performance compared to the one-bit Sigma Delta sampling

关键词： Quantization (signal) signal reconstruction signal processing algorithms Receivers signal resolution Sigma-delta modulation Sensors Kaczmarz algorithm one-bit quantization PnP-ADMM modulo ADCs unlimited sampling

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