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Design of Denoising Method for Underwater Communication Based on Adaptive Chunking Algorithm and Quantum Mechanics Theory

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IEEE INTERNET OF THINGS JOURNAL 2025年第3期12卷 2498-2513页

作者： Liu, Xin Meng, Xiangxu Xu, Yinan Wu, Yujing Lee, Hojun Liu, Bingqi Xu, Yihu Yanbian Univ Coll Engn Yanji 133002 Peoples R China Hoseo Univ Asan Dept Informat & Commun Engn Asan 31499 South Korea

With the deepening exploitation of marine resources, the application of underwater communication is becoming increasingly widespread, and the noise in the underwater channel will severely impact the reliability of underwater communication. Therefore, researchers have focused on improving the accuracy and reliability of underwater communication through denoising. Currently, denoising methods are widely used based on algorithms, such as Fourier transform, wavelet transform, convolutional neural network, and time-space domain filtering. Still, they all have some areas for improvement in the application of underwater communication. In this regard, inspired by quantum mechanical tools and adaptive algorithms, this article proposes a quantum adaptive chunk denoising algorithm applied in underwater communication. First, this article proposes an adaptive chunking algorithm to dynamically adjust the chunk size according to the local signal characteristics to capture the signal details better. Second, the chunked signal and image are taken as potentials in the discrete Schr & ouml;dinger equation and solved for the eigenvalues and eigenvectors of the discrete Hamiltonian matrix constructed by the adaptive chunking algorithm. Finally, the solved values are subjected to total variational denoising and reconstruction after denoising. The algorithm proposed in this article is compared with wavelet denoising based on hard and soft thresholding, Denoising Algorithm Based on an ideal low-pass adaptive filter, sparsity-assisted signal smoothing (SASS) algorithm and Quantum Mechanics Adaptive Basis Denoising Algorithm. The experimental results show that the quantum adaptive chunking denoising algorithm proposed in this article can effectively remove underwater noise from signals and images under the condition of low-signal-to-noise ratio, which improves the reliability and signal quality of underwater communication and meets the denoising standard of underwater communication.

关键词： Noise Noise reduction Quantum mechanics Underwater communication signal processing algorithms Thermal noise Mathematical models Wavelet transforms Classification algorithms Time-frequency analysis

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Interacting Large Language Model Agents Bayesian Social Learning Based Interpretable Models

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IEEE ACCESS 2025年 13卷 25465-25504页

作者： Jain, Adit Krishnamurthy, Vikram Cornell Univ Sch Elect & Comp Engn Ithaca NY 14853 USA

This paper discusses the theory and algorithms for interacting large language model agents (LLMAs) using methods from statistical signal processing and microeconomics. While both fields are mature, their application to decision-making involving interacting LLMAs remains unexplored. Motivated by Bayesian sentiment analysis on online platforms, we construct interpretable models and stochastic control algorithms that enable LLMAs to interact and perform Bayesian inference. Because interacting LLMAs learn from both prior decisions and external inputs, they can exhibit bias and herding behavior. Thus, developing interpretable models and stochastic control algorithms is essential to understand and mitigate these behaviors. This paper has three main results. First, we show using Bayesian revealed preferences from microeconomics that an individual LLMA satisfies the necessary and sufficient conditions for rationally inattentive (bounded rationality) Bayesian utility maximization and, given an observation, the LLMA chooses an action that maximizes a regularized utility. Second, we utilize Bayesian social learning to construct interpretable models for LLMAs that interact sequentially with each other and the environment while performing Bayesian inference. Our proposed models capture the herding behavior exhibited by interacting LLMAs. Third, we propose a stochastic control framework to delay herding and improve state estimation accuracy under two settings: 1) centrally controlled LLMAs and 2) autonomous LLMAs with incentives. Throughout the paper, we numerically demonstrate the effectiveness of our methods on real datasets for hate speech classification and product quality assessment, using open-source models like LLaMA and Mistral and closed-source models like ChatGPT. The main takeaway of this paper, based on substantial empirical analysis and mathematical formalism, is that LLMAs act as rationally bounded Bayesian agents that exhibit social learning when interacting. Tradi

关键词： Bayes methods Stochastic processes signal processing algorithms signal processing Microeconomics Inference algorithms Engines Data models Numerical models Bayesian social learning large language models Bayesian revealed preferences structural results optimal stopping POMDPs self-attention rational inattention model collapse model collapse

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An Overview and Classification of Machine Learning Approaches for Radar signal Deinterleaving

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IEEE ACCESS 2025年 13卷 28008-28028页

作者： Lesieur, Louis Le Caillec, Jean-Marc Khenchaf, Ali Guardia, Vincent Toumi, Abdelmalek Inst Polytech Paris ENSTA Lab STICC UMR CNRS 6285 F-29806 Brest France Thales F-29200 Brest France IMT Atlantique Lab STICC UMR CNRS 6285 F-29285 Brest France

Electronic Warfare (EW) receivers are passive systems that are designed to detect and identify active radar emitters in the environment. The radar pulses emitted by multiple sources are received and must be deinterleaved, in other words, sorted according to the waveform to which they belong, i.e. according to their emitter. As radar signals are more complex and observations are denser, new Machine Learning (ML) approaches appear in the literature to enhance traditional Radar signal Deinterleaving (RSD). In this paper, we propose an overview of the ML approaches to RSD. To this end, we identify some criteria to characterize a method: its used technique, underlying assumptions, exploited parameters, input characterization, and architectural pattern. First, the problem of RSD is detailed with its challenges and operational requirements. We then outline the methods of the literature inside a taxonomy based on the technique criterion: the first category includes PRI estimation methods including histogram-based methods, and ML techniques constitute three other categories: clustering, RNN, CNN. Finally, the other identified criteria are explained and discussed.

关键词： Receivers Radar Radio frequency Indexes Frequency modulation Vectors Taxonomy signal processing algorithms Radar signal processing Real-time systems Electronic warfare radar deinterleaving pulse sorting machine learning segmentation

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Factorized Fourier Operator Based Neural Network for Fiber Nonlinearity Compensation in Coherent Optical Communication Systems

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JOURNAL OF LIGHTWAVE TECHNOLOGY 2025年第8期43卷 3612-3622页

作者： Yi, Anlin Fei, Jianxiong Huang, Junling Yan, Lianshan Luo, Bin He, Xingchen Jiang, Lin Pan, Wei Southwest Jiaotong Univ Ctr Informat Photon & Commun Sch Informat Sci & Technol Chengdu 610031 Peoples R China

In long-haul and high-capacity coherent optical communications systems, it is essential to reduce the complexity of the underlying algorithms in the practical implementation of digital signal processing techniques for compensation of transmission impairments. Here, we investigate a factorized Fourier operator (F-FNO) based neural network (NN) for compensation the linear and nonlinear signal distortion in fiber simultaneously. The combination of the factorized Fourier transforms and residual connection enables the F-FNO based NN to compensate fiber impairments with higher accuracy, lower computational complexity and faster convergence rate, compared with our previous Fourier operator (FNO) based NN. The time consumption is further decreased by sharing the Fourier-domain weight matrices. The feasibility of the proposed scheme is verified experimentally in 32GBaud polarization division multiplexed (PDM) uniformly distributed 16/32/64-ary quadrature amplitude modulation (PDM-16/32/64QAM) as well as probabilistically shaped 16/32/64QAM (PDM-PS-16/32/64QAM) coherent optical communication systems. The experimental results demonstrate that the Q-factor improvement is extremely close to the standard digital back propagation (DBP) with 16 steps per span (16StPS-DBP) even in the highly nonlinear region, while with much lower computational complexity and time consumption.

关键词： Artificial neural networks Optical fiber networks Fast Fourier transforms Optical fiber polarization Optical fiber dispersion Training Symbols Computational complexity signal processing algorithms Modulation Digital back-propagation (DBP) factorized Fourier operator fiber nonlinearity compensation neural networks (NN)

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XCF-LSTMSATNet: A Classification Approach for EEG signals Evoked by Dynamic Random Dot Stereograms

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2025年 33卷 502-513页

作者： Zhang, Tingting Yan, Xu Chen, Xin Mao, Yi Hohai Univ Minist Educ Key Lab Maritime Intelligent Cyberspace Technol Nanjing 210000 Peoples R China Nanjing Univ Nanjing Drum Tower Hosp Affiliated Hosp Med SchDept Cardiol Nanjing 210008 Peoples R China

Stereovision is the visual perception of depth derived from the integration of two slightly different images from each eye, enabling understanding of the three-dimensional space. This capability is deeply intertwined with cognitive brain functions. To explore the impact of stereograms with varied motions on brain activities, we collected Electroencephalography (EEG) signals evoked by Dynamic Random Dot Stereograms (DRDS). To effectively classify the EEG signals induced by DRDS, we introduced a novel hybrid neural network model, XCF-LSTMSATNet, which integrates an XGBoost Channel Feature Optimization Module with the EEGNet and an LSTM Self-Attention Modules. Initially, in the channel selection phase, XGBoost is employed for preliminary classification and feature weight analysis, which can enhance our channel selection strategy. Following this, EEGNet employs deep convolutional layers to extract spatial features, while separable convolutions are subsequently used to derive high-dimensional spatial-temporal features. Meanwhile, the LSTMSAT Module, with its capability to learn long-term dependencies in time-series signals, is deployed to capture temporal continuity information. The incorporation of the self-attention mechanism further amplifies the model's ability to grasp long-distance dependencies and enables dynamic weight allocation to the extracted features. In the end, both temporal and spatial features are integrated into the classification module, enabling precise prediction across three categories of EEG signals. The proposed XCF-LSTMSATNet was extensively tested on both a custom dataset and the public datasets SRDA and SRDB. The results demonstrate that the model exhibits solid classification performance across all three datasets, effectively showcasing its robustness and generalization capabilities.

关键词： Electroencephalography Feature extraction Brain modeling Stereo image processing Optimization Long short term memory Convolution signal processing algorithms Hospitals Electrodes EEG DRDS LSTM EEGNet XGBoost

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A Low-Rank Projected Proximal Gradient Method for Spectral Compressed Sensing

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IEEE TRANSACTIONS ON signal processing 2025年 73卷 691-705页

作者： Yao, Xi Dai, Wei China Mobile Shanghai Ind Res Inst Shanghai 200135 Peoples R China Imperial Coll London Dept Elect & Elect Engn London SW7 2BU England

This paper presents a new approach to the recovery of spectrally sparse signals (SSS) from partially observed entries, addressing challenges posed by large-scale data and heavy-noise environments. The SSS reconstruction can be formulated as a non-convex low-rank Hankel recovery problem. Traditional formulations for SSS recovery often suffer from reconstruction inaccuracies due to unequally weighted norms and over-relaxation of the Hankel structure in noisy conditions. Additionally, a critical limitation of standard proximal gradient (PG) methods for solving this optimization problem is their slow convergence. We overcome this issue by introducing a more accurate formulation and proposing the Low-rank Projected Proximal Gradient (LPPG) method, designed to efficiently converge to stationary points through a two-step process. The first step involves a modified PG approach, allowing for a constant step size independent of the signal size, significantly accelerating the gradient descent phase. The second step employs a subspace projection strategy, optimizing within a low-rank matrix space to further decrease the objective function. Both steps of the LPPG method are meticulously tailored to exploit the intrinsic low-rank and Hankel structures of the problem, thereby enhancing computational efficiency. Numerical simulations demonstrate substantial improvements in both efficiency and recovery accuracy of the LPPG method compared to existing benchmark algorithms. This performance gain is particularly pronounced in scenarios with significant noise, showcasing the method's robustness and applicability to large-scale SSS recovery tasks.

关键词： Matrix decomposition Convergence Optimization Compressed sensing signal processing algorithms Vectors Noise Linear programming Computational complexity Accuracy Hankel matrix low-rank matrix completion non-convex optimization spectral compressed sensing

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Three-Dimensional Localization of Mixed Near-Field and Far-Field Sources Based on a Unified Exact Propagation Model

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IEEE TRANSACTIONS ON signal processing 2025年 73卷 245-258页

作者： Fang, Jiaxiong Chen, Hua Liu, Wei Yang, Songjie Yuen, Chau So, Hing Cheung Ningbo Univ Fac Elect Engn & Comp Sci Ningbo 315211 Peoples R China Hong Kong Polytech Univ Dept Elect & Elect Engn Hong Kong Peoples R China Univ Elect Sci & Technol China Natl Key Lab Wireless Commun Chengdu 610054 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China

In applications like speaker localization using a microphone array, the collected signals are typically a mixture of far-field (FF) and near-field (NF) sources. To find the positions of both NF and FF sources, a three-dimensional spatial-temporal localization algorithm based on a unified exact propagation geometry is developed in this paper, which avoids approximating the spatial phase difference with the first-order and second-order Taylor expansions applied to FF and NF sources, respectively. Our scheme utilizes cross-correlation to produce virtual observations for establishing a third-order parallel factor data model with the use of spatial and temporal information. The array's steering vectors can be extracted by trilinear decomposition. The amplitude and phase information of the whole array elements is jointly exploited to classify the source types and obtain the location estimates via a least squares method. Moreover, the proposed algorithm is computationally efficient since no spectral searches, high-order statistics calculations or parameter pairing procedures are required. The deterministic Cram & eacute;r-Rao bound is also derived as a performance benchmark, and numerical results are provided to demonstrate the effectiveness of the developed method.

关键词： Noise measurement Location awareness Classification algorithms Geometry Three-dimensional displays signal processing algorithms Phased arrays Multiple signal classification Apertures Microphone arrays Array signal processing source localization spatial-temporal near-field far-field

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Stochastic Halfspace Approximation Method for Convex Optimization With Nonsmooth Functional Constraints

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2025年第1期70卷 479-486页

作者： Singh, Nitesh Kumar Necoara, Ion Natl Univ Sci & Technol Politehn Bucharest Automat Control & Syst Engn Dept Bucharest 060042 Romania Romanian Acad Gheorghe Mihoc Caius Iacob Inst Math Stat & Appl M Roma TX USA

In this work, we consider convex optimization problems with smooth objective function and nonsmooth functional constraints. We propose a new stochastic gradient algorithm, called the stochastic halfspace approximation method (SHAM), to solve this problem, where at each iteration we first take a gradient step for the objective function and then we perform a projection step onto one halfspace approximation of a randomly chosen constraint. We propose various strategies to create this stochastic halfspace approximation and we provide a unified convergence analysis that yields new convergence rates for the SHAM algorithm in both optimality and feasibility criteria evaluated at some average point. In particular, we derive convergence rates of order O(1/root k) , when the objective function is only convex, and O(1/k) when the objective function is strongly convex. The efficiency of SHAM is illustrated through detailed numerical simulations.

关键词： Convergence Convex functions Linear programming Approximation algorithms signal processing algorithms Optimization Systems engineering and theory Convex optimization nonsmooth functional constraints stochastic halfspace approximation stochastic projection gradient methods

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A Dual Inexact Nonsmooth Newton Method for Distributed Optimization

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IEEE TRANSACTIONS ON signal processing 2025年 73卷 188-203页

作者： Niu, Dunbiao Hong, Yiguang Song, Enbin Tongji Univ Coll Elect & Informat Engn Dept Control Sci & Engn Natl Key Lab Autonomous Intelligent Unmanned Syst Shanghai 201804 Peoples R China Sichuan Univ Coll Math Chengdu 610065 Sichuan Peoples R China

In this paper, we propose a novel dual inexact nonsmooth Newton (DINN) method for solving a distributed optimization problem, which aims to minimize a sum of cost functions located among agents by communicating only with their neighboring agents over a network. Our method is based on the Lagrange dual of an appropriately formulated primal problem created by introducing local variables for each agent and enforcing a consensus constraint among these variables. Due to the decomposed structure of the dual problem, the DINN method guarantees a superlinear (or even quadratic) convergence rate for both the primal and dual iteration sequences, achieving the same convergence rate as its centralized counterpart. Furthermore, by exploiting the special structure of the dual generalized Hessian, we design a distributed iterative method based on Nesterov's acceleration technique to approximate the dual Newton direction with suitable precision. Moreover, in contrast to existing second-order methods, the DINN method relaxes the requirement for the objective function to be twice continuously differentiable by using the linear Newton approximation of its gradient. This expands the potential applications of distributed Newton methods. Numerical experiments demonstrate that the DINN method outperforms the current state-of-the-art distributed optimization methods.

关键词： Convergence Jacobian matrices Vectors Newton method Linear programming Costs signal processing algorithms Approximation algorithms Distributed databases Cost function Inexact nonsmooth Newton method distributed optimization superlinear convergence rate communication cost

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Low-Power Real-Time Seizure Monitoring Using AI-Assisted Sonification of Neonatal EEG

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING 2025年第1期13卷 80-89页

作者： Nguyen, Tien Daly, Aengus Gomez-Quintana, Sergi O'Sullivan, Feargal Temko, Andriy Popovici, Emanuel Univ Coll Cork Dept Elect & Elect Engn Coll Rd Cork T12 K8AF Ireland

Detecting seizures in neonates requires continuous electroencephalography (EEG) monitoring, a costly process that demands trained experts. Although recent advancements in machine learning offer promising solutions for automated seizure detection, the opaque nature of these algorithms poses significant challenges to their adoption in healthcare settings. A prior study demonstrated that integrating machine learning with sonification-an interpretation method that converts bio-signals into sound-can mitigate the black-box problem while enhancing seizure detection performance. This AI-assisted sonification algorithm can provide a valuable complementary tool in seizure monitoring besides the traditional visualization method. A low-power and affordable implementation of the algorithm is presented in this study using a microcontroller. To improve its practicality, we also introduce a real-time design that allows the sonification algorithm to function in parallel with data acquisition. The system consumes 12 mW in average, making it suitable for a battery-powered device.

关键词： Electroencephalography Sonification Pediatrics Real-time systems Instruction sets signal processing algorithms Vocoders Monitoring Interpolation Detection algorithms Artificial intelligence biomedical monitoring biomedical signal processing electroencephalography embedded software Edge AI internet of medical things machine learning microprocessors real-time systems sonification signal processing algorithms

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