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Fundamental Picking Analysis System for Electric Guitar Using Wrist-Worn Inertial Motion Sensors

IEEE SENSORS JOURNAL 2025年第5期25卷 8849-8856页

作者： Matsushita, Soichiro Takamoto, Ayaka Tokyo Univ Technol Sch Comp Sci Entertainment Lab Hachioji Tokyo 1920982 Japan

A wrist-worn inertial motion-sensing device with a high sampling frequency was applied to evaluate fundamental electric guitar picking. A combinatorial analysis using wrist-twisting angular jerk and time-differential vertical jerk signals detected on the player's dominant wrist achieved accurate picking timing estimation. The developed signal-processing algorithm eliminated motion artifacts such as vibration from the guitar strings, especially in the case of the palm mute technique. The timing differences between the motion-based method using a sampling frequency of 1024 Hz and a sound-based onset timing analysis method as ground truth were less than 10 ms. In addition, it was found that the amplitude of sound and the time duration of picking for each note can be determined in the form of motion parameters without using sound signals. A population test in a 14-week-long guitar lesson class with 12 beginners showed convincing results that reflected the difficulties of the chord strumming and the single-note picking techniques.

关键词： Timing Sensors Hands Rhythm Accuracy Wrist Vibrations signal processing algorithms Gyroscopes Bars Angular jerk electric guitar inertial motion sensor jerk picking wrist-worn

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An Open-Access Repository of Synchrophasor Data Quality Examples: Curation and Example Applications

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IEEE ACCESS 2025年 13卷 66445-66457页

作者： Biswas, Shuchismita Yin, Tianzhixi Naqvi, Syed Ahsan Raza Follum, Jim Varghese, Antos Cheeramban Ahmad, Tawsif Etingov, Pavel Pacific Northwest Natl Lab Richland WA 99354 USA

Synchrophasor measurements are critical in providing wide-area situational awareness to power system operators. However, data artifacts may be introduced in these measurements due to various factors such as loss of communication, loss of GPS signal, internal clock error, and vendor-specific implementation of phasor estimation algorithms. Tools designed to provide actionable insights from synchrophasor data, hence, must be designed to be robust to these data quality issues. To test the performance of algorithms in the presence of bad data encountered in field measurements, researchers need access to real-world examples of such data artifacts. To address this need, two years of synchrophasor data sourced from multiple electric utilities in the United States were analyzed to identify examples of data quality problems. These examples were then labeled and published in the Grid Event Signature Library, a publicly available repository of power system measurements hosted by the Oak Ridge National Laboratory. This paper describes the data curation process, and illustrates two application use cases where the dataset can be valuable to the research community. In the first use case, a random forest (RF) classifier is trained to distinguish power system disturbance signatures from data anomalies introduced in synchrophasor measurements due to clock errors. With optimally tuned class weights, the RF classifier achieves an F1 score of 0.999. The second use case studies the impact of data quality issues on an example synchrophasor application (specifically, event start time determination). It is found that the introduction of quantization noise and intermittent data anomalies can increase the root mean square error for event start time determination by 1-4 seconds. The use cases demonstrate how the repository curated in this work can be valuable for developing and testing tools aimed at analyzing synchrophasor measurements.

关键词： Phasor measurement units Data integrity Quantization (signal) Libraries Voltage measurement Loss measurement signal processing algorithms Power measurement Noise Measurement uncertainty Phasor measurement unit time synchronization data quality data drops

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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 Online Fault Diagnosis Method for PEMFC Based on Output Voltage and Transfer Convolutional Neural Network

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2025年

作者： Guo, Zhirui Ma, Rui Ma, Haoran Li, Zhanyu Xiong, Peiyao Jiang, Wentao Zhou, Yang Northwestern Polytech Univ Sch Automat Xian 710129 Peoples R China

To achieve accurate and fast online fault diagnosis of proton exchange membrane fuel cell (PEMFC) under complex operating conditions, this article proposes a diagnostic method based on voltage-transfer learning convolutional neural network (V-TCNN). The sensitive analysis for PEMFC is conducted for pattern identifications first, and the output voltage response which can accurately characterize the internal mechanisms of the fuel cell stacks is applied to improve the fault diagnosis accuracy. Besides, to reduce the computation time and improve the portability of the diagnostic algorithm, transfer learning methods are applied to optimize the conventional neural network (CNN) architecture. The comparison tests show that the proposed algorithm can improve the diagnostic accuracy by 7.5% and reduce the computation time by 20% compared with the current existing deeply layered neural networks. Comprehensive analysis shows that the algorithm can reach a fault diagnosis accuracy of 99.56%, which can help to contribute to the development and implementation of fuel cell online fault diagnosis methods in fuel cell electric vehicles (FCEVs).

关键词： Fault diagnosis Voltage Fuel cells Testing Transfer learning Training Data models Accuracy signal processing algorithms Protons online output voltage response proton exchange membrane fuel cell (PEMFC) transfer learning

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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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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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Two-Stage Residual Projection Network for Image Bit-Depth Enhancement

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IEEE ACCESS 2025年 13卷 31215-31227页

作者： Liu, Zepeng Wang, Yizong Sang, Yayuan Liu, Chao Tian, Jiya Xinjiang Inst Technol Sch Informat Engn Aksu 843100 Peoples R China

Low bit-depth (LBD) images produce stubborn false contour artifacts and make detailed information disappear, making bit-depth enhancement (BDE) a challenging task. Considering the mixture of structural distortions and real edges in LBD images, multi-scale features are crucial for the BDE tasks. However, existing CNN-based methods suffer from structural bottlenecks, which make it difficult to capture sufficient LBD features in a single-stage network. To overcome this issue, this paper proposes a two-stage residual projection network (TRPN) to explore the multi-scale features of BDE. An encoder-decoder structure based on alternating up and down sampling is proposed to learn wide context information in stage 1. In stage 2, a residual projection module based on dense connection is proposed to preserve the detailed texture as much as possible and avoid over-smoothing in non-flat regions, which is caused by alternating up and down sampling. To efficiently utilize multi-scale features, we introduce a supervised attention module that improves network ability by dynamically adjusting the attention weights within the model. Finally, extensive experiments demonstrate that our method achieves outstanding performance improvements both quantitatively and qualitatively, which illustrates its effectiveness.

关键词： Feature extraction Convolution Image color analysis Image reconstruction Distortion Deconvolution Training signal processing algorithms Colored noise Visualization Bit-depth enhancement multi-scale features encoder-decoder alternating up and down sampling residual projection

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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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