arXiv - EE - Signal Processing最新文献_第9页

Addressing Pilot Contamination in Channel Estimation with Variational Autoencoders 利用变异自编码器解决信道估计中的先导污染问题

arXiv - EE - Signal Processing Pub Date : 2024-09-11 DOI: arxiv-2409.07071

Amar Kasibovic, Benedikt Fesl, Michael Baur, Wolfgang Utschick

引用次数: 0

Joint Energy and SINR Coverage Probability in UAV Corridor-assisted RF-powered IoT Networks 无人机走廊辅助射频供电物联网网络中的联合能量和 SINR 覆盖概率

arXiv - EE - Signal Processing Pub Date : 2024-09-11 DOI: arxiv-2409.07333

Harris K. Armeniakos, Petros S. Bithas, Konstantinos Maliatsos, Athanasios G. Kanatas

引用次数: 0

Variability in Grasp Type Distinction for Myoelectric Prosthesis Control Using a Non-Invasive Brain-Machine Interface 使用无创脑机接口控制肌电假肢的抓握类型区分变异性

arXiv - EE - Signal Processing Pub Date : 2024-09-11 DOI: arxiv-2409.07207

Corentin Piozin, Lisa Bouarroudj, Jean-Yves Audran, Brice Lavrard, Catherine Simon, Florian Waszak, Selim Eskiizmirliler

{"title":"Variability in Grasp Type Distinction for Myoelectric Prosthesis Control Using a Non-Invasive Brain-Machine Interface","authors":"Corentin Piozin, Lisa Bouarroudj, Jean-Yves Audran, Brice Lavrard, Catherine Simon, Florian Waszak, Selim Eskiizmirliler","doi":"arxiv-2409.07207","DOIUrl":"https://doi.org/arxiv-2409.07207","url":null,"abstract":"Decoding multiple movements from the same limb using electroencephalographic\u0000(EEG) activity is a key challenge with applications for controlling prostheses\u0000in upper-limb amputees. This study investigates the classification of four hand\u0000movements to control a modified Myobock prosthesis via EEG signals. We report\u0000results from three EEG recording sessions involving four amputees and twenty\u0000able-bodied subjects performing four grasp movements under three conditions:\u0000Motor Execution (ME), Motor Imagery (MI), and Motor Observation (MO). EEG\u0000preprocessing was followed by feature extraction using Common Spatial Patterns\u0000(CSP), Wavelet Decomposition (WD), and Riemannian Geometry. Various\u0000classification algorithms were applied to decode EEG signals, and a metric\u0000assessed pattern separability. We evaluated system performance across different\u0000electrode combinations and compared it to the original setup. Our results show\u0000that distinguishing movement from no movement achieved 100% accuracy, while\u0000classification between movements reached 70-90%. No significant differences\u0000were found between recording conditions in classification performance.\u0000Able-bodied participants outperformed amputees, but there were no significant\u0000differences in Motor Imagery. Performance did not improve across the sessions,\u0000and there was considerable variability in EEG pattern distinction. Reducing the\u0000number of electrodes by half led to only a 2% average accuracy drop. These\u0000results provide insights into developing wearable brain-machine interfaces,\u0000particularly for electrode optimization and training in grasp movement\u0000classification.","PeriodicalId":501034,"journal":{"name":"arXiv - EE - Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142175954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Survey of Anomaly Detection in In-Vehicle Networks 车载网络异常检测调查

arXiv - EE - Signal Processing Pub Date : 2024-09-11 DOI: arxiv-2409.07505

Övgü Özdemir, M. Tuğberk İşyapar, Pınar Karagöz, Klaus Werner Schmidt, Demet Demir, N. Alpay Karagöz

{"title":"A Survey of Anomaly Detection in In-Vehicle Networks","authors":"Övgü Özdemir, M. Tuğberk İşyapar, Pınar Karagöz, Klaus Werner Schmidt, Demet Demir, N. Alpay Karagöz","doi":"arxiv-2409.07505","DOIUrl":"https://doi.org/arxiv-2409.07505","url":null,"abstract":"Modern vehicles are equipped with Electronic Control Units (ECU) that are\u0000used for controlling important vehicle functions including safety-critical\u0000operations. ECUs exchange information via in-vehicle communication buses, of\u0000which the Controller Area Network (CAN bus) is by far the most widespread\u0000representative. Problems that may occur in the vehicle's physical parts or\u0000malicious attacks may cause anomalies in the CAN traffic, impairing the correct\u0000vehicle operation. Therefore, the detection of such anomalies is vital for\u0000vehicle safety. This paper reviews the research on anomaly detection for\u0000in-vehicle networks, more specifically for the CAN bus. Our main focus is the\u0000evaluation of methods used for CAN bus anomaly detection together with the\u0000datasets used in such analysis. To provide the reader with a more comprehensive\u0000understanding of the subject, we first give a brief review of related studies\u0000on time series-based anomaly detection. Then, we conduct an extensive survey of\u0000recent deep learning-based techniques as well as conventional techniques for\u0000CAN bus anomaly detection. Our comprehensive analysis delves into anomaly\u0000detection algorithms employed in in-vehicle networks, specifically focusing on\u0000their learning paradigms, inherent strengths, and weaknesses, as well as their\u0000efficacy when applied to CAN bus datasets. Lastly, we highlight challenges and\u0000open research problems in CAN bus anomaly detection.","PeriodicalId":501034,"journal":{"name":"arXiv - EE - Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142175951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-scale spatiotemporal representation learning for EEG-based emotion recognition 基于脑电图的情绪识别多尺度时空表征学习

arXiv - EE - Signal Processing Pub Date : 2024-09-11 DOI: arxiv-2409.07589

Xin Zhou, Xiaojing Peng

{"title":"Multi-scale spatiotemporal representation learning for EEG-based emotion recognition","authors":"Xin Zhou, Xiaojing Peng","doi":"arxiv-2409.07589","DOIUrl":"https://doi.org/arxiv-2409.07589","url":null,"abstract":"EEG-based emotion recognition holds significant potential in the field of\u0000brain-computer interfaces. A key challenge lies in extracting discriminative\u0000spatiotemporal features from electroencephalogram (EEG) signals. Existing\u0000studies often rely on domain-specific time-frequency features and analyze\u0000temporal dependencies and spatial characteristics separately, neglecting the\u0000interaction between local-global relationships and spatiotemporal dynamics. To\u0000address this, we propose a novel network called Multi-Scale Inverted Mamba\u0000(MS-iMamba), which consists of Multi-Scale Temporal Blocks (MSTB) and\u0000Temporal-Spatial Fusion Blocks (TSFB). Specifically, MSTBs are designed to\u0000capture both local details and global temporal dependencies across different\u0000scale subsequences. The TSFBs, implemented with an inverted Mamba structure,\u0000focus on the interaction between dynamic temporal dependencies and spatial\u0000characteristics. The primary advantage of MS-iMamba lies in its ability to\u0000leverage reconstructed multi-scale EEG sequences, exploiting the interaction\u0000between temporal and spatial features without the need for domain-specific\u0000time-frequency feature extraction. Experimental results on the DEAP, DREAMER,\u0000and SEED datasets demonstrate that MS-iMamba achieves classification accuracies\u0000of 94.86%, 94.94%, and 91.36%, respectively, using only four-channel EEG\u0000signals, outperforming state-of-the-art methods.","PeriodicalId":501034,"journal":{"name":"arXiv - EE - Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142175950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Refracting Reconfigurable Intelligent Surface Assisted URLLC for Millimeter Wave High-Speed Train Communication Coverage Enhancement 用于毫米波高速列车通信覆盖增强的可折射可重构智能表面辅助 URLLC

arXiv - EE - Signal Processing Pub Date : 2024-09-11 DOI: arxiv-2409.06946

Changzhu Liu, Ruisi He, Yong Niu, Shiwen Mao, Bo Ai, Ruifeng Chen

{"title":"Refracting Reconfigurable Intelligent Surface Assisted URLLC for Millimeter Wave High-Speed Train Communication Coverage Enhancement","authors":"Changzhu Liu, Ruisi He, Yong Niu, Shiwen Mao, Bo Ai, Ruifeng Chen","doi":"arxiv-2409.06946","DOIUrl":"https://doi.org/arxiv-2409.06946","url":null,"abstract":"High-speed train (HST) has garnered significant attention from both academia\u0000and industry due to the rapid development of railways worldwide. Millimeter\u0000wave (mmWave) communication, known for its large bandwidth is an effective way\u0000to address performance bottlenecks in cellular network based HST wireless\u0000communication systems. However, mmWave signals suffer from significant path\u0000loss when traversing carriage, posing substantial challenges to cellular\u0000networks. To address this issue, reconfigurable intelligent surfaces (RIS) have\u0000gained considerable interest for its ability to enhance cell coverage by\u0000reflecting signals toward receiver. Ensuring communication reliability, a core\u0000performance indicators of ultra-reliable and low-latency communications (URLLC)\u0000in fifth-generation systems, is crucial for providing steady and reliable data\u0000transmissions along railways, particularly for delivering safety and control\u0000messages and monitoring HST signaling information. In this paper, we\u0000investigate a refracting RIS-assisted multi-user multiple-input single-output\u0000URLLC system in mmWave HST communications. We propose a sum rate maximization\u0000problem, subject to base station beamforming constraint, as well as refracting\u0000RIS discrete phase shifts and reliability constraints. To solve this\u0000optimization problem, we design a joint optimization algorithm based on\u0000alternating optimization method. This involves decoupling the original\u0000optimization problem into active beamforming design and packet error\u0000probability optimization subproblem, and discrete phase shift design\u0000subproblems. These subproblems are addressed exploiting Lagrangian dual method\u0000and the local search method, respectively. Simulation results demonstrate the\u0000fast convergence of the proposed algorithm and highlight the benefits of\u0000refracting RIS adoption for sum rate improvement in mmWave HST networks.","PeriodicalId":501034,"journal":{"name":"arXiv - EE - Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142175988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ART: Artifact Removal Transformer for Reconstructing Noise-Free Multichannel Electroencephalographic Signals ART：用于重构无噪声多通道脑电信号的去伪变压器

arXiv - EE - Signal Processing Pub Date : 2024-09-11 DOI: arxiv-2409.07326

Chun-Hsiang Chuang, Kong-Yi Chang, Chih-Sheng Huang, Anne-Mei Bessas

{"title":"ART: Artifact Removal Transformer for Reconstructing Noise-Free Multichannel Electroencephalographic Signals","authors":"Chun-Hsiang Chuang, Kong-Yi Chang, Chih-Sheng Huang, Anne-Mei Bessas","doi":"arxiv-2409.07326","DOIUrl":"https://doi.org/arxiv-2409.07326","url":null,"abstract":"Artifact removal in electroencephalography (EEG) is a longstanding challenge\u0000that significantly impacts neuroscientific analysis and brain-computer\u0000interface (BCI) performance. Tackling this problem demands advanced algorithms,\u0000extensive noisy-clean training data, and thorough evaluation strategies. This\u0000study presents the Artifact Removal Transformer (ART), an innovative EEG\u0000denoising model employing transformer architecture to adeptly capture the\u0000transient millisecond-scale dynamics characteristic of EEG signals. Our\u0000approach offers a holistic, end-to-end denoising solution for diverse artifact\u0000types in multichannel EEG data. We enhanced the generation of noisy-clean EEG\u0000data pairs using an independent component analysis, thus fortifying the\u0000training scenarios critical for effective supervised learning. We performed\u0000comprehensive validations using a wide range of open datasets from various BCI\u0000applications, employing metrics like mean squared error and signal-to-noise\u0000ratio, as well as sophisticated techniques such as source localization and EEG\u0000component classification. Our evaluations confirm that ART surpasses other\u0000deep-learning-based artifact removal methods, setting a new benchmark in EEG\u0000signal processing. This advancement not only boosts the accuracy and\u0000reliability of artifact removal but also promises to catalyze further\u0000innovations in the field, facilitating the study of brain dynamics in\u0000naturalistic environments.","PeriodicalId":501034,"journal":{"name":"arXiv - EE - Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142175953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust Square Root Unscented Kalman filter of graph signals 图信号的鲁棒平方根无痕卡尔曼滤波器

arXiv - EE - Signal Processing Pub Date : 2024-09-11 DOI: arxiv-2409.06981

Jinhui Hu, Haiquan Zhao, Yi Peng

{"title":"Robust Square Root Unscented Kalman filter of graph signals","authors":"Jinhui Hu, Haiquan Zhao, Yi Peng","doi":"arxiv-2409.06981","DOIUrl":"https://doi.org/arxiv-2409.06981","url":null,"abstract":"Considering the problem of nonlinear and non-gaussian filtering of the graph\u0000signal, in this paper, a robust square root unscented Kalman filter based on\u0000graph signal processing is proposed. The algorithm uses a graph topology to\u0000generate measurements and an unscented transformation is used to obtain the\u0000priori state estimates. In addition, in order to enhance the numerical\u0000stability of the unscented Kalman filter, the algorithm combines the double\u0000square root decomposition method to update the covariance matrix in the graph\u0000frequency domain. Furthermore, to handle the non-Gaussian noise problem in the\u0000state estimation process, an error augmentation model is constructed in the\u0000graph frequency domain by unifying the measurement error and state error, which\u0000utilizes the Laplace matrix of the graph to effectively reduce the cumulative\u0000error at each vertex. Then the general robust cost function is adopted as the\u0000optimal criterion to deal with the error, which has more parameter options so\u0000that effectively suppresses the problems of random outliers and abnormal\u0000measurement values in the state estimation process. Finally, the convergence of\u0000the error of the proposed algorithm is firstly verified theoretically, and then\u0000the robustness of the proposed algorithm is verified by experimental\u0000simulation.","PeriodicalId":501034,"journal":{"name":"arXiv - EE - Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142175958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PRIME: Phase Reversed Interleaved Multi-Echo acquisition enables highly accelerated distortion-free diffusion MRI PRIME：相位反转交错多回波采集实现了高度加速的无失真弥散磁共振成像

arXiv - EE - Signal Processing Pub Date : 2024-09-11 DOI: arxiv-2409.07375

Yohan Jun, Qiang Liu, Ting Gong, Jaejin Cho, Shohei Fujita, Xingwang Yong, Susie Y Huang, Lipeng Ning, Anastasia Yendiki, Yogesh Rathi, Berkin Bilgic

{"title":"PRIME: Phase Reversed Interleaved Multi-Echo acquisition enables highly accelerated distortion-free diffusion MRI","authors":"Yohan Jun, Qiang Liu, Ting Gong, Jaejin Cho, Shohei Fujita, Xingwang Yong, Susie Y Huang, Lipeng Ning, Anastasia Yendiki, Yogesh Rathi, Berkin Bilgic","doi":"arxiv-2409.07375","DOIUrl":"https://doi.org/arxiv-2409.07375","url":null,"abstract":"Purpose: To develop and evaluate a new pulse sequence for highly accelerated\u0000distortion-free diffusion MRI (dMRI) by inserting an additional echo without\u0000prolonging TR, when generalized slice dithered enhanced resolution (gSlider)\u0000radiofrequency encoding is used for volumetric acquisition. Methods: A\u0000phase-reversed interleaved multi-echo acquisition (PRIME) was developed for\u0000rapid, high-resolution, and distortion-free dMRI, which includes two echoes\u0000where the first echo is for target diffusion-weighted imaging (DWI) acquisition\u0000with high-resolution and the second echo is acquired with either 1)\u0000lower-resolution for high-fidelity field map estimation, or 2) matching\u0000resolution to enable efficient diffusion relaxometry acquisitions. The sequence\u0000was evaluated on in vivo data acquired from healthy volunteers on clinical and\u0000Connectome 2.0 scanners. Results: In vivo experiments demonstrated that 1) high\u0000in-plane acceleration (Rin-plane of 5-fold with 2D partial Fourier) was\u0000achieved using the high-fidelity field maps estimated from the second echo,\u0000which was made at a lower resolution/acceleration to increase its SNR while\u0000matching the effective echo spacing of the first readout, 2) high-resolution\u0000diffusion relaxometry parameters were estimated from dual-echo PRIME data using\u0000a white matter model of multi-TE spherical mean technique (MTE-SMT), and 3)\u0000high-fidelity mesoscale DWI at 550 um isotropic resolution could be obtained in\u0000vivo by capitalizing on the high-performance gradients of the Connectome 2.0\u0000scanner. Conclusion: The proposed PRIME sequence enabled highly accelerated,\u0000high-resolution, and distortion-free dMRI using an additional echo without\u0000prolonging scan time when gSlider encoding is utilized.","PeriodicalId":501034,"journal":{"name":"arXiv - EE - Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142175963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Role of Explainable AI in Revolutionizing Human Health Monitoring 可解释人工智能在人类健康监测革命中的作用

arXiv - EE - Signal Processing Pub Date : 2024-09-11 DOI: arxiv-2409.07347

Abdullah Alharthi, Ahmed Alqurashi, Turki Alharbi, Mohammed Alammar, Nasser Aldosari, Houssem Bouchekara, Yusuf Shaaban, Mohammad Shoaib Shahriar, Abdulrahman Al Ayidh

{"title":"The Role of Explainable AI in Revolutionizing Human Health Monitoring","authors":"Abdullah Alharthi, Ahmed Alqurashi, Turki Alharbi, Mohammed Alammar, Nasser Aldosari, Houssem Bouchekara, Yusuf Shaaban, Mohammad Shoaib Shahriar, Abdulrahman Al Ayidh","doi":"arxiv-2409.07347","DOIUrl":"https://doi.org/arxiv-2409.07347","url":null,"abstract":"The complex nature of disease mechanisms and the variability of patient\u0000symptoms present significant obstacles in developing effective diagnostic\u0000tools. Although machine learning has made considerable advances in medical\u0000diagnosis, its decision-making processes frequently lack transparency, which\u0000can jeopardize patient outcomes. This underscores the critical need for\u0000Explainable AI (XAI), which not only offers greater clarity but also has the\u0000potential to significantly improve patient care. In this literature review, we\u0000conduct a detailed analysis of analyzing XAI methods identified through\u0000searches across various databases, focusing on chronic conditions such as\u0000Parkinson's, stroke, depression, cancer, heart disease, and Alzheimer's\u0000disease. The literature search revealed the application of 9 trending XAI\u0000algorithms in the field of healthcare and highlighted the pros and cons of each\u0000of them. Thus, the article is concluded with a critical appraisal of the\u0000challenges and future research opportunities for XAI in human health\u0000monitoring.","PeriodicalId":501034,"journal":{"name":"arXiv - EE - Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142175955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0