arXiv - EE - Signal Processing最新文献_第6页

Efficient FPGA Implementation of an Optimized SNN-based DFE for Optical Communications 为光通信高效实现基于 SNN 的优化 DFE

arXiv - EE - Signal Processing Pub Date : 2024-09-13 DOI: arxiv-2409.08698

Mohamed Moursi, Jonas Ney, Bilal Hammoud, Norbert Wehn

引用次数: 0

Symbol-Level Precoding-Based Self-Interference Cancellation for ISAC Systems 基于符号级预编码的 ISAC 系统自干扰消除

arXiv - EE - Signal Processing Pub Date : 2024-09-13 DOI: arxiv-2409.08608

Shu Cai, Zihao Chen, Ya-Feng Liu, Jun Zhang

引用次数: 0

On the Restricted Isometry Property of Kronecker-structured Matrices 论克罗内克结构矩阵的限制等势特性

arXiv - EE - Signal Processing Pub Date : 2024-09-13 DOI: arxiv-2409.08699

Yanbin He, Geethu Joseph

引用次数: 0

Low Complexity DoA-ToA Signature Estimation for Multi-Antenna Multi-Carrier Systems 多天线多载波系统的低复杂度 DoA-ToA 信号估计

arXiv - EE - Signal Processing Pub Date : 2024-09-13 DOI: arxiv-2409.08650

Chandrashekhar Rai, Debarati Sen

{"title":"Low Complexity DoA-ToA Signature Estimation for Multi-Antenna Multi-Carrier Systems","authors":"Chandrashekhar Rai, Debarati Sen","doi":"arxiv-2409.08650","DOIUrl":"https://doi.org/arxiv-2409.08650","url":null,"abstract":"Accurate direction of arrival (DoA) and time of arrival (ToA) estimation is\u0000an stringent requirement for several wireless systems like sonar, radar,\u0000communications, and dual-function radar communication (DFRC). Due to the use of\u0000high carrier frequency and bandwidth, most of these systems are designed with\u0000multiple antennae and subcarriers. Although the resolution is high in the large\u0000array regime, the DoA-ToA estimation accuracy of the practical on-grid\u0000estimation methods still suffers from estimation inaccuracy due to the spectral\u0000leakage effect. In this article, we propose DoA-ToA estimation methods for\u0000multi-antenna multi-carrier systems with an orthogonal frequency division\u0000multiplexing (OFDM) signal. In the first method, we apply discrete Fourier\u0000transform (DFT) based coarse signature estimation and propose a low complexity\u0000multistage fine-tuning for extreme enhancement in the estimation accuracy. The\u0000second method is based on compressed sensing, where we achieve the\u0000super-resolution by taking a 2D-overcomplete angle-delay dictionary than the\u0000actual number of antenna and subcarrier basis. Unlike the vectorized 1D-OMP\u0000method, we apply the low complexity 2D-OMP method on the matrix data model that\u0000makes the use of CS methods practical in the context of large array regimes.\u0000Through numerical simulations, we show that our proposed methods achieve the\u0000similar performance as that of the subspace-based 2D-MUSIC method with a\u0000significant reduction in computational complexity.","PeriodicalId":501034,"journal":{"name":"arXiv - EE - Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142251431","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

Fast Structured Orthogonal Dictionary Learning using Householder Reflections 利用豪斯顿反射进行快速结构化正交字典学习

arXiv - EE - Signal Processing Pub Date : 2024-09-13 DOI: arxiv-2409.09138

Anirudh Dash, Aditya Siripuram

引用次数: 0

Train-On-Request: An On-Device Continual Learning Workflow for Adaptive Real-World Brain Machine Interfaces 按需训练：用于自适应真实世界脑机接口的设备上持续学习工作流程

arXiv - EE - Signal Processing Pub Date : 2024-09-13 DOI: arxiv-2409.09161

Lan Mei, Cristian Cioflan, Thorir Mar Ingolfsson, Victor Kartsch, Andrea Cossettini, Xiaying Wang, Luca Benini

{"title":"Train-On-Request: An On-Device Continual Learning Workflow for Adaptive Real-World Brain Machine Interfaces","authors":"Lan Mei, Cristian Cioflan, Thorir Mar Ingolfsson, Victor Kartsch, Andrea Cossettini, Xiaying Wang, Luca Benini","doi":"arxiv-2409.09161","DOIUrl":"https://doi.org/arxiv-2409.09161","url":null,"abstract":"Brain-machine interfaces (BMIs) are expanding beyond clinical settings thanks\u0000to advances in hardware and algorithms. However, they still face challenges in\u0000user-friendliness and signal variability. Classification models need periodic\u0000adaptation for real-life use, making an optimal re-training strategy essential\u0000to maximize user acceptance and maintain high performance. We propose TOR, a\u0000train-on-request workflow that enables user-specific model adaptation to novel\u0000conditions, addressing signal variability over time. Using continual learning,\u0000TOR preserves knowledge across sessions and mitigates inter-session\u0000variability. With TOR, users can refine, on demand, the model through on-device\u0000learning (ODL) to enhance accuracy adapting to changing conditions. We evaluate\u0000the proposed methodology on a motor-movement dataset recorded with a\u0000non-stigmatizing wearable BMI headband, achieving up to 92% accuracy and a\u0000re-calibration time as low as 1.6 minutes, a 46% reduction compared to a naive\u0000transfer learning workflow. We additionally demonstrate that TOR is suitable\u0000for ODL in extreme edge settings by deploying the training procedure on a\u0000RISC-V ultra-low-power SoC (GAP9), resulting in 21.6 ms of latency and 1 mJ of\u0000energy consumption per training step. To the best of our knowledge, this work\u0000is the first demonstration of an online, energy-efficient, dynamic adaptation\u0000of a BMI model to the intrinsic variability of EEG signals in real-time\u0000settings.","PeriodicalId":501034,"journal":{"name":"arXiv - EE - Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142251372","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

Turbo Equalization with Coarse Quantization using the Information Bottleneck Method 使用信息瓶颈法进行粗量化的 Turbo 均衡

arXiv - EE - Signal Processing Pub Date : 2024-09-13 DOI: arxiv-2409.09004

Philipp Mohr, Jasper Brüggmann, Gerhard Bauch

引用次数: 0

Using Ear-EEG to Decode Auditory Attention in Multiple-speaker Environment 利用耳电子脑电图解码多扬声器环境中的听觉注意力

arXiv - EE - Signal Processing Pub Date : 2024-09-13 DOI: arxiv-2409.08710

Haolin Zhu, Yujie Yan, Xiran Xu, Zhongshu Ge, Pei Tian, Xihong Wu, Jing Chen

{"title":"Using Ear-EEG to Decode Auditory Attention in Multiple-speaker Environment","authors":"Haolin Zhu, Yujie Yan, Xiran Xu, Zhongshu Ge, Pei Tian, Xihong Wu, Jing Chen","doi":"arxiv-2409.08710","DOIUrl":"https://doi.org/arxiv-2409.08710","url":null,"abstract":"Auditory Attention Decoding (AAD) can help to determine the identity of the\u0000attended speaker during an auditory selective attention task, by analyzing and\u0000processing measurements of electroencephalography (EEG) data. Most studies on\u0000AAD are based on scalp-EEG signals in two-speaker scenarios, which are far from\u0000real application. Ear-EEG has recently gained significant attention due to its\u0000motion tolerance and invisibility during data acquisition, making it easy to\u0000incorporate with other devices for applications. In this work, participants\u0000selectively attended to one of the four spatially separated speakers' speech in\u0000an anechoic room. The EEG data were concurrently collected from a scalp-EEG\u0000system and an ear-EEG system (cEEGrids). Temporal response functions (TRFs) and\u0000stimulus reconstruction (SR) were utilized using ear-EEG data. Results showed\u0000that the attended speech TRFs were stronger than each unattended speech and\u0000decoding accuracy was 41.3% in the 60s (chance level of 25%). To further\u0000investigate the impact of electrode placement and quantity, SR was utilized in\u0000both scalp-EEG and ear-EEG, revealing that while the number of electrodes had a\u0000minor effect, their positioning had a significant influence on the decoding\u0000accuracy. One kind of auditory spatial attention detection (ASAD) method,\u0000STAnet, was testified with this ear-EEG database, resulting in 93.1% in\u00001-second decoding window. The implementation code and database for our work are\u0000available on GitHub: https://github.com/zhl486/Ear_EEG_code.git and Zenodo:\u0000https://zenodo.org/records/10803261.","PeriodicalId":501034,"journal":{"name":"arXiv - EE - Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142251427","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

Finite Sample Analysis of Distribution-Free Confidence Ellipsoids for Linear Regression 线性回归无分布置信椭圆的有限样本分析

arXiv - EE - Signal Processing Pub Date : 2024-09-13 DOI: arxiv-2409.08801

Szabolcs Szentpéteri, Balázs Csanád Csáji

{"title":"Finite Sample Analysis of Distribution-Free Confidence Ellipsoids for Linear Regression","authors":"Szabolcs Szentpéteri, Balázs Csanád Csáji","doi":"arxiv-2409.08801","DOIUrl":"https://doi.org/arxiv-2409.08801","url":null,"abstract":"The least squares (LS) estimate is the archetypical solution of linear\u0000regression problems. The asymptotic Gaussianity of the scaled LS error is often\u0000used to construct approximate confidence ellipsoids around the LS estimate,\u0000however, for finite samples these ellipsoids do not come with strict\u0000guarantees, unless some strong assumptions are made on the noise distributions.\u0000The paper studies the distribution-free Sign-Perturbed Sums (SPS) ellipsoidal\u0000outer approximation (EOA) algorithm which can construct non-asymptotically\u0000guaranteed confidence ellipsoids under mild assumptions, such as independent\u0000and symmetric noise terms. These ellipsoids have the same center and\u0000orientation as the classical asymptotic ellipsoids, only their radii are\u0000different, which radii can be computed by convex optimization. Here, we\u0000establish high probability non-asymptotic upper bounds for the sizes of SPS\u0000outer ellipsoids for linear regression problems and show that the volumes of\u0000these ellipsoids decrease at the optimal rate. Finally, the difference between\u0000our theoretical bounds and the empirical sizes of the regions are investigated\u0000experimentally.","PeriodicalId":501034,"journal":{"name":"arXiv - EE - Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142251426","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

Domain Adaptation for DoA Estimation in Multipath Channels with Interferences 在有干扰的多径信道中进行 DoA 估算的域自适应

arXiv - EE - Signal Processing Pub Date : 2024-09-12 DOI: arxiv-2409.07782

Amitay Bar, Joseph S. Picard, Israel Cohen, Ronen Talmon

引用次数: 0