2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)最新文献_第10页

Deep Learning-Based Modulation Classification for OFDM Systems Without Symbol-Level Synchronization 基于深度学习的无符号级同步OFDM系统调制分类

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) Pub Date : 2023-06-04 DOI: 10.1109/ICASSPW59220.2023.10193676

Byungjun Kim, V. Sathyanarayanan, C. Mecklenbräuker, P. Gerstoft

引用次数: 0

Angular Central Gaussian and Watson Mixture Models for Assessing Dynamic Functional Brain Connectivity During a Motor Task 角中心高斯和沃森混合模型在运动任务中评估动态功能脑连接

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) Pub Date : 2023-06-04 DOI: 10.1109/ICASSPW59220.2023.10193021

A. S. Olsen, Emil Ortvald, K.H. Madsen, Mikkel N. Schmidt, Morten Mørup

引用次数: 0

In-Band Full-Duplex Solutions in the Paradigm of Integrated Sensing and Communication 集成传感与通信范式中的带内全双工解决方案

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) Pub Date : 2023-06-04 DOI: 10.1109/ICASSPW59220.2023.10193639

A. Harutyunyan, P. Sen

引用次数: 1

Site Diversity Switching Prediction AT Q Band Using Deep Learning Techniques in Satellite Communications 卫星通信中使用深度学习技术的站点分集交换预测

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) Pub Date : 2023-06-04 DOI: 10.1109/ICASSPW59220.2023.10193159

Maria Kaselimi, A. J. Roumeliotis, A. Z. Papafragkakis, A. Panagopoulos, N. Doulamis

引用次数: 0

Human Activity Recognition From Motion and Acoustic Sensors Using Contrastive Learning 利用对比学习从运动和声音传感器中识别人类活动

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) Pub Date : 2023-06-04 DOI: 10.1109/ICASSPW59220.2023.10192969

Rui Zhou, Running Zhao, Edith C. H. Ngai

引用次数: 0

A Unified Semantic Segmentation and Object Detection Framework for Synthetic Aperture Sonar Imagery 合成孔径声纳图像的统一语义分割与目标检测框架

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) Pub Date : 2023-06-04 DOI: 10.1109/ICASSPW59220.2023.10193155

Shannon-Morgan Steele

{"title":"A Unified Semantic Segmentation and Object Detection Framework for Synthetic Aperture Sonar Imagery","authors":"Shannon-Morgan Steele","doi":"10.1109/ICASSPW59220.2023.10193155","DOIUrl":"https://doi.org/10.1109/ICASSPW59220.2023.10193155","url":null,"abstract":"Manually identifying objects in synthetic aperture sonar (SAS) imagery is costly and time consuming, making identification through computer vision and deep learning techniques an appealing alternative. Depending on the application, a generalized map (semantic segmentation) and/or a characterization of each individual object (object detection) may be desired. Here, we demonstrate a framework that allows us to simultaneously generate both semantic segmentation maps and object detections with a single deep learning model by chaining together a U-Net model with k-means clustering and connected components. This framework streamlines the model training phase by allowing us to utilize a set of semantically segmented training data to yield both semantic segmentation and bounding box predictions. We demonstrate that the deep learning model can achieve accurate predictions with a small training set through transfer learning from a convolutional neural network pretrained on optical imagery. Results from this unified framework will be presented on images of boulders collected during various surveys using a Kraken Robotics miniature SAS (MINSAS).","PeriodicalId":158726,"journal":{"name":"2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124146083","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

Topological Analysis of Low Dimensional Phase Space Trajectories of High Dimensional EEG Signals For Classification of Interictal Epileptiform Discharges 高维脑电图信号低维相空间轨迹拓扑分析用于癫痫样间期放电分类

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) Pub Date : 2023-06-04 DOI: 10.1109/ICASSPW59220.2023.10193167

A. Stiehl, M. Flammer, F. Anselstetter, N. Ille, H. Bornfleth, S. Geißelsöder, C. Uhl

{"title":"Topological Analysis of Low Dimensional Phase Space Trajectories of High Dimensional EEG Signals For Classification of Interictal Epileptiform Discharges","authors":"A. Stiehl, M. Flammer, F. Anselstetter, N. Ille, H. Bornfleth, S. Geißelsöder, C. Uhl","doi":"10.1109/ICASSPW59220.2023.10193167","DOIUrl":"https://doi.org/10.1109/ICASSPW59220.2023.10193167","url":null,"abstract":"A new topology based feature extraction method for classification of interictal epileptiform discharges (IEDs) in EEG recordings from patients with epilepsy is proposed. After dimension reduction of the recorded EEG signal, using dynamical component analysis (DyCA) or principal component analysis (PCA), a persistent homology analysis of the resulting phase space trajectories is performed. Features are extracted from the persistent homology analysis and used to train and evaluate a support vector machine (SVM). Classification results based on these persistent features are compared with statistical features of the dimension-reduced signals and combinations of all of these features. Combining the persistent and statistical features improves the results (accuracy 94.7 %) compared to using only statistical feature extraction, whereas applying only persistent features does not achieve sufficient performance. For this classification example the choice of the dimension reduction technique does not significantly influence the classification performance of the algorithm.","PeriodicalId":158726,"journal":{"name":"2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124225333","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

Sign Language Avatar Animation Search: An Ani2Ani Search Application 手语头像动画搜索:Ani2Ani搜索应用程序

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) Pub Date : 2023-06-04 DOI: 10.1109/ICASSPW59220.2023.10193345

Mathew Huerta-Enochian, Chang-Seog Ko

引用次数: 0

Truedepth Measurements of Facial Expressions: Sensitivity to the Angle Between Camera and Face 面部表情的真深度测量:对相机和面部之间角度的敏感性

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) Pub Date : 2023-06-04 DOI: 10.1109/ICASSPW59220.2023.10193107

L. Esselink, M. Oomen, F. Roelofsen

引用次数: 0

EEG Source Estimation Using Deep Prior Without a Subject’s Individual Lead Field 无个体导联场的深度先验脑电源估计

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) Pub Date : 2023-06-04 DOI: 10.1109/ICASSPW59220.2023.10193746

Naoki Hojo, H. Yano, R. Takashima, T. Takiguchi, Seiji Nakagawa

{"title":"EEG Source Estimation Using Deep Prior Without a Subject’s Individual Lead Field","authors":"Naoki Hojo, H. Yano, R. Takashima, T. Takiguchi, Seiji Nakagawa","doi":"10.1109/ICASSPW59220.2023.10193746","DOIUrl":"https://doi.org/10.1109/ICASSPW59220.2023.10193746","url":null,"abstract":"Estimating current sources in the brain using an electroencephalogram (EEG) is affected by the accuracy of the lead field, which represents signal propagation from the cortical sources to the scalp. To accurately compute the lead field, one must know the subject’s head structure. However, imaging methods for brain structure require large-scale equipment. In this paper, we propose a novel method of EEG source estimation that does not require the lead field of each subject obtained in advance. The current sources in the brain and the lead field are simultaneously estimated using implicit prior distributions expressed by an untrained convolutional neural network (CNN), namely Deep Prior, and a pre-trained CNN using the lead field of an average subject, respectively. The proposed method requires only a noisy EEG observation and the lead field of the average subject. We showed that the proposed method was more accurate than the conventional methods, and was also as accurate as the Deep Prior-based method with the lead field of each subject.","PeriodicalId":158726,"journal":{"name":"2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125125111","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