Traitement Du Signal最新文献_第8页

Advancements in Image Feature-Based Classification of Motor Imagery EEG Data: A Comprehensive Review 基于图像特征的运动图像脑电数据分类研究进展

4区计算机科学

Traitement Du Signal Pub Date : 2023-10-30 DOI: 10.18280/ts.400507

Cagatay Murat Yilmaz, Bahar Hatipoglu Yilmaz

{"title":"Advancements in Image Feature-Based Classification of Motor Imagery EEG Data: A Comprehensive Review","authors":"Cagatay Murat Yilmaz, Bahar Hatipoglu Yilmaz","doi":"10.18280/ts.400507","DOIUrl":"https://doi.org/10.18280/ts.400507","url":null,"abstract":"Non-invasive acquisition and analysis of human brain signals play a crucial role in the development of brain-computer interfaces, enabling their widespread applicability in daily life. Motor imagery has emerged as a prominent technique for the advancement of such interfaces. While initial machine and deep learning studies have shown promising results in the context of motor imagery, several challenges remain to be addressed prior to their extensive adoption. Deep learning, renowned for its automated feature extraction and classification capabilities, has been successfully employed in various domains. Notably, recent research efforts have focused on processing and classifying motor imagery EEG signals using two-dimensional data formats, yielding noteworthy advancements. Although existing literature encompasses reviews primarily centered on machine learning or deep learning techniques, this paper uniquely emphasizes the review of methods for constructing two-dimensional image features, marking the first comprehensive exploration of this subject. In this study, we present an overview of datasets, survey a range of signal-to-image conversion methods, and discuss classification approaches. Furthermore, we comprehensively examine the current challenges and outline future directions for this research domain.","PeriodicalId":49430,"journal":{"name":"Traitement Du Signal","volume":"31 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136068344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Utilizing Deep Learning-Based Fusion of Laser Point Cloud Data and Imagery for Digital Measurement in Steel Truss Member Applications 基于深度学习的激光点云数据和图像融合在钢桁架构件应用中的数字测量

4区计算机科学

Traitement Du Signal Pub Date : 2023-10-30 DOI: 10.18280/ts.400516

Wenxian Li, Zhimin Liu

引用次数: 0

Comparative Analysis of Photoplethysmography Signal Quality from Right and Left Index Fingers 左右食指脉搏波信号质量的比较分析

4区计算机科学

Traitement Du Signal Pub Date : 2023-10-30 DOI: 10.18280/ts.400537

Kehkashan Kanwal, Muhammad Asif, Syed Ghufran Khalid, Sarwar Wasi, Farhana Zafar, Iffat Kiran, Saad Abdullah

引用次数: 0

ulti-Modal Medical Image Matching Based on Multi-Task Learning and Semantic-Enhanced Cross-Modal Retrieval 基于多任务学习和语义增强跨模态检索的多模态医学图像匹配

4区计算机科学

Traitement Du Signal Pub Date : 2023-10-30 DOI: 10.18280/ts.400522

Yilin Zhang

引用次数: 0

Hybrid Deep Learning Approach for 6G MIMO Channel Estimation and Interference Alignment HetNet Environments 6G MIMO信道估计与干扰对准的混合深度学习方法

4区计算机科学

Traitement Du Signal Pub Date : 2023-10-30 DOI: 10.18280/ts.400514

Ranjith Subramanian, Jesu Jayarin, Arumugam Chandrasekar

引用次数: 0

Real-Time Recognition and Feature Extraction of Stratum Images Based on Deep Learning 基于深度学习的地层图像实时识别与特征提取

4区计算机科学

Traitement Du Signal Pub Date : 2023-10-30 DOI: 10.18280/ts.400542

Tong Wang, Yu Yan, Lizhi Yuan, Yanhong Dong

引用次数: 0

Automated Identification and Categorization of COVID-19 via X-Ray Imagery Leveraging ROI Segmentation and CART Model 利用ROI分割和CART模型的x射线图像自动识别和分类COVID-19

4区计算机科学

Traitement Du Signal Pub Date : 2023-10-30 DOI: 10.18280/ts.400543

Bayan Alsaaidah, Zaid Mustafa, Moh’d Rasoul Al-Hadidi, Lubna A. Alharbi

引用次数: 0

Multi-Modal Fusion for Moving Object Detection in Static and Complex Backgrounds 静态和复杂背景下运动目标检测的多模态融合

4区计算机科学

Traitement Du Signal Pub Date : 2023-10-30 DOI: 10.18280/ts.400513

Huali Jiang, Xin Li

{"title":"Multi-Modal Fusion for Moving Object Detection in Static and Complex Backgrounds","authors":"Huali Jiang, Xin Li","doi":"10.18280/ts.400513","DOIUrl":"https://doi.org/10.18280/ts.400513","url":null,"abstract":"Moving object detection from video sequences remains a focal point of research. To address the limitations evident in current methodologies, a synthesis of optical flow method and salient object fusion algorithm has been applied. Utilising the Graph-based Visual Saliency (GBVS) algorithm, significant target region signals from both static and dynamic images can be obtained. This technique captures valuable image target information, highlighting conspicuous targets within dynamic visuals. Concurrently, target signals can be isolated employing the Harmony Search (HS) algorithm, enhancing the accuracy in identifying moving objects. A weighted fusion of the extracted salient regions by the GBVS algorithm and the moving objects identified by the HS algorithm was executed in this study. This amalgamation demonstrates efficacy in extracting static objects in rudimentary environments and complex backgrounds alike. MATLAB simulation experiments have indicated that such a multi-modal fusion not only diminishes background noise but also proficiently isolates the entirety of the target. Building on traditional frame difference and background difference methods and considering the properties of the field programmable gate array (FPGA) alongside off-chip synchronous dynamic memory's access control prerequisites, adaptations for these algorithms were conceived using FPGA logic units.","PeriodicalId":49430,"journal":{"name":"Traitement Du Signal","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136102642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Utilizing a Hybrid Model for Human Injury Severity Analysis in Traffic Accidents 基于混合模型的交通事故人身伤害严重程度分析

4区计算机科学

Traitement Du Signal Pub Date : 2023-10-30 DOI: 10.18280/ts.400540

Uddagiri Sirisha, Bolem Sai Chandana

{"title":"Utilizing a Hybrid Model for Human Injury Severity Analysis in Traffic Accidents","authors":"Uddagiri Sirisha, Bolem Sai Chandana","doi":"10.18280/ts.400540","DOIUrl":"https://doi.org/10.18280/ts.400540","url":null,"abstract":"Road safety has been prioritized by governments globally, resulting in the implementation of numerous initiatives aimed at curtailing traffic accidents. Despite these efforts, the complete eradication of accidents remains unattainable. Therefore, swift and accurate responses to accident sites, accompanied by appropriate medical aid, are paramount in saving lives. Existing systems, primarily designed to alert medical personnel in the aftermath of an accident, rely solely on Vehicle Damage (V d ) to assess accident severity, neglecting Human Injury (H i ) considerations. This study proposes a hybrid model equipped with an attention mechanism, designed to classify accident severity based on both V d and H i . The proposed model accepts video or image inputs and classifies accident severity levels accordingly. Moreover, an extension of the model has been developed to obfuscate sensitive areas in accident imagery based on severity, particularly when such images are disseminated on public platforms without obtaining necessary consent. The proposed hybrid model, therefore, not only facilitates a more comprehensive severity assessment of traffic accidents but also ensures the protection of privacy and promotes ethical image sharing practices.","PeriodicalId":49430,"journal":{"name":"Traitement Du Signal","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136103314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Vibration Signal Analysis of Complex Mechanical Systems and Early Wear Detection and Forecasting for Gears 复杂机械系统振动信号分析及齿轮早期磨损检测与预测

4区计算机科学

Traitement Du Signal Pub Date : 2023-10-30 DOI: 10.18280/ts.400527

Suzhen Wu

引用次数: 0