IEEE Systems Man and Cybernetics Magazine最新文献_第2页

Deep Vision in Analysis and Recognition of Radar Data: Achievements, Advancements, and Challenges 雷达数据分析与识别中的深度视觉:成就、进步与挑战

IEEE Systems Man and Cybernetics Magazine Pub Date : 2023-10-01 DOI: 10.1109/msmc.2022.3216943

Qi Liu, Zhiyun Yang, Ru Ji, Yonghong Zhang, Muhammad Bilal, Xiaodong Liu, S. Vimal, Xiaolong Xu

引用次数: 0

Dynamic Hand Gesture Recognition Based on A-Mode Ultrasound Sensing: Proposing an Algorithm Based on the Long Short-Term Memory Framework 基于a型超声传感的动态手势识别:提出一种基于长短期记忆框架的算法

IEEE Systems Man and Cybernetics Magazine Pub Date : 2023-10-01 DOI: 10.1109/msmc.2023.3299431

Donghan Liu, Dinghuang Zhang, Gongyue Zhang, Honghai Liu

{"title":"Dynamic Hand Gesture Recognition Based on A-Mode Ultrasound Sensing: Proposing an Algorithm Based on the Long Short-Term Memory Framework","authors":"Donghan Liu, Dinghuang Zhang, Gongyue Zhang, Honghai Liu","doi":"10.1109/msmc.2023.3299431","DOIUrl":"https://doi.org/10.1109/msmc.2023.3299431","url":null,"abstract":"Hand gesture recognition plays a crucial role in the field of human–computer interaction (HCI). In terms of the multimodal sensing of hand gestures, the A-mode ultrasound (AUS) signal is far less investigated, especially for dynamic hand gestures, than its counterparts, such as surface electromyography (sEMG). In this article, we explore the recognition of dynamic hand gestures by proposing an AUS-based deep learning algorithm that codes time correlation in the long short-term memory (LSTM) framework. First, a dynamic handwritten numbers 0 through 9 dataset was created and recorded. Then, after preprocessing the data, we propose an algorithm based on the deep learning framework. Also, we designed two different strategies that used two different structures for comparison. Finally, through experiments, the accuracy of different deep learning structures [convolutional neural network (CNN) and LSTM] and traditional feature extraction [support vector machine (SVM)] on dynamic gesture recognition of ultrasonic (US) signals are compared, and we prove that LSTM has better performance. The experiment results prove that the proposed method achieves 89.5% accuracy, which outperforms its counterparts. It paves the way for potential HCI applications involving dynamic hand gestures. It is anticipated that more uses of dynamic gesture recognition will be discussed in the future to bring the research into real-life applications.","PeriodicalId":43649,"journal":{"name":"IEEE Systems Man and Cybernetics Magazine","volume":"189 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135707714","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

Distributed Minmax Strategy for Consensus Tracking in Differential Graphical Games: A Model-Free Approach 微分图形博弈共识跟踪的分布式极小策略:一种无模型方法

IEEE Systems Man and Cybernetics Magazine Pub Date : 2023-10-01 DOI: 10.1109/msmc.2023.3282774

Yan Zhou, Jialing Zhou, Guanghui Wen, Minggang Gan, Tao Yang

引用次数: 0

Report of the First IEEE International Summer School (Online) on Environments—Classes, Agents, Roles, Groups, and Objects and Its Applications [Conference Reports] 第一届IEEE国际暑期学校(在线)环境-类，代理，角色，组和对象及其应用报告[会议报告]

IEEE Systems Man and Cybernetics Magazine Pub Date : 2023-10-01 DOI: 10.1109/msmc.2023.3308445

Haibin Zhu

引用次数: 0

Saeid Nahavandi: Academic, Innovator, Technopreneur, and Thought Leader [Society News] Saeid Nahavandi:学者、创新者、技术企业家和思想领袖[社会新闻]

IEEE Systems Man and Cybernetics Magazine Pub Date : 2023-10-01 DOI: 10.1109/msmc.2023.3284811

Saeid Nahavandi

引用次数: 0

Online Summer School 网上暑期学校

IF 3.2

IEEE Systems Man and Cybernetics Magazine Pub Date : 2023-07-01 DOI: 10.1109/msmc.2023.3275041

引用次数: 0

The 19th IEEE International Conference on Networking, Sensing, and Control [Conference Reports] 第19届IEEE网络、传感和控制国际会议[会议报告]

IF 3.2

IEEE Systems Man and Cybernetics Magazine Pub Date : 2023-07-01 DOI: 10.1109/msmc.2023.3273460

Q. Kang, Shuaiyu Yao

引用次数: 0

An ASD Classification Based on a Pseudo 4D ResNet: Utilizing Spatial and Temporal Convolution 基于伪四维ResNet的ASD分类:利用时空卷积

IF 3.2

IEEE Systems Man and Cybernetics Magazine Pub Date : 2023-07-01 DOI: 10.1109/MSMC.2022.3228381

Shuaiqi Liu, Siqi Wang, Hong Zhang, Shui-Hua Wang, Jie Zhao, Jingwen Yan

{"title":"An ASD Classification Based on a Pseudo 4D ResNet: Utilizing Spatial and Temporal Convolution","authors":"Shuaiqi Liu, Siqi Wang, Hong Zhang, Shui-Hua Wang, Jie Zhao, Jingwen Yan","doi":"10.1109/MSMC.2022.3228381","DOIUrl":"https://doi.org/10.1109/MSMC.2022.3228381","url":null,"abstract":"The psychiatric condition known as autism spectrum disorder (ASD) affects children and adults alike. As a medical imaging technology, functional magnetic resonance imaging (fMRI) is widely used to study the brains of persons with ASD. This study introduces a novel technique: a pseudo 4D ResNet (P4D ResNet) to simultaneously extract and classify the brain activity of ASD patients. A P4D ResNet can extract both temporal and spatial information from fMRI data, which mainly consists of two different residual blocks stacked together. In a P4D ResNet, to reduce computational and parametric quantities, each residual block is combined with a 3D spatial filter and a 1D temporal filter instead of a 4D spatiotemporal convolution, which can perform parallel computation. Due to the high dimensionality of the complete data and the limited amount of data, in this article, each piece of fMRI data are sampled at equal intervals of a set length in the time dimension for data expansion. Compared with other existing models, the experiments show that the proposed model for ASD classification achieved better results.","PeriodicalId":43649,"journal":{"name":"IEEE Systems Man and Cybernetics Magazine","volume":"10 1","pages":"9-18"},"PeriodicalIF":3.2,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83509478","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

Tooth.AI: Intelligent Dental Disease Diagnosis and Treatment Support Using Semantic Network 牙齿。AI:基于语义网络的智能牙病诊疗支持

IF 3.2

IEEE Systems Man and Cybernetics Magazine Pub Date : 2023-07-01 DOI: 10.1109/MSMC.2023.3245814

Hossam A. Gabbar, Abderrazak Chahid, Md. Jamiul Alam Khan, Oluwabukola Grace-Adegboro, Matthew Immanuel Samson

引用次数: 1

Edge Processing: A LoRa-Based LCDT System for Smart Building With Energy and Delay Constraints 边缘处理:一种基于lora的具有能量和延迟约束的智能建筑LCDT系统

IF 3.2

IEEE Systems Man and Cybernetics Magazine Pub Date : 2023-07-01 DOI: 10.1109/MSMC.2022.3204848

B. Shilpa, Hari Prabhat Gupta, R. K. Jha

{"title":"Edge Processing: A LoRa-Based LCDT System for Smart Building With Energy and Delay Constraints","authors":"B. Shilpa, Hari Prabhat Gupta, R. K. Jha","doi":"10.1109/MSMC.2022.3204848","DOIUrl":"https://doi.org/10.1109/MSMC.2022.3204848","url":null,"abstract":"A smart building is an emerging technology that has the potential to be used in a variety of ubiquitous computing applications. The majority of existing work for smart building monitoring consumes a significant amount of energy to communicate the sensory data from the building to the end users (EUs). This work presents a low-cost data transmission (LCDT) system for a smart building in the context of a noisy environment. The system uses the long-range (LoRa) communication protocol to conserve energy and enable long-distance communication. The smart building sensors generate data in the form of a multivariate time series (MTS). The system compresses such an MTS before transmission by utilizing deep learning (DL) techniques. A channel to reduce the transmission noise of sensory data is also designed using the DL method. The system decompresses the received data at the receiver end and obtains the original MTS. Additionally, we also conducted experiments to demonstrate the utility of the system. The experimental results demonstrate that selecting a finite number of distinct edge device (ED) types aids in developing an LCDT system subject to energy and latency constraints.","PeriodicalId":43649,"journal":{"name":"IEEE Systems Man and Cybernetics Magazine","volume":"345 1","pages":"37-43"},"PeriodicalIF":3.2,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79641809","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