Pub Date : 2023-10-01DOI: 10.1109/msmc.2022.3216943
Qi Liu, Zhiyun Yang, Ru Ji, Yonghong Zhang, Muhammad Bilal, Xiaodong Liu, S. Vimal, Xiaolong Xu
Radars are widely used to obtain echo information for effective prediction, such as precipitation nowcasting. In this article, recent relevant scientific investigation and practical efforts using deep learning (DL) models for weather radar data analysis and pattern recognition have been reviewed. In addition, this work presents and discusses recent achievements, as well as recent developments and existing problems, in an attempt to establish plausible potentials and trends in this highly concerned field, particularly, in the fields of beam blockage correction, radar echo extrapolation, and precipitation nowcast. Compared to traditional approaches, present DL methods depict better performance and convenience but suffer from stability and generalization.
{"title":"Deep Vision in Analysis and Recognition of Radar Data: Achievements, Advancements, and Challenges","authors":"Qi Liu, Zhiyun Yang, Ru Ji, Yonghong Zhang, Muhammad Bilal, Xiaodong Liu, S. Vimal, Xiaolong Xu","doi":"10.1109/msmc.2022.3216943","DOIUrl":"https://doi.org/10.1109/msmc.2022.3216943","url":null,"abstract":"Radars are widely used to obtain echo information for effective prediction, such as precipitation nowcasting. In this article, recent relevant scientific investigation and practical efforts using deep learning (DL) models for weather radar data analysis and pattern recognition have been reviewed. In addition, this work presents and discusses recent achievements, as well as recent developments and existing problems, in an attempt to establish plausible potentials and trends in this highly concerned field, particularly, in the fields of beam blockage correction, radar echo extrapolation, and precipitation nowcast. Compared to traditional approaches, present DL methods depict better performance and convenience but suffer from stability and generalization.","PeriodicalId":43649,"journal":{"name":"IEEE Systems Man and Cybernetics Magazine","volume":"73 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":"135707260","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}
Pub Date : 2023-10-01DOI: 10.1109/msmc.2023.3299431
Donghan Liu, Dinghuang Zhang, Gongyue Zhang, Honghai Liu
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.
{"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}
Pub Date : 2023-10-01DOI: 10.1109/msmc.2023.3282774
Yan Zhou, Jialing Zhou, Guanghui Wen, Minggang Gan, Tao Yang
This article focuses on the design of distributed minmax strategies for multiagent consensus tracking control problems with completely unknown dynamics in the presence of external disturbances or attacks. Each agent obtains its distributed minmax strategy by solving a multiagent zero-sum differential graphical game, which involves both nonadversarial and adversarial behaviors. Solving such a game is equivalent to solving a game algebraic Riccati equation (GARE). By making slight assumptions concerning performance matrices, ${cal{L}}_{2}$ stability and asymptotic stability of the closed-loop consensus error systems are strictly proven. Furthermore, inspired by data-driven off-policy reinforcement learning (RL), a model-free policy iteration (PI) algorithm is presented for each follower to generate the minmax strategy. Finally, simulations are performed to demonstrate the effectiveness of the proposed theoretical results.
{"title":"Distributed Minmax Strategy for Consensus Tracking in Differential Graphical Games: A Model-Free Approach","authors":"Yan Zhou, Jialing Zhou, Guanghui Wen, Minggang Gan, Tao Yang","doi":"10.1109/msmc.2023.3282774","DOIUrl":"https://doi.org/10.1109/msmc.2023.3282774","url":null,"abstract":"This article focuses on the design of distributed minmax strategies for multiagent consensus tracking control problems with completely unknown dynamics in the presence of external disturbances or attacks. Each agent obtains its distributed minmax strategy by solving a multiagent zero-sum differential graphical game, which involves both nonadversarial and adversarial behaviors. Solving such a game is equivalent to solving a game algebraic Riccati equation (GARE). By making slight assumptions concerning performance matrices, <inline-formula xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><tex-math notation=\"LaTeX\">${cal{L}}_{2}$</tex-math></inline-formula> stability and asymptotic stability of the closed-loop consensus error systems are strictly proven. Furthermore, inspired by data-driven off-policy reinforcement learning (RL), a model-free policy iteration (PI) algorithm is presented for each follower to generate the minmax strategy. Finally, simulations are performed to demonstrate the effectiveness of the proposed theoretical results.","PeriodicalId":43649,"journal":{"name":"IEEE Systems Man and Cybernetics Magazine","volume":"59 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":"135707720","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}
Pub Date : 2023-10-01DOI: 10.1109/msmc.2023.3308445
Haibin Zhu
Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
提供社会信息，可能包括新闻，评论或技术笔记，从业者和研究人员应该感兴趣。
{"title":"Report of the First IEEE International Summer School (Online) on Environments—Classes, Agents, Roles, Groups, and Objects and Its Applications [Conference Reports]","authors":"Haibin Zhu","doi":"10.1109/msmc.2023.3308445","DOIUrl":"https://doi.org/10.1109/msmc.2023.3308445","url":null,"abstract":"Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.","PeriodicalId":43649,"journal":{"name":"IEEE Systems Man and Cybernetics Magazine","volume":"4 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":"135707004","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}
Pub Date : 2023-10-01DOI: 10.1109/msmc.2023.3284811
Saeid Nahavandi
Distinguished Prof. Saeid Nahavandi is the inaugural associate deputy vice-chancellor research and chief of defence innovation of Swinburne University of Technology, Melbourne, VIC, Australia. Nahavandi completed a Ph.D. degree in automation and control from Durham University, United Kingdom, and then served at Massey University, New Zealand for seven years. Prior to joining Swinburne, he was Alfred Deakin Professor and founding director of the Institute for Intelligent Systems Research and Innovation, as well as pro vice-chancellor at Deakin University, Waurn Ponds, VIC, Australia.
{"title":"Saeid Nahavandi: Academic, Innovator, Technopreneur, and Thought Leader [Society News]","authors":"Saeid Nahavandi","doi":"10.1109/msmc.2023.3284811","DOIUrl":"https://doi.org/10.1109/msmc.2023.3284811","url":null,"abstract":"Distinguished Prof. Saeid Nahavandi is the inaugural associate deputy vice-chancellor research and chief of defence innovation of Swinburne University of Technology, Melbourne, VIC, Australia. Nahavandi completed a Ph.D. degree in automation and control from Durham University, United Kingdom, and then served at Massey University, New Zealand for seven years. Prior to joining Swinburne, he was Alfred Deakin Professor and founding director of the Institute for Intelligent Systems Research and Innovation, as well as pro vice-chancellor at Deakin University, Waurn Ponds, VIC, Australia.","PeriodicalId":43649,"journal":{"name":"IEEE Systems Man and Cybernetics Magazine","volume":"15 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":"135707138","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}
Pub Date : 2023-07-01DOI: 10.1109/msmc.2023.3275041
{"title":"Online Summer School","authors":"","doi":"10.1109/msmc.2023.3275041","DOIUrl":"https://doi.org/10.1109/msmc.2023.3275041","url":null,"abstract":"","PeriodicalId":43649,"journal":{"name":"IEEE Systems Man and Cybernetics Magazine","volume":"58 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82329084","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}
Pub Date : 2023-07-01DOI: 10.1109/msmc.2023.3273460
Q. Kang, Shuaiyu Yao
{"title":"The 19th IEEE International Conference on Networking, Sensing, and Control [Conference Reports]","authors":"Q. Kang, Shuaiyu Yao","doi":"10.1109/msmc.2023.3273460","DOIUrl":"https://doi.org/10.1109/msmc.2023.3273460","url":null,"abstract":"","PeriodicalId":43649,"journal":{"name":"IEEE Systems Man and Cybernetics Magazine","volume":" 10","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72497627","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}
Pub Date : 2023-07-01DOI: 10.1109/MSMC.2022.3228381
Shuaiqi Liu, Siqi Wang, Hong Zhang, Shui-Hua Wang, Jie Zhao, Jingwen Yan
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.
{"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}
Pub Date : 2023-07-01DOI: 10.1109/MSMC.2023.3245814
Hossam A. Gabbar, Abderrazak Chahid, Md. Jamiul Alam Khan, Oluwabukola Grace-Adegboro, Matthew Immanuel Samson
The emerging fourth industrial revolution (industry 4.0) is leading the healthcare system toward more digitalization and smart management. For instance, recent digital healthcare solutions can help dentists/practitioners save time by managing their schedules and managing diagnosis and treatment. The proposed solution is a diagnostic module that can be integrated into existing dental software. This module is based on artificial intelligence (AI) that allows the diagnosis of X-ray images/volumes and helps in the early detection and diagnosis of oral health diseases. The solution presents a smart and automated assistive platform to aid dental practitioners in identifying underlying tooth diseases and accessing doctors in treatment suggestions.
{"title":"Tooth.AI: Intelligent Dental Disease Diagnosis and Treatment Support Using Semantic Network","authors":"Hossam A. Gabbar, Abderrazak Chahid, Md. Jamiul Alam Khan, Oluwabukola Grace-Adegboro, Matthew Immanuel Samson","doi":"10.1109/MSMC.2023.3245814","DOIUrl":"https://doi.org/10.1109/MSMC.2023.3245814","url":null,"abstract":"The emerging fourth industrial revolution (industry 4.0) is leading the healthcare system toward more digitalization and smart management. For instance, recent digital healthcare solutions can help dentists/practitioners save time by managing their schedules and managing diagnosis and treatment. The proposed solution is a diagnostic module that can be integrated into existing dental software. This module is based on artificial intelligence (AI) that allows the diagnosis of X-ray images/volumes and helps in the early detection and diagnosis of oral health diseases. The solution presents a smart and automated assistive platform to aid dental practitioners in identifying underlying tooth diseases and accessing doctors in treatment suggestions.","PeriodicalId":43649,"journal":{"name":"IEEE Systems Man and Cybernetics Magazine","volume":"7 1","pages":"19-27"},"PeriodicalIF":3.2,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87436457","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}
Pub Date : 2023-07-01DOI: 10.1109/MSMC.2022.3204848
B. Shilpa, Hari Prabhat Gupta, R. K. Jha
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.
{"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}
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