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This book includes selected papers from the 13th IEEE International Conference on Multisensor Integration and Fusion for Intelligent Systems (MFI 2017) held in Daegu, Korea, November 16–22, 2017. It covers various topics, including sensor/actuator networks, distributed and cloud architectures, bio-inspired systems and evolutionary approaches, methods of cognitive sensor fusion, Bayesian approaches, fuzzy systems and neural networks, biomedical applications, autonomous land, sea and air vehicles, localization, tracking, SLAM, 3D perception, manipulation with multifinger hands, robotics, micro/nano systems, information fusion and sensors, and multimodal integration in HCI and HRI. The book is intended for robotics scientists, data and information fusion scientists, researchers and professionals at universities, research institutes and laboratories.
sensor actuator networks, distributed and cloud architectures, bio inspired systems and evolutionary approaches, methods of cognitive sensor fusion, Bayesian approaches, fuzzy systems and neural networks, biomedical applications, autonomous vehicles of land, sea and air, localization, tracking, SLAM, 3D perception, manipulation with multifinger hands, robotics, micro nano systems, information fusion, sensors, multimodal integration in HCI and HRI, etc
I3CAC provides a premier interdisciplinary platform for researchers, practitioners and educators to present and discuss not only the most recent innovations, trends, and concerns but also practical challenges encountered and solutions adopted in the fields of computing, communication and control systems. Participation of three renowned speakers and oral presentations of the 128 authors were presented in our conference. We strongly believe that the I3CAC 2021 conference provides a good forum for all researchers, developers and practitioners to discuss.
This book highlights the recent research on hybrid intelligent systems and their various practical applications. It presents 45 selected papers from the 20th International Conference on Hybrid Intelligent Systems (HIS 2021) and 16 papers from the 17th International Conference on Information Assurance and Security, which was held online, from December 14 to 16, 2021. A premier conference in the field of artificial intelligence and machine learning applications, HIS-IAS 2021 brought together researchers, engineers and practitioners whose work involves intelligent systems, network security and their applications in industry. Including contributions by authors from over 20 countries, the book offers a valuable reference guide for all researchers, students and practitioners in the fields of computer science and engineering.
This three-volume set (CCIS 1367-1368) constitutes the refereed proceedings of the 5th International Conference on Computer Vision and Image Processing, CVIP 2020, held in Prayagraj, India, in December 2020. Due to the COVID-19 pandemic the conference was partially held online. The 134 papers papers were carefully reviewed and selected from 352 submissions. The papers present recent research on such topics as biometrics, forensics, content protection, image enhancement/super-resolution/restoration, motion and tracking, image or video retrieval, image, image/video processing for autonomous vehicles, video scene understanding, human-computer interaction, document image analysis, face, iris, emotion, sign language and gesture recognition, 3D image/video processing, action and event detection/recognition, medical image and video analysis, vision-based human GAIT analysis, remote sensing, and more.
The book presents the latest advances and research findings in the fields of computational science and communication. The areas covered include smart innovation; systems and technologies; embedded knowledge and intelligence; innovation and sustainability; advanced computing; and networking and informatics. It also focuses on the knowledge-transfer methodologies and the innovation strategies employed to make these effective. This fascinating compilation appeals to researchers, academics and engineers around the globe.
Argumentation has traditionally been studied across a number of fields, notably philosophy, cognitive science, linguistics and jurisprudence. The study of computational models of argumentation is a more recent endeavor, bringing together researchers from traditional fields and computer science and engineering within a rich, interdisciplinary matrix. Computational models of argumentation have been identified and used since the 1980s, and more recently an important role for argumentation in leading to principled decisions has emerged in several settings. This book presents the proceedings of COMMA 2022 the 9th International Conference on Computational Models of Argument, held in Cardiff, Wales, United Kingdom, during 14 - 16 September 2022. The book contains 27 regular papers and 16 demo papers from a total of 75 submissions, as well as 3 invited talks from Prof Paul Dunne (University of Liverpool), Prof Iryna Gurevych (TU Darmstadt), and Prof Antonis Kakas (University of Cyprus), which reflect the diverse nature of the field. Papers are a mix of theoretical and practical contributions; theoretical contributions include new formal models, the study of formal or computational properties of models, design for implemented systems and experimental research; practical papers include applications to law, machine learning and explainability. Abstract and structured accounts of argumentation are covered, as are relations between different accounts. Many papers focus on the evaluation of arguments or their conclusions given a body of arguments, with a continuation of a recent trend to study gradual or probabilistic notions of evaluation. The book offers an overview of recent and current research and will be of interest to all those working with computational models of argumentation.
User care at home is a matter of great concern since unforeseen circumstances might occur that affect people's well-being. Technologies that assist people in independent living are essential for enhancing care in a cost-effective and reliable manner. Assisted care applications often demand real-time observation of the environment and the resident’s activities using an event-driven system. As an emerging area of research and development, it is necessary to explore the approaches of the user care system in the literature to identify current practices for future research directions. Therefore, this book is aimed at a comprehensive review of data sources (e.g., sensors) with machine learning for various smart user care systems. To encourage the readers in the field, insights of practical essence of different machine learning algorithms with sensor data (e.g., publicly available datasets) are also discussed. Some code segments are also included to motivate the researchers of the related fields to practically implement the features and machine learning techniques. It is an effort to obtain knowledge of different types of sensor-based user monitoring technologies in-home environments. With the aim of adopting these technologies, research works, and their outcomes are reported. Besides, up to date references are included for the user monitoring technologies with the aim of facilitating independent living. Research that is related to the use of user monitoring technologies in assisted living is very widespread, but it is still consists mostly of limited-scale studies. Hence, user monitoring technology is a very promising field, especially for long-term care. However, monitoring of the users for smart assisted technologies should be taken to the next level with more detailed studies that evaluate and demonstrate their potential to contribute to prolonging the independent living of people. The target of this book is to contribute towards that direction.
This book introduces the challenges of robotic tactile perception and task understanding, and describes an advanced approach based on machine learning and sparse coding techniques. Further, a set of structured sparse coding models is developed to address the issues of dynamic tactile sensing. The book then proves that the proposed framework is effective in solving the problems of multi-finger tactile object recognition, multi-label tactile adjective recognition and multi-category material analysis, which are all challenging practical problems in the fields of robotics and automation. The proposed sparse coding model can be used to tackle the challenging visual-tactile fusion recognition problem, and the book develops a series of efficient optimization algorithms to implement the model. It is suitable as a reference book for graduate students with a basic knowledge of machine learning as well as professional researchers interested in robotic tactile perception and understanding, and machine learning.
ARTIFICIAL INTELLIGENT TECHNIQUES FOR WIRELESS COMMUNICATION AND NETWORKING The 20 chapters address AI principles and techniques used in wireless communication and networking and outline their benefit, function, and future role in the field. Wireless communication and networking based on AI concepts and techniques are explored in this book, specifically focusing on the current research in the field by highlighting empirical results along with theoretical concepts. The possibility of applying AI mechanisms towards security aspects in the communication domain is elaborated; also explored is the application side of integrated technologies that enhance AI-based innovations, insights, intelligent predictions, cost optimization, inventory management, identification processes, classification mechanisms, cooperative spectrum sensing techniques, ad-hoc network architecture, and protocol and simulation-based environments. Audience Researchers, industry IT engineers, and graduate students working on and implementing AI-based wireless sensor networks, 5G, IoT, deep learning, reinforcement learning, and robotics in WSN, and related technologies.