Presents a probabilistic and information-theoretic framework for a search for static or moving targets in discrete time and space. Probabilistic Search for Tracking Targets uses an information-theoretic scheme to present a unified approach for known search methods to allow the development of new algorithms of search. The book addresses search methods under different constraints and assumptions, such as search uncertainty under incomplete information, probabilistic search scheme, observation errors, group testing, search games, distribution of search efforts, single and multiple targets and search agents, as well as online or offline search schemes. The proposed approach is associated with path planning techniques, optimal search algorithms, Markov decision models, decision trees, stochastic local search, artificial intelligence and heuristic information-seeking methods. Furthermore, this book presents novel methods of search for static and moving targets along with practical algorithms of partitioning and search and screening. Probabilistic Search for Tracking Targets includes complete material for undergraduate and graduate courses in modern applications of probabilistic search, decision-making and group testing, and provides several directions for further research in the search theory. The authors: Provide a generalized information-theoretic approach to the problem of real-time search for both static and moving targets over a discrete space. Present a theoretical framework, which covers known information-theoretic algorithms of search, and forms a basis for development and analysis of different algorithms of search over probabilistic space. Use numerous examples of group testing, search and path planning algorithms to illustrate direct implementation in the form of running routines. Consider a relation of the suggested approach with known search theories and methods such as search and screening theory, search games, Markov decision process models of search, data mining methods, coding theory and decision trees. Discuss relevant search applications, such as quality-control search for nonconforming units in a batch or a military search for a hidden target. Provide an accompanying website featuring the algorithms discussed throughout the book, along with practical implementations procedures.

Intelligent Autonomy of UAVs: Advanced Missions and Future Use provides an approach to the formulation of the fundamental task typical to any mission and provides guidelines of how this task can be solved by different generic robotic problems. As such, this book aims to provide a systems engineering approach to UAV projects, discovering the real problems that need to be resolved independently of the application. After an introduction to the rapidly evolving field of aerial robotics, the book presents topics such as autonomy, mission analysis, human-UAV teams, homogeneous and heterogeneous UAV teams, and finally, UAV-UGV teams. It then covers generic robotic problems such as orienteering and coverage. The book next introduces deployment, patrolling, and foraging, while the last part of the book tackles an important application: aerial search, tracking, and surveillance. This book is meant for both scientists and practitioners. For practitioners, it presents existing solutions that are categorized according to various missions: surveillance and reconnaissance, 3D mapping, urban monitoring, precision agriculture, forestry, disaster assessment and monitoring, security, industrial plant inspection, etc. For scientists, it provides an overview of generic robotic problems such as coverage and orienteering; deployment, patrolling and foraging; search, tracking, and surveillance. The design and analysis of algorithms raise a unique combination of questions from many fields, including robotics, operational research, control theory, and computer science.

Mechatronics and automation technology has led to technological change and innovation in all engineering fields, affecting various disciplines, including machine technology, electronics, and computing. It plays a vital role in improving production efficiency, reducing energy consumption and improving product quality and safety, and will be central to the further advancement of technology and industry, bringing convenience and innovation to even more areas. This book presents the proceedings of ICMAT 2023, the 2nd International Conference on Mechatronics and Automation Technology, held as a virtual event on 27 October 2023. The aim of the conference was to provide a platform for scientists, scholars, engineers and researchers from universities and scientific institutes around the world to share the latest research achievements in mechatronics and automation technology, explore key challenges and research directions, and promote the development and application of theory and technology in this field. A total of 121 submissions were received for the conference, of which 77 were ultimately accepted after a rigorous peer-review process. The papers cover a wide range of topics falling within the scope of mechatronics and automation technology, including smart manufacturing; digital manufacturing; additive manufacturing; robotics; sensors; control; electronic and electrical engineering; intelligent systems; and automation technology, as well as other related fields. Providing an overview of recent developments in mechatronics and automation technology, the book will be of interest to all those working in the field.

Probabilistic Graphical Models for Computer Vision introduces probabilistic graphical models (PGMs) for computer vision problems and teaches how to develop the PGM model from training data. This book discusses PGMs and their significance in the context of solving computer vision problems, giving the basic concepts, definitions and properties. It also provides a comprehensive introduction to well-established theories for different types of PGMs, including both directed and undirected PGMs, such as Bayesian Networks, Markov Networks and their variants. - Discusses PGM theories and techniques with computer vision examples - Focuses on well-established PGM theories that are accompanied by corresponding pseudocode for computer vision - Includes an extensive list of references, online resources and a list of publicly available and commercial software - Covers computer vision tasks, including feature extraction and image segmentation, object and facial recognition, human activity recognition, object tracking and 3D reconstruction

A unified framework for developing planning and control algorithms for active sensing, with examples of applications for specific sensor technologies. Active sensor systems, increasingly deployed in such applications as unmanned vehicles, mobile robots, and environmental monitoring, are characterized by a high degree of autonomy, reconfigurability, and redundancy. This book is the first to offer a unified framework for the development of planning and control algorithms for active sensing, with examples of applications for a range of specific sensor technologies. The methods presented can be characterized as information-driven because their goal is to optimize the value of information, rather than to optimize traditional guidance and navigation objectives.

This book provides a broad overview of both the technical challenges in sensor network development, and the real-world applications of distributed sensing. Important aspects of distributed computing in large-scale networked sensor systems are analyzed in the context of human behavior understanding, including topics on systems design tools and techniques. Additionally, the book examines a varied range of applications. Features: contains valuable contributions from an international selection of leading experts in the field; presents a high-level introduction to the aims and motivations underpinning distributed sensing; describes decision-making algorithms in the presence of complex sensor networks; provides a detailed analysis of the design, implementation, and development of a distributed network of homogeneous or heterogeneous sensors; reviews the application of distributed sensing to human behavior understanding and autonomous intelligent vehicles; includes a helpful glossary and a list of acronyms.

The five-volume set LNCS 11536, 11537, 11538, 11539, and 11540 constitutes the proceedings of the 19th International Conference on Computational Science, ICCS 2019, held in Faro, Portugal, in June 2019. The total of 65 full papers and 168 workshop papers presented in this book set were carefully reviewed and selected from 573 submissions (228 submissions to the main track and 345 submissions to the workshops). The papers were organized in topical sections named: Part I: ICCS Main Track Part II: ICCS Main Track; Track of Advances in High-Performance Computational Earth Sciences: Applications and Frameworks; Track of Agent-Based Simulations, Adaptive Algorithms and Solvers; Track of Applications of Matrix Methods in Artificial Intelligence and Machine Learning; Track of Architecture, Languages, Compilation and Hardware Support for Emerging and Heterogeneous Systems Part III: Track of Biomedical and Bioinformatics Challenges for Computer Science; Track of Classifier Learning from Difficult Data; Track of Computational Finance and Business Intelligence; Track of Computational Optimization, Modelling and Simulation; Track of Computational Science in IoT and Smart Systems Part IV: Track of Data-Driven Computational Sciences; Track of Machine Learning and Data Assimilation for Dynamical Systems; Track of Marine Computing in the Interconnected World for the Benefit of the Society; Track of Multiscale Modelling and Simulation; Track of Simulations of Flow and Transport: Modeling, Algorithms and Computation Part V: Track of Smart Systems: Computer Vision, Sensor Networks and Machine Learning; Track of Solving Problems with Uncertainties; Track of Teaching Computational Science; Poster Track ICCS 2019 Chapter “Comparing Domain-decomposition Methods for the Parallelization of Distributed Land Surface Models” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.