Innovations In Defence Support Systems 3

This book is a continuation of our previous volumes on Innovations in Defence Support Systems. This book includes a sample of recent advances in intelligent monitoring. The contributions include: · Data fusion in modern surveillance · Distributed intelligent surveillance systems modeling for performance evaluation · Incremental learning on trajectory clustering · Pedestrian speed profiles from video sequence · System-wide tracking of individuals · A scalable approach based on normality components for intelligent surveillance · Distributed camera overlap estimation · Multi-robot team for environmental monitoring The book is directed to the security experts, engineers, scientists, students and professors who are interested in intelligent monitoring.

Innovations in the area of Defence Support Systems are multi-disciplinary, cover a broad range of technologies, and could not possibly be covered within a single volume. This research book presents a sample of research as below: • On the Transition of Innovation and Technology in Defence • Inserting Innovations In-service • Classification of Battlefield Ground Vehicles based on the Acoustic Emissions • Convoy Movement Problem – An Optimization Perspective • Machine Vision Algorithms for Autonomous Aerial Refueling for UAVs using the USAF Refueling Boom Method • Motion Optimization Scheme for Cooperative Mobile Robots • An Automated Decision System for Landmine Detection and Classification The book is directed to the application engineers, research students, professors, decision makers and scientists & engineers working in defence and related areas.

Innovations in Defence Support Systems - 2 presents a sample of the state-of-art research on defence support systems. The focus of the volume is on the design and optimization of socio-technical systems and their performance in defence contexts. Conceptual and methodological considerations for the development of such systems and criteria likely to be useful in their evaluation are discussed, along with their conceptual underpinnings in total system performance analysis.

This book is a continuation of our previous volumes on Innovations in Defence Support Systems. This book includes a sample of recent advances in intelligent monitoring. The contributions include: · Data fusion in modern surveillance · Distributed intelligent surveillance systems modeling for performance evaluation · Incremental learning on trajectory clustering · Pedestrian speed profiles from video sequence · System-wide tracking of individuals · A scalable approach based on normality components for intelligent surveillance · Distributed camera overlap estimation · Multi-robot team for environmental monitoring The book is directed to the security experts, engineers, scientists, students and professors who are interested in intelligent monitoring.

The design of most modern engineering systems entails the consideration of a good trade-off between the several targets requirements to be satisfied along the system life such as high reliability, low redundancy and low operational costs. These aspects are often in conflict with one another, hence a compromise solution has to be sought. Innovative computing techniques, such as genetic algorithms, swarm intelligence, differential evolution, multi-objective evolutionary optimization, just to name few, are of great help in founding effective and reliable solution for many engineering problems. Each chapter of this book attempts to using an innovative computing technique to elegantly solve a different engineering problem.

With the rapid expansion of the Internet over the last 20 years, event-based distributed systems are playing an increasingly important role in a broad range of application domains, including enterprise management, environmental monitoring, information dissemination, finance, pervasive systems, autonomic computing, collaborative working and learning, and geo-spatial systems. Many different architectures, languages and technologies are being used for implementing event-based distributed systems, and much of the development has been undertaken independently by different communities. However, a common factor is an ever-increasing complexity. Users and developers expect that such systems are able not only to handle large volumes of simple events but also to detect complex patterns of events that may be spatially distributed and may span significant periods of time. Intelligent and logic-based approaches provide sound foundations for addressing many of the research challenges faced and this book covers a broad range of recent advances, contributed by leading experts in the field. It presents a comprehensive view of reasoning in event-based distributed systems, bringing together reviews of the state-of-the art, new research contributions, and an extensive set of references. It will serve as a valuable resource for students, faculty and researchers as well as industry practitioners responsible for new systems development.

Self-organizing approaches inspired from biological systems, such as social insects, genetic, molecular and cellular systems under morphogenesis, and human mental development, has enjoyed great success in advanced robotic systems that need to work in dynamic and changing environments. Compared with classical control methods for robotic systems, the major advantages of bio-inspired self-organizing robotic systems include robustness, self-repair and self-healing in the presence of system failures and/or malfunctions, high adaptability to environmental changes, and autonomous self-organization and self-reconfiguration without a centralized control. “Bio-inspired Self-organizing Robotic Systems” provides a valuable reference for scientists, practitioners and research students working on developing control algorithms for self-organizing engineered collective systems, such as swarm robotic systems, self-reconfigurable modular robots, smart material based robotic devices, unmanned aerial vehicles, and satellite constellations.

Computational Intelligence (CI) community has developed hundreds of algorithms for intelligent data analysis, but still many hard problems in computer vision, signal processing or text and multimedia understanding, problems that require deep learning techniques, are open. Modern data mining packages contain numerous modules for data acquisition, pre-processing, feature selection and construction, instance selection, classification, association and approximation methods, optimization techniques, pattern discovery, clusterization, visualization and post-processing. A large data mining package allows for billions of ways in which these modules can be combined. No human expert can claim to explore and understand all possibilities in the knowledge discovery process. This is where algorithms that learn how to learnl come to rescue. Operating in the space of all available data transformations and optimization techniques these algorithms use meta-knowledge about learning processes automatically extracted from experience of solving diverse problems. Inferences about transformations useful in different contexts help to construct learning algorithms that can uncover various aspects of knowledge hidden in the data. Meta-learning shifts the focus of the whole CI field from individual learning algorithms to the higher level of learning how to learn. This book defines and reveals new theoretical and practical trends in meta-learning, inspiring the readers to further research in this exciting field.