Pramod K. Varshney – författare

This book provides an introduction to decision making in a distributed computational framework. When most computations were performed by a central processor, classical detection theory could assume that the processor could make decisions based on complete information. The development of distributed processors working in parallel on different parts of the same computational problem makes it necessary to make local decisions that are then conveyed to other processors, where ultimately a fusion center must make global decisions. Using numerous examples throughout the book, the author discusses such distributed detection processes under various different formulations and in a wide variety of network topologies. By providing a unified treatment of the recent advances, this book should prove valuable not only to researchers active in the field, but also to graduate students and others embarking on research in detection, signal processing, and statistical decision theory. Some prior knowledge of detection theory is assumed.

This compilation focuses on multisensor fusion aspects of video monitoring and surveillance. It addresses several current research and application issues by providing a selective set of invited contributions on topics ranging from complex event detection and human behaviour understanding to biometric feature extraction for combatting terrorism. Contributors to this volume are from the leading international laboratories in the field of video surveillance. The authors include coverage of numerous technical problems related to image monitoring of complex sites using multiple sensors, and apply cutting edge techniques to actual problems in different environments. The book is intended for researchers and technical personnel involved in the design of video surveillance systems in the fields of transportation and security services. Professionals and advanced level students with an interest in computer-based analysis of video sequences, intelligent transport systems, and related areas will find this work especially useful.

This book provides an introductory treatment of the fundamentals of decision-making in a distributed framework. Classical detection theory assumes that complete observations are available at a central processor for decision-making. More recently, many applications have been identified in which observations are processed in a distributed manner and decisions are made at the distributed processors, or processed data (compressed observations) are conveyed to a fusion center that makes the global decision. Conventional detection theory has been extended so that it can deal with such distributed detection problems. A unified treatment of recent advances in this new branch of statistical decision theory is presented. Distributed detection under different formulations and for a variety of detection network topologies is discussed. This material is not available in any other book and has appeared relatively recently in technical journals. The level of presentation is such that the hook can be used as a graduate-level textbook. Numerous examples are presented throughout the book. It is assumed that the reader has been exposed to detection theory. The book will also serve as a useful reference for practicing engineers and researchers. I have actively pursued research on distributed detection and data fusion over the last decade, which ultimately interested me in writing this book. Many individuals have played a key role in the completion of this book.

Monitoring of public and private sites is increasingly becoming a very important and critical issue, especially after the recent flurry of terrorist attacks including the one on the Word Trade Center in September 2001. It is, therefore, imperative that effective multisensor surveillance systems be developed to protect the society from similar attacks in the future. The new generation of surveillance systems to be developed have a specific requirement: they must be able to automatically identify criminal and terrorist activity without sacrificing individual privacy to the extent possible. Privacy laws concerning monitoring and surveillance systems vary from country to country but, in general, they try to protect the privacy of their citizens. Monitoring and visual surveillance has numerous other applications. It can be employed to help invalids or handicapped and to monitor the activities of elderly people. It can be used to monitor large events such as sporting events, as well. Nowadays, monitoring is employ~d in several different contexts including transport applications, such as monitoring of railway stations and airports, dangerous environments like nuclear facilities or traffic flows on roads and bridges. The latest generation of surveillance systems mainly rely on hybrid analog-digital, or completely digital video communications and processing methods and take advantage of the greater of flexibility offered by video processing algorithms that are capable focusing a human operator's attention on a set of interesting situations.

Over the last fifty years, a large number of spaceborne and airborne sensors have been employed to gather information regarding the earth's surface and environment. As sensor technology continues to advance, remote sensing data with improved temporal, spectral, and spatial resolution is becoming more readily available. This widespread availability of enormous amounts of data has necessitated the development of efficient data processing techniques for a wide variety of applications. In particular, great strides have been made in the development of digital image processing techniques for remote sensing data. The goal has been efficient handling of vast amounts of data, fusion of data from diverse sensors, classification for image interpretation, and development of user-friendly products that allow rich visualization. This book presents some new algorithms that have been developed for high­ dimensional datasets, such as multispectral and hyperspectral imagery. The contents of the book are based primarily on research carried out by some members and alumni of the Sensor Fusion Laboratory at Syracuse University.

Over the last fifty years, a large number of spaceborne and airborne sensors have been employed to gather information regarding the earth's surface and environment. As sensor technology continues to advance, remote sensing data with improved temporal, spectral, and spatial resolution is becoming more readily available. This widespread availability of enormous amounts of data has necessitated the development of efficient data processing techniques for a wide variety of applications. In particular, great strides have been made in the development of digital image processing techniques for remote sensing data. The goal has been efficient handling of vast amounts of data, fusion of data from diverse sensors, classification for image interpretation, and development of user-friendly products that allow rich visualization. This book presents some new algorithms that have been developed for high­ dimensional datasets, such as multispectral and hyperspectral imagery. The contents of the book are based primarily on research carried out by some members and alumni of the Sensor Fusion Laboratory at Syracuse University.

The series Studies in Computational Intelligence (SCI) publishes new developments and advances in the various areas of computational intelligence – quickly and with a high quality. The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the fields of engineering, computer science, physics and life science, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automata, self-organizing systems, soft computing, fuzzy systems and hybrid intelligent systems. Critical to both contributors and readers are the short publication time and world-wide distribution – this permits a rapid and broad dissemination of research results.The field of “Artificial Intelligence” developed important concepts for simulating human intelligence. Its sister field, “Applied Intelligence”, has focused on techniques for developing intelligent systems for solving real life problems in all disciplines including science, social science, art, engineering, and finance.  The objective of the International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems (IEA-AIE) is to promote and disseminate research in Applied Intelligence. It seeks quality papers on a wide range of topics in applied intelligence that are employed in developing intelligent systems for solving  real life problems in all disciplines. Every year this conference brings together scientists, engineers and practitioners, who work on designing and developing applications that use intelligent techniques or work on intelligent techniques and apply them to application domains. The book is comprised of seventeen chapters providing up-to-date and state-of-the-art research on theapplications of applied Intelligence techniques.

The two volume set LNAI 6703 and LNAI 6704 constitutes the thoroughly refereed conference proceedings of the 24th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2011, held in Syracuse, NY, USA, in June/July 2011. The total of 92 papers selected for the proceedings were carefully reviewed and selected from 206 submissions. The papers cover a wide number of topics including feature extraction, discretization, clustering, classification, diagnosis, data refinement, neural networks, genetic algorithms, learning classifier systems, Bayesian and probabilistic methods, image processing, robotics, navigation, optimization, scheduling, routing, game theory and agents, cognition, emotion, and beliefs.

The two volume set LNAI 6703 and LNAI 6704 constitutes the thoroughly refereed conference proceedings of the 24th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2011, held in Syracuse, NY, USA, in June/July 2011. The total of 92 papers selected for the proceedings were carefully reviewed and selected from 206 submissions. The papers cover a wide number of topics including feature extraction, discretization, clustering, classification, diagnosis, data refinement, neural networks, genetic algorithms, learning classifier systems, Bayesian and probabilistic methods, image processing, robotics, navigation, optimization, scheduling, routing, game theory and agents, cognition, emotion, and beliefs.

The series Studies in Computational Intelligence (SCI) publishes new developments and advances in the various areas of computational intelligence – quickly and with a high quality. The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the fields of engineering, computer science, physics and life science, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automata, self-organizing systems, soft computing, fuzzy systems and hybrid intelligent systems. Critical to both contributors and readers are the short publication time and world-wide distribution – this permits a rapid and broad dissemination of research results.The field of “Artificial Intelligence” developed important concepts for simulating human intelligence. Its sister field, “Applied Intelligence”, has focused on techniques for developing intelligent systems for solving real life problems in all disciplines including science, social science, art, engineering, and finance.  The objective of the International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems (IEA-AIE) is to promote and disseminate research in Applied Intelligence. It seeks quality papers on a wide range of topics in applied intelligence that are employed in developing intelligent systems for solving  real life problems in all disciplines. Every year this conference brings together scientists, engineers and practitioners, who work on designing and developing applications that use intelligent techniques or work on intelligent techniques and apply them to application domains. The book is comprised of seventeen chapters providing up-to-date and state-of-the-art research on theapplications of applied Intelligence techniques.

The book presents theory and algorithms for secure networked inference in the presence of Byzantines. It derives fundamental limits of networked inference in the presence of Byzantine data and designs robust strategies to ensure reliable performance for several practical network architectures. In particular, it addresses inference (or learning) processes such as detection, estimation or classification, and parallel, hierarchical, and fully decentralized (peer-to-peer) system architectures. Furthermore, it discusses a number of new directions and heuristics to tackle the problem of design complexity in these practical network architectures for inference.