Wireless sensor networks promise an unprecedented fine-grained interface between the virtual and physical worlds. They are one of the most rapidly developing new information technologies, with applications in a wide range of fields including industrial process control, security and surveillance, environmental sensing, and structural health monitoring. This book is motivated by the urgent need to provide a comprehensive and organized survey of the field.Ideal for researchers and designers seeking to create new algorithms and protocols, and engineers implementing integrated solutions, it also contains many exercises and can be used by graduate students taking courses in Networks.

This monograph breaks with the tradition and studies a number of critical elements of a theory of representations for GEAs and applies them to the empirical study of various important idealized test functions and problems of commercial import.

As the world economy becomes more interdependent and competition for business continues to be more globally oriented, it has become necessary to address the issues of IT management from a broader global focus. Information technology is not only the enabler of the global marketplace but also a potential solution base for achieving global competitive advantage. The transfer of IT worldwide will be met with many technological barriers and managerial challenges. As global networks and electronic communication become a way of life for business, the management of these emerging technologies worldwide will require new directions and innovative solutions, many of which are addressed by Managing Globally with Information Technology.

About the Author
Sherif Kamel is an assistant professor of MIS and associate director of the Management Center at the American University in Cairo. He is the associate editor of the Annals of Cases on Information Technology Applications and Management in Organizations.

This scholarly discussion of managerial challenges details the most recent research on how organizations can better create, share, and exploit knowledge. Spanning the business and public service context, the information provided covers practical issues such as measuring returns, establishing trust, and integrating technology. Also discussed are knowledge management systems, Internet support, and information systems development.About the Author
Elayne Coakes is a senior lecturer in business information management at the University of Westminster. She is the author of Socio-Technical and Human Cognition Elements of Information Systems, The New Sociotech, and Knowledge Management in the Socio-Technical World.

Most books on operating systems deal with theory while ignoring practice. While the usual principles are covered in detail, the book describes a small, but real UNIX-like operating system: MINIX. The book demonstrates how it works while illustrating the principles behind it. Operating Systems: Design and Implementation Second Edition provides the MINIX source code. The relevant selections of the MINIX code are described in detail. When it first came out, MINIX caused something of a revolution. Within weeks, it had its own newsgroup on USENET, with 40,000 people. Most wanted to make MINIX bigger and fancier. Instead, Linux was created. That has become quite popular, very large, and complicated. MINIX, on the other hand, has remained small and suitable for instruction and example. The book has been revised to include updates in MINIX, which started out as a v 7 unix clone for a floppy-disk only 8088. It is now aimed at 386, 486, and pentium machines and is based on the international posix standard instead of on v7. There are now also versions of MINIX for the Macintosh and SPARC available. Professional programmers will find this book to be a valuable resource and reference book.

Innovative uses of global and local networks of linked computers make new ways of collaborative working, learning, and acting possible. In Group Cognition Gerry Stahl explores the technological and social reconfigurations that are needed to achieve computer-supported collaborative knowledge building–group cognition that transcends the limits of individual cognition. Computers can provide active media for social group cognition where ideas grow through the interactions within groups of people; software functionality can manage group discourse that results in shared understandings, new meanings, and collaborative learning. Stahl offers software design prototypes, analyzes empirical instances of collaboration, and elaborates a theory of collaboration that takes the group, rather than the individual, as the unit of analysis.

Stahl’s design studies concentrate on mechanisms to support group formation, multiple interpretive perspectives, and the negotiation of group knowledge in applications as varied as collaborative curriculum development by teachers, writing summaries by students, and designing space voyages by NASA engineers. His empirical analysis shows how, in small-group collaborations, the group constructs intersubjective knowledge that emerges from and appears in the discourse itself. This discovery of group meaning becomes the springboard for Stahl’s outline of a social theory of collaborative knowing. Stahl also discusses such related issues as the distinction between meaning making at the group level and interpretation at the individual level, appropriate research methodology, philosophical directions for group cognition theory, and suggestions for further empirical work.

Artificial Intelligence and Innovations (AIAI) will interest researchers, IT professionals and consultants by examining technologies and applications of demonstrable value. The conference focused on profitable intelligent systems and technologies.

AIAI focuses on real world applications; therefore authors should highlight the benefits of AI technology for industry and services. Novel approaches solving business and industrial problems, using AI, will emerge from this conference.

Reverse engineering is a critical set of techniques and tools for understanding what software is really all about. Formally, it is “the process of analyzing a subject system to identify the system’s components and their interrelationships and to create representations of the system in another form or at a higher level of abstraction

Increasingly, researchers in many branches of science are coming into contact with Bayesian statistics or Bayesian probability theory. By encompassing both inductive and deductive logic, Bayesian analysis can improve model parameter estimates by many orders of magnitude. It provides a simple and unified approach to all data analysis problems, allowing the experimenter to assign probabilities to competing hypotheses of interest, on the basis of the current state of knowledge. This book provides a clear exposition of the underlying concepts with large numbers of worked examples and problem sets. The book also discusses numerical techniques for implementing the Bayesian calculations, including an introduction to Markov Chain Monte-Carlo integration and linear and nonlinear least-squares analysis seen from a Bayesian perspective. In addition, background material is provided in appendices and supporting Mathematica notebooks are available, providing an easy learning route for upper-undergraduates, graduate students, or any serious researcher in physical sciences or engineering.

The goal of this book is to address the use of several important machine learning techniques into computer vision applications. An innovative combination of computer vision and machine learning techniques has the promise of advancing the field of computer vision, which contributes to better understanding of complex real-world applications. The effective usage of machine learning technology in real-world computer vision problems requires understanding the domain of application, abstraction of a learning problem from a given computer vision task, and the selection of appropriate representations for the learnable (input) and learned (internal) entities of the system. In this book, we address all these important aspects from a new perspective: that the key element in the current computer revolution is the use of machine learning to capture the variations in visual appearance, rather than having the designer of the model accomplish this. As a bonus, models learned from large datasets are likely to be more robust and more realistic than the brittle all-design models.