Electrical Engineering and Intelligent Systems

This book examines the state of the art in electrical engineering and intelligent systems, offering research articles by researchers who participated in the Electrical Engineering and Intelligent Systems conference at the 2011 World Congress on Engineering.

The revised and extended papers collected in this volume represent the cutting-edge of research at the nexus of electrical engineering and intelligent systems. They were selected from well over 1000 papers submitted to the high-profile international World Congress on Engineering held in London in July 2011. The chapters cover material across the full spectrum of work in the field, including computational intelligence, control engineering, network management, and wireless networks. Readers will also find substantive papers on signal processing, Internet computing, high performance computing, and industrial applications. The Electrical Engineering and Intelligent Systems conference, as part of the 2011 World Congress on Engineering was organized under the auspices of the non-profit International Association of Engineers (IAENG). With more than 30 nations represented on the conference committees alone, the Congress features the best and brightest scientific minds from a multitude of disciplines related to engineering. These peer-reviewed papers demonstrate the huge strides currently being taken in this rapidly developing field and reflect the excitement of those at the frontiers of this research.

Covers recent advances in robot navigation and task allocation in robot teams Discusses detector for color images and using pattern substitution Presents research on fuzzy nets, fuzzy optimization, and swarm optimization

Autorentext

Dr. Ao is an professor at the University of Hong Kong.
Dr. Gelman is a professor at the Department of Applied Mathematics and Computing School of Engineering, Cranfield University.

Inhalt
From the Contents: Robot Navigation Using View Sequences and Sliding Window Search.- Animal Food Chain Based Particle Swarm Optimization.- A Fuzzy Optimization Model for Single-Period Inventory Problem.- Project Scheduling to Maximize Fuzzy Net Present Value.