ISSNIP 2005 Tutorial Topics

 Speaker Name

Wireless communication is rapidly developing into one of the most important enabling factors for the next-generation in information technology. A largely untapped opportunity lies in low data-rate low-cost wireless transceivers, assembled into distributed networks of computation, sensor and actuator nodes. This enables so-called “ambient-intelligence” applications such as smart buildings, innovative user interfaces, everyday computing, and new forms of entertainment, amongst others. While the aggregate system processes large amounts of data, individual nodes participate in a small fraction only. These ubiquitous networks require that the individual nodes are tiny, easily integratable into the environment, and have negligible cost. Most importantly, the nodes must be self-contained in terms of energy via a one-time battery charge or a replenishable supply of energy scavenged from the environment. While the continued scaling of silicon technology goes a long way towards reducing the size and power dissipation of electronic components, achieving the required ultra-low power-dissipation levels and mesoscale component size requires innovations from the system architecture down to the circuit technology. This tutorial introduces a number of techniques to accomplish this and presents a roadmap towards truly affordable ambient intelligence.

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Professor JAN M. RABAEY received the EE and Ph.D degrees in applied sciences from the Katholieke Universiteit Leuven, Belgium, respectively in 1978 and 1983. From 1983 till 1985, he was connected to the University of California, Berkeley as a Visiting Research Engineer. From 1985 till 1987, he was a research manager at IMEC, Belgium, and in 1987, he joined the faculty of the Electrical Engineering and Computer Science department of the University of California, Berkeley, where he is now holds the Donald O. Pederson Distinguished Professorship. He has been a visiting professor at the University of Pavia (Italy), Waseda University (Japan), Technical University Delft (Netherlands), Victoria Technical University and the University of New South Wales (Australia). He was the Associate Chair (EE) of the EECS Dept. at Berkeley from 1999 till 2002, and is currently the Scientific co-director of the Berkeley Wireless Research Center (BWRC, as well as the director of the GigaScale Systems Research Center (GSRC). Jan Rabaey authored or co-authored a wide range of papers in the area of signal processing and design automation. He received numerous scientific awards, including the 1985 IEEE Transactions on Computer Aided Design Best Paper Award (Circuits and Systems Society), the 1989 Presidential Young Investigator award, and the 1994 Signal Processing Society Senior Award, and the 2002 ISSCC Jack Raper Award.  In 1995, he became an IEEE Fellow  He is past chair of the VLSI Signal Processing Technical Committee of the Signal Processing Society and chaired the executive committee of the Design Automation Conference.  He is serving on the Technical Advisory Board of a wide range of companies. His current research interests include the conception and implementation of next-generation integrated wireless systems. This includes the analysis and optimization of communication algorithms and networking protocols, the study of low-energy implementation architectures and circuits, and the supporting design automation environments.

 Speaker Name

Swarm intelligence is one of the many branches of bio-inspired computing. It deals with the collective “intelligence” that can emerge by making a number of unsophisticated agents/particles/individuals to interact (cooperate, compete and coordinate) with each other in a decentralized fashion. Among the many biologically-inspired computing techniques, swarm intelligence is the main approach devoted to the study and simulation of insect societies such as the ant colony. The application of swarm intelligence is numerous including routing in telecommunication networks, task allocation and robotics. Particle swarm is another bio-inspired computing algorithm which is derived from birds flocking, fish schooling. The algorithm is sometimes compared to evolutionary algorithms (EAs) of various sorts, as it comprises a population of individuals and random fluctuations, which are properties of EAs. The particle swarm arose from studies in social psychology and differs significantly from evolutionary methods. Applications of particle swarm are numerous including neural network training, evolving digital circuits, robotics and power system optimization. Most of this tutorial will focus on the fundamentals and the applications of swarm intelligence for neural network training, evolvable hardware, placement and routing in FPGAs, collective robotic search and UAVs.

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 Speaker Name

 Traditionally, Information Fusion systems assume that the information is gathered from known sensors over proprietary communication networks and using fixed rules of information fusion and designated computing resources. Emerging technologies like wireless sensors networks, TEDS enabled legacy sensors, ubiquitous computing devices and all IP next generation networks are  challenging the rationale of conventional information fusion systems. The technology has matured to a point where it is not unreasonable to discover  sensors based on the context, establish relevance, query for appropriate data, and fuse it using the most appropriate fusion rule, using ubiquitous computing  and communication environment in an opportunistic manner. The converging  technologies leading to the design of such opportunistic information fusion  systems will be covered in this course. This new paradigm enables sensors to  provide opportunistic services to multiple applications at the same time and  deliver new, non-zero sum benefits of information fusion. This course brings forth the fundamental challenges and present several applications where  significant benefits of opportunistic information fusion are demonstrated.

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 Speaker Name

Pervasive computing technologies and associated software are being employed to facilitate such applications as telemedicine, education, space endeavors, manufacturing, crisis management, transportation, and defense for all the time and everywhere use. In pervasive computing environments, hardware and software entities are expected to function autonomously, continually and correctly. Recent advances in hardware, software agents, and middleware technologies have been mainly responsible for the emergence of pervasive computing as perhaps the most exciting area of computing in recent times. Pervasive computing encompasses mobile computing and distributed computing and more – agent technologies, middleware, situation-aware computing etc.  Pervasive computing is about providing ‘where you want, when you want, what you want and how you want’ services to users, applications and devices. There have been many outstanding papers and research and developmental works in recent years, highlighting the challenges of pervasive computing [Sat01, Ban 00]. These issues and challenges can be listed as - invisibility, interoperability and heterogeneity, proactivity, mobility, intelligence and security.

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