Nature-inspired methods for hardware and software system design and information processing

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My research investigates the use of knowledge engineering, heuristics and nature-inspired computation for system design and analysis. Its basic postulate is that most system design, modeling, and analysis problems can be represented as classification or optimization problems for which a natural equivalent exist and have been solved over the course of evolution, or for which innovative search algorithms can be developed using heuristic search and/or data mining. In doing so, I seek alternative approaches to existing formal techniques when they are inapplicable or too slow. In the past few years, my research included the development and application of intelligent algorithms for data analysis and system design using neural networks, particle swarm optimization and data mining. This led to several original contributions at the algorithmic and applicative levels, namely in instrumentation, embedded software design, computer aided design tools, and medical image processing, In the next five years, I intend to move on to the area of design automation by investigating the development of a reuse-based framework for electronic circuit design that either recalls complete stored designs or finds and integrates fragments from different example designs in the database. I will validate my findings automated RF and microwave circuit design.I expect my research not only to advance the state of the art in intelligent system design and signal processing, and on developing efficient tools for the computer aided design of hardware and software systems, but also to contribute significant advances to rapid system prototyping, hence helping to shorten the design cycle of complex designs and reduce development cost.