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Plan Background Papers Evolving, Adaptable Visual Processing System
Bowling Green State University, Firelands, US has reference to this Academic Journal, Evolving, Adaptable Visual Processing System Simon Fung-Kee-Fung Background Papers Lucian Prodan, Gianluca Tempesti, Daniel Mange, Andr Stauffer, ?Biology Meets Electronics: The Path so that a Bio-Inspired FPGA? , Proceedings from the 3rd International Conference on Evolvable Systems: From Biology so that Hardware, pp 189 ?196, Springer Verlag 2000 Also: T. Higuchi, M. Iwata, Isamu Kajutani, Hitoshi Iba, Yuji Hirao, Tatsumi Furuya, Bernard Manderick, ?Evolvable Hardware in addition to Its Applications so that Pattern Recognition in addition to Fault-Tolerant Systems? , Towards Evolvable Hardware: The Evolutionary Engineering Approach, pp118-135, Springer Verlag 1996 Plan Introduction Embryonics Project Conclusions of Authors Relevance so that our Project Advantages of Evolvable Hardware
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Introduction Adaptive Machines Plasticity Vs. Conventional Computer Hardware Evolvable Hardware Used in development of on-line adaptive machines An example: Embryonics Project Embryonics Project Embryonics = Embryo + Electronics Goals Similarity Effectiveness Ontogenesis: the development of a single organism from a single cell so that an adult.
Fundamental Features Multicellular organization Cellular Division Cellular Differentiation Artificial Cells Simple Processor Set of Instructions Functionality = Parallel operation
CPE/CSC 481: Knowledge-Based Systems Course Overview Overview Introduction Logistics Textbooks Bridge-In Pre-Test Motivation Objectives Evaluation Criteria What is an Expert System (ES)? Definitions ?Expert System? Main Components of an ES Main ES Components General Concepts in addition to Characteristics of ES Development of ES Technology Rules in addition to Humans Early ES Success Stories The Key so that ES Success When (Not) so that Use ESs ES Tools ES Elements ES Structure Rule-Based ES Example Rules MYCIN Sample Rule Inference Engine Cycle Forward in addition to Backward Chaining Foundations of Expert Systems Post Production Systems Markov Algorithms Rete Algorithm ES Advantages ES Problems Post-Test Evaluation Summary Introduction Important Concepts in addition to Terms
Cyclic vs. Addressable Memory Implementation Each cell stores the entire genome Conventional Addressable Memory relatively complex addressing in addition to decoding logic Contrary so that requirement that cells be as simple as possible Cyclic Memory In living cells, the genetic information is processed sequentially CM does not require any addressing Data access is similar so that how the ribosome processes the genome in a living cell Artificial Molecules FPGA ? a two-dimensional array of programmable logic elements Uniform surface of of programmable elements (our molecules) Can be assigned a function at runtime via a software configuration
Self Repair Cellular Level – Each cell stores the entire genome Molecular Level ? All molecules are identical Conclusions Programmable circuits necessary Need so that vary the cellular structure as a function of the application. Need so that efficiently store the important amount of memory required by a genome-based approach Relevance Image analysis by FPGAs Break down using multi-level approach Each section represents a receptive field Edge Detection More complex = smaller receptive field Smaller receptive field = more cells/area System needs so that adapt so that real-time video
Benefits of Evolvable Hardware Run-time reconfigurability Higher performance than general- purpose processors More flexible than ASICs Customization THE BIG PICTURE Establish a model of the retina Devise a system that can be used so that help certain people alongside visual impairments see better
Curtis, Jake Host
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