The capabilities of current fault-handling techniques for Field Programmable Gate Arrays (FPGAs) develop a descriptive classification ranging from simple passive techniques to robust dynamic methods. Fault-handling methods not requiring modification of the FPGA device architecture or user intervention to recover from faults are examined and evaluated against overhead-based and sustainability-based performance metrics such as additional resource requirements, throughput reduction, fault capacity, and fault coverage. This classification alongside these performance metrics forms a standard for confident comparisons.

While the fault repair capability of Evolvable Hardware (EH) approaches have been previously demonstrated, further improvements to fault handling capability can be achieved by exploiting population diversity during all phases of the fault handling process. A new paradigm for online EH regeneration using Genetic Algorithms (GAs) called Consensus Based Evaluation (CBE) is developed where the performance of individuals is assessed based on broad consensus of the population instead of a conventional fitness function. Adoption of CBE enables information contained in the population to not only enrich the evolutionary process, but also support fault detection and isolation. On-line regeneration of functionality is achieved without additional test vectors by using the results of competitions between individuals in the population

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Abstract-This paper presents an integrated research and teaching model that has resulted from an NSF-funded effort to introduce results of current Machine Learning research into the engineering and computer science curriculum at the University of Central Florida (UCF). While in-depth exposure to current topics in Machine Learning has traditionally occurred at the graduate level, the model developed affords an innovative and feasible approach to expanding the depth of coverage in research topics to undergraduate students. The model has been self-sustaining as evidenced by its continued operation during the years after the NSF grant's expiration, and is transferable to other institutions due to its use of modular and faculty-specific technical content. This model offers a tightly-coupled teaching and research approach to introducing current topics in Machine Learning research to undergraduates, while also involving them in the research process itself. The approach has provided new mechanisms to increase faculty participation in undergraduate research, has exposed approximately 15 undergraduates annually to research at UCF, and has effectively prepared a number of these students for graduate study through active involvement in the research process and co-authoring of publications.Index Terms-Curriculum development, integrated research and teaching, machine learning, team teaching models, undergraduate research experiences I. INTRODUCTION Current models of undergraduate research such as Research Experiences forUndergraduate Students (REU), Honors Theses, and senior-year projects frequently Manuscript received 28 September, 2007. This work was supported in part by the National Science Foundation grant #0203446. Corresponding author: M. Georgiopoulos 407-823-5338; fax: 407-823-5835; e-mail: michaelg@mail.ucf.edu 1 M. Georgiopoulos, R. F. DeMara, A. J. Gonzalez, A. S. Wu, M. Mollaghasemi, M. Kysilka, J. Secretan, and C. A. Sharma are with the University of Central Florida, Orlando, FL 32816 USA. 2 E. Gelenbe is with the Imperial College, London SW7 2AZ, UK.3 A. J. Alnsour is visiting faculty the University of Central Florida (on sabbatical leave from Al-Isra Private University, Amman, Jordan). A Sustainable Model for Integrating Current Topics in Machine Learning Research into the Undergraduate Curriculum2 serve as effective means to introduce undergraduate students to research [1].However, these interactions can reveal challenges with regards to sustaining undergraduate research over an extended period of time [2]. The Sustainable Model for Assimilating Research and Teaching (SMART) at UCF integrates current research into the undergraduate curriculum through a course sequence that has propagated beyond an NSF-funded Combined Research and Curriculum Development (CRCD) award [3], [4]. SMART reaches a wide audience of undergraduate students who may not otherwise have considered well-established research programs for undergraduates, such as the NSF-funded Research Experiences for Undergraduates (REUs). The effort describ...

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