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His research interests are in the general area of reconfigurable computing. Current research focuses on the development of a suite of software tools that allow scientists to benefit from the potential order of magnitude speedup in execution time offered by reconfigurable computers over typical desktop computers. Dr. Gloster has also conducted research in the area of technology-based curriculum development, distance education, and VLSI design for testability.Dr. Gloster has taught courses on digital system design, ASIC design, microprocessor system applications, FPGA-based system design, and VLSI design for testability (using VHDL/Verilog). He has served on the program committee and as session chair for several international conferences. He received best paper and presentation awards for a paper presented at the International Conference on Computer Design This presentation will provide evaluation results and lessons learned during the first year of this project, including the effects of strategies, such as mentorship and early exposure to university-based STEM resources. Recommendations for developing a prototypical process for developing programs that broaden participation of underrepresented students will also be discussed.

In this paper we show that learning schemes can be utilized to generate robot grasping points. These learning schemes can be based on the geometrical similarity of objects and the functional similarity of tasks. This approach will drastically increase the speed of the search process and enrich the system's knowledge base.A Neural Network model that acquires data from a sold modeling data base is suggested. This model combines the completeness of information provided by solid modeling with the uncertainty encountered in the grouping process to perform geometrical classification of objects.