Florida Institute of Technology
High Tech with a Human Touch
HAM, Fredric M.
Dean, College of Engineering and Harris Professor of Electrical Engineering
Electrical and Computer Engineering, College of Engineering
Educational Background
B.S. Iowa State University
M.S. Iowa State University
Ph.D. Iowa State University
Recognition & Awards
IEEE Fellow
SPIE Fellow
INNS Fellow
Professional Experience
Dr. Ham is Dean of the College of Engineering and Harris Professor of Electrical Engineering. He is also a consultant to industry in systems analysis. He teaches courses in control theory at the graduate and undergraduate levels, and advanced filtering and neural networks at the graduate level. His graduate students are conducting research in neural networks, infrasound signal processing, modeling of acoustic structures, biomedical signal processing, non-invasive glucose monitoring, digital signal and image processing, independent component analysis and pattern recognition. Before joining Florida Tech, Dr. Ham was a staff engineer at the Harris Corporation, working in digital communications and control system design for large space structures. Prior to Harris, Dr. Ham was a geophysicist with Shell Oil Company, processing marine seismic data. He is a senior member of IEEE, a member of the International Neural Networks Society and currently the president of the society, Eta Kappa Nu, Tau Beta Pi, Phi Kappa Phi and Sigma Xi. He is also the associate editor of IEEE Transactions on Neural Networks, was voted Teacher of the Year in the College of Engineering (1994-1995), received the university-wide Faculty Excellence Award for Research (1996-1997) and the College of Engineering Teaching Award (1996-1997). Dr. Ham has authored over 100 technical publications, holds three patents, and authored the textbook, “Principles of Neurocomputing for Science and Engineering,” McGraw-Hill Higher Education, 2001.
Current Research
Development of a Universal Neural network classifier for infrasound events.
Selected Publications
F.M. Ham, K. Rekab, R. Acharyya and Y-C Lee, 2008. Infrasound Signal Classification Using Parallel RBF Neural Networks. International Journal of Signal, Image and Systems Engineering. 1 (3 & 4): 155-167.
Ham, F.M. and R. Acharyya, 2007. A Universal Neural Network-Based Infrasound Event Classifier. Signal and Image Processing for Remote Sensing, Part I, Chapter 3, Ed.: C.H. Chen, CRC Press (Taylor & Francis Group).
Park, S. and Ham, F.M., 2005. Secure Medical Image Transmission Using Independent Component Analysis of Digitally Modulated Mixtures. Journal of Neural, Parallel and Scientific Computation, 13: 37-50.
Delgado, H.J., Thursby, M.H. and Ham, F.M., 2005. A Novel Neural Network used for Synthesis of Antennas and Microwave Devices. IEEE Transactions on Neural Networks. 16 (6): 1590-1600.
Hicks, C.L. and F.M. Ham. 2001. An Efficient Method for Performing Discrete Convolution Using Kronecker Products. Journal of Neural, Parallel, and Scientific Computations, 9 (3&4): 369-388.
Bard, J.D. and F.M. Ham. 1999. Time Difference of Arrival Dilution of Precision and Applications. IEEE Transactions on Signal Processing 47 (2): 521-523.
Ham, F.M., I. Kostanic, G.M. Cohen and B.R. Gooch. 1997. Determination of Glucose Concentrations in an Aqueous Matrix from NIR Spectra using Optimal Time-Domain Filtering and Partial Least-Squares Regression. IEEE Transactions on Biomedical Engineering 44 (6): 475-485.