Daniel M. Litynski is Professor Emeritus of Electrical & Computer Engineering (ECE). He completed 7 years as Vice President for Research (VPR) & previously completed three years with NSF as Program Director (Physics) and Acting Division Director for Undergraduate Education (DUE). From 1999 to 2004, he was Dean of the College of Engineering and Applied Sciences (CEAS), Provost and Vice President for Academic Affairs, and President (interim) for WMU when it was a national Doctoral Research Extensive University with over 270 programs and almost 30,000 students. While Dean of CEAS, the college grew from approximately 2300 to 3000 students, new programs were initiated/accredited, plans for a new 270 acre Parkview Campus for CEAS done, construction neared completion (finished while Provost at approximately $100 million), an included Business Technology & Research Park implemented, and a strategic planning process completed. Brigadier General (retired) Litynski served with Armor and Ordnance units in Vietnam and Germany, several US R&D organizations, and the United States Military Academy including Professor and Head of the Department of Electrical Engineering and Computer Science. He’s done research and teaching in electrical engineering, optics, and physics for over forty years including twenty-five different courses (two initiated in Laser Physics and Photonics Engineering), and authored or co-authored many international conference presentations, technical papers, book chapters, and a patent. Past President of the IEEE Education Society. Honors and Awards: US Distinguished Service Medal, Bronze Star (2OLC), Meritorious Service Medal, Army Commendation Medal (OLC), Knight's Cross of Merit by President of Poland, NSF Director’s Award, IEEE Senior Member, ASEE Meritorious Service Award, six honor societies, eight professional societies. Education: Ph.D. (Physics) Rensselaer Polytechnic Institute (RPI), M.S. (Optics) University of Rochester, B.S. (Physics) RPI.
Acousto-photorefractive holographic interferometric correlator for progressive pattern recognition using wavelet transforms
Experimental results from a smart pixel implementation of the wavelet transformation for signal processing