Eric Dieckman, Ph.D.
Ph.D., Applied Science, The College of William and Mary
M.S., Architectural Acoustics, Rensselaer Polytechnic Institute
B.S., Physics, Truman State University
Dr. Dieckman has a background in acoustic modeling, signal processing, and machine learning with a degree concentration in Nondestructive Evaluation. He is the author or co-author on more than 30 conference presentations, reports, and patent applications, covering a wide range of topics. Results from his work have been presented across the United States and in Mexico, Canada, France, and Spain. Recent projects include development of an ultrasonic system to detect counterfeit microelectronic components, design of an acoustic method to benignly exclude pest birds from a geographic area, and numerical simulations of acoustic scattering.
As part of his dissertation research, Dr. Dieckman investigated a number of sensor modalities for mobile robotics applications, including passive thermal infrared, active near-infrared depth-mapping, a coffee-can radar, and echolocation using an acoustic parametric array. This work identified a new low-cost, long-range acoustic echolocation sensor which, coupled with advanced signal processing and machine learning algorithms, allows the automated detection and classification of oncoming vehicles at distances exceeding 50 m.
Dr. Dieckman is a co-founder of the Wavelet Signal Processing and Nondestructive Evaluation Systems Testing (WaSP-NEST) Lab at the Sonalysts Waterford campus, where he is directing the exploration of new methods of ultrasonic non-destructive evaluation of complex structures using advanced signal processing and machine learning approaches. Recent projects include guided wave ultrasonic inspection of thermal-spray coatings for ship structures, Pareto optimization of UUV operations, data mining of sonar operator performance metrics, and physical modeling of satellite motion for software training systems.
Selected Publications and Presentations
’Automated classification of oncoming ground vehicles using acoustic echolocation and supervised machine learning’ (with M. Hinders), Invited for The 167th Meeting of the Acoustical Society of America, Providence, RI, 6 May 2014
’Benign exclusion of birds using acoustic parametric arrays’ (with E. Skinner, G. Mahjoub, J. Swaddle, and M. Hinders), The 165th Meeting of the Acoustical Society of America and 21st International Congress on Acoustics, Montreal, Quebec, Canada, 6 June 2013
’Acoustic echolocation for mobile robots with parametric arrays’ (with M.Hinders), Invited for the Acoustical Society of America’s World-Wide Press Room at the 164th Meeting in Kansas City, MO 22-26 October 2012
’High-frequency contact ultrasound for subsurface characterization of microelectronics’ (with M. Hinders and J. Stevens), The 19th Annual Research Symposium and Spring Meeting of the American Society of Nondestructive Testing, Williamsburg, VA, 23 March 2010
’Porous material parameter estimation: A Bayesian approach’ (with C. Fackler and N. Xiang), Proceedings of MaxEnt 2011 31st International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, Waterloo, Canada, 10-15 July 2011
’Spatially enveloping reverberation in sound fixing, processing, and room-acoustic simulations using coded sequences’ (with N. Xiang and U. Trivedi), U.S. Patent Application #12/615,655, Filed 10 Nov 2009