Dr. Harold H. Szu
Senior Scientist at US Army RDECOM CERDEC NVESD
SPIE Involvement:
Fellow status | Conference Chair | Conference Program Committee | Track Chair | Author | Editor | Instructor
Area of Expertise:
Artificial Neural Networks , Dual Infrared Spectral Cancer Screening , Statistical Physics , Unsupervised Learning , Wavelets, ICA, Compressive Sampling , Image Video Processing
Profile Summary

Army NVESD MS/Human Signature Exploitation Ft. Belvoir, VA 22060 703-704-0532
• Fellow of American. Institute Medicine & BioEngineering 2004 for breast cancer passive spectrogram diagnoses.
• Fellow of IEEE (1997) for bi-sensor fusion;
• Foreign Academician, Russian Academy of Nonlinear Sciences, 1999, for unsupervised learning.
• Fellow of Optical Society America (1996) for adaptive wavelet
• Fellow of International Optical Engineering (SPIE since 1995) for neural nets.
• Fellow of INNS (2010) for a founding governor and former president of INNS
Dr. Szu has been a champion of brain-style computing for 2 decades; a founder, former president, and a current governor of International Neural Network Society (INNS), he received the INNS D. Gabor Award in 1997 “for outstanding contribution to neural network applications in information sciences and pioneer implementations of fast simulated annealing search,” and the Eduardo R. Caianiello Award in 1999 from the Italy Academy for “elucidating and implementing a chaotic neural net as a dynamic realization for fuzzy logic membership function.” Recently, he contributed to the unsupervised learning theory of the thermodynamic free energy of sensory pair for fusion. Because of this contribution, Dr. Szu is a foreign academician of Russian Academy of Nonlinear Sciences in 1999 for the unsupervised learning based on a homeostasis constant Cybernetic brain temperature. Recently, SPIE awarded him with the Nanoengineering Award and the Biomedical Wellness Engineering Award.
Besides 300 publications, a dozen patents, numerous books & journals, Dr. Szu taught students “how to be creative in interdisciplinary sciences” according to the Uhlenbeck’s Royal Dutch tradition and guided a dozen PhD students. His practice of the creativity is itemized as follows:
• Initiate Biomedical Wellness Engineering for the quality of life of aging societies.
• Promote Nano-Robot for high-yield Nanoengineering based on Nanosciences and Na
Publications (246)

Proceedings Article | 3 June 2016
Proc. SPIE. 9871, Sensing and Analysis Technologies for Biomedical and Cognitive Applications 2016
KEYWORDS: Sensors, Image processing, Video, Feature extraction, Surveillance, Sensor networks, Image sensors, Sensing systems, Algorithm development, Smart sensors

Proceedings Article | 3 June 2016
Proc. SPIE. 9871, Sensing and Analysis Technologies for Biomedical and Cognitive Applications 2016
KEYWORDS: Microelectromechanical systems, Digital signal processing, Seismic sensors, Sensors, Earthquakes, Data processing, Signal processing, Gyroscopes, Electronic filtering, Filtering (signal processing)

Proceedings Article | 3 June 2015
Proc. SPIE. 9496, Independent Component Analyses, Compressive Sampling, Large Data Analyses (LDA), Neural Networks, Biosystems, and Nanoengineering XIII
KEYWORDS: Radar, Signal to noise ratio, Backscatter, Scattering, Sensors, Surveillance, Neural networks, Microwave radiation, Thermodynamics, RGB color model

Proceedings Article | 3 June 2015
Proc. SPIE. 9496, Independent Component Analyses, Compressive Sampling, Large Data Analyses (LDA), Neural Networks, Biosystems, and Nanoengineering XIII
KEYWORDS: Signal to noise ratio, Digital signal processing, Electrodes, Fourier transforms, Field programmable gate arrays, Data processing, Electroencephalography, Neurons, Magnetoencephalography, Brain

Proceedings Article | 2 June 2015
Proc. SPIE. 9496, Independent Component Analyses, Compressive Sampling, Large Data Analyses (LDA), Neural Networks, Biosystems, and Nanoengineering XIII
KEYWORDS: Human-machine interfaces, Digital signal processing, Field programmable gate arrays, Head, Data processing, Image enhancement, Electroencephalography, Brain-machine interfaces, Data analysis, Brain