Dr. Harold H. Szu
Senior Scientist at DEVCOM C5ISR
SPIE Involvement:
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 (222)

Proceedings Article | 3 June 2016 Paper
Proceedings Volume 9871, 98710W (2016) https://doi.org/10.1117/12.2239982
KEYWORDS: Surveillance, Sensor networks, Sensing systems, Sensors, Image processing, Video, Feature extraction, Algorithm development, Smart sensors, Image sensors

Proceedings Article | 3 June 2016 Paper
Jerry Wu, Jing Liang, Harold Szu
Proceedings Volume 9871, 98710X (2016) https://doi.org/10.1117/12.2239983
KEYWORDS: Earthquakes, Microelectromechanical systems, Sensors, Digital signal processing, Signal processing, Data processing, Filtering (signal processing), Gyroscopes, Seismic sensors, Electronic filtering

Proceedings Article | 3 June 2015 Paper
Jerry Wu, Harold Szu, Yuechen Chen, Ran Guo, Xixi Gu
Proceedings Volume 9496, 94960S (2015) https://doi.org/10.1117/12.2184655
KEYWORDS: Electroencephalography, Brain, Field programmable gate arrays, Digital signal processing, Signal to noise ratio, Electrodes, Neurons, Fourier transforms, Data processing, Magnetoencephalography

Proceedings Article | 3 June 2015 Paper
Harold Szu, Charles Hsu, Jefferson Willey, Joseph Landa, Minder Hsieh, Louis Larsen, Alan Krzywicki, Binh Tran, Philip Hoekstra, John Dillard, Keith Krapels, Michael Wardlaw, Kai-Dee Chu
Proceedings Volume 9496, 94960G (2015) https://doi.org/10.1117/12.2176082
KEYWORDS: Microwave radiation, Radar, RGB color model, Scattering, Thermodynamics, Surveillance, Signal to noise ratio, Backscatter, Sensors, Neural networks

Proceedings Article | 2 June 2015 Paper
Harold Szu, Charles Hsu, Joseph Landa, Jae H. Cha, Keith Krapels
Proceedings Volume 9496, 94960H (2015) https://doi.org/10.1117/12.2176093
KEYWORDS: Cameras, Black bodies, Brain, Neurons, Visualization, Thermodynamics, Sensing systems, RGB color model, Neural networks, Surveillance

Showing 5 of 222 publications
Proceedings Volume Editor (24)

Showing 5 of 24 publications
Conference Committee Involvement (25)
Independent Component Analyses, Compressive Sampling, Large Data Analyses (LDA), Neural Networks, Biosystems, and Nanoengineering XIII
23 April 2015 | Baltimore, MD, United States
Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XII
7 May 2014 | Baltimore, MD, United States
Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XI
1 May 2013 | Baltimore, Maryland, United States
Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering X
25 April 2012 | Baltimore, Maryland, United States
Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering IX
27 April 2011 | Orlando, Florida, United States
Showing 5 of 25 Conference Committees
Course Instructor
SC715: Independent Component Analysis and Beyond: Blind Signal Processing and its Applications
Blind Signal Processing (BSP) is an emerging area of research and technology with solid theoretical foundations and many potential applications. The problems of separating or extracting of the source signals from sensor arrays, without knowledge of the transmission channel characteristics and the real sources, can be expressed briefly as a number of blind source separation (BSS) or related generalized component analysis (GCA) methods: Independent Component Analysis (ICA) (and its extensions), Sparse Component Analysis (SCA), Sparse Principal Component Analysis (SPCA), Non-negative Matrix Factorization (NMF), Time-Frequency Component Analyzer (TFCA) and Multichannel Blind Deconvolution (MBD). BSP is not limited to ICA or BSS. With BSP we aim to discover and validate principles or laws which govern relationships between inputs (hidden components) and outputs (observations) when the information about the propagation Multi-Input Multi-Output (MIMO) system and its inputs are limited or hindered. BSP incorporates many problems, like blind identification of channels of unknown systems or a problem of suitable decomposition of signals into basic latent (hidden) components which do not necessary represent true sources but rather some of their features or sub-components. This four-hour course presents the fundamentals of blind signal processing, especially blind source separation and extraction, and in the remaining time discusses their applications in several important signal processing areas including estimation of sources, novel enhancement, denoising, artifact removal, filtering, detection, classification of multi-sensory signals and data, especially in biomedical applications and Brain Computer Interface (BCI).
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