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
• 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
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You will have access to both the presentation and article (if available).
This will count as one of your downloads.
You will have access to both the presentation and article (if available).
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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