Prof. Soo-Young Lee
Professor, Department of Electrical Engineering at KAIST
SPIE Involvement:
Author | Instructor
Publications (13)

Proceedings Article | 12 September 2014 Paper
Proc. SPIE. 9193, Novel Optical Systems Design and Optimization XVII
KEYWORDS: Refractive index, Light sources, Resonators, Dielectrics, Near field, Optical resonators, Geometrical optics, Dielectric polarization, Light

Proceedings Article | 24 June 2014 Paper
Proc. SPIE. 9118, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XII
KEYWORDS: Electrodes, Ions, Diffusion, Head, Electroencephalography, Feedback loops, Neuroimaging, Thermodynamics, Correlation function, Brain

Proceedings Article | 22 May 2014 Paper
Proc. SPIE. 9118, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XII
KEYWORDS: Biometrics, Independent component analysis, Visualization, Interference (communication), Linear filtering, System identification, Electroencephalography, Analytical research, Information security, Brain

Proceedings Article | 15 May 2012 Paper
Proc. SPIE. 8401, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering X
KEYWORDS: Visual process modeling, Sensors, Wavelets, Artificial neural networks, Computer vision technology, Machine vision, Optical flow, Visual system, Mathematics, Neurons

Proceedings Article | 14 June 2011 Paper
Proc. SPIE. 8058, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering IX
KEYWORDS: Principal component analysis, Independent component analysis, Detection and tracking algorithms, Data modeling, Feature extraction, Machine learning, Feature selection, Electrical engineering, Algorithm development, Neurons

Showing 5 of 13 publications
Proceedings Volume Editor (1)

Conference Committee Involvement (9)
Sensing and Analysis Technologies for Biomedical and Cognitive Applications 2016
17 April 2016 | Baltimore, Maryland, United States
Independent Component Analyses, Compressive Sampling, Large Data Analyses (LDA), Neural Networks, Biosystems, and Nanoengineering XIII
23 April 2015 | Baltimore, Maryland, United States
Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XII
7 May 2014 | Baltimore, Maryland, 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
Showing 5 of 9 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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