Prof. Richard G. Baraniuk
at Rice Univ
SPIE Involvement:
Conference Program Committee | Author | Instructor
Publications (40)

PROCEEDINGS ARTICLE | June 8, 2012
Proc. SPIE. 8365, Compressive Sensing
KEYWORDS: Target detection, Long wavelength infrared, Signal to noise ratio, Mirrors, Fabry–Perot interferometers, Imaging systems, Cameras, Sensors, Digital micromirror devices, Compressed sensing

PROCEEDINGS ARTICLE | September 27, 2007
Proc. SPIE. 6701, Wavelets XII
KEYWORDS: Digital filtering, Computing systems, Linear filtering, Quantization, Sensing systems, Associative arrays, Feedback loops, Analog electronics, Optimization (mathematics), Compressed sensing

PROCEEDINGS ARTICLE | September 27, 2007
Proc. SPIE. 6701, Wavelets XII
KEYWORDS: Diffraction, Wavelets, Fourier transforms, Phase retrieval, Signal processing, Terahertz radiation, Chemical elements, Algorithm development, Detection theory, Compressed sensing

PROCEEDINGS ARTICLE | February 28, 2007
Proc. SPIE. 6498, Computational Imaging V
KEYWORDS: Optical filters, Image compression, Cameras, Matrices, Error analysis, Image filtering, Image classification, Digital micromirror devices, Target recognition, Compressed sensing

PROCEEDINGS ARTICLE | February 2, 2006
Proc. SPIE. 6065, Computational Imaging IV
KEYWORDS: Mirrors, Compressive imaging, Image compression, Imaging systems, Cameras, Sensors, Wavelets, Photodiodes, Reconstruction algorithms, Digital micromirror devices

PROCEEDINGS ARTICLE | September 17, 2005
Proc. SPIE. 5914, Wavelets XI
KEYWORDS: Principal component analysis, Image processing, Error analysis, 3D modeling, Image analysis, Image registration, Calculus, Image understanding, Algorithm development, 3D image processing

Showing 5 of 40 publications
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 published special sections
Course Instructor
SC902: Compressive Sensing: Theory and Applications
Sensors and signal processing hardware and algorithms are under increasing pressure to accommodate ever larger and higher-dimensional data sets; ever faster capture, sampling, and processing rates; ever lower power consumption; communication over ever more difficult channels; and radically new sensing modalities. This four-hour course presents the fundamental theory and selected applications of Compressive Sensing, a new approach to data acquisition in which analog signals are digitized for processing not via uniform sampling but via inner products with random test functions. Unlike Nyquist-rate sampling, which completely describes a signal by exploiting its bandlimitedness, Compressive Sensing reduces the number of measurements required to completely describe a signal by exploiting its compressibility. The implications are promising for many applications and enable the design of new kinds of analog-to-digital converters, imaging systems and cameras, and radar systems, among others.
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