J
Jason N. Laska
Researcher at Rice University
Publications - 41
Citations - 10267
Jason N. Laska is an academic researcher from Rice University. The author has contributed to research in topics: Compressed sensing & Signal. The author has an hindex of 27, co-authored 41 publications receiving 9515 citations. Previous affiliations of Jason N. Laska include BAE Systems & University of Illinois at Urbana–Champaign.
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Journal ArticleDOI
Single-Pixel Imaging via Compressive Sampling
Marco F. Duarte,Mark A. Davenport,Dharmpal Takhar,Jason N. Laska,Ting Sun,Kevin F. Kelly,Richard G. Baraniuk +6 more
TL;DR: A new camera architecture based on a digital micromirror device with the new mathematical theory and algorithms of compressive sampling is presented that can operate efficiently across a broader spectral range than conventional silicon-based cameras.
Journal ArticleDOI
Beyond Nyquist: Efficient Sampling of Sparse Bandlimited Signals
TL;DR: A new type of data acquisition system, called a random demodulator, that is constructed from robust, readily available components that supports the empirical observations, and a detailed theoretical analysis of the system's performance is provided.
Journal ArticleDOI
Robust 1-Bit Compressive Sensing via Binary Stable Embeddings of Sparse Vectors
TL;DR: This paper investigates an alternative CS approach that shifts the emphasis from the sampling rate to the number of bits per measurement, and introduces the binary iterative hard thresholding algorithm for signal reconstruction from 1-bit measurements that offers state-of-the-art performance.
Proceedings ArticleDOI
A new compressive imaging camera architecture using optical-domain compression
Dharmpal Takhar,Jason N. Laska,Michael B. Wakin,Marco F. Duarte,Dror Baron,Shriram Sarvotham,Kevin F. Kelly,Richard G. Baraniuk +7 more
TL;DR: A new camera architecture is developed that employs a digital micromirror array to perform optical calculations of linear projections of an image onto pseudorandom binary patterns that can be adapted to image at wavelengths that are currently impossible with conventional CCD and CMOS imagers.
Proceedings ArticleDOI
Theory and Implementation of an Analog-to-Information Converter using Random Demodulation
TL;DR: The new theory of compressive sensing enables direct analog-to-information conversion of compressible signals at sub-Nyquist acquisition rates and proves the concept under the effect of circuit nonidealities.