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Title: SWinzip v. 1.0

Abstract

SWinzip is a Matlab and C++ library for scientific lossy data compression and reconstruction using compressed sensing and tree-wavelets transforms. These methods are known for their large compression and usefulness in data analytics such as features extraction. Compressed sensing and wavelets methods rely heavily on sparse and dense linear algebra operations implemented through the Boost codes in our library. SWinzip accommodates data represented on both regular grids (e.g. image data) and point-clouds (e.g. unstructured meshes).

Authors:
 [1];  [1];  [1];  [1]
  1. Sandia National Laboratories
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1331313
Report Number(s):
SWinzip; 005016MLTPL00
SCR #2103
DOE Contract Number:  
AC04-94AL85000
Resource Type:
Software
Software Revision:
00
Software Package Number:
005016
Software CPU:
MLTPL
Open Source:
Yes
Source Code Available:
Yes
Related Software:
Matlab R2015a, Boost 1.57 and BLAS/LAPACK bindings
Country of Publication:
United States

Citation Formats

Salloum, Maher, Hensinger, David, Fabian, Nathan, and Lee, Jina. SWinzip v. 1.0. Computer software. https://www.osti.gov//servlets/purl/1331313. Vers. 00. USDOE. 8 Aug. 2016. Web.
Salloum, Maher, Hensinger, David, Fabian, Nathan, & Lee, Jina. (2016, August 8). SWinzip v. 1.0 (Version 00) [Computer software]. https://www.osti.gov//servlets/purl/1331313.
Salloum, Maher, Hensinger, David, Fabian, Nathan, and Lee, Jina. SWinzip v. 1.0. Computer software. Version 00. August 8, 2016. https://www.osti.gov//servlets/purl/1331313.
@misc{osti_1331313,
title = {SWinzip v. 1.0, Version 00},
author = {Salloum, Maher and Hensinger, David and Fabian, Nathan and Lee, Jina},
abstractNote = {SWinzip is a Matlab and C++ library for scientific lossy data compression and reconstruction using compressed sensing and tree-wavelets transforms. These methods are known for their large compression and usefulness in data analytics such as features extraction. Compressed sensing and wavelets methods rely heavily on sparse and dense linear algebra operations implemented through the Boost codes in our library. SWinzip accommodates data represented on both regular grids (e.g. image data) and point-clouds (e.g. unstructured meshes).},
url = {https://www.osti.gov//servlets/purl/1331313},
doi = {},
year = {Mon Aug 08 00:00:00 EDT 2016},
month = {Mon Aug 08 00:00:00 EDT 2016},
note =
}

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