PyGASP: Python-based GPU-accelerated signal processing
| Autor: | Greg Wolffe, Erin Carrier, Nathaniel Bowman |
|---|---|
| Rok vydání: | 2013 |
| Předmět: |
Discrete wavelet transform
Signal processing Mathematical model Computer science business.industry Python (programming language) Computational science Multidimensional signal processing Computer engineering business computer Scientific disciplines Digital signal processing computer.programming_language |
| Zdroj: | EIT |
| DOI: | 10.1109/eit.2013.6632683 |
| Popis: | Computational science is the application of computing technology to evaluate mathematical models in order to solve problems in the scientific disciplines. Many scientific fields are experiencing an explosion of data, with signal processing being a crucial technique for aiding interpretation and for distinguishing meaningful information from noise. This process requires tools that can be easily used by researchers from all branches of science and which are fast enough to manage the enormous amount of data being generated. This paper presents such a toolkit: an intuitive, high-performance Python library for facilitating large-scale signal analysis. Of particular interest is a novel PyCUDA implementation of the Discrete Wavelet Transform (DWT), several applications of which are demonstrated in this paper. |
| Databáze: | OpenAIRE |
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