MATLAB and FPGA-based interactive tool for exploring concepts on compressed sensing. Rico-Aniles, D., Ramirez-Cortes, J., M., Rangel-Magdaleno, J., Gomez-Gil, P., Peregrina-Barreto, H., & Alarcon-Aquino, V. Computer Applications in Engineering Education, 23(6):921-930, 11, 2015. ![MATLAB and FPGA-based interactive tool for exploring concepts on compressed sensing [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website doi abstract bibtex This paper presents an educational platform developed to explore some concepts related to the relatively new signal acquisition paradigm known as Compressed Sensing (CS). CS aims to acquire a signal with sparse or compressible representation in a suitable domain, using a number of samples under the limit established by the Nyquist-Shannon sampling theorem. The application consists of a graphical user interface in MATLAB, and a low-cost FPGA Xilinx Spartan 6, which in that way form a powerful and low-cost design station adequate to perform a number of CS experiments. Reconstruction of signals is carried out using a Greedy algorithm which solves the underdetermined system in real time with a novel Chebyshev-type matrix inversion. Effects related to the number of samples defined in the measurement matrix, and the use of different spaces, such as Discrete Cosine Transform, or Discrete Wavelet Transform, can be easily studied with the described tool. Results derived from its use in graduate courses are discussed.
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title = {MATLAB and FPGA-based interactive tool for exploring concepts on compressed sensing},
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abstract = {This paper presents an educational platform developed to explore some concepts related to the relatively new signal acquisition paradigm known as Compressed Sensing (CS). CS aims to acquire a signal with sparse or compressible representation in a suitable domain, using a number of samples under the limit established by the Nyquist-Shannon sampling theorem. The application consists of a graphical user interface in MATLAB, and a low-cost FPGA Xilinx Spartan 6, which in that way form a powerful and low-cost design station adequate to perform a number of CS experiments. Reconstruction of signals is carried out using a Greedy algorithm which solves the underdetermined system in real time with a novel Chebyshev-type matrix inversion. Effects related to the number of samples defined in the measurement matrix, and the use of different spaces, such as Discrete Cosine Transform, or Discrete Wavelet Transform, can be easily studied with the described tool. Results derived from its use in graduate courses are discussed.},
bibtype = {article},
author = {Rico-Aniles, Daniel and Ramirez-Cortes, Juan Manuel and Rangel-Magdaleno, Jose and Gomez-Gil, Pilar and Peregrina-Barreto, Hayde and Alarcon-Aquino, Vicente},
doi = {10.1002/cae.21664},
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