<abstract xmlns="http://www.w3.org/1999/xhtml"><p>This paper presents a way of acquiring a sparse signal by taking only a limited number of samples; sampling and compression are performed in one step by the analog to information conversion. The signal is recovered with minimal information loss from the reduced data record via compressed sensing reconstruction. Several methods of analog to information conversion are described with focus on numerical complexity and implementation in existing embedded devices. Two novel analog to information conversion methods are proposed, distinctive by their computational simplicity - direct subsampling and subsampling with integration. Proposed sensing methods are intended for and evaluated with real water parameter signals measured by a wireless sensor network. Compressed sensing proves to reduce the data transfer rate by >80 % with very little signal processing performed at the sensing side and no appreciable distortion of the reconstructed signal.</p></abstract>

This paper presents a way of acquiring a sparse signal by taking only a limited number of samples; sampling and compression are performed in one step by the analog to information conversion. The signal is recovered with minimal information loss from the reduced data record via compressed sensing reconstruction. Several methods of analog to information conversion are described with focus on numerical complexity and implementation in existing embedded devices. Two novel analog to information conversion methods are proposed, distinctive by their computational simplicity - direct subsampling and subsampling with integration. Proposed sensing methods are intended for and evaluated with real water parameter signals measured by a wireless sensor network. Compressed sensing proves to reduce the data transfer rate by >80 % with very little signal processing performed at the sensing side and no appreciable distortion of the reconstructed signal.

Sparse Signal Acquisition via Compressed Sensing and Principal Component Analysis

Measurement Science Review

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

This paper presents a way of acquiring a sparse signal by taking only a limited number of samples; sampling and compression are performed in one step by the...

Sparse Signal Acquisition via Compressed Sensing and Principal Component Analysis

Published Online: Oct 17, 2018

Page range: 175 - 182

Received: Apr 13, 2018

Accepted: Sep 03, 2018

DOI: https://doi.org/10.1515/msr-2018-0025

Keywords
Compressed sensing, analog-to-information conversion, sparse signal, signal compression

© 2018 Imrich Andráš, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Sparse Signal Acquisition via Compressed Sensing and Principal Component Analysis

Published Online: Oct 17, 2018

Page range: 175 - 182

Received: Apr 13, 2018

Accepted: Sep 03, 2018

DOI: https://doi.org/10.1515/msr-2018-0025

KeywordsCompressed sensing, analog-to-information conversion, sparse signal, signal compression

© 2018 Imrich Andráš, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Keywords
Compressed sensing, analog-to-information conversion, sparse signal, signal compression