<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Compressive sensing is a processing approach aiming to reduce the data stream from the observed object with the inherent sparsity using the optimal signal models. The compression of the sparse input signal in time or in the transform domain is performed in the transmitter by the Analog to Information Converter (AIC). The recovery of the compressed signal using optimization based on the differential evolution algorithm is presented in the article as an alternative to the faster pseudoinverse algorithm. Pseudoinverse algorithm results in an unambiguous solution associated with lower compression efficiency. The selection of the mathematically appropriate signal model affects significantly the compression efficiency. On the other hand, the signal model influences the complexity of the algorithm in the receiving block. The suitability of both recovery methods is studied on examples of the signal compression from the passive infrared (PIR) motion sensors or the ECG bioelectric signals.</p></abstract>

Compressive sensing is a processing approach aiming to reduce the data stream from the observed object with the inherent sparsity using the optimal signal models. The compression of the sparse input signal in time or in the transform domain is performed in the transmitter by the Analog to Information Converter (AIC). The recovery of the compressed signal using optimization based on the differential evolution algorithm is presented in the article as an alternative to the faster pseudoinverse algorithm. Pseudoinverse algorithm results in an unambiguous solution associated with lower compression efficiency. The selection of the mathematically appropriate signal model affects significantly the compression efficiency. On the other hand, the signal model influences the complexity of the algorithm in the receiving block. The suitability of both recovery methods is studied on examples of the signal compression from the passive infrared (PIR) motion sensors or the ECG bioelectric signals.

Optimization Paradigm in the Signal Recovery after Compressive Sensing

Faculty of electrical engineering and informatics

Measurement Science Review

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Optimization Paradigm in the Signal Recovery after Compressive Sensing"}

Compressive sensing is a processing approach aiming to reduce the data stream from the observed object with the inherent sparsity using the optimal signal...

Optimization Paradigm in the Signal Recovery after Compressive Sensing

Online veröffentlicht: 23. Feb. 2019

Seitenbereich: 35 - 42

Eingereicht: 25. Mai 2018

Akzeptiert: 24. Jan. 2019

DOI: https://doi.org/10.2478/msr-2019-0006

Schlüsselwörter
Compressed sensing, Analog to Information Converter

© 2019 Linus Michaeli et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Optimization Paradigm in the Signal Recovery after Compressive Sensing

Online veröffentlicht: 23. Feb. 2019

Seitenbereich: 35 - 42

Eingereicht: 25. Mai 2018

Akzeptiert: 24. Jan. 2019

DOI: https://doi.org/10.2478/msr-2019-0006

SchlüsselwörterCompressed sensing, Analog to Information Converter

© 2019 Linus Michaeli et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Compressed sensing, Analog to Information Converter