Compressive Sensing and Algebraic Coding: Connections and Challenges

Vidyasagar, M.; Lotfi, Mahsa

doi:10.1007/978-3-030-04630-9_8

Cited by 1 publication

(3 citation statements)

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“…Applying compressive sensing in solving any problem involves investigating the efficiency and the relevance of the measurement matrices as well as the recovery algorithms. A number of evaluation metrics have been proposed and used in different applications [25]. These metrics include coherence, sparsity, recovery error, correlation, recovery time, processing time, compression ratio, and phase transition diagram.…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…Null space condition metric provides accurate performance evaluation of the sampling matrix. However, it is highly expensive and not practical [25].…”

Section: Null Space Propertymentioning

confidence: 99%

“…This condition is called null space property (NSP) [25]. In case this condition is not satisfied, it would be impossible to recover the signal, x, and distinguish it from 𝑥′ based only on the signal measurements, 𝑦 .…”

Section: Null Space Propertymentioning

confidence: 99%

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Metrics for Evaluating the Efficiency of Compressing Sensing Techniques

Salahdine

Ghribi

Kaabouch

2020

2020 International Conference on Information Networking (ICOIN)

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Compressive sensing has been receiving a great deal of interest from researchers in many areas because of its ability in speeding up data acquisition. This framework allows fast signal acquisition and compression when signals are sparse in some domains. It extracts the main information from high dimensional sparse signals using only a few samples, then the sparse signals are recovered from the few measurements. There are two main points to consider when it comes to using compressive sensing. The first one is how to design the linear measurement matrix to ensure that the compressive sensing is meeting the objectives of the application. The second is how to recover the sparse signal from few measurements. Performing compressive sensing requires analyzing and investigating the efficiency of the measurement matrix and the recovery algorithm. To date, constructing explicit measurement matrices and developing efficient recovery algorithms are still open challenges in applications. Thus, this paper describes metrics to evaluate the performance of compressive sensing techniques.

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Section: Evaluation Metricsmentioning

confidence: 99%

“…Null space condition metric provides accurate performance evaluation of the sampling matrix. However, it is highly expensive and not practical [25].…”

Section: Null Space Propertymentioning

confidence: 99%