2021
DOI: 10.1088/1757-899x/1099/1/012012
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Compressive Sensing: Methods, Techniques, and Applications

Abstract: According to the latest research, it is very much clear that in future we require a huge amount of data as modern advancement in communication and signal processing generates a large number of bytes some examples are 5G peak data rate about 300 Mb/sec, an image of black hole required several petabytes to store & in medical signal processing huge amount of data required. So, by these examples, we can easily understand the scarcity of storage in near future. To overcome this problem of data scarcity such typ… Show more

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Cited by 18 publications
(7 citation statements)
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“…[ 38 ] To be faithfully reconstructed in the CS process, a signal must satisfy the conditions of measurement incoherence and sparsity. [ 39 ] Signals are usually not sparse in their original form. However, they may be sparse in different representations, such as frequency or time‐frequency.…”
Section: Innovations In Multiphoton Microscopymentioning
confidence: 99%
“…[ 38 ] To be faithfully reconstructed in the CS process, a signal must satisfy the conditions of measurement incoherence and sparsity. [ 39 ] Signals are usually not sparse in their original form. However, they may be sparse in different representations, such as frequency or time‐frequency.…”
Section: Innovations In Multiphoton Microscopymentioning
confidence: 99%
“…Compressive sensing is already known from sensor networks [152], [153]. Fundamental concepts and widely used algorithms for compressive sensing are comprised in [154]. Improved sampling is envisioned to have a significant positive impact on the CEX and a relative effect on the energy savings.…”
Section: B Energy Reduction Potential Beyond 5gmentioning
confidence: 99%
“…This approach is called compressed sensing, or more generally compressive sampling. Compressed sensing has recently emerged as a potential framework for signal acquisition in many applications [17], especially to monitor a wideband spectrum [18]. In [19,20], the authors showed that a finite-dimensional signal which has a sparse representation in some basis can be recovered from a small set of linear measurements.…”
Section: Introductionmentioning
confidence: 99%