Abstract—Recently, a new direction in signal processing – “Compressed Sensing " is being actively developed. A number of authors have pointed out a connection between the Compressed Sensing problem and the problem of estimating the Kolmogorov widths, studied in the seventies and eighties of the last century. In this paper we make the above mentioned connection more precise. DOI: 10.1134/S000143460711019
Is compressive sensing overrated? Or can it live up to our expectations? What will come aft...
Since publication of the initial papers in 2006, compressed sensing has captured the imagination of ...
Compressive sensing is a relatively new technique in the signal processing field which allows acquir...
Compressed sensing (CS) is an emerging field that has attracted considerable research interest over ...
Compressed sensing is a new concept in signal processing where one seeks to minimize the number of m...
Mathematical approaches refer to make quantitative descriptions, deductions and calculations through...
This book is aimed at presenting concepts, methods and algorithms ableto cope with undersampled and ...
This paper provides an important extension of compressed sensing which bridges a substantial gap bet...
Compressed sensing is a novel research area, which was introduced in 2006, and since then has alread...
Abstract Compressed sensing was introduced some ten years ago as an effective way of acquiring signa...
Compressed sensing has roused great interest in research and many industries over the last few decad...
After introducing the concept of compressed sensing as a complementary measurement mode to...
Compressed sensing (CS) is an area of signal processing and statistics that emerged in the late 1990...
Compressed sensing hinges on the sparsity of signals to allow their reconstruction starting from a ...
The classical Shannon/Nyquist sampling theorem tells us that in order to not lose information when u...
Is compressive sensing overrated? Or can it live up to our expectations? What will come aft...
Since publication of the initial papers in 2006, compressed sensing has captured the imagination of ...
Compressive sensing is a relatively new technique in the signal processing field which allows acquir...
Compressed sensing (CS) is an emerging field that has attracted considerable research interest over ...
Compressed sensing is a new concept in signal processing where one seeks to minimize the number of m...
Mathematical approaches refer to make quantitative descriptions, deductions and calculations through...
This book is aimed at presenting concepts, methods and algorithms ableto cope with undersampled and ...
This paper provides an important extension of compressed sensing which bridges a substantial gap bet...
Compressed sensing is a novel research area, which was introduced in 2006, and since then has alread...
Abstract Compressed sensing was introduced some ten years ago as an effective way of acquiring signa...
Compressed sensing has roused great interest in research and many industries over the last few decad...
After introducing the concept of compressed sensing as a complementary measurement mode to...
Compressed sensing (CS) is an area of signal processing and statistics that emerged in the late 1990...
Compressed sensing hinges on the sparsity of signals to allow their reconstruction starting from a ...
The classical Shannon/Nyquist sampling theorem tells us that in order to not lose information when u...
Is compressive sensing overrated? Or can it live up to our expectations? What will come aft...
Since publication of the initial papers in 2006, compressed sensing has captured the imagination of ...
Compressive sensing is a relatively new technique in the signal processing field which allows acquir...
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