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Sub-Nyquist Sampling -- Bridging theory and practice (2011)
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BibTeX
@MISC{Mishali11sub-nyquistsampling,
author = {Moshe Mishali and Yonina C. Eldar},
title = {Sub-Nyquist Sampling -- Bridging theory and practice},
year = {2011}
}
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Abstract
Signal processing methods have changed substantially over the last several decades. In modern applications, an increasing number of functions is being pushed forward to sophisticated software algorithms, leaving only delicate finely tuned tasks for the circuit level. Sampling theory, the gate to the digital world, is the key enabling this revolution, encompassing all aspects related to the conversion of continuous-time signals to discrete streams of numbers. The famous Shannon-Nyquist theorem has become a landmark: a mathematical statement that has had one of the most profound impacts on industrial development of digital signal processing (DSP) systems. Over the years, theory and practice in the field of sampling have developed in parallel routes. Contributions by many research groups suggest a multitude of methods, other than uniform sampling, to acquire analog signals [1]–[6]. The math has deepened, leading to abstract signal spaces and innovative sampling techniques. Within generalized sampling theory, bandlimited signals have no special preference, other than historic. At the same time, the market adhered to the Nyquist paradigm;
Keyphrases
sub-nyquist sampling bridging theory signal processing method profound impact mathematical statement nyquist paradigm signal space digital signal processing many research group famous shannon-nyquist theorem modern application analog signal uniform sampling tuned task continuous-time signal industrial development sophisticated software algorithm parallel route special preference innovative sampling technique digital world circuit level last several decade
