Abstract / truncated to 115 words (read the full abstract)

Signal acquisition is a main topic in signal processing. The well-known Shannon-Nyquist theorem lies at the heart of any conventional analog to digital converters stating that any signal has to be sampled with a constant frequency which must be at least twice the highest frequency present in the signal in order to perfectly recover the signal. However, the Shannon-Nyquist theorem provides a worst-case rate bound for any bandlimited data. In this context, Compressive Sensing (CS) is a new framework in which data acquisition and data processing are merged. CS allows to compress the data while is sampled by exploiting the sparsity present in many common signals. In so doing, it provides an efficient way to ... toggle 4 keywords

compressive sensing spectrum sensing cognitive radio through-the-wall radar imaging

Information

Author
Lagunas, Eva
Institution
Universitat Politecnica de Catalunya
Supervisors
Publication Year
2014
Upload Date
March 23, 2014

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