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The problem of determining the intrinsic quality of a signal processing system with respect to the inference of an unknown deterministic parameter $\theta$ is considered. While the Fisher information measure $F(\theta)$ forms a classical tool for such a problem, direct computation of the information measure can become difficult in various situations. For the estimation theoretic performance analysis...
Compressive sensing is a recent technique for estimating a sparse vector from a reduced number of observations. Several algorithms have been developed and studied in this context. However, the reduction of number of samples (or the sampling rate) is one aspect of compressive sensing. In fact, the quantization of the observations, which is generally unavoidable in practice (due to the analog-to-digital...
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