CM
Chandra R. Murthy
4 records found
1
Low-resolution compressed sensing and beyond for communications and sensing
Trends and opportunities
This survey paper examines recent advancements in low-resolution signal processing, emphasizing quantized compressed sensing. Rising costs and power demands of high-sampling-rate data acquisition drive the interest in quantized signal processing, particularly in wireless communic
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We consider the problem of jointly estimating the states and sparse inputs of a linear dynamical system using noisy low-dimensional observations. We exploit the underlying sparsity in the inputs using fictitious sparsity-promoting Gaussian priors with unknown variances (as hyperp
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The stabilizability of a linear dynamical system (LDS) refers to the existence of control inputs that drive the system state to zero. In this article, we analyze both the theoretical and algorithmic aspects of the stabilizability of an LDS using sparse control inputs with potenti
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The emergence of compressive sensing and the associated ℓ1 recovery algorithms and theory have generated considerable excitement and interest in their applications. This chapter will examine recent developments and a complementary set of tools based on a Bayesian frame
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