Learn More
In this paper, we consider power spectral density estimation of ban-dlimited, wide-sense stationary signals from sub-Nyquist sampled data. This problem has recently received attention from within the emerging field of cognitive radio for example, and solutions have been proposed that use ideas from compressed sensing and the theory of digital alias-free(More)
This paper presents a novel power spectral density estimation technique for band-limited, wide-sense stationary signals from sub-Nyquist sampled data. The technique employs multi-coset sampling and incorporates the advantages of compressed sensing (CS) when the power spectrum is sparse, but applies to sparse and nonsparse power spectra alike. The estimates(More)
The Random Demodulator (RD) and the Modulated Wideband Converter (MWC) are two recently proposed compressed sensing (CS) techniques for the acquisition of continuous-time spectrally-sparse signals. They extend the standard CS paradigm from sampling discrete, finite dimensional signals to sampling continuous and possibly infinite dimensional ones, and thus(More)
Empirical divergence maximization is an estimation method similar to empirical risk minimization whereby the Kullback-Leibler divergence is maximized over a class of functions that induce probability distributions. We use this method as a design strategy for quantiz-ers whose output will ultimately be used to make a decision about the quantizer's input. We(More)
—In the design of distributed quantization systems one inevitably confronts two types of constraints—those imposed by a distributed system's structure and those imposed by how the distributed system is optimized. Structural constraints are inherent properties of any distributed quantization system and are normally summarized by functional relationships(More)
We develop a simulated annealing technique to jointly optimize a distributed quantiza-tion structure meant to maximize the asymptotic error exponent of a downstream classifier or detector. This distributed structure sequentially processes an input vector and exploits broadcasts to improve the best possible error exponents. The annealing approach is a robust(More)
X-ray Computed Tomography (CT) reconstruction from sparse number of views is becoming a powerful way to reduce either the radiation dose or the acquisition time in CT systems but still requires a huge computational time. This paper introduces an approximate Bayesian inference framework for CT reconstruction based on a family of denoising approximate message(More)
The problem of efficient sampling of wideband Radar signals for Electronic Support Measures (ESM) is investigated in this paper. Wideband radio frequency sampling generally needs a sampling rate at least twice the maximum frequency of the signal, i.e. Nyquist rate, which is generally very high. However, when the signal is highly structured, like wideband(More)