Suppose x is an unknown vector in Ropfm (a digital image or signal); we plan to measure n general linear functionals of x and then reconstruct. If x is known to be compressible by transform coding with a known transform, and we reconstruct via the nonlinear procedure defined here, the number of measurements n can be dramatically smaller than the size m. Thus, certain natural classes of images with m pixels need only n=O(m1/4log5/2(m)) nonadaptive nonpixel samples for faithful recovery, as opposed to the usual m pixel samples. More specifically, suppose x has a sparse representation in some orthonormal basis (e.g., wavelet, Fourier) or tight frame (e.g., curvelet, Gabor)-so the coefficients belong to an lscrp ball for 0<ples1. The N most important coefficients in that expansion allow reconstruction with lscr2 error O(N1/2-1p/). It is possible to design n=O(Nlog(m)) nonadaptive measurements allowing reconstruction with accuracy comparable to that attainable with direct knowledge of the N most important coefficients. Moreover, a good approximation to those N important coefficients is extracted from the n measurements by solving a linear program-Basis Pursuit in signal processing. The nonadaptive measurements have the character of "random" linear combinations of basis/frame elements. Our results use the notions of optimal recovery, of n-widths, and information-based complexity. We estimate the Gel'fand n-widths of lscrp balls in high-dimensional Euclidean space in the case 0<ples1, and give a criterion identifying near- optimal subspaces for Gel'fand n-widths. We show that "most" subspaces are near-optimal, and show that convex optimization (Basis Pursuit) is a near-optimal way to extract information derived from these near-optimal subspaces

Compressed sensing

In this paper we survey and critically comment on some basic concepts and notions relevant to the methodology dealing with process capability indices (PCI's). The emphasis is on distributional and inferential aspects. The intent is to contribute to the ..

Distributional and Inferential Properties of Process Capability Indices

In this paper, we present relay-assisted transmission as a powerful fading mitigation tool for free-space optical systems operating in atmospheric turbulence channels. We study both serial (i.e., multi-hop transmission) and parallel (i.e., cooperative diversity) relaying encoupled with amplify-and-forward and decode-and-forward modes. We consider an aggregated channel model which takes into account both path-loss and turbulence-induced log-normal fading. Since fading variance is distance-dependent in free-space optical systems, relay-assisted transmission takes advantage of the resulting shorter hops and yields significant performance improvements. We derive outage probability of the relaying schemes under consideration which are further confirmed through Monte-Carlo simulations. Our outage probability analysis demonstrates that an impressive performance improvement of 18.5 dB is possible with the use of a single relay at a target outage probability of 10-6.

/pdf/relay-assisted-free-space-optical-communication-8tvjw2m9mc.pdf

Relay-Assisted Free-Space Optical Communication

The overlapping coefficient is defined as a measure of the agreement between two probability distributions. Its relationship to the dissimilarity index and its propertie are described. An extensive treatment of maximum-likelihood estimation of the overlap between two normal distributions is presented as an example of estimating the overlapping coefficient from sample data.

The overlapping coefficient as a measure of agreement between probability distributions and point estimation of the overlap of two normal densities

A nonlinear general equilibrium model of the term structure of interest rates

Measurements are frequently recorded without their algebraic sign. As a consequence, the underlying distribution of measurements is replaced by a distribution of absolute measurements. When the underlying distribution is normal, the resulting distribution is called the “folded normal distribution”. The authors describe methods for estimating the mean and standard deviation of the normal distribution based on estimates of the mean and standard deviation determined from the folded normal. Tables are provided to assist in the estimation procedure and an example included.

https://www.jstor.org/stable/info/1266560

L. S. Nelson

Papers

The Folded Normal Distribution