Consider the K-user Gaussian interference channel with static channel coefficients. Recent efforts have characterized the capacity region to within a constant gap for several important special cases, including two-user, very weak, many-to-one, and one-to-many interference channels. The general case remains open, primarily due to the challenge of characterizing the achievable rate for interferen...

It is well-known that Gaussian filter is the most important model in image denoising. However, the inverse of the Gaussian model for image sharpening is seriously ill-posed. In this paper, we propose several variations of the Gaussian model, which are derived from the varied diffusion equations. Explicit forms for these models (filters) are given in the Fourier space, which facilitate the usage...

A subspace-based method for denoising with a frame works as follows: If a signal is known to have a sparse representation with respect to the frame, the signal can be estimated from a noise-corrupted observation of the signal by finding the best sparse approximation to the observation. The ability to remove noise in this manner depends on the frame being designed to efficiently represent the si...

l’tlt: probIc:rn of dztc~ctin~ iri:,c:il::ity c!~arlges in images is canonical in \ isIon. lXi;e del,ectlou opc.1 :ltc,rs arc typ]Lally designed to optimally estimate first or second derival.ivr over some (usually small) support. Other criteria such as output signal to noise ratio or bandwidth have also been argued for. This paper describes an attempt to formulate a set of edge detection criter...

NASA Tech Briefs, November 2010 yield this behavior. Previous work on mean-square error characterization for ML estimators has predominantly focused on additive Gaussian noise. This work demonstrates that the discrete nature of the Poisson noise process leads to a distinctly different error behavior. This work was done by Baris I. Erkmen and Bruce E. Moision of Caltech for NASA’s Jet Propulsion...

This paper introduces our actual work in front-end techniques to obtain robust speech recognition devices in mismatch conditions (additive noise mismatch and channel mismatch). Two algorithms have been combined to compensate the distortions due to different channel characteristics and additive noise: 1) A Cepstral Mean Normalization and Variance Scaling technique (MNVS) and 2) An Adaptive Gauss...

A coding theorem and converse are proved for abstract channels with time structure that contain continuous-time continuous-valued channels and the result by Kadota and Wyner (1972) as special cases. As main contribution the coding theorem is proved for a significantly weaker condition on the channel output memory and without imposing extra measurability requirements to the channel. These improv...

This work studies the stability and the stochastic properties of neural activity evoked by external stimulation. The underlying nonlocal model describes the spatiotemporal response dynamics of neural populations involving both synaptic delay and axonal transmission delay. We show that the linear model recasts to a set of affine delay differential equations in spatial Fourier space. Besides a st...

We study the robustness of classifiers to various kinds of random noise models. In particular, we consider noise drawn uniformly from the `p ball for p ∈ [1,∞] and Gaussian noise with an arbitrary covariance matrix. We characterize this robustness to random noise in terms of the distance to the decision boundary of the classifier. This analysis applies to linear classifiers as well as classifie...

Abstract We consider the complex case of the S-inequality. It concerns the behaviour of Gaussian measures of dilations of convex and rotationally symmetric sets in Cn. We pose and discuss a conjecture that among all such sets measures of cylinders (i.e. the sets {z ∈ Cn | |z1| ≤ p}) decrease the fastest under dilations. Our main result in this paper is that this conjecture holds under the addit...