Noise (signal processing)

About: Noise (signal processing) is a research topic. Over the lifetime, 61013 publications have been published within this topic receiving 621165 citations.

Signal processing with alpha-stable distributions and applications

Abstract: The Stable Distribution Symmetric Stable Random Processes Covariation and Conditional Expectation Parameter Estimates for Symmetric Stable Distributions Estimation of Covariations Parametric Models of Stable Processes Linear Theory of Stable Processes Symmetric Stable Models for Impulsive Noise Signal Detection in Stable Noise Current and Future Trends in Signal Processing with Alpha-Stable Distributions.

Noise enhancement of information transfer in crayfish mechanoreceptors by stochastic resonance

Abstract: IN linear information theory, electrical engineering and neurobiology, random noise has traditionally been viewed as a detriment to information transmission. Stochastic resonance (SR) is a nonlinear, statistical dynamics whereby information flow in a multistate system is enhanced by the presence of optimized, random noise1–4. A major consequence of SR for signal reception is that it makes possible substantial improvements in the detection of weak periodic signals. Although SR has recently been demonstrated in several artificial physical systems5,6, it may also occur naturally, and an intriguing possibility is that biological systems have evolved the capability to exploit SR by optimizing endogenous sources of noise. Sensory systems are an obvious place to look for SR, as they excel at detecting weak signals in a noisy environment. Here we demonstrate SR using external noise applied to crayfish mechanoreceptor cells. Our results show that individual neurons can provide a physiological substrate for SR in sensory systems.

On a simple algorithm to calculate the 'energy' of a signal

Abstract: A simple algorithm is derived that permits on-the-fly calculation of the energy required to generate, in a certain sense, a signal. The results of applying this algorithm to a number of well-known signals are shown. Some of the invariance and noise properties of the algorithm are derived and verified by simulation. The implementation of the algorithm and its application to speech processing are briefly discussed. >

Stochastic Approximation Algorithms and Applications

Abstract: Applications and issues application to learning, state dependent noise and queueing applications to signal processing and adaptive control mathematical background convergence with probability one - Martingale difference noise convergence with probability one - correlated noise weak convergence - introduction weak convergence methods for general algorithms applications - proofs of convergence rate of convergence averaging of the iterates distributed/decentralized and asynchronous algorithms.

Hyperspectral Subspace Identification

Abstract: Signal subspace identification is a crucial first step in many hyperspectral processing algorithms such as target detection, change detection, classification, and unmixing. The identification of this subspace enables a correct dimensionality reduction, yielding gains in algorithm performance and complexity and in data storage. This paper introduces a new minimum mean square error-based approach to infer the signal subspace in hyperspectral imagery. The method, which is termed hyperspectral signal identification by minimum error, is eigen decomposition based, unsupervised, and fully automatic (i.e., it does not depend on any tuning parameters). It first estimates the signal and noise correlation matrices and then selects the subset of eigenvalues that best represents the signal subspace in the least squared error sense. State-of-the-art performance of the proposed method is illustrated by using simulated and real hyperspectral images.