White noise

About: White noise is a research topic. Over the lifetime, 16496 publications have been published within this topic receiving 318633 citations.

Log-correlated Gaussian fields: an overview

Abstract: We survey the properties of the log-correlated Gaussian field (LGF), which is a centered Gaussian random distribution (generalized function) h on ℝ d , defined up to a global additive constant.

Self-adaptive partial discharge signal de-noising based on ensemble empirical mode decomposition and automatic morphological thresholding

Abstract: This paper proposes a self-adaptive technique for partial discharge (PD) signal denoising with automatic threshold determination based on ensemble empirical mode decomposition (EEMD) and mathematical morphology. By introducing extra noise in the decomposition process, EEMD can effectively separate the original signal into different intrinsic mode functions (IMFs) with distinctive frequency scales. Through the kurtosis-based selection criterion, the IMFs embedded with PD impulses can be extracted for reconstruction. On the basis of mathematical morphology, an automatic morphological thresholding (AMT) technique is developed to form upper and lower thresholds for automatically eliminating the residual noise while maintaining the PD signals. The results on both simulated and real PD signals show that the above PD denoising technique is superior to wavelet transform (WT) and conventional EMD-based PD de-noising techniques.

A New Class of White Noise Generalized Functions

Abstract: The S-transform is studied as a mapping from a space of tensors to a space of functions over a complex space. The range of this transform is characterized in terms of analyticity and growth. These results are applied to a broad class of generalized functions in white noise analysis. These correspond to completions of the Gaussian L2-space which preserve orthogonality of Hermite polynomials. The S-transform is defined for the new generalized functions, and the range of this S-transform is identified in terms of analyticity and growth. Examples of the new spaces of generalized functions are given; these include distributions considered by Kondratiev and Streit, as well as new classes of distributions whose S-transforms have growth bounded by iterated exponentials.

Blind fractionally-spaced equalization of noisy FIR channels: adaptive and optimal solutions

Abstract: Blind fractionally spaced (FS) equalizers only require output samples taken at rates higher than the symbol rate to estimate the channel or the equalizer. Methods for finding FIR zero-forcing equalizers directly from the observations are described and adaptive versions are developed. In contrast, most methods require channel estimation as a first step to estimating the equalizer. For the noisy channel, the FIR equalizer is shown to be minimum mean-square error. FS equalizers are not unique, thereby allowing optimum zero-forcing parametric FIR or nonparametric IIR equalizers to be derived such that in addition to being zero-forcing, they also minimize the noise power at the output. These optimum equalizers do not depend on the input distribution and are also valid for deterministic inputs. Finally, if the additive noise is white, they do not depend on the SNR.