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White noise

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


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Journal ArticleDOI
TL;DR: A numerical simulation technique is presented that combines the advantages of the discrete Fourier transform (DFT) algorithm and a digital filtering scheme to generate continuous long-duration multivariate random processes.
Abstract: A numerical simulation technique is presented that combines the advantages of the discrete Fourier transform (DFT) algorithm and a digital filtering scheme to generate continuous long-duration multivariate random processes. This approach offers the simple convenience of conventional fast Fourier transform (FFT) based simulation schemes; however, it does not suffer from the drawback of the large computer memory requirement that in the past has precluded the generation of long-duration time series utilizing FFT-based approaches. Central to this technique is a simulation of a large number of time series segments by utilizing the FFT algorithm, which are subsequently synthesized by means of a digital filter to provide the desired duration of simulated processes. This approach offers computational efficiency, convenience, and robustness. The computer code based on the present methodology does not require users to have experience in determining optimal model parameters, unlike the procedures based on parametric models. The effectiveness of this methodology is demonstrated by means of examples concerning the simulation of a multivariate random wind field and the spatial variation of wave kinematics in a random sea with prescribed spectral descriptions. The simulated data showed excellent agreement with the target spectral characteristics. The proposed technique has immediate applications to the simulation of real-time processes.

101 citations

Proceedings ArticleDOI
29 Nov 2004
TL;DR: This work proposes a turbo decoding which is suitable for AWAN channels and shows the BER (bit error rate) performance of the proposed turbo decoding in a class A noise environment by computer simulation.
Abstract: Power line channels often suffer from impulsive interference generated by electrical appliances. Therefore, power line communication (PLC) degrades due to such impulsive interference. Middleton's class A noise model is frequently utilized for the modeling of such impulsive noise environments. We deal with turbo decoding for turbo codes over an additive white class A noise (AWAN) channel. We propose a turbo decoding which is suitable for AWAN channels. In addition, we show the BER (bit error rate) performance of the proposed turbo decoding in a class A noise environment by computer simulation.

101 citations

Journal ArticleDOI
TL;DR: In this article, a technique for the identification of arcing high impedance faults using burst noise signals at frequencies near the power system fundamental and low order harmonics is described, which can be differentiated from synchronous power system signals in the frequency bands of interest.
Abstract: Previous papers have described a method for the detection of arcing fallen distribution primiary conductor faults using the electrical noise in feeder current above 2kHz. While this method provided improved detection of such faults, this high frequency signal often would not propagate past capacitor banks. In the present paper, we describe a technique for the identification of arcing high impedance faults using burst noise signals at frequencies near the power system fundamental and low order harmonics. Arcing generates non-synchronous burst noise signals which approximate white noise, providing a signal which can be differentiated from synchronous power system signals in the frequency bands of interest. The primary advantage of monitoring frequencies near the fundamental is that this arcing fault signal at low frequencies will exhibit little attenuation from capacitor banks or other sources. This paper provides preliminary results that arcing faults can be detected effectively using frequency components below 60 Hz or between low order harmonics of 60 Hz. The technique is demonstrated through analysis of analog signals recorded during numerouis staged utility downed conductor tests.

101 citations

Journal ArticleDOI
TL;DR: A new idea, enhancing speech based on auditory evidence, is explored for the problem of enhancing speech degraded by stationary and nonstationary additive white noise; a significant reduction of such noise and an improvement in speech quality are achieved.
Abstract: A new idea, enhancing speech based on auditory evidence, is explored for the problem of enhancing speech degraded by stationary and nonstationary additive white noise. Distinguishing different objectives for heavy and light noise interference, two related algorithms are developed. For speech degraded by heavy noise, the improvement in signal-to-noise ratio (SNR) is as high as 12 dB; for lightly noisy speech, the improvement is modest and decreases as the SNR of the noisy speech increases. Quantizing noise is used to assess the capacity for reducing nonstationary noise using these algorithms; a significant reduction of such noise and an improvement in speech quality are achieved. The advantages of the proposed algorithms for speech enhancement include no need for prior knowledge of the noise and only a modest computational requirement. >

101 citations

Journal ArticleDOI
TL;DR: In this paper, a systematic analysis of the EnKF for small ensemble size is presented, where the authors view the method as a state estimator, and not as an algorithm which approximates the true filtering distribution.
Abstract: The ensemble Kalman filter (EnKF) is a method for combining a dynamical model with data in a sequential fashion. Despite its widespread use, there has been little analysis of its theoretical properties. Many of the algorithmic innovations associated with the filter, which are required to make a useable algorithm in practice, are derived in an ad hoc fashion. The aim of this paper is to initiate the development of a systematic analysis of the EnKF, in particular to do so for small ensemble size. The perspective is to view the method as a state estimator, and not as an algorithm which approximates the true filtering distribution. The perturbed observation version of the algorithm is studied, without and with variance inflation. Without variance inflation well-posedness of the filter is established; with variance inflation accuracy of the filter, with respect to the true signal underlying the data, is established. The algorithm is considered in discrete time, and also for a continuous time limit arising when observations are frequent and subject to large noise. The underlying dynamical model, and assumptions about it, is sufficiently general to include the Lorenz '63 and '96 models, together with the incompressible Navier–Stokes equation on a two-dimensional torus. The analysis is limited to the case of complete observation of the signal with additive white noise. Numerical results are presented for the Navier–Stokes equation on a two-dimensional torus for both complete and partial observations of the signal with additive white noise.

100 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023238
2022535
2021488
2020541
2019558
2018537