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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
17 Mar 2011-PLOS ONE
TL;DR: Examination of noise, sampling frequency and time series length influence various measures of entropy when applied to human center of pressure (CoP) data, as well as in synthetic signals with known properties, suggests long-range correlations should be removed from CoP data prior to calculating entropy.
Abstract: BACKGROUND: Over the last two decades, various measures of entropy have been used to examine the complexity of human postural control. In general, entropy measures provide information regarding the health, stability and adaptability of the postural system that is not captured when using more traditional analytical techniques. The purpose of this study was to examine how noise, sampling frequency and time series length influence various measures of entropy when applied to human center of pressure (CoP) data, as well as in synthetic signals with known properties. Such a comparison is necessary to interpret data between and within studies that use different entropy measures, equipment, sampling frequencies or data collection durations. METHODS AND FINDINGS: The complexity of synthetic signals with known properties and standing CoP data was calculated using Approximate Entropy (ApEn), Sample Entropy (SampEn) and Recurrence Quantification Analysis Entropy (RQAEn). All signals were examined at varying sampling frequencies and with varying amounts of added noise. Additionally, an increment time series of the original CoP data was examined to remove long-range correlations. Of the three measures examined, ApEn was the least robust to sampling frequency and noise manipulations. Additionally, increased noise led to an increase in SampEn, but a decrease in RQAEn. Thus, noise can yield inconsistent results between the various entropy measures. Finally, the differences between the entropy measures were minimized in the increment CoP data, suggesting that long-range correlations should be removed from CoP data prior to calculating entropy. CONCLUSIONS: The various algorithms typically used to quantify the complexity (entropy) of CoP may yield very different results, particularly when sampling frequency and noise are different. The results of this study are discussed within the context of the neural noise and loss of complexity hypotheses.

93 citations

Journal ArticleDOI
TL;DR: In this article, the authors describe and analyze a particular application of high duty-cycle time-division multiplexing to the separation and identification of signals from an interferometric sensor array.
Abstract: This paper describes and analyzes a particular application of high duty-cycle time-division multiplexing to the separation and identification of signals from an interferometric sensor array. Using the method discussed here, the coherence length of the laser is no longer a severe design constraint. Also, the source phase-induced intensity noise which limits some other multiplexing methods may be overcome, leading to a higher sensitivity. The arrays of all-passive remote sensors exhibit minimal crosstalk between sensors, and have downlead insensitivity. A synthetic heterodyne demodulation technique prevents environmentally induced signal fading. Analysis includes coupling ratios for all directional couplers in the system, signal and noise spectra, minimum detectable phase shift, and the effect of ac coupling on noise and crosstalk. An experimental all-fiber implementation of a two sensor array has yielded a measured sensitivity of approximately 10 μrad/ \sqrt{Hz} over a range of signal frequencies, and a crosstalk level of better than 55 dB.

93 citations

Journal ArticleDOI
TL;DR: Using compact closed form formulas for the Cramer Rao Bound corresponding to the joint estimation of the directions-of-arrival, the signal covariance matrix, and the noise variance, it is observed that under certain conditions, correlation phase has a strong effect on DOA estimation accuracy.
Abstract: In this paper we present compact closed form formulas for the Cramer Rao Bound corresponding to the joint estimation of the directions-of-arrival, the signal covariance matrix, and the noise variance. Using these formulas we investigate the effect of signal correlation on the achievable accuracy of direction finding system in a correlated signal environment. As expected, estimation accuracy decreases with increasing correlation magnitude. We observe that under certain conditions (small aperture, high correlation magnitude), correlation phase has a strong effect on DOA estimation accuracy.

93 citations

Journal ArticleDOI
TL;DR: The signal processing technique is capable of characterizing the signal characteristics quite accurately even when the amplitude of currents is as small as 5-10 fA, and a technique is provided by which channel currents originating from the sum of two or more independent single channels are decomposed so that each process can be separately characterized.
Abstract: Techniques for characterizing very small single-channel currents buried in background noise are described and tested on simulated data to give confidence when applied to real data. Single channel currents are represented as a discrete-time, finite-state, homogeneous, Markov process, and the noise that obscures the signal is assumed to be white and Gaussian. The various signal model parameters, such as the Markov state levels and transition probabilities, are unknown. In addition to white Gaussian noise, the signal can be corrupted by deterministic interferences of known form but unknown parameters, such as the sinusoidal disturbance stemming from AC interference and a drift of the base line owing to a slow development of liquid-junction potentials. To characterize the signal buried in such stochastic and deterministic interferences, the problem is first formulated in the framework of a Hidden Markov Model and then the Expectation Maximization algorithm is applied to obtain the maximum likelihood estimates of the model parameters (state levels, transition probabilities), signals, and the parameters of the deterministic disturbances. Using fictitious channel currents embedded in the idealized noise, we first show that the signal processing technique is capable of characterizing the signal characteristics quite accurately even when the amplitude of currents is as small as 5-10 fA. The statistics of the signal estimated from the processing technique include the amplitude, mean open and closed duration, open-time and closed-time histograms, probability of dwell-time and the transition probability matrix. With a periodic interference composed, for example, of 50 Hz and 100 Hz components, or a linear drift of the baseline added to the segment containing channel currents and white noise, the parameters of the deterministic interference, such as the amplitude and phase of the sinusoidal wave, or the rate of linear drift, as well as all the relevant statistics of the signal, are accurately estimated with the algorithm we propose. Also, if the frequencies of the periodic interference are unknown, they can be accurately estimated. Finally, we provide a technique by which channel currents originating from the sum of two or more independent single channels are decomposed so that each process can be separately characterized. This process is also formulated as a Hidden Markov Model problem and solved by applying the Expectation Maximization algorithm. The scheme relies on the fact that the transition matrix of the summed Markov process can be construed as a tensor product of the transition matrices of individual processes.

93 citations

Journal ArticleDOI
TL;DR: In this paper, the authors provide an overview along with a critical appraisal of available methods for uncertainty propagation of linear systems subjected to dynamic loading, where uncertain structural properties are treated as random quantities by employing a stochastic approach.

93 citations


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