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To summarize, the effect of correlations in the noise is in general to increase the magnitude of the errors. For correlations in time which are short compared to the period of the signal, the result is to multiply the previously obtained variances by D, the number of consecutive data points which are correlated.
A white noise process is one with a mean zero and no correlation between its values at different times.
What does oversampled mean?
In signal processing, oversampling is the process of sampling a signal at a sampling frequency significantly higher than the Nyquist rate. A signal is said to be oversampled by a factor of N if it is sampled at N times the Nyquist rate.
What are the two examples of correlated noise?
For example, a light curve of a star might contain contain correlated noise caused by the star’s rotation or stellar activity. Quasar light curves also might contain correlated noise due to slow changes in the accretion rate of the central black hole.
Is noise correlated?
Given that noise correlations are important for neural decoding, they may also influence decoding of fMRI data. In contrast to signal correlations, noise correlations reflect the extent to which noise in the activity of a voxel is correlated with noise in the activity of other voxels in the population.
Why is white noise importance in time series?
White noise is an important concept in time series forecasting. If a time series is white noise, it is a sequence of random numbers and cannot be predicted. If the series of forecast errors are not white noise, it suggests improvements could be made to the predictive model.
How does oversampling affect white Gaussian noise correlation?
If we receive a signal assuming white Gaussian noise channel and we want to over-sample the signal to ensure high correlation between signal samples, does the oversampling affect the noise correlation (which is suppose to be uncorrelated). Join ResearchGate to ask questions, get input, and advance your work.
Do you need oversampling for correlation based detectors?
In most cases, the hardware bandwidth equals the bandwidth of the signals from a channel. Correlation-based detectors sometimes require oversampling to guarantee a correlated primary signal.
What does uncorrelated noise mean in signal processing?
Uncorrelated noise refers to noise that has a zero autocorrelation function. So, every point in the noise signal is “independent” of every other point. So, even if you have signal values for large time epochs, you cannot predict the next value.
When is a signal perfectly correlated with itself?
If a signal is periodic, then the signal will be perfectly correlated with a version of itself if the time-delay is an integer number of periods. That fact, along with related experiments, has implicated autocorrelation as a potentially important part of signal processing in human hearing.