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AWGN is additive in nature by definition, so you won't need any deconvolution to estimate transmitted symbols. Received samples are obtained as : $$y[n] = x[n] + w[n]$$ where $w[n]$ are Complex Circular Symmetric White Gaussian Noise samples. In OFDM receiver when you strip-off cyclic prefix and take FFT of the received samples, you get Freq-Domain vectors ...


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I think we need to be a fair bit more specific: No matter what random variable has its distribution, … If that distribution has a finite variance and mean (counterexample: Cauchy-distributed variables, e.g. $\operatorname*{Im}(z)/\operatorname*{Re}(z)$ of complex normal $z$), if we pick n samples and mean (or just sum) them and do this many time, those ...


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You can add random variables with different means and variances. For example, look at two continuous uncorrelated normal-distributed random variables $x_1$ and $x_2$ with means and variances $\mu_1, \sigma_1^2$ and $\mu_2, \sigma_2^3$ Then $x_3 = x_1 + x_2 $ is also a normal-distributed random variable with $\mu_3 = \mu_1 + \mu_2$ and $ \sigma_3^2 = \sigma_1^...


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This should be doable with ssqueezepy's extract_ridges; try varying penalty and bw (see their docstrings). As last resort, feeding a cropped image that excludes region without ridges may work better, as the algorithm assumes the ridge spans the entire frame. You can automate this by finding indices at which column energies fall below a set threshold, e.g. np....


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Does the "Energy Threshold" carry information about the end of the noise and the beginning of the packet due to the calculated arithmetic mean? There's no such thing as "end of noise": Nothing says that noise can't take values that are as large (or larger) than your signal's power; in "benign" noise, these values are just rare. ...


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The random does not play a role when averaging is used to remove the noise. The distribution of noise does. Averaging works when the mean of the noise is zero. The assumption is that averaging noise that has a zero mean results in canceling noise component while the signal components remain mostly unchanged. Take for example the power line noise as 60Hz ...


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Window-average filter is an example of lowpass filter. That means, if interesting frequencies in your signal are few times lower than noise, then yes, you can do that. If they are close or higher, then no. Use bandstop/highpass filter if your signal frequencies are much higher than noise, or adaptive filter like Recursive Least Squares instead, if your ...


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Looks like pre-ringing. That happens whenever you apply a linear phase filter to a signal. In your case that could be the sigma-delta converters or any type of sample rate conversion or other unknown processing that the operating system or the drivers doo.


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Since your signal seems to be periodic (at least in the excerpt we see), we can't really tell, but my guess: You've already solved the mystery of what that is: it's a fade-in. It might be intentional (sounds a lot like Android), so that recordings with sudden high amplitudes on the first samples don't lead to "cracks" from the speaker, or it might ...


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A remarkably terse specification of the instrumentation (sensors, probes) and techniques used to measure "the vibration of a structure" leaves open the issues of applicability of the Wiener filter vs "the other (more classic) filtering methods" to processing of "the-vibration-of-a-structure" data. Well established techniques are ...


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The Wiener filter considers statistical behaviours of the noise and the signal, and thus, (theoretically) achieves optimum separation of them for a class of signals and systems, which is not the case for more classical approaches. Wiener filter frequency response is such that, at those frequencies where noise power is dominant (a.k.a. low SNR), the gain is ...


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