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How MATLAB makes the distinction between P-Cores and E-Cores?

  It is known that modern CPUs have both Performance cores (P-cores) and efficiency cores (E-cores), different types of CPU cores that have different purposes and are designed for different tasks. P-cores typically have higher clock speeds and designed for high-performance tasks, while E-cores operate at lower clock speeds and focus on energy-efficient processing. In MATLAB, maxNumCompThreads returns the current maximum number of computational threads. Currently, the maximum number of computational threads is equal to the number of physical cores on your machine. How MATLAB makes the distinction between P-Cores and E-Cores ? NOTE:- Matlabsolutions.com  provide latest  MatLab Homework Help, MatLab Assignment Help  ,  Finance Assignment Help  for students, engineers and researchers in Multiple Branches like ECE, EEE, CSE, Mechanical, Civil with 100% output.Matlab Code for B.E, B.Tech,M.E,M.Tech, Ph.D. Scholars with 100% privacy guaranteed. Get MATLAB projects...
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Why is noise required to get expected Magnitude Squared Coherence (mscohere)

 I am missing something with magnitude Squared Coherence and/or its algorithm. If two signals are compared without or with little noise I get unexpected results. As an example taking from the ML help page:

 
Fs = 1000; t = 0:1/Fs:1-1/Fs;
 
x = cos(2*pi*100*t)+sin(2*pi*200*t)+0.5*randn(size(t)); y = 0.5*cos(2*pi*100*t-pi/4)+0.35*sin(2*pi*200*t-pi/2)+ ... 0.5*randn(size(t)); [Pxy,F] = mscohere(x,y,hamming(100),80,100,Fs);
 
gives the expected two peak response. I would have thought that with no noise the mscohere would be similar and even stronger but it is not. Run the same  code without the noise
 
x = cos(2*pi*100*t)+sin(2*pi*200*t); y = 0.5*cos(2*pi*100*t-pi/4)+0.35*sin(2*pi*200*t-pi/2);
 
[Pxy,F] = mscohere(x,y,hamming(100),80,100,Fs);
 
and rather than getting two strong peaks and the rest near or at zero, you get unity for all frequencies.
 
You don't need much noise, 0.5% or -46dB will do. Below this and the results get real funky.
 
Furthermore, without some noise the algorithm sees harmonics very strongly even though they are not in both signals:
 
x = cos(2*pi*100*t)+sin(2*pi*200*t)+0.5*randn(size(t)); y = 0.5*cos(2*pi*100*t-pi/4);
 
still gives two strong peaks at 100 and 200 unless y has noise. Then all is as expected.

ANSWER



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You might be thinking of CPSD instead of MSCOHERE. MSCOHERE will normalize CPSD by the PSD of each signal (i.e. Cxy = (abs(Pxy).^2)./(Pxx.*Pyy), where Pxy is the CPSD. When the spectrum of Pxy, Pxx and Pyy are very near zero, you'll be looking at division of two numbers very close to zero. Adding the noise decouples them somewhat.

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