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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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Use of modalfrf for operating measurements

 I need to calculate the FRF for a mechanical system where the exitation has its origin from a gearbox. I generated simulation data while the mechanical systems rotates under operating conditions. I generated data of velocity an acceleration. For my acoustical analysis I need the frequency response function. So I wanted to calculate them by using the function modalfrf. But I am not sure if I can use this function due to the fact that I do not have a hammer or shaker exitation. So is it possible to calculate the FRF with this function or do I need to use another function? 



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This is essentially a system identification problem. You have non-parametric (tfestimate, spa, spafdr, etfe) techniques and parametric techniques (ssest, tfest). MODALFRF implements both approaches. When the 'Estimator' is H1 or H2, it is doing something similar to tfestimate and producing a non-parametric estimate of frequency response using correlation analysis (e.g., H = Syy/Sxy, where Syy is output spectrum = fft(Ryy), where Ryy is auto-correlation sequence, etc). When you use 'Estimator'/'subspace', it implements a parametric subspace identification (n4sid) algorithm to first estimate a state-space model and then compute its frequency response using FREQRESP.
If input  signal is periodic, you will get much better speed by using a non-parametric technique. But the results are going to be sensitive to choice of technique (H1 or H2), frame-size, window shape and amount of overlapping. Parametric methods are much slower but less sensitive to tuning parameters (such as model order) and are more reliable IMO. You also get the additional insights into system dynamics in terms of poles/zeros, damping, I/O delays and feedthrough.
If you have system identification toolbox, I would recommend the following:


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