some parameters need to be scaled to fit the data set. The above parameters seem to work well on the scale we are working with, size of a room, but they would need to be significantly decreased to handle smaller objects,
Since the normal distribution occurs frequently in economic and financial modeling, one often needs a method to transform low-discrepancy sequences from the uniform distribution to the normal distribution. Two well known methods used with pseudorandom numbers are the Box–Muller and the inverse ...
The array x[] holds interleaved data (real, imag, real, imag, etc.): The code also shows cycle counts for the various operations, and we see that a single butterfly takes 23 cycles on a Cortex®-M4. Looking more closely at the cycle count we see that 13 cycles are due to memory...
To date, the ultrasonic WFC has been investigated only for the FFT of 2D data for image processing, however, 1-D FFT for signal processing is important and how to use such a WFC system for signal processing remains unknown. On the other hand, the WFC system using GHz ultrasonic waves11,...
phase rotation matrix and MP-WFRFT parameter pool are constructed by using 2D-CPA hyperchaotic sequence respectively to complete the constellation amplitude phase encryption and MP-WFRFT dynamic transformation encryption process, the statistical characteristics of data are further eliminated and the anti-pa...
computations are usually needed, excessive energy will be consumed and the efficiency could be also low if the resolution of the input signal is high (large 1-D input data). Additionally, the computational complexity of the 1-D FFT for signals isO(NlogN). Similar to the 2-D FFT ...
control, I'm willing to make an exception for the.matfiles containing the test data. My reasoning is that they represent the decoupling of my code from the MATLAB code, and if the two projects were separated, they would be considered a part of the Python code, not the original MATLAB ...
Function f(t) can be approximated by fitting a regression surface to the data by determining a local neighbourhood of an arbitrary (t0). These neighbour- ing points are weighted depending on their distance from (t0). The closer points get larger wi weights. The estimate fˆ is obtained ...
Wavelet transform has been successfully applied to waveform data analysis in fault diagnostics of gears [72,73], bearings [74,75] and other mechanical systems [76,77]. Dalpiaz and Rivola [78] assessed and compared the effectiveness and reliability of wavelet transform to other vibration signal ...
The energy scalograms for four categories (atrial fibrillation, normal, other, and noise) are shown in Fig.5to illustrate input data for deep learning training. In Fig.5(a), the AF rhythm (no. A_3111.jpg) is depicted, while Fig.5(b) shows a normal sinus rhythm (no. N_181.jpg)...