Wireless Communication and Machine Learning Applications

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DFT and FFT with Python and It is applications on various signals

Fast Fourier Transform (FFT) is one of the most important algorithms in computer science, electronics and signal processing engineering. It is a fast solver for Discrete Fourier Transform (DFT). Basically, DFT or FFT transforms signals from time-amplitude domain to frequency-amplitude domain. The reverse form of the FFT is known as Inverse Fast Fourier Transform which converts, naturally, signals from frequency domain to time domain. FFT is heavily used in communication, radar or computer systems. For example OFDM (orthogonal frequency division multiplexing) is developed based on IFFT and FFT. Since Python is most common used scientific programming language beside Matlab, I would like to present some information about FFT and using it in Python. Python or Koala This blog post ( https://jakevdp.github.io/blog/2013/08/28/understanding-the-fft/ ) includes the basics of the FFT and very clear comparison of it to DFT. Another blog post ( https://www.ritchievink.com/blog/2017/04/

Matlab Phased Array Toolbox and Radar Examples

Matlab is one of the best software that can be for scientific and engineering research and computation, in addition to Python. Phased Array Toolbox of the Matlab provides a solid solution for antenna array analysis and radar research. Furthermore, Mathworks, owner company of Matlab, presents an extensive documentation of this toolbox. Here, I would like to present useful examples and documentation of Matlab radar studies, mainly from Mathworks website. Radar Data Cube: Fundamental data structure for received radar data. https://uk.mathworks.com/help/phased/gs/radar-data-cube.html Building and Processing a Radar Data Cube: https://uk.mathworks.com/company/newsletters/articles/building-and-processing-a-radar-data-cube.html Designing a Basic Monostatic Pulse Radar: https://uk.mathworks.com/help/phased/examples/designing-a-basic-monostatic-pulse-radar.html Basic Radar Using Phase-Coded Waveform: https://uk.mathworks.com/help/phased/ug/basic-radar-using-phase-coded-waveform.ht

On the performance of Matlab and Parallel Computing

MATLAB is one of the most powerful scientific computing tools along with Python. Although Python is my favorite scientific programming language since it is opensource, well-documented and has plenty of libraries, I sometimes use MATLAB especially while dealing with very large matrices as MATLAB is highly optimized for large-scale matrix operations, consequently, it performs better at processing very large matrices. From a parallel computing perspective, MATLAB actually strives to utilize all available CPU cores in a parallel way to maximize its performance and reduce the computation time when it is possible. Therefore, it does a kind of parallel computing when it is possible such as in matrix operations as these operations are very suitable to be run parallelly. However, the parallel operation of the MATLAB might be restricted by bad coding practice of the users especially using for or while loops, because those loops are generally performed in a serial manner with an increa

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