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Electromagnetic Modelling and Antenna Simulation via Opensource Software

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Commercial electromagnetic simulation (EM) software packages such as CST Microwave Studio and  ANSYS HFSS are widely used in commercial applications and educational purposes. Based on my experience, they provide very accurate results which match measurements in most antenna works. On the other hand, there are also very solid opensource software and applications which may also provide similar results in some applications. Antennas are also used in radio telescopes While commercial EM software suits usually have very good documentations, easy-to-use interference, and result visualisation and navigation tools, opensource EM software suits might consist of only the solver and documentation which explains how it should be used and implemented for design and simulation via an interference and a programming language such as Python, MATLAB, C++ . As they are opensource, it is also possible to edit their codes and advance their functions and performance. Thus, these features makes opensource
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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 increasi
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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. 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/2017/04/23/understanding-the-f
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