Treffer: A Taste of Python - Discrete and Fast Fourier Transforms.

This paper is an attempt to present the development and application of a practical teaching module introducing Python programming techniques to electronics, computer, and bioengineering students at an undergraduate level before they encounter digital signal processing and its applications in junior or senior level courses. The Fourier transform takes a signal in time domain, switches it into the frequency domain, and vice versa. Fourier Transforms are extensively used in engineering and science in a vast and wide variety of fields including concentrations in acoustics, digital signal processing, image processing, geophysical processing, wavelet theory, and optics and astronomy. The Discrete Fourier Transform (DFT) is an essential digital signal processing tool that is highly desirable if the integral form of the Fourier Transform cannot be expressed as a mathematical equation. The key to spectral analysis is to choose a window length that suits the signal to be analyzed, since the length of the window used for DFT calculations has a substantial impact on the information the DFT can provide. The operation count of the DFT algorithm is time-intensive, and as such a number of Fast Fourier Transform methods have been developed to adequately perform DFT efficiently. This paper will explain how this learning and teaching module was instrumental in progressive learning for students by presenting Python programming and the general theory of the Fourier Transform in order to demonstrate how the DFT and FFT algorithms are derived and computed through leverage of the Python data structures. This paper thereby serves as an innovative way to expose technology students to this difficult topic and gives them a fresh taste of Python programming while having fun learning the Discrete and Fast Fourier Transforms. [ABSTRACT FROM AUTHOR]

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