Discrete Fourier Transform and Spectral Analysis (MATLAB)

[mitsumi]

MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 17 lectures (4h 48m) | Size: 1 GB

Introduction to Fourier Transform and Spectral Analysis - Part 2

What you'll learn:
Understanding Discrete Fourier transform basics, implementing DFT, convolution and correlation in Matlab/Octave
Essential signal processing skills using Matlab/Octave

Requirements
Familiarity with Fourier transform and spectral analysis
My course "Introduction to Fourier Transform and Spectral Analysis Part 1" on UDEMY is desirable prerequisite

Description
This course is continuation of Fourier transform and spectral analysis series. In this course I will introduce discrete Fourier Transform, explain concepts of frequency bins and frequency resolution and illustrate spectral leakage effect.

The best way to understand what happens with signals and spectral components is to generate test signals and spectra. The shortest route is to learn Matlab (or use compatible open-source Octave program). I will describe very simple basic set of Matlab programming skills and after a couple of short lectures you will be able to edit and run simple scripts and Description your output results.

The rest of the course illustrates using Matlab for signal processing. It is always useful to have source code of programs - it saves a lot of time and provides "prototyping" for program development. Each lecture will have attached downloadable script.

In the video lecture I will explain all program steps and show real-time results of script execution. I will start from very simple generation of sinusoidal signals and calculation of FFT, going to more complicated examples such as up- and down-conversion, convolution and cross-correlation, frequency measurement using phase approximation.

After taking this course you will have a set of essential skills of signal processing and FFT analysis using Matlab. I will explain a number of useful tricks which will help you to develop and run your signal processing programs.

Who this course is for
Electrical Engineering, Physics, Data Science, Biomedical Science

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