Скачать или смотреть Discrete Fourier Transform (DFT) Explained | MATLAB examples

The Discrete Fourier Transform (DFT) is a fundamental concept in digital signal processing (DSP) that allows us to analyze signals in the frequency domain. By transforming signals from the time domain to the frequency domain, DFT helps in spectral analysis, signal decomposition, and frequency component extraction. This transformation is widely used in wireless communications, image processing, data compression, radar systems, and biomedical signal analysis.

The Inverse Discrete Fourier Transform (IDFT) reverses this process, converting frequency components back into their original time-domain representation. Both DFT and IDFT are essential in fields like audio processing, telecommunications, and real-time signal analysis, enabling efficient filtering, modulation, and reconstruction of signals.

In this tutorial, we’ll break down the mathematical foundations of DFT and IDFT, explain how they work, and demonstrate their MATLAB implementation. We will also compare DFT vs. FFT (Fast Fourier Transform), an algorithm that significantly improves computational efficiency. By the end of this session, you will understand how to compute DFT using matrix multiplication, visualize the signal spectrum, and reconstruct signals using Inverse DFT.

🚀 What You’ll Learn:
✔ What the Fourier Transform (FT) is and how it works
✔ The difference between DFT and IDFT
✔ How to compute DFT using matrix multiplication
✔ How to use MATLAB’s fft and ifft functions for fast computation
✔ How to visualize signal spectra and interpret frequency components

⏱️ Chapters
0:00 - Introduction
0:29 - Understanding Fourier Transform, DFT, and FFT
2:05 - DFT: Matrix Representation and Procedure
4:25 - Invserse DFT
5:20 - FT and DFT: Mathematical Formulation
6:03 - MATLAB Code Example

💻 MATLAB Code:
Download the full MATLAB script here: https://github.com/marwyp/WaveformAca...

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