Explore MATLAB source code curated for "DFT" with clean implementations, documentation, and examples.
MATLAB-based compressed sensing implementation featuring DCT, DWT, DFT orthogonal bases and overcomplete dictionaries for sparse signal decomposition and reconstruction, including algorithm workflows and key function demonstrations
Implementation of FFT and DFT algorithms using MATLAB with execution speed benchmarking and code optimization insights.
Applying Discrete Cosine Transform (DCT) and Discrete Fourier Transform (DFT) to an image with analysis of high and low frequency component coefficients, including implementation approaches and key mathematical operations
MATLAB source code implementation for Discrete Fourier Transform (DFT) of finite sequences, featuring algorithm explanations and key function descriptions
1. Deepen comprehension of Fast Fourier Transform (FFT) fundamental principles through practical experiments. 2. Explore the relationship between FFT point count and spectral resolution, and analyze connections between zero-padded sequence FFT and original sequence FFT implementations. Discrete Fourier Transform (DFT) and convolution represent two fundamental operations in signal processing, involving extensive applications in signal and system analysis/synthesis. These operations are interconnected: convolution can be implemented via DFT, while numerous algorithms including correlation, filtering, and spectral estimation can be DFT-based. Conversely, DFT can also be realized through convolution operations, demonstrating their mathematical equivalence in digital signal processing.
DFT-based instantaneous frequency measurement algorithm with interpolation techniques demonstrated through graphical frequency analysis results and code implementation details.
MATLAB-based OFDM simulation program featuring comprehensive implementation of mapping, IDFT/DFT transformations, synchronization (time and frequency), channel estimation, and frequency offset compensation algorithms in functional form.
Analysis and comparison of various channel estimation methods for MIMO-OFDM systems, including Least Squares (LS), Linear Minimum Mean Square Error (LMMSE), and Discrete Fourier Transform (DFT) approaches, focusing on their implementation algorithms and performance characteristics.
Windowed Discrete Fourier Transform - Conduct spectral analysis of discrete signals using DFT, employing various window functions to suppress spectral leakage caused by signal truncation
Discrete Fourier Transform (DFT) Implementation in MATLAB - Note: While MATLAB provides built-in FFT functions for efficient computation, FFT is essentially a fast algorithm to compute DFT. This implementation demonstrates the foundational DFT computation process.