Explore MATLAB source code curated for "FFT" with clean implementations, documentation, and examples.
Multiple MATLAB programs for interferometer direction finding with cross-compatibility features. Includes FFT-based implementations and five core algorithms with essential computational modules for signal processing and direction of arrival estimation.
This MATLAB 7.0 implementation package includes practical programs demonstrating Continuous Wavelet Transform (CWT), Discrete Wavelet Transform (DWT), Fast Fourier Transform (FFT), and FFT Shift operations for signal processing applications.
Image compressive sensing implementation using FFT (constructing orthogonal transformation matrix) or DWT (measuring high-frequency coefficients) as sparsity bases, with Orthogonal Matching Pursuit (OMP) algorithm for reconstruction
This source code implements a comparison between marginal spectrum and FFT within the Hilbert-Huang Transform (HHT) framework. Personally tested and verified, the generated plots meet basic requirements. The implementation includes key signal processing components for empirical mode decomposition (EMD) and spectral analysis.
Beginner-friendly implementations of Fast Fourier Transform (FFT) and basic digital filters with code explanations and algorithmic insights for signal processing applications.
Implementation of FFT and DFT algorithms using MATLAB with execution speed benchmarking and code optimization insights.
MATLAB implementation approaches for radar moving target detection using two signal processing methods: FFT-based frequency analysis and FIR filtering techniques
A beginner-friendly audio processing program utilizing Fast Fourier Transform (FFT) for spectral analysis and implementing noise reduction algorithms, serving as an ideal experimental framework for learning digital signal processing fundamentals.
FFT spectral analysis can be employed to extract EEG signals from various frequency bands. The extracted signals enable diagnosis of neurological disorders and analysis of brain electrical activity and functional states. Implementation involves: 1) Converting experimental EEG data files (pre-processed with 50Hz notch filtering) to text format (e.g., 0661.txt) for MATLAB compatibility; 2) Importing data into MATLAB, extracting Fp1 channel signals, applying FFT to isolate α, β, θ, and δ bands, and performing inverse FFT for time-domain reconstruction; 3) Calculating power spectral density for each frequency band.
An EMD programming implementation example demonstrating signal processing techniques including Fast Fourier Transform (FFT), power spectral analysis, Hilbert-Huang Transform (HHT), and envelope spectrum computation, with enhanced code-related explanations for algorithm implementation.