Explore MATLAB source code curated for "时域" with clean implementations, documentation, and examples.
Comprehensive modal processing program suite featuring time-domain analysis, frequency analysis, auto-spectrum, cross-spectrum, coherence analysis, frequency response, time-frequency analysis, HHT (Hilbert-Huang Transform), random decrement technique, and other essential methods. Excellent reference implementation with practical code examples and algorithm explanations.
MATLAB-based scrambling and descrambling simulation system enabling time-domain and frequency-domain waveform analysis of signals before and after scrambling/descrambling processes. Includes implementation details of scrambling algorithms and visualization techniques for understanding digital signal scrambling concepts.
Deepen understanding of image transformation techniques. Compute Fourier transform J of an image, perform spectral shifting and visualization; calculate inverse FFT of J and display results. Implementation approaches include using fft2() for 2D Fourier transform, fftshift() for spectrum centering, and ifft2() for inverse transformation.
This program implements the time-frequency domain separation algorithm in blind source separation, featuring wide application scope and practical implementation approaches for signal processing tasks.
Time-domain representation using Short-Time Fourier Transform (STFT) with 1000 sampling points, applying windowing functions, and visualizing original signals through 3D spectrograms and contour plots.
The five-point cubic smoothing method serves as an effective technique for smoothing signals in both time and frequency domains. In the time domain, it reduces high-frequency random noise mixed into vibration signals. For frequency-domain applications, it enables smoother spectral curves by applying a cubic polynomial fit over five consecutive data points.
Implementation of time domain waveform generation and power spectral density analysis for Bipolar Non-Return-to-Zero (NRZ), Bipolar Return-to-Zero (RZ), and Differential codes using MATLAB signal processing techniques
Comprehensive Signal Feature Extraction Using 16 Statistical Metrics: Mean, Standard Deviation, Variance, Skewness, Kurtosis, Peak-to-Peak, Peak Value, RMS Amplitude, Average Amplitude, Root Amplitude, Waveform Factor, and Crest Factor with Code Implementation Insights
Simulation of time-domain, frequency-domain, and ambiguity functions for various radar pulse compression signals with code implementation insights
Generate noise frequency modulation jamming signals and plot their time-domain characteristics and power spectrum using MATLAB implementation