Explore MATLAB source code curated for "幅度" with clean implementations, documentation, and examples.
This radar clutter simulation program generates Weibull-distributed amplitude clutter with Gaussian power spectrum, implemented in time-domain I/Q channels representing real and imaginary components respectively.
Generating symmetric 16QAM, 64QAM, and 256QAM constellation diagrams to visualize signal amplitude and phase characteristics, including implementation approaches for plotting modulation schemes.
Design approach for Quadrature Mirror Filters starting from fundamental principles, with code-enabled observation and analysis of phase and magnitude characteristics through practical implementation.
Implementation of image processing in the frequency domain using Fourier transform techniques, comparing differences between image restoration from magnitude and phase components, with successful debugging and validation
Regarding simu1: During the initialization process before running Simulink, channel time delay and amplitude estimation is completed. Multipath combining weight coefficients estimation employs optional EG/MR/MMSE combining schemes. Channel options include AWGN (Additive White Gaussian Noise) and IEEE 802.15.3a UWB standard channel models (CM1-CM4). For simplification, the simulation data package from IEEE 802.15.3a channel models is used here. The Eb/No parameter in the model refers to the energy per bit to noise power spectral density ratio. In AWGN channel, when Eb/No reaches 4.31 dB, the bit error rate is 0.01.
Apply Fast Fourier Transform (FFT) to input images, extract magnitude and phase components for separate reconstruction, and compare restoration results using MATLAB/Python implementation examples
When certain data points in a curve are excessively large, making VC plots visually unappealing, you can apply reduction techniques while preserving trend information through appropriate algorithms and scaling methods.
Extracting seven instantaneous information-based features from signals: maximum value of zero-centered normalized instantaneous amplitude power spectral density, standard deviation of zero-centered normalized instantaneous amplitude absolute value, standard deviation of absolute value of instantaneous phase nonlinear component in zero-centered non-weak signal segments, standard deviation of instantaneous phase nonlinear component in zero-centered non-weak signal segments, standard deviation of absolute value of zero-centered normalized instantaneous frequency in non-weak signal segments, maximum value of normalized instantaneous frequency power spectral density in a signal segment, and a novel feature parameter based on different projection characteristics of QPSK and 16QAM signals on the XI axis.
Implementation of standard MATLAB functions for accurate phase and amplitude computation with robust algorithm design