Explore MATLAB source code curated for "信噪比" with clean implementations, documentation, and examples.
Implementation of adaptive interference cancellation technique for Additive White Gaussian Noise removal, featuring two correlated noise generation methods - one with single random noise source and another with dual random noise sources. The program calculates signal-to-noise ratio improvement and mean square error gain after denoising, demonstrating algorithm performance through quantitative metrics.
Computation methods and algorithms for evaluating signal-to-noise ratio and key performance metrics in sigma-delta analog-to-digital converters, including MATLAB implementation approaches for noise analysis and spectral estimation.
LPC encoding implementation demonstrating bit error rate performance variation with signal-to-noise ratio under additive white Gaussian noise conditions, featuring MATLAB code structure for BER simulation and LPC parameter configuration.
Relationship Between Mutual Coupling Coefficient Errors and SNR in Array Signal Processing with Code Implementation Considerations
A decoding implementation of digital fountain codes processing original data blocks of length 1000, generating encoded sequences of length 1100, maintaining robust decoding accuracy across varying signal-to-noise ratio conditions
MATLAB algorithm implementation for computing signal-to-noise ratio in noisy images with code explanations
Signal-to-Noise Ratio (SNR), calculated as signal energy divided by noise power, helps analyze signal components; implementation involves calculating signal and noise powers using MATLAB functions like mean(), var(), or bandpower()
Simulate OFDM communication system using AWGN channel, generate BER vs SNR performance curves, and compare simulation results with theoretical benchmarks using MATLAB/Octave implementation
MATLAB implementation for SNR computation supporting both mono and stereo audio signals, featuring accurate mathematical formulas and well-structured code with preprocessing capabilities
MATLAB-based simulation of MIMO system capacity under different configurations of transmit antennas, receive antennas, and signal-to-noise ratios, demonstrating how MIMO channels can multiply wireless channel capacity without increasing bandwidth or transmit power