BPSK Signal Power Spectral Density with MATLAB Implementation
MATLAB programming code for calculating and visualizing the power spectral density of BPSK signals, including waveform generation and spectral analysis using Welch's method.
Computing Power Spectral Density for Two Signals with Spectral Correlation Control
Calculate power spectral density for two signals using spectral correlation coefficients to determine frequency point inclusion in superposition calculations, enabling quality control of PSD results through spectral similarity analysis.
Power Spectral Density Simulation
This program simulates power spectral density for educational purposes, featuring implementation details using signal processing algorithms suitable for technical analysis and research applications.
Power Spectral Density and Constellation Diagrams of QPSK Modulation with Rectangular and Root Raised Cosine Pulses under Different SNR Channel Conditions
MATLAB implementation for analyzing power spectral density and constellation diagrams of QPSK modulation using rectangular and root raised cosine pulses across various signal-to-noise ratio (SNR) channels. The code includes pulse shaping techniques, modulation/demodulation algorithms, and SNR simulation modules. Simply execute the main function to generate all results.
Computing Power Spectral Density and Estimating Bandwidth of Input Signals with Plotting
Calculate the power spectral density of input signals and estimate signal bandwidth using frequency domain analysis, with comprehensive plotting capabilities for signal visualization.
Comprehensive Source Code Collection for EEG Analysis
A diverse collection of source code for EEG analysis, including power spectral density estimation, event-related synchronization/desynchronization analysis, topographic map visualization, and related signal processing techniques
Power Spectral Density Simulation Comparison
This program performs power spectral density simulation comparisons using three signal sources described in "Introduction to Modern Digital Signal Processing" Volume 1, Page 202, Exercise 5. The implementation employs three spectral estimation methods: Periodogram method, Autocorrelation method, and Covariance method, with MATLAB code demonstrating different algorithmic approaches for spectrum analysis.
Extracting Seven Instantaneous Information-Based Signal Features
Extraction of seven instantaneous information-based signal features implemented through MATLAB algorithms: 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 components for zero-centered non-weak signal segments, standard deviation of instantaneous phase nonlinear components for zero-centered non-weak signal segments, standard deviation of absolute value of instantaneous frequency for zero-centered normalized non-weak signal segments, maximum value of normalized instantaneous frequency power spectral density for a signal segment, and feature parameters derived from distinct XI-axis projection characteristics of QPSK and 16QAM signals.
Analysis of Power Spectral Density in Simulated Direct Sequence Ultra-Wideband Systems
Simulation-based Power Spectral Density Analysis of Direct Sequence Ultra-Wideband (DS-UWB) Communications