Parameterizing neural power spectra into periodic & aperiodic components.
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Updated
May 4, 2024 - Python
Parameterizing neural power spectra into periodic & aperiodic components.
Compares FBMC to OFDM based schemes. Reproduces all figures from “Filter bank multicarrier modulation schemes for future mobile communications”, IEEE Journal on Selected Areas in Communications, 2017.
Simulates pruned DFT spread FBMC and compares the performance to OFDM, SC-FDMA and conventional FBMC. The included classes (QAM, DoublySelectiveChannel, OFDM, FBMC) can be reused in other projects.
Allows to reproduce all figures from "Pruned DFT Spread FBMC: Low PAPR, Low Latency, High Spectral Efficiency", IEEE Transactions on Communications, 2018
Spectrum Analyzer with Arduino: An Arduino Due and a PC give you the frequency response of any device, filter or amplifier, up to 100kHz.
Least-squares (sparse) spectral estimation and (sparse) LPV spectral decomposition.
Functions for creating speech features in MATLAB.
Adaptive, sine-multitaper power spectral density estimation in R
A bundle of codes targeted on analyses of IQ data in the context of Schottky spectroscopy
das2 stream utilities and catalog client in C
Power spectral density for patchy images
Spectral analysis of ECG signals based on Fast Fourier Transform and Power Spectral Density
Senior Design Project at UH
Modelling and simulation of major components in a digital communication system
Explore this repository for a modified implementation of OpenCV's periodic noise removal filter tutorial.
Calculation of PSD of seismic waves in order to measure the impact of culture noise during the COVID-19 pandemic.
Minimalist Matlab implementation of a random process generation in one point
Statistical Digital Signal Processing and Modeling
Ground acceleration records are simulated using the non-stationnary Kanai–Tajimi model
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