Parameterizing neural power spectra into periodic & aperiodic components.
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Updated
Aug 8, 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.
Allows to reproduce all figures from "Pruned DFT Spread FBMC: Low PAPR, Low Latency, High Spectral Efficiency", IEEE Transactions on Communications, 2018
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.
Spectrum Analyzer with Arduino: An Arduino Due and a PC give you the frequency response of any device, filter or amplifier, up to 100kHz.
Functions for creating speech features in MATLAB.
Least-squares (sparse) spectral estimation and (sparse) LPV spectral decomposition.
Senior Design Project at UH
Adaptive, sine-multitaper power spectral density estimation in R
Ground acceleration records are simulated using the non-stationnary Kanai–Tajimi model
Calculation of PSD of seismic waves in order to measure the impact of culture noise during the COVID-19 pandemic.
This web app allows you to decompose your signal data or time series using FFT and gives the opportunity to interactively investigate the signal and its spectrum (frequency spectrum, power spectrum, periodogram, and its power spectral density) using the advantage of Plotly package.
das stream utilities and catalog client in C
Scripts to determine the power spectral density (PSD) of blazar light curves in python2
Minimalist Matlab implementation of a random process generation in one point
Modelling and simulation of major components in a digital communication system
A bundle of codes targeted on analyses of IQ data in the context of Schottky spectroscopy
Statistical Digital Signal Processing and Modeling
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