The software for computational methods developed by the Signal Science Lab are publicly available to researchers via denoising.cornell.edu. The detailed documentation for software usage can be found on the website.
The software can be applied to recover any 1D signal with SNR >= 1. The method uses wavelet denoising approaches developed at the Signal Science Lab and contains advanced noise thresholding procedures.
The software is specialized for Pulsed Dipolar ESR signals (or any signal with exponential decay). The method incorporates NERD with additional signal processing features.
The software uses Srivastava-Freed Singular Value Decomposition Method to solve ill-posed methods for obtaining the approximate solution. It also contains the uncertaintly analysis program.
We maintain a GitHub repository (https://github.com/Signal-Science-Lab/Sparsity-Based-Decomposition-Level-Selection) containing MATLAB scripts to replicate our simulations and other analyses.
We maintain a GitHub repository (https://github.com/SignalScienceLab/ESR-Hyperfine-Extraction-Simulation-Data-and-Codes.git) containing MATLAB scripts to replicate our simulations as well as data generated in our paper “Hyperfine Decoupling of ESR Spectra Using Wavelet Transform“.