readMe.m

function readMe()
% 
% I. Introduction
%     The MATLAB code contained in this directory implements the algorithm
%     derived in the paper [1] using Majorization-Minimization technique. 
%     This file describes the details and procedures for the examples
%     contained in [1]. 
%     
% II. Files contained and their description
%     This directory contains the following files-
%     1. demo.m
%         This file demonstrates the CNC FLSA [1] for denoising a 
%         sparse piecewise constant signal. This file reproduces example 1 
%         in the paper [1]. 
%         
%     3. ECG_demo.m
%         This file demonstrates the denoising of a synthetic ECG signal, 
%         generated using ECGSYN (see [1] for details). 
%         The synthetic ECG signal is obtained by the following commands
%         
%         fs = 256;
%         ecg = ecgsyn(fs, 20); % 20 is the number of beats to be simulated.                                                   
%      
%         
%     5. CNC_FLSA.m
%         This function minimizes the CNC FLSA objective function
%         F(x) = 0.5||y-x||_2^2 + lam0*phi(x,a0) + lam1*phi(Dx,a1)
%         using the majorization-minimization technique, where phi
%         is a non-convex penalty function. type `help CNC_FLSA' for more details
%          
%     6. soft.m
%         This file implements the soft thresholding rule. 
%         type `help soft.m' for more details
%     
%     7. tvd.c, tvd.mexmaci, tvd.mexmaci64, and tvd.mex64
%         C++ implementation of TV denoising (see [1] for details)
% 
% For questions/comments contact: Ankit Parekh (ankit.parekh@nyu.edu)
% 
% [1] Convex fused lasso denoising with non-convex regularization and its 
%     use for pulse detection. 
%     Ankit Parekh and Ivan W. Selesnick, 2015. 
%     

clc; help readMe