Here you will find daily news and tutorials about R, contributed by over 750 bloggers.
There are many ways to follow us - By e-mail:On Facebook: If you are an R blogger yourself you are invited to add your own R content feed to this site (Non-English R bloggers should add themselves- here)

Compressed sensing (CS) is pretty much appealing all current signal processing research community. At the same time, popularity of R language gaining a strong foot in the research and industry. Even though historically MATLAB is a de-facto standard in signal processing community, R is becoming a serious alternative to this. For example, the quality of R in time-series analysis or medical imaging is now an accepted fact. Last year, I have demonstrated how one can use R in compressed sensing research in a short tutorial in the pub, close to Liverpool street in London. The package R1magic is available in CRAN. Package provides basic interface to perform 1-D compressed sensing with l1, TV penalized minimization. There are other packages doing similar regularized minimization. However the interface of R1magic is particularly designed for CS i.e. appearance of sparse bases in the objective function. Here is one simple example (Version 0.1): library(R1magic)# Signal components N <- 100 # Sparse components K <- 4 # Up to Measurements > K LOG (N/K) M <- 40 # Measurement Matrix (Random Sampling Sampling) phi <- GaussianMatrix(N,M) # R1magic generate random signal xorg <- sparseSignal(N, K, nlev=1e-3) y <- phi %*% xorg ;# generate measurement T <- diag(N) ;# Do identity transform p <- matrix(0, N, 1) ;# initial guess # R1magic Convex Minimization ! (unoptimized-parameter) ll <- solveL1(phi, y, T, p) x1 <- ll$estimate plot( 1:100, seq(0.011,1.1,0.011), type = “n”,xlab=””,ylab=””) title(main=”Random Sparse Signal Recovery”, xlab=”Signal Component”,ylab=”Spike Value”) lines(1:100, xorg , col = “red”) lines(1:100, x1, col = “blue”, cex = 1.5) # shifted by 5 for clearity

Blue line is the reconstructed signal with R1magic.

Related

To leave a comment for the author, please follow the link and comment on their blog: Scientific Memo.