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R and Signal Processing

by Joseph Rickert R would probably not be the first language that pops into the mind of an engineer who is looking for signal processing software. However, I think anyone who looks into what R can do in this area would be very pleasantly surprised. The bulk of R’s basic signal processing capability comes from the signal package which was ported over from the open source project Octave. This package contains a number of functions for filtering, filter generation, resampling, interpolation and the visualization of filter models that Matlab packages up in its Signal Processing Toolbox. The functions in the R's signal package retain the look and feel of MatLab originals. Working with these functions should make it easy for anyone familiar with MatLab to make the transition to R. Here is some sample code adapted from D.M. Etter’s book "Engineering Problem Solving with Matlab" for computing the transfer functions for four digital filters and plotting the results. Remember that a transfer function H(z) may be described as the ratio of two complex ploynomials: H(z) = B(z) / A(z) where A(z) = a(0) + a(1) * z^-1 + . . . + a(n) * z^-n  andB(z) = b(0) + b(1) * z^-1 + . . . + b(n) * z^-n # Digital Tranfer Function Example # from pages 333 - 337 of Engineering Problem Solving with Matlab # D.M. Etter, Prentice Hall 1993   # Compute and plot digital transfer functions library(signal) data(package="signal") # to see the data sets availale in the package signal ls("package:signal") # to list all of the objects in signal lsf.str("package:signal") # to list all of the functions in signal     # Filter H1(z) B1 <- c(0.2066,0.4131,0.2066) # coefficients of numerator polynomial a1 >

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More Stories By David Smith

David Smith is Vice President of Marketing and Community at Revolution Analytics. He has a long history with the R and statistics communities. After graduating with a degree in Statistics from the University of Adelaide, South Australia, he spent four years researching statistical methodology at Lancaster University in the United Kingdom, where he also developed a number of packages for the S-PLUS statistical modeling environment. He continued his association with S-PLUS at Insightful (now TIBCO Spotfire) overseeing the product management of S-PLUS and other statistical and data mining products.<

David smith is the co-author (with Bill Venables) of the popular tutorial manual, An Introduction to R, and one of the originating developers of the ESS: Emacs Speaks Statistics project. Today, he leads marketing for REvolution R, supports R communities worldwide, and is responsible for the Revolutions blog. Prior to joining Revolution Analytics, he served as vice president of product management at Zynchros, Inc. Follow him on twitter at @RevoDavid

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