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Signal Processing Software
Signal Processing Software
GCVSPL: Spline Smoothing & Differentiation, GGLPEX: Extrapolation, QuickSAND: Smoothing and Differentiation, BioProc: Analog data processing.
GCVSPL
Herman J. Woltring
GCVSPL is a Fortran package for spline smoothing and differentiation, by the late Herman J. Woltring. The methods were described in Woltring HJ (1986) A FORTRAN package for generalized, cross-validatory spline smoothing and differentiation. Adv. Engng. Softw. 8:104-113. Woltring has shown that spline smoothing is equivalent to a double Butterworth filter (see Release Notes). The differences are that with splines it is possible to process data with unequal sampling intervals and the boundary conditions are well defined. The original subroutine package is available, as well as versions for C and Matlab programmers. Also some front-end programs for UNIX and PC.
pdf gcvspl.memo (3 KB) - Release Notes (read first)
pdf gcvspl.f (46 KB) - Subroutine package - VMS Fortran version
pdf gcvspl.fortran (54 KB) - Subroutine package - Fortran 77 version
pdf gcvtst.fortran (6 KB) - Demo/Test Program - including Vaughan's falling golf ball data
GCVSPL Front End
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A front-end program in Fortran 77 (last updated April 12, 2000). The program is interactive and self-explanatory. Tested on UNIX (Sun and SGI) and Windows NT.
pdf filter.pdf (95 KB) - Notes on Digital Filtering
pdf gcvspl.zip (53 KB) - Complete package
GCVSPL in C (D. Meglan)
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A version of GCVSPL in the C language.
GCVSPL in C (D. Twisk)
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A version of GCVSPL in the C language.
GCVSPL for Matlab (D. Carta)
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A Matlab MEX interface for the GCVSPL package.
GCVSPL for Matlab (T. Reina)
Tony Reina
A Matlab MEX interface for the GCVSPL and SPLDER packages based on Dwight Meglan's C Code.
GGLPEX
Giannis Giakas
Software that uses a linear prediction method to extrapolate data.
Abstract
Two extrapolation techniques for recursive digital filtering are presented and compared with common padding methods such as linear and reflection (reverse mirror) extrapolation. The case in which the endpoints of position data lead to peak accelerations after filtering and differentiation is examined. The first technique, "least squares", is based on fitting a third degree polynomial to the final ten data points in both to the forward and backward directions and extending the signal by 20 data points using the polynomial coefficients. The second technique, "prediction", is based on a linear autoregressive model with 20 coefficients, which is applied in both directions and the signal is extrapolated by 20 points. The lowest cumulative error of the endpoint accelerations (22.8 rad s-2) represented just one third of the error when the common padding methods were used in optimal digital filtering (69.7 rad s-2). It also represented approximately half the lowest cumulative error in optimal smoothing with quintic splines (48.0 rad s-2).
default gglpex.exe - Executable F (.exe) file (23 KB)
QuickSAND
Jeff Walker
Quick Smoothing And Numerical Differentiation for Power Macintosh. This software has numerous algorithms for smoothing and differentiating noisy data, including the Woltring spline routines, Butterworth filters, Wiener filters, and more.
(link out of date - http://usm.maine.edu/~walker/software.html)
BioProc
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A program to read and display Kistler, APAS and other file formats. Includes algorithms for smoothing and ensemble averaging of signals.
(link out of date - http://isbweb.org/software/bioproc)
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