PLEASE NOTE : This is the documentation for the avs module executable spectest1D, which contains the following modules: darpsd dminorm dmusic Any mention of xvimage is actually a "field 2D". Also, the INPUTs and OUTPUTs, which are mapped to avs parameters, inputs, and outputs, are for the khoros library routine. ********************************************************************************* Documentation for avs module darpsd INPUT image pointer to VIFF structure containing image data to be processed. nfreq number of data points for PSD estimate (nfreq >= 0). arfrom AR coefficients: come from a tapped delay filter(0) or from a lattice filter(1). estimate Spectral estimate: phase spectral estimate(0) or PSD estimate(1). normalize normalize PSD estimate: if yes (0) then nor- malize, else no (1) do nothing. procdir process direction: 0 indicated vector oriented processing, 1 indicates band oriented processing. OUTPUT image pointer to VIFF structure containing image data after processing. Return Value: 1 on success, 0 on failure. DESCRIPTION darpsd 1D AR PSD/Phase Estimation. Determines the 1D autoregressive (AR) PSD and Phase spectral estimate of the input file. The input file consists of autoregressive parameters. The autoregressive parameters can originate either on a tapped-delay or lattice filter realizations. The user is allowed to extrapolate the PSD or Phase estimate to the desired number of points. It can operate on Real and Complex data. NOTE 1: complex data is expected to be in rectangular (not polar) form. NOTE 2: if input data is real the Phase spectra will be 0.0. darpsd does not work on explicit location data and will return an error when such a file is encountered. No output file will be created. darpsd works only on VFF_TYP_FLOAT or VFF_TYP_COMPLEX data files. Input/Output Files The input file must be of type VFF_TYP_FLOAT or VFF_TYP_COMPLEX. The output file returned will be of size specified by the user. If the data is of type VFF_TYP_COMPLEX and complex aithmetic is selected then the output file will be of type VFF_TYP_FLOAT. Process direction Data can be processed with either band or vector orientation (-d option). The default data processing direction is in the vector direction (-d = 0). pixel location. The direc- tion of processing can be changed so that each band is pro- cessed as a signal (instead of each vector being a signal) by setting the -d option to 1. SEE ALSO darpsd(1), intro(3), vipl(3), verror(3), vutils(3) RESTRICTIONS works only on VFF_TYP_COMPLEX and VFF_TYP_FLOAT. darpsd does not work on explicit location data and will return an error when such a data file is encountered. AUTHOR Ramiro Jordan COPYRIGHT Copyright 1992, University of New Mexico. All rights reserved. ********************************************************************************* Documentation for avs module dminorm INPUT image pointer to VIFF structure containing image data to be processed. order order of the linear combiner. freq number of computed spectral values. kdim used to determine the dimension of the noise subspace. If it is a positive, non-zero integer, then it represents the actual value of the noise subspace dimension. If it is zero, then dminorm will use the Akaike (AIC) criteria to determine the noise subspace dimension. If it is -1, then the minimum description length (MDL) criteria will be used. auto_type type of auto correlation to be used. 0 specifies a biased estimate, 1 specifies an unbiased estimate, 2 specifies an FFT based estimate. arith_type type of arithmetic to be used on complex data. 0 specifies scalar arithmetic and 1 specifies vector arithmetic. psd_type specifies the centering of the power spectral density estimate. 0 specifies not centered, 1 specifies centered. procdir process direction: 0 indicated vector oriented processing, 1 indicates band oriented processing. OUTPUT image pointer to VIFF structure containing image data after processing. Return Value: 1 on success, 0 on failure. DESCRIPTION An implementation of the Minimum Norm for spectral estima- tion. Minimum Norm is a spectral estimation technique based on eigendecomposition of the autocorrelation matrix of a sampled random process. The Minimum Norm algorithm attempts to eliminate the effects of spurious zeros, which result in spurious peaks in the power spectrum, by pushing them inside the unit circle. The algorithm involves generating estimates of the eigen- values and eigenvectors of a Toelitz and Hermitian auto- correlation matrix estimate. The eigenvectors are then sorted by ascending order of their corresponding eigen- values. The eigenvectors corresponding to the k smallest, approximately equal eigenvalues are selected. k is the dimension of the noise subspace, and can be specified by the user or may be computed using the Akaike (AIC) or Minimum Description Length (MDL) information theoretic criteria. The Minimum Norm power spectrum is computed by solving an ordinary linear prediction problem. For a more theoretical discussion of the Minimum Norm algo- rithm see: Optimum Signal Processing by Sophocles Orfranidis, McGraw-Hill, 1988; or Eigenvector-Based Parametric Modeling of Time-Domain Signals: Application to Nuclear Magnetic Resonance Spectroscopy by Glen Abousleman (Master's thesis, University of New Mexico, Dept. of EECE, 1990). dminorm does not work on explicit location data and will return an error when such a file is encountered. No output file will be created. dminorm works only on VFF_TYP_FLOAT or VFF_TYP_COMPLEX data files. Input/Output Files The input file must be of type VFF_TYP_FLOAT or VFF_TYP_COMPLEX. The output file returned will be of size specified by the user. If the data is of type VFF_TYP_COMPLEX and complex aithmetic is selected then the output file will be of type VFF_TYP_FLOAT. Process direction Data can be processed with either band or vector orientation (-d option). The default data processing direction is in the vector direction (-d = 0). This means that data stored in multiband format will be processed as a set pixel loca- tion. The direction of processing can be changed so that each band is processed as a signal (instead of each vector being a signal) by setting the -d option to 1. SEE ALSO dminorm(1), intro(3), vipl(3), verror(3), vutils(3) RESTRICTIONS works only on VFF_TYP_COMPLEX and VFF_TYP_FLOAT. dminorm does not work on explicit location data and will return an error when such a data file is encountered. AUTHOR Jeremy Worley, Ramiro Jordan, Glen Abousleman COPYRIGHT Copyright 1992, University of New Mexico. All rights reserved. ********************************************************************************* Documentation for avs module dmusic INPUT image pointer to VIFF structure containing image data to be processed. order order of the linear combiner. freq number of computed spectral values. kdim used to determine the dimension of the noise subspace. If it is a positive, non-zero integer, then it represents the actual value of the noise subspace dimension. If it is zero, then dmusic will use the Akaike (AIC) criteria to determine the noise subspace dimension. If it is -1, then the minimum description length (MDL) criteria will be used. auto_type type of auto correlation to be used. 0 specifies a biased estimate, 1 specifies an unbiased estimate, 2 specifies an FFT based estimate. arith_type type of arithmetic to be used on complex data. 0 specifies scalar arithmetic and 1 specifies vector arithmetic. psd_type specifies the centering of the power spectral density estimate. 0 specifies not centered, 1 specifies centered. procdir process direction: 0 indicated vector oriented processing, 1 indicates band oriented processing. OUTPUT image pointer to VIFF structure containing image data after processing. Return Value: 1 on success, 0 on failure. DESCRIPTION An implementation of the MUSIC (Multiple Signal Classifica- tion) for spectral estimation. MUSIC is a spectral estima- tion technique based on eigendecomposition of the autocorre- lation matrix of a sampled random process. The process involves generating estimates of the eigenvalues and eigenvectors of a Toelitz and Hermitian autocorrelation matrix estimate. The eigenvectors are then sorted by ascending order of their corresponding eigenvalues. The eigenvectors corresponding to the k smallest, approximately equal eigenvalues are selected. k is the dimension of the noise subspace, and can be specified by the user or may be computed using the Akaike (AIC) or Minimum Description Length (MDL) information theoretic criteria. k power spectrum estimates corresponding to the k selected eigenvectors are computed and averaged to form the MUSIC power spectrum estimate. For a more theoretical discussion of the MUSIC algorithm see: Optimum Signal Processing by Sophocles Orfranidis, McGraw-Hill, 1988; or Eigenvector-Based Parametric Modeling of Time-Domain Signals: Application to Nuclear Magnetic Resonance Spectroscopy by Glen Abousleman (Master's thesis, University of New Mexico, Dept. of EECE, 1990). dmusic does not work on explicit location data and will return an error when such a file is encountered. No output file will be created. dmusic works only on VFF_TYP_FLOAT or VFF_TYP_COMPLEX data files. Input/Output Files The input file must be of type VFF_TYP_FLOAT or VFF_TYP_COMPLEX. The output file returned will be of size specified by the user. If the data is of type VFF_TYP_COMPLEX and complex aithmetic is selected then the output file will be of type VFF_TYP_FLOAT. Process direction Data can be processed with either band or vector orientation (-d option). The default data processing direction is in the vector direction (-d = 0). This means that data stored in multiband format will be processed as a set pixel loca- tion. The direction of processing can be changed so that each band is processed as a signal (instead of each vector being a signal) by setting the -d option to 1. SEE ALSO dmusic(1), intro(3), vipl(3), verror(3), vutils(3) RESTRICTIONS works only on VFF_TYP_COMPLEX and VFF_TYP_FLOAT. dmusic does not work on explicit location data and will return an error when such a data file is encountered. AUTHOR Jeremy Worley, Ramiro Jordan, Glen Abousleman COPYRIGHT Copyright 1992, University of New Mexico. All rights reserved. *********************************************************************************