NASA Scientific and Technical Aerospace Reports

properties of hyperbolic Dirichlet functions are discussed. Using the new characterizations of the hyperbolic Q(sub p)
functions established in this thesis, two results dealing with composition operators are proved in Section 3. For further
discussion about these classes, several questions about these hyperbolic classes and open problems are presented in Section
4.
Derived from text
Analytic Functions; Function Space; Hyperbolic Functions; Geometry
20060001593 SUPELEC, Gif-sur-Yvette, France
Some Properties of Prediction and Interpolation Errors
Picinbono, B.; Kerilis, J. M.; IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP ‘87);
Volume 1; 1987, pp. 9.7.1 - 9.7.4; In English; See also 20060001583; Copyright; Avail.: Other Sources
Linear prediction and interpolation are widely used in signal processing. Usually they are obtained by linear filters of finite
memory, and the prediction and interpolation errors decrease when this memory increases. If the asymptotic values of these
errors are null, the signal is singular. In this paper a numerical method to compute the prediction and interpolation errors for
signals with a given power spectrum is presented. A large number of results obtained with this method are discussed, especially
the difference between regular and singular signals.
Author
Interpolation; Linear Prediction; Errors; Signal Processing
20060001619 International Business Machines Corp., USA
Fast Image Segmentation for Some Machine Vision Applications
Hinkle, Eric B.; Sanz, Jorge L. C.; IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP ‘87);
Volume 1; 1987, pp. 7.7.1-7.7.4; In English; See also 20060001583; Copyright; Avail.: Other Sources
This paper describes the use of an image contrast measure for producing binary segmentations of images in a certain class
of applications. This method is well-suited for fast pipeline implementations, because the contrast measure uses only two local
features in the image. To eliminate segmentation noise, we post-process the segmentations using binary morphological
operations. This method has been applied to three different micro-electronics inspection problems, with consistently good
results, and experimental results from each of these applications are presented here. Also, we discuss this technique in terms
of the theory of polynomial classifiers.
Author
Imaging Techniques; Segments; Image Contrast; Computer Vision
20060001626 Air Force Geophysics Lab., Hanscom AFB, MA, USA
On the Extreme Accuracy of Maximum Entropy Spectrum Estimation from an Error-Free Autocorrelation Function
Fougere, Paul F.; IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP ‘87); Volume 1; 1987,
pp. 9.8.1 - 9.8.4; In English; See also 20060001583; Copyright; Avail.: Other Sources
The Maximum Entropy Method (MEM) is compared to the periodogram method (DFT) for the estimation of line spectra
given an error-free autocorrelation function (ACF). In one computer simulation run, a 250 lag ACF was generated as the sum
of 63 cosinusoids with given amplitudes, Ai, and wave numbers, f. The wave numbers cover a band from 0 to 89.239/cm with
a Nyquist of 90/ cm and are given to an accuracy of six significant figures. The amplitudes range over four orders of magnitude
and are given to five significant figures. The resulting normalized ACF was truncated to five significant figures. The resulting
MEM spectrum was remarkably accurate. The lowest 53 spectral peaks reproduced the input frequencies exactly (to six
significant figures) while the largest error in the remaining 10 lines occurred at line 63: input = 89.2814; MEM = 89.2934.
The relative amplitude of this tiny line is only 2x10(exp -4) and yet its frequency was recovered to one part in 9000.
Author
Maximum Entropy Method; Autocorrelation; Errors; Line Spectra; Frequencies
20060001713 State Univ. Coll. of New York, Buffalo, NY, USA
Lemniscate Transform: A New Efficient Technique for Shape Coding and Representation
Kundu, Amlan; IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP ‘87); Volume 1; 1987,
pp. 7.8.1-7.8.4; In English; See also 20060001583; Copyright; Avail.: Other Sources
In this paper, a new algorithm for visual shape coding and representation using a powerful theorem on algebraic curve
has be.en presented. The algorithm codes the closed shape, which does never intersect itself, by means of the foci of the closed
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