NASA Scientific and Technical Aerospace Reports
NASA Scientific and Technical Aerospace Reports
NASA Scientific and Technical Aerospace Reports
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estimators, <strong>and</strong> investigate the effectiveness of various combinations of the partial derivative estimates in detecting blurred<br />
steps <strong>and</strong> lines. We also touch briefly on the problem of sensitivity to various types of edge structures, <strong>and</strong> develop an isotropic<br />
operator with reduced sensitivity to isolated spikes.<br />
Author<br />
Edge Detection; Differential Equations<br />
20060001721 Oklahoma State Univ., Stillwater, OK, USA<br />
CARMA Model Methods of Two-Dimensional Shape Classification: An Eigensystem Approach vs. the LP Norm<br />
Malakooti, Mohammad V.; Teague, Keith A.; IEEE International Conference on Acoustics, Speech, <strong>and</strong> Signal Processing<br />
(ICASSP ‘87); Volume 1; 1987, pp. 583-586; In English; See also 20060001583; Copyright; Avail.: Other Sources<br />
Because of periodicity of the time series derived from the N angularly equispaced radii, the correlation matrix has an<br />
invariant feature under rotation, translation, <strong>and</strong> scaling. The periodic characteristics possessed by the time series can be<br />
utilized to obtain improvement for texture boundary detection. A new circular ARMA (CARMA) model is introduced to<br />
represent the time series obtained for shape classification. This model is compared with a regular ARMA model <strong>and</strong> its high<br />
resolution <strong>and</strong> accuracy is tested for several two dimensional objects. Singular value decomposition (SVD) is used to calculate<br />
the insensitive features for shape classification <strong>and</strong> boundary reconstruction. The invariant right singular vectors of the<br />
correlation matrix are used as an orthogonal basis for the solution space. The dimension of the spanned space (model order)<br />
is calculated from a new nullity algorithm. To show the high resolution of the eigensystem approach, L(sub 1) <strong>and</strong> classical<br />
L(sub 2) solutions are compared.<br />
Author<br />
Algorithms; Shapes; Two Dimensional Models; Eigenvectors<br />
20060001802 NorthWest Research Associates, Inc., Bellevue, WA USA<br />
Laboratory <strong>and</strong> Numerical Studies of the Effects of Shear on 3-D Vortex Evolution<br />
Delisi, Donald P.; Robins, Robert E.; Aug. 17, 2005; 45 pp.; In English; Original contains color illustrations<br />
Contract(s)/Grant(s): N00014-01-C-0316<br />
Report No.(s): AD-A440279; NWRA-BECR-05-R305; No Copyright; Avail.: CASI: A03, Hardcopy<br />
This report documents the results of ONR Contract N00014-01-C-0316. The goal of this study was to begin to underst<strong>and</strong><br />
the effects of vertical shear on the evolution of a vortex pair. Vorticity is always generated from a three-dimensional lifting<br />
surface. Behind this surface, the vorticity typically rolls up quickly into a pair of counter-rotating vortices. Since vortices<br />
transport mass <strong>and</strong> momentum, their evolution <strong>and</strong> persistence are important in naval hydrodynamics.<br />
DTIC<br />
Numerical Analysis; Shear Properties; Vortices<br />
20060001803 Massachusetts Univ., Amherst, MA USA<br />
Intrinsically Motivated Reinforcement Learning<br />
Singh, Satinder; Barto, Andrew G.; Chentanez, Nuttapong; Jan. 1, 2005; 9 pp.; In English; Original contains color illustrations<br />
Report No.(s): AD-A440280; No Copyright; Avail.: Defense <strong>Technical</strong> Information Center (DTIC)<br />
Psychologists call behavior intrinsically motivated when it is engaged in for its own sake rather than as a step toward<br />
solving a specific problem of clear practical value. But what we learn during intrinsically motivated behavior is essential for<br />
our development as competent autonomous entities able to efficiently solve a wide range of practical problems as they arise.<br />
In this paper we present initial results from a computational study of intrinsically motivated reinforcement learning aimed at<br />
allowing artificial agents to construct <strong>and</strong> extend hierarchies of reusable skills that are needed for competent autonomy.<br />
DTIC<br />
Learning; Problem Solving<br />
20060001822 Maryl<strong>and</strong> Univ., College Park, MD USA<br />
A Novel Non-Orthogonal Joint Diagonalization Cost Function for ICA<br />
Afsari, Bijan; Krishnaprasad, P. S.; Jan. 1, 2005; 14 pp.; In English<br />
Contract(s)/Grant(s): DAAD19-01-1-0465<br />
Report No.(s): AD-A440311; ISR-TR-2005-106; No Copyright; Avail.: Defense <strong>Technical</strong> Information Center (DTIC)<br />
We present a new scale-invariant cost function for non-orthogonal joint-diagonalization of a set of symmetric matrices<br />
with application to Independent Component Analysis (ICA). We derive two gradient minimization schemes to minimize this<br />
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