Scientific and Technical Aerospace Reports Volume 39 April 6, 2001

of the neural network and hence, the performance of the mass CAD scheme, should improve. Preliminary results have exhibited
this improvement.
DTIC
Cancer; Computer Techniques; Detection; Genetic Algorithms; Mammary Glands; Medical Science
64
NUMERICAL ANALYSIS
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20010021922 Institute for Computer Applications in Science and Engineering, Hampton, VA USA
Multigrid approaches to non-linear diffusion problems on unstructured meshes Final Report
Mavriplis, Dimitri J., Institute for Computer Applications in Science and Engineering, USA; February 2001; 16p; In English
Contract(s)/Grant(s): NAS1-97046; RTOP 505-90-52-01
Report No.(s): NASA/CR-2001-210660; NAS 1.26:210660; ICASE-2001-3; No Copyright; Avail: CASI; A03, Hardcopy; A01,
Microfiche
The efficiency of three multigrid methods for solving highly non-linear diffusion problems on two-dimensional unstructured
meshes is examined. The three multigrid methods differ mainly in the manner in which the nonlinearities of the governing equations
are handled. These comprise a non-linear full approximation storage (FAS) multigrid method which is used to solve the nonlinear
equations directly, a linear multigrid method which is used to solve the linear system arising from a Newton linearization
of the non-linear system, and a hybrid scheme which is based on a non-linear FAS multigrid scheme, but employs a linear solver
on each level as a smoother. Results indicate that all methods are equally effective at converging the non-linear residual in a given
number of grid sweeps, but that the linear solver is more efficient in cpu time due to the lower cost of linear versus non-linear grid
sweeps.
Author
Diffusion; Multigrid Methods; Nonlinearity; Unstructured Grids (Mathematics)
20010023924 Defence Research Establishment Ottawa, Ottawa, Ontario Canada
Linear and Quadratic Time-Frequency Representations
Thayaparan, Thayananthan, Defence Research Establishment Ottawa, Canada; Nov. 2000; 115p; In English
Report No.(s): AD-A385576; DREO-TM-2000-080; No Copyright; Avail: CASI; A02, Microfiche; A06, Hardcopy
This report is reviewing both linear and quadratic time-frequency representations. The linear representations discussed are
Short-Time Fourier Transform and S-transform. The quadratic representation discussed is Wigner distribution. We outline the
motivations, interpretations, mathematical fundamentals, properties, and applications of these linear and quadratic time-frequency
representations. We also compare these three different time-frequency analysis techniques and show that each technique
has its strengths and drawbacks. The simulated data sets have been used for the comparison. The choice of the particular time-frequency
representation depends upon the specific area of application and what we aim to achieve with a local frequency analysis.
We show that time-frequency analysis methods should enable us to classify signals with a considerably greater interpretation of
the physical situation than can be achieved by the conventional Fourier Transform method alone.
DTIC
Quantum Mechanics; Fourier Transformation; Power Spectra
20010025276 NASA Goddard Space Flight Center, Greenbelt, MD USA
The Vertical Error Characteristics of GOES-derived Winds: Description and Impact on Numerical Weather Prediction
Rao, P. Anil, Maryland Univ., USA; Velden, Christopher S., Wisconsin Univ., USA; Braun, Scott A., NASA Goddard Space Flight
Center, USA; [2001]; 49p; In English; No Copyright; Avail: CASI; A03, Hardcopy; A01, Microfiche
Errors in the height assignment of some satellite-derived winds exist because the satellites sense radiation emitted from a
finite layer of the atmosphere rather than a specific level. Potential problems in data assimilation may arise because the motion
of a measured layer is often represented by a single-level value. In this research, cloud and water vapor motion winds that are
derived from the Geostationary Operational Environmental Satellites (GOES winds) are compared to collocated rawinsonde
observations (RAOBs). An important aspect of this work is that in addition to comparisons at each assigned height, the GOES
winds are compared to the entire profile of the collocated RAOB data to determine the vertical error characteristics of the GOES
winds. The impact of these results on numerical weather prediction is then investigated. The comparisons at individual vector
height assignments indicate that the error of the GOES winds range from approx. 3 to 10 m/s and generally increase with height.
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