Determination of mean surface position and sea state from the radar return of a short-pulse satellite altimeter

Using the specular point theory of scatter from a very rough surface, the average backscatter cross section per unit area per radar cell width is derived for a cell located at a given height above the mean sea surface. This result is then applied to predict the average radar cross section observed by a short-pulse altimeter as a function of time for two modes of operation: pulse-limited and beam-limited configurations. For a pulse-limited satellite altimeter, a family of curves is calculated showing the distortion of the leading edge of the receiver output signal as a function of sea state (i.e., wind speed). A signal processing scheme is discussed that permits an accurate determination of the mean surface position--even in high seas--and, as a by-product, the estimation of the significant seawave height (or wind speed above the surface). Comparison of these analytical results with experimental data for both pulse-limited and beam-limited operation lends credence to the model. Such a model should aid in the design of short-pulse altimeters for accurate determination of the geoid over the oceans, as well as for the use of such altimeters for orbital sea-state monitoring

Determination of RMS height of a rough surface using radar waves

Root mean square height of rough surface determined by measuring correlation between two scattered radar waves at different frequencies as function of frequency separatio

Gating characteristics of photomultiplier tubes for Lidar applications

A detector test facility was developed and applied in the evaluation and characterization of lidar detectors in support of the multipurpose airborne differential absorption lidar (DIAL) system based at the Langley Research Center (LaRC). A performance data base of various detector configurations available to the DIAL system was obtained for optimum lidar detector selection. Photomultiplier tubes (PMT's) with multialkaline and bialkaline photocathodes were evaluated in voltage-divider networks (bases) by using either the focusing electrode or dynodes as a gating mechanism. Characteristics used for detector evaluation included gain stability, signal rise time, and the ability to block unwanted high light levels

November Snowfall Variability and Trends Around Lake Michigan: Sensitivity to Temperature and Teleconnection Patterns

Using available long-term stations, November climatology of temperature and snowfall since 1950 has been composited for the region near Lake Michigan. Daily data was examined for the available stations to explore the monthly temperature and snowfall, the number of days with snowfall, and snow cover. The characteristics of six sub-region composites were compared using composites around Lake Michigan, respectively. Early season snowfall is much more common in the eastern sub-regions, implying a dominant role of lake-effect snowfall to the overall climatology. The number of days with snowfall is greater in the eastern sub-regions. Western and eastern sub-regions both exhibit a negative correlation between snowfall and temperature. The snowfall is particularly sensitive to the number of near to below freezing days. Teleconnection processes were also examined and a deviation was found with El Niño Novembers as opposed to neutral and La Niña Novembers. A weaker relation to snowfall was seen to the North Atlantic Oscillation (NAO). There is a trend toward less November monthly snowfall and number of days with snow. For some stations in lake-effect prone areas, there are virtually no snowless Novembers in the initial decades, yet three or four out of ten Novembers have been essentially snowless since 1990

Manifold Learning in MR spectroscopy using nonlinear dimensionality reduction and unsupervised clustering

Purpose To investigate whether nonlinear dimensionality reduction improves unsupervised classification of 1H MRS brain tumor data compared with a linear method. Methods In vivo single-voxel 1H magnetic resonance spectroscopy (55 patients) and 1H magnetic resonance spectroscopy imaging (MRSI) (29 patients) data were acquired from histopathologically diagnosed gliomas. Data reduction using Laplacian eigenmaps (LE) or independent component analysis (ICA) was followed by k-means clustering or agglomerative hierarchical clustering (AHC) for unsupervised learning to assess tumor grade and for tissue type segmentation of MRSI data. Results An accuracy of 93% in classification of glioma grade II and grade IV, with 100% accuracy in distinguishing tumor and normal spectra, was obtained by LE with unsupervised clustering, but not with the combination of k-means and ICA. With 1H MRSI data, LE provided a more linear distribution of data for cluster analysis and better cluster stability than ICA. LE combined with k-means or AHC provided 91% accuracy for classifying tumor grade and 100% accuracy for identifying normal tissue voxels. Color-coded visualization of normal brain, tumor core, and infiltration regions was achieved with LE combined with AHC. Conclusion Purpose To investigate whether nonlinear dimensionality reduction improves unsupervised classification of 1H MRS brain tumor data compared with a linear method. Methods In vivo single-voxel 1H magnetic resonance spectroscopy (55 patients) and 1H magnetic resonance spectroscopy imaging (MRSI) (29 patients) data were acquired from histopathologically diagnosed gliomas. Data reduction using Laplacian eigenmaps (LE) or independent component analysis (ICA) was followed by k-means clustering or agglomerative hierarchical clustering (AHC) for unsupervised learning to assess tumor grade and for tissue type segmentation of MRSI data. Results An accuracy of 93% in classification of glioma grade II and grade IV, with 100% accuracy in distinguishing tumor and normal spectra, was obtained by LE with unsupervised clustering, but not with the combination of k-means and ICA. With 1H MRSI data, LE provided a more linear distribution of data for cluster analysis and better cluster stability than ICA. LE combined with k-means or AHC provided 91% accuracy for classifying tumor grade and 100% accuracy for identifying normal tissue voxels. Color-coded visualization of normal brain, tumor core, and infiltration regions was achieved with LE combined with AHC. Conclusion The LE method is promising for unsupervised clustering to separate brain and tumor tissue with automated color-coding for visualization of 1H MRSI data after cluster analysis

Scattering from surfaces with different roughness scales, analysis and interpretation

Statistical analysis and physical interpretation of scattering from surfaces with different roughness scale

SYSTEMS ANALYSIS APPROACH TO SELECTION OF FARM EQUIPMENT

Farm Management,

4 Tricks To Make Her Orgasm- AskMen

Her shoulder, white like a page. Her sex; her words