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students, and postdoctoral fellows and quali ed candidates of the aforementioned elds from other institutions. Each year the available positions are (postdoctoral, Predoctoral) (2, 2), (3, 3), (3, 3), (3, 3), (3, 3), and selected candidates can be reappointed annually. Annual stipend and partial tuition/fees are provided for the successful candidates. NIH has supported many training and research programs throughout the years as a way to guide potential candidates to leadership in selected elds. Examples are the two programs funded by the National Cancer Institute (NCI), NIH in the mid-1980s and the early 1990s when the concept of PACS had just started (entitled Biomedical Physics Training Program and PACS in Radiology ) to the author of this book. At that time he was with UCLA (University of California, Los Angeles) and later relocated to UCSF (University of California, San Francisco); and Professor Michael McNitt-Gray and Professor Hooshang Kangarloo of UCLA took over the principal investigatorship of the two programs, respectively. These two grants are still active, and have provided opportunities for a good number of individuals who are now the leaders in medical imaging and PACS. The Biomedical Physics Training Program has helped developing the Medical Imaging Division at UCLA; many trainees got their PhD and/or PD training credentials there and have since moved on to work generate barcode rdlc
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Scripts that are useful for managing databases fall into at least four categories, described in the following sections.
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1. If you did the exercise on drawing limits, use that drawing or open ab05-02.dwg from the Results folder of the CD-ROM. 2. Save the drawing as ab05-03.dwg in your AutoCAD Bible folder. 3. Choose Tools Drafting Settings. 4. On the Snap and Grid tab, the snap spacing is set to 1 2". In the Grid section, change the X and Y spacing to 1". Make sure the Snap Type is set to Grid Snap and Rectangular Snap. Click OK. 5. Click SNAP and GRID on the status bar to turn them on. Make sure OSNAP is turned off. 6. Choose Format Units. Change the Angle Type back to Decimal Degrees. Click OK. 7. Using the coordinate display as your guide, start the LINE command and draw line segments from 2.5, 1.5 to .5<270 to 11<0 to .5<90. End the LINE command. 8. Restart the LINE command. Again use the coordinate display to draw line segments from 2,2 to .5<270 to 12<0 to .5<90. End the LINE command. 9. Save your drawing. It should look like Figure 5-5. Notice how the grid and snap settings facilitate the drawing process. The architectural units create a different drawing experience than decimal units would. Setting up a drawing creates a drawing environment suited to your work needs.
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After selecting U.S. Prepress Defaults, I click the OK button. The source environment is fully prepared. Now to save an image and send it on its way.
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FIGURE 8.7 Measured return loss for (a) port 1 excitation and (b) port 2 excitation of antenna 1 shown in Figure 8.6; (c) measured isolation S21 between ports 1 and 2 for antenna 1; L = 60 mm, h = 12.8 mm, H1 = 19 mm, H2 = 21 mm, t = 4 mm, and ground-plane size = 100 100 mm2 .
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The drawing used in the following exercise on defining primary units, ab15-03.dwg, is in the Results folder on the CD-ROM.
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PCA is used to extract features and visualize data. The most important PCA application is the reduction in the number of wavelengths. Examples of nonhierarchical clustering [22] methods include Gaussian mixture models, K means, and fuzzy C means. They can be subdivided into hard and soft clustering methods. Hard classi cation methods such as K means assign pixels to membership of only one cluster whereas soft classi cations such as fuzzy C means assign degrees of fractional membership in each cluster. Supervised classi cations use spectral and class membership information [23]. Mathematical models are rst computed with a calibration set containing spectral and class information. This model is then applied to predict new sample classes. Supervised pattern recognition algorithms fall into three main categories of contrasting pairs. The rst comprises methods based on discrimination [e.g., linear discriminant analysis (LDA)] and those emphasizing similarity within a class [e.g., soft independent modeling of class analogy (SIMCA)]. The second comprises linear and nonlinear methods such as neural methods. The third comprises parametric and nonparametric computations. In parametric techniques such as LDA, the decision rules use statistical parameters of normal sample distribution. In summary, classical supervised classi cation methods are distance-based LDA, SIMCA, and PLS discriminant analysis (PLS-DA), which performs regression between spectra and class memberships. Reference spectra choice is critical when applying supervised pattern recognition methods. The rst solution is to use pure compound spectra as references. The drawback is that mixture spectra in data cubes often differ from the reference spectra. Applying the model may therefore give wrong results. The second solution, suitable in a few studies, is to select image pixels where only one compound is present in order to obtain the calibration sets.
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