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and dark rings, spreading out with increasing defocus. This cannot be seen very clearly in Figure 11.6 but is more evident in Figure 11.8, which is again a set of isophots, but taken over a greater range of both the coordinates and showing only one quadrant of the symmetrical pattern. The isophots are labeled with the logarithm, to base 10, of the relative intensity, so as to give a greater range of intensities in the diagram. From Figure 11.8 it can be seen that a typical defocused pattern, easily recognizable by anyone who has looked at point spread functions, will show a series of roughly equally spaced rings, increasing in intensity toward the outside and having the outermost ring brighter, wider, and larger in diameter than would be expected from the general progression of the rings. In fact, the rings are not so regularly graded as they appear at rst to be; this can be seen from a very careful inspection of the monochromatic point spread function or from a study of Figure 11.8. Nevertheless, this apparent regularity is easily upset by

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You can use this feature to make your Hello, world! example prettier by adding a proper HTML header and footer and including a CSS style sheet. Enter the following code and save it as hello_ pretty.php in your document root folder:

To create a new database, all you have to do is use the CREATE DATABASE command. Type the following to create a new database called mydatabase:

GENERAL PRINCIPLES Orbital interaction theory as introduced and utilized in the previous chapters of the book has served to point us in a direction along a reaction coordinate We have been able to nd the most favorable geometry in which two molecules can approach each other by considering the interactions of the frontier orbitals, and we have seen that, carried to the logical extreme, the initial interactions imply making and breaking of bonds and the formation of new molecules Indeed, the initial trajectory implied from the orbital interaction theory very often does indeed lead to the expected products when the reaction is exothermic For endothermic reactions, information with respect to the structure and stability of the transition state is necessary to decide upon the reaction pathway, and this could be deduced from examination of the reactive intermediates (or products if the reaction is concerted).

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.