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Introduction and Background 17.1.1 Introduction 17.1.2 Background Image/Data Security Methods 17.2.1 Four Steps in Digital Signature and Digital Envelope 17.2.2 General Methodology Digital Envelope 17.3.1 Image Signature, Envelope, Encryption, and Embedding 17.3.1.1 Image Preprocessing 17.3.1.2 Hashing (Image Digest) 17.3.1.3 Digital Signature 17.3.1.4 Digital Envelope 17.3.1.5 Data Embedding 17.3.2 Data Extraction and Decryption 17.3.3 Methods of Evaluation of the Digital Envelope

80 inches height 31 inches width 59 inches depth 13,000 51,000 BTU/hour 2,666 pounds (1,209 kg) 12 multimode fiber

Notice in Figure 2-8 the same six navigation items are listed among the menu commands. In addition, each of these navigation items has corresponding keyboard modifiers that produce the same action. The keystrokes for page navigation are listed in Table 2-1.

Historically, one motivation for performing NIRS was to develop fast and noninvasive quantitative analysis To achieve this, it is not suf cient to extract PCs from the data There must be a regression method relating these PCs to the constituent, analyte, or physical property for which the calibration is performed Regression concerns all methods attempting to t a model to the observed data The tted model may be used to describe the relationship between two groups of variables or to predict values of unknown samples If X and Y are the two data matrices involved in regression, the purpose is to compute a Y = f(X) model, which tries to explain, or predict, the variations in the Y variable(s) from those in the X variable(s).

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The link between X and Y is explored through a common set of samples from which both X and Y values have been collected and are clearly known Building a regression model involves collecting variable values for selected samples and tting a mathematical relationship to the corresponding spectral data For example, spectroscopic measurements are performed on solutions with known concentrations of a given compound Regression is used to relate the concentration to the spectrum Once the regression model is build, the unknown concentration for new samples can be indirectly predicted using the spectroscopic measurements as predictors The advantage appears obvious if one considers noninvasive and nondestructive measurement by NIR If the concentration is dif cult or expensive to measure directly, spectroscopic analysis offers an alternative and much cheaper method of determination.

Thus the indication for using regression as a predictive tool is its potential for performing fast and low-cost measurement as a substitute for more expensive or time-consuming alternatives The data may require appropriate preprocessing before a regression can be built and used The calibration step must be followed by a validation step, that is, the model must be checked for its ef ciency in predicting independent data Once the number of components has been selected based on the calibration and validation variances, the model is diagnosed by interpreting the loading and score plots (for PCR and PLS), the loading weight plots (for PLS), and B coef cients (PCR), and examined for the prediction of new data 42312 Multiple Linear Regression.

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where l=k is the maximum allowed error due to variations in y. Typically, to have a reasonably small error, we at least require that ty2 l : 4 1:12

g1(t ) iLS(t ) g2(t )

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(ii)