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the higher dimensional feature space. Because of this, SVM has the advantage that it can handle the classes with complex nonlinear decision boundaries. SVM has now evolved into an active area of research [18 21]. This chapter will first introduce the major feature extraction methods LDA and PCA. The MCE algorithm for integrated feature extraction and classification and the nonlinear formulation of SVM are then introduced. Feature extraction and compression with MCE and SVM are discussed subsequently. The performances of these feature extraction and classification algorithms are compared and discussed based on the experimental results on Deterding vowels and TIMIT continuous speech databases.



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PE Executables contain a list of special optional directories, which are essentially additional data structures that executables can contain. Most directories have a special data structure that describes their contents, and none of them is required for an executable to function properly.





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If observed event distortion is less than the distortion bound, ie, Di < D , then the event is deemed to be reliably detected Else, appropriate action needs to be taken to assure the desired reliability level in the event-to-sink communication The main rationale behind such event-to-sink reliability notion is that the data generated by the sensors are temporally correlated, which tolerates individual packets to be lost to the extent where the desired event distortion D is not exceeded Let f be the reporting frequency of a sensor node de ned as the number of samples taken and hence packets sent out per unit time by that node for a sensed phenomenon This reporting frequency can be attributed to increase in sampling rate as in Section 532.

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Hence, the reporting frequency f controls the amount of traf c injected to the sensor eld while regulating the number of temporally correlated samples taken from the phenomenon This, in turn, affects the observed event distortion that is, event detection reliability Thus, the reliable event transport problem in WSN is to determine the reporting rate (f ) of source nodes so that the maximum event estimation distortion bound D is not exceeded; that is, required event detection reliability is achieved at the sink, with minimum resource utilization..

The goal of linear discriminant analysis is to separate the classes by projecting class samples from p-dimensional space onto a finely orientated line. For a K-class problem, m = min K 1 p different lines will be involved. Thus, the projection is from a p-dimensional space to a c-dimensional space [22]. Suppose we have K classes, X1 X2 XK . Let the ith observation vector from the Xj be xji , where j=1 J and i = 1 Nj . J is the number of classes and Nj is the number of observations from class j. The within-class covariance matrix Sw and between-class covariance matrix Sb are defined as:

Figure 3.4 The dynamic linking process and how modules can be interconnected using their import and export tables.

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Table 3.1 NAME Export Table The Optional Directories in the Portable Executable File Format. DESCRIPTION Lists the names and RVAs of all exported functions in the current module. ASSOCIATED DATA STRUCTURE IMAGE_EXPORT_ DIRECTORY

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(16.1)

Lists the names of module IMAGE_IMPORT_ and functions that are DESCRIPTOR imported from the current module. For each function, the list contains a name string (or an ordinal) and an RVA that points to the current function s import address table entry. This is the entry that receives the actual pointer to the imported function in runtime, when the module is loaded. Points to the executable s resource directory. A resource directory is a static definition or various user-interface elements such as strings, dialog box layouts, and menus. Contains a list of addresses within the module that must be recalculated in case the module gets loaded in any address other than the one it was built for. IMAGE_RESOURCE_ DIRECTORY

Reporting Frequency (s 1)

Contains debugging IMAGE_DEBUG_ information for the executable. DIRECTORY This is usually presented in the form of a link to an external symbol file that contains the actual debugging information. Points to a special thread-local section in the executable that can contain thread-local variables. This functionality is managed by the loader when the executable is loaded. IMAGE_TLS_ DIRECTORY

The projection from observation space to feature space is accomplished by a linear transformation matrix T: y = TT x The corresponding within-class and between-class covariance matrices in the feature space are: Sw = Sb = where j =

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