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Caution As nifty as this automatic server-side validation may seem, it has its deficits. It s probably more trouble than it s worth. We address these problems later in this section. But because you may encounter it, it s worth understanding. And for small forms, its benefits may outweigh its deficits.

1, the Fisherface algorithm [39], which is a classical LDA approach, and the uncorrelated LDA (ULDA) algorithm [51] are included for comparison between LDA and MLDA For LDA and ULDA, a 32 32 face image is represented as a 1024 1 vector for input In the experiments, the number of iterations for the MLDA variants was set to 10 For DATER, GTDA, and TR1DA, up to 300 features were tested For UMLDA, up to 100 features were tested The maximum number of features tested for LDA and ULDA was C 1, where C is the number of subjects (classes) in training For the TR1DA algorithm, we tested several values of for each L and the best one for each L was used: = 2 for L = 5, = 08 for L = 10, and = 06 for L = 20, 30.

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It s a simple idea, really. It enables you to place information on the form that identifies which form fields are to be validated and what validation should be done for each field. The validation then takes place automatically. Say that you have a form field such as the following:

This field prompts the user to enter his username, naturally, and may be used on a login screen, as shown in Figure 20-1. In such an application, you really need to require the user to enter a username; otherwise, the user can t log in. So this is a good opportunity for a required validation. Although we show a more effective client-side validation for this in the section Adding validation by using CFINPUT, later in this chapter, you first need to see how to do it by using automatic server-side processing.

For UMLDA, a xed regularization parameter = 5 104 was empirically chosen and all initial projection vectors are set to all ones (1) for simplicity..

0 2 4 6 Depth, Z (m)

The way that this automatic server-side validation works is that you place a hidden field (as discussed in 6) on the form. This hidden field has the same name as the field to be validated, adding one of several predefined validation directives. In the example in the preceding section, to make the username required input, you would use the following code:

Notice the validation directive, required, which is appended to the form s field name, with an underscore connecting them. This states that the field with the name username requires a value to be entered. That s all that you need to do. (You see later that you could use several other such directives.) The code for the form, with this added field (and one for a password as well), is as follows:

The detection of ducial points increases the complexity of ECG-based applications. In addition, there are no de nitive rules or methods for localizing the wave boundaries, especially in varying heart rates or heart anomalies. Motivated by these dif culties, various methods have been suggested for the non ducial feature extraction from ECGs. In general, the use of windowing techniques, as a precursor to the feature extraction, has been found to overcome several serious problems, due to pulse localization and synchronization, in ducial methods. Plataniotis et al. [23] were among the earliest to report a non ducial technique for extracting feature from ECG segments. The autocorrelation of nonoverlapping ECG windows was used as a source of discriminative information, followed by the discrete cosine transform for dimensionality reduction. Classi cation was carried out using two similarity measures, namely, the normalized Euclidean and the Gaussian log-likelihood distances. The method was tested on 14 subjects; and 100% subject and window recognition rates were achieved. Wang et al. [24] demonstrated a systematic analysis of ECG signals for methodologies with and without ducial points detection. A ducial framework that combined analytic and appearance features was compared to a feature extraction technique from autocorrelated ECG windows. The proposed techniques were tested on two public data sets. It was shown that the non ducial methods can achieve high-recognition performance compared to the ducial ones.

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