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The use of beans in a JSP page emphasizes the fundamental principle of JSP, which is to separate programming from page authoring or designing. The programmer implements the business logic in the bean component by defining the properties that are applicable to the logic. Alternatively, the page designer uses the bean properties to access the values that are used in the pages of the application. As a result, the business logic is encapsulated in a Java object represented by the bean component, which is then instantiated and accessed from a JSP page. Although this mechanism does seem complex, it involves the use of three JSP standard actions already discussed in 5, Using JSP Directives and Action Elements. Let s now look at the implementation of beans in a JSP page. We begin with a detailed discussion on the JSP tags that are used to work with beans in a JSP page. In the final section of the chapter, we apply the concepts in a scenario-based example to create and implement beans in JSP. Before we begin the discussion on the JSP tags used for the bean implementation, let s look at the output generated by the use of a bean in a JSP page. Figure 9.4 displays the output of a JSP page, in which a bean component is used to retrieve the Hello World message. At the end of this chapter, we will have created a page for the bank application by using a bean component. In the course of defining tags for bean implementation, we will use the code of the Hello World message bean example.

In the opening paragraph of their chapter about the BAI, Inbau et al. (2001, p. 173) state that research has demonstrated that innocent subjects tend to respond differently to these specialized questions than do

Since u > 0, it follows that P(h( ) > u ) P(h( ) > uLh ). De ne A := {x RN , h(x) }. Then Au {y RN : h(y) + uLh }. In fact, according to the de nition of Au , if w Au , one can write w = x + z, where h(x) , z u. So h(w) h(x) + uLh . It follows, using (2.22), that P(h( ) > u ) P( AC ). u (2.23) (2.22)

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In Section 3.2 of 2 we discussed computation of the covariance matrix, which describes the statistically expected level of errors in the estimated Cartesian coordinates. Here we show how to transform the error estimates into topocentric coordinates. This turns out to be crucial later in the book for road navigation algorithms to rule out highly improbable solutions. One cannot assume that errors in Cartesian geocentric coordinates are a reasonable measure of the error in topocentric coordinates, because quite frequently the error in height is far worse than in the East and North directions. Fortunately, the computation is quite straightforward, as it follows from the law of propagation of errors. First of all, note that a position error can be represented as a small displacement, and therefore it transforms according to: venv = Gvxyz where the geocentric coordinate matrix vxyz = ( x, y, z)T and the topocentric coordinate matrix venv = ( e, n, h)T. We now derive how to transform the covariance matrix of coordinates from the geocentric system to the topocentric system:

Theorem 5.6. Let X = {X(t) : t R} be Gaussian centered and stationary, with covariance normalized by (0) = 1. Assume that has a Taylor expansion at the origin which is absolutely convergent at t = 2T . Then the conclusion of Theorem 5.1 holds true, so that the Rice series converges and FMT (u) can be computed by means of (5.2). Proof. Again we assume, with no loss of generality, that T = 1. Notice that the hypothesis implies that the spectral moments k exist and are nite for every k = 0, 1, 2, . . . . We will obtain the result assuming that H1 : 2k C1 (k!)2 . It is easy to verify that if has a Taylor expansion at zero that is absolutely convergent at t = 2, then H1 holds true. (In fact, both conditions are only slightly different, since H1 implies that the Taylor expansion of at zero is absolutely convergent in {|}t| < 2.) Let us check that the hypotheses of Theorem 5.1 are satis ed. First, pX(1/2) (x) D = (2 ) 1/2 . Second, let us show a sequence {ck } that satis es (5.1). We have P( X(2k 1)

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