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Although the key names appear in the array, the values remain back in the structure. After calling StructSort(), Listing 15-3 loops through keyArray, which contains the sorted key names. During this loop, keyArray[i] contains the current key name, which can in turn be used to supply the key name to the FruitCosts structure. If you follow the ColdFusion processing engine along step-by-step, the resolution of this reference is as follows:

Step 1: Step 2: Step 3: Result: #FruitCosts[keyArray[i]]# #FruitCosts[keyArray[1]]# #FruitCosts[ Apples ]# 1.50

The performance of a ngerprint identi cation system heavily relies on the quality of the ridge valley structures of the input ngerprint. Unfortunately, due to different skin conditions, sensor noise, incorrect nger pressure, and inherently low-quality ngerprints, several images contain poor-quality regions where the ridge pattern is very noisy and corrupted. The aim of an enhancement algorithm is to improve the clarity of the ridge structures, thus making the successive feature extraction more reliable. The most effective ngerprint enhancement approaches proposed in the literature [1] are based on contextual ltering where the image is locally convolved with lters tuned according to the local context (i.e., ridge orientation and frequency). These are quite standard image processing techniques where the use of learning is typically limited to an initial tuning of the system aimed at nding out the best parameters. However, some isolated attempts have been introduced where the role of learning is

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and E / E = 1. The points identi ed as sample are the results of the Monte Carlo simulation. The maximum standard deviation of the differential settlement occurs when the separation distance has the same order of magnitude as the correlation distance for the modulus, a result consistent with other studies discussed above.

But what if you want to sort by a key in a nested structure We ve modified the FruitCosts structure from earlier in the chapter by using the following code:

14

<cfset <cfset <cfset <cfset <cfset <cfset <cfset <cfset <cfset <cfset <cfset <cfset <cfset <cfset <cfset <cfset FruitCosts = StructNew()> FruitCosts[ Oranges ] = StructNew()> FruitCosts[ Oranges ][ lb ] = 1.99> FruitCosts[ Oranges ][ sack ] = 15.50> FruitCosts[ Apples ] = StructNew()> FruitCosts[ Apples ][ lb ] = 1.50> FruitCosts[ Apples ][ sack ] = 13.00> FruitCosts[ Peaches ] = StructNew()> FruitCosts[ Peaches ][ lb ] = 1.75> FruitCosts[ Peaches ][ sack ] = 16.25> FruitCosts[ Cherries ] = StructNew()> FruitCosts[ Cherries ][ lb ] = 2.25> FruitCosts[ Cherries ][ sack ] = 14.00> FruitCosts[ Lemons ] = StructNew()> FruitCosts[ Lemons ][ lb ] = 1.65> FruitCosts[ Lemons ][ sack ] = 15.00>

Each element in the FruitCosts structure is a substructure containing two keys: lb (price per pound) and sack (price per sack). To help you visualize this structure, look at Figure 15-12.

reference 26. Copyright 2000, IEEE.)

So now that you have this set of nested structures, how do you sort by price per pound A fourth attribute of StructSort()describes a dot path to the sort value, as shown in Listing 15-4.

central: The main drawback of these approaches is to collect a reliable training set with proper ground truth. In the Ghosal et al. method [26] the lter coef cients are learned through a learnby-example paradigm from a small set of training ngerprints and the corresponding set of binary ridge maps manually drawn by an expert (see Figure 14.6). Bal et al. [27] proposed a supervised ltering technique that makes use of a recurrent neural network. Supervised ltering employs a xed-sized lter mask and a convolution operation, as shown in Figure 14.7, where hi,j are the weights of the lter mask, b is a scalar bias value, f is a xed nonlinear activation function, t is the iteration step, and am,n (t) is the pixel intensity at the step t. The recurrent ow of supervised ltering is described by the convolution between the input and the lter mask and then by a summation of the bias scalar value in the nonlinear activation function. The learning process consists in nding the optimal values for the weights hi,j and the bias value b that minimize the difference between the current output and the desired output.

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