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Step 4 The predicted positions of the target are used by the UW-API protocols [2] to create new cells that the target is likely to enter This is illustrated in Figure 148, where the three dotted cells represent the regions that the target is likely to enter after the current active cell (cell A in Fig 148) A subset of these cells is activated by the UW-API protocols for subsequent detection and tracking of the target Step 5 Once the target is detected in one of the new cells, it is designated as the new active cell and the nodes in the original active cell (cell A in Fig 148) can be put in the standby state to conserve energy Steps 1 5 are repeated for the new active cell, and this forms the basis of detecting and tracking a single target.

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If Windows can t connect to any of the networks that are currently available, it tries to connect to the ones found in your list of preferred networks (previously shown in Figure 3-7). If Windows can t connect to those preferred networks, it uses your wireless card to scan for the presence of any wireless networks within range. If you are using the Windows Automatically connect to nonpreferred networks feature (see Figure 3-9), your computer automatically connects to the rst wireless network it nds. If you have disabled this feature (as in Figure 3-10), Windows does not connect to any wireless networks it nds. Instead, Windows displays a message that says, One or more wireless networks are available (previously shown in Figure 3-6). This gives you complete control over which wireless network your computer accesses.

For each detected target, an information eld containing tracking information, such as the location of the target at certain past times, is usually passed from one active cell to the next one This is particularly important in the case of multiple targets 14424 Multiple-Target Tracking Figure 148 illustrates detection and tracking of a single target If multiple targets are suf ciently separated in space or time, that is, they occupy distinct space time cells, essentially the same procedure as described in Section 14423 may be used: a different track is initiated and maintained for each target Suf cient separation in time means that the energy detector output of a particular sensor exhibits distinguishable peaks corresponding to the CPAs of the two targets.

where Y is the DV and X and e are both IVs. In fact, the Bentler-Weeks model is a regression model, expressed in matrix algebra: =B + , (24.2)

Thus, an = bn implies that an and bn are equal to the rst order in the exponent. We can now restate the above results: If n 0 and n 0, then

Similarly, suf cient separation in space means that at a given instant the spatial target signatures exhibit distinguishable peaks corresponding to nodes that are closest to the targets at that instant The assumption of suf cient separation in space and/or time may be too restrictive in general In such cases, classi cation algorithms are needed that operate on spatiotemporal target signatures to classify them This necessarily requires a priori statistical knowledge of typical signatures for different target classes 14425 Target Classi cation Here, we focus on single-node (no collaboration between nodes) classi cation based on temporal target signatures: a time series segment is generated for each detected event at a node and processed for classi cation Some form of temporal processing, such as a fast Fourier transform (FFT), is performed and the transformed vector is fed to a bank of classi ers corresponding to different target classes.

A B H S C D E F G H I J K L M N O P Q R S T U V W X Y Z N U W B J M T A V Y F Q L X D Z G P E R C O I K

The outputs of the classi ers that detect the target, active classi ers, are reported to the manager nodes as opposed to the energy detector outputs Steps (1) to (5) in Subsection 14423 are repeated for all the active classi er outputs to generate and maintain tracks for different classi ed targets In some cases, both energy-based CPA information and classi er outputs may be needed Now we brie y describe the three classi ers explored in this chapter Given a set of N-dimensional feature vectors {x; x [ RN }, we assume that each of them is assigned a class label, vc [ V {v1 , v2 , , vm }, that belongs to a set of m elements We denote by p(vc ) the prior probability that a feature vector belongs.

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