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The sink is interested in estimating the expected value of the event during a decision interval that is given by (5.28). Assuming each sensor node sends information at a rate of f samples/sec, this estimation can simply be found by S( , f, M) = 1 fM

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To start, let's declare the test-report-view extension We can either edit the pluginxml directly or we can use PDE to guide us through the extension creation An extension point should define its attributes and expected values in an extension point schema PDE uses the schema information to both validate the manifest and guide you through extension point creation Since we know which extension point we want to define and since we haven't used the PDE schema-based extension until now, let's use it to define the extension point for the test report view Open the pluginxml in the manifest editor and switch to theExtensions tab, shown in Figure 233

(5.32)

See Appendices F and H for some research on these topics performed by Vanguard. Overall, we applaud the changes that 401(k) plans are adopting to solve real problems with the actual operation of their plans. These changes address many of the shortcomings in 401(k) plans. However, participants still bear the investment risk associated with these plans. We believe the financial services industry will continue to work to bring cost-effective solutions that will help participants shift risk to organizations more suited to handle these risks, such as insurance companies.

where M is the number of sensor nodes that send samples of the observed point source. M nodes are chosen among the nodes in the network to represent the point source and, hence, are referred to as representative nodes. Consequently, the distortion achieved by this estimation is given by [2] Dp ( , f, M) = E (S( ) S( , f, M))2 (5.33)

With the Add button we can add the definition of a new extension PDE offers wizards to define extensions that are either driven by the extension point's schema or custom extension templates, as shown in Figure 234 The templates are hand-crafted for a particular extension and can therefore generate a significant portion of the code Browse the available extension templates and use them when they fit your needs

where the subscript p denotes the point source. Using (5.24), (5.25), (5.28), (5.31), and (5.32), (5.33) can be expressed as

We use the schema-based extension for creating our view extension On the next page, select the orgeclipseuiviews extension point and click Finish (the view extension point doesn't require a point identifier or name and you can leave the corresponding fields empty) This will create a new view extension definition that we can refine using the New action in the context menu and theProperties view Double click an element to make its properties visible in the Properties view To complete the views extension point, we add a new view element using the New action as shown inFigure 235

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In the small and mid-size marketplace, providers are pulling together all functions associated with retirement plan management. And in many cases, they are also providing bundled services for both defined contribution and DB services. These changes are being driven by both plan

(i, j, k, l)

We also need to enter values for the class, icon, id, and name properties in the Properties view, shown in Figure 236 When entering the class name, you also have the option to generate the initial implementation of the class The generated class conforms to the type specified in the schema information for the extension point In our case, this is ViewPart We won't use this feature now, so we can develop our view step-by-step

(5.34)

Having entered all this information, you should get the following extension point definition in the manifest editor's Source page:

and di = (xi + yi ), and (i, j, k, l) is the spatiotemporal correlation function given in (5.27). Spatiotemporal Characteristics of Field Sources. In some WSN applications such as temperature monitoring and seismic monitoring, the physical phenomenon is dispersed over the sensor eld and, hence, can be modeled as a eld source. Thus, here we explore the spatiotemporal characteristics of observing such a phenomenon in WSNs. As in Section 5.3.3, the event signal f (x, y, t) is assumed to be a Gaussian random 2 process with N(0, s ). The sink is interested in estimating the signal f (x0 , y0 , t) over the decision interval at location (x0 , y0 ). Assuming that the observed signal f (x, y, t) is wide-sense stationary (WSS), the expectation of the signal over the decision interval [i.e., S( )] can be calculated by the time average of the observed signal as S( ) = 1