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6 Some Results This section presents two examples to demonstrate 3-D wavelet compression results compared to those obtained by other methods The rst example uses a 3-D MRI data set with 124 images each with 256 256 16 bits Both 2-D and 3-D wavelet compressions are used; the 2-D compression method is similar to that of the 3-D compression algorithm except that a two-dimensional wavelet transform is applied to each slice Figure 618 compares the compression ratios using the 3-D and 2-D algorithms for a xed PSNR; the horizontal axis is the peak signal-to-noise ratio (PSNR) de ned in Eq (69), and the vertical axis represents the compression ratio At the same PSNR, compression ratios of the 3-D method are about 40% to 90% higher than that of the 2-D method The second example following the same example shown in Figure 6.

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limited and incremental communications add signi cant latency to the propagation of local-to-regional network context information The technical approach described below addresses these challenges Network Context Generation: Network context generation is the process of matching evidence from the measurements to the situation models to generate hypotheses of what is observed by the node and the state of the network from the perspective of the node Initially, the network context is generated based on local information at the node including the measurements, component con guration, and mission knowledge Situation hypotheses are representations derived from alternative matches of evidence to models The situation hypotheses for network context are maintained within the Network Context Manager and are updated as new measurements are made available A representation based on Bayesian Networks is used to capture the uncertainties in the measurements and the matching process.

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6 where the same 3-D body CT image volume was used Figures 619 and 620 depict the same section from the body CT image volume with 2-D wavelet comparison In addition 3-D wavelet was used to compress the 3-D volume, with the reconstructed.

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The approach based on probabilistic relational models complements the overall component-based guiding principle of this architecture Model fragments corresponding to the different network protocols are built and maintained within a knowledge base At run time the component con gurations and measured parameters are used to retrieve the appropriate model fragments and are composed into the network context Coordination: Coordination for supervisory control is accomplished by constructing a hierarchical fusion framework Each individual node s network context is represented as a Bayesian Network, as described earlier, which is a compact representation of the relationships among a set of variables that represent network state A number of component level Bayesian Network fragments (which represent information about individual traf c ows, etc) are aggregated into node level Bayesian Networks, and groups of node-level Bayesian Networks are combined within a coordination group (a set of nodes sharing information eg.

gives the result for the time constants: (8.35) Therefore, using Robinson's theorem, we see that the damping time in the horizontal degree of freedom is (8.36)

At the producer level, pharmaceutical companies must decide whether targeted drug development is economically feasible Will there be a suf cient return on investment to justify the risks involved in pharmacogenomic research Will companies only pursue research and drug development for diseases for which there is.

, a group of one-hop neighbors) to create a group network context The group network contexts are then combined to create the global aggregate network context The composition of all node Bayesian Networks constitutes the global Bayesian Network that represents the global belief state Note that the global belief state is never represented explicitly at any single location but is maintained implicitly across the network When control actions need to be taken, some coordination across nodes may be required Such coordination is organized around the concept of a coordination task, for which a dynamically formed set of nodes agree to share contexts and coordinate behaviors Within a coordination task, each node has a role and shares with other session members only those aspects of the context and behavior that are relevant to the node s role within the session so that bandwidth overhead is minimized.

Public key encryption is a better option for securely exchanging files with other users. You learn more about encryption and other methods of securing files, folders, and disks in 13.

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