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Finally, the robot maintains a probabilistic transition model for the deployed network graph, and can compute the action policy at each node for any destination point. However, a more attractive solution is to compute the action policy distributively in the deployed network. The idea is that every node in the network updates its utility and computes the optimal navigation action (for a robot in its vicinity) on its own. When the navigation destination is determined (either a robot requiring to be guided to a certain node, or a node requiring a robot s assistance), the node that is closest to the destination triggers the computation by injecting a Start Computation packet into the network containing its id. Every node redirects this packet to its neighbors using ooding, and updates the utilities according to equation (3.18). After the utilities are computed, every node computes an optimal policy for itself according to equation (3.19). Neighboring nodes are queried once again for the nal utility values. The computed optimal action is stored at each node and is sent as part of a suggestion packet that the robot would receive if it is in the vicinity of the node.

TABLE 3.10.2 Propagation Times in Four Driving Methods for Selected Interconnection Widths Interconnection Width (mm) 0.1 0.2 0.5 1.0 2.0 5.0 10.0 GaAs MESFET (ns) 0.98 0.50 0.39 0.33 0.31 0.25 0.27 Minimum-Size Repeaters (ns) 3.01 3.10 3.56 4.03 4.5 a a Optimum-Size Repeaters (ns) 0.04 0.08 0.15 0.20 0.35 a a Cascaded Drivers (ns) 0.85 0.52 0.38 0.46 0.70 1.38 2.39

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3.6.5.3. Probabilistic navigation With this algorithm [192], assuming neither a map nor a GPS are available, the robot can navigate through the environment from point A to point B communicating with a sensor network. The algorithm has two stages: Planning: when the navigation goal is speci ed (either the robot requests to be guided to a certain place, or a sensor node requires the robot s assistance), the node that is closest to the destination triggers the navigation eld computation. During this computation, every node probabilistically determines the optimal direction in which the robot should move when in its vicinity. The computed optimal directions of all nodes in conjunction compose the navigation eld. The navigation eld provides the robot with the best possible direction that has to be followed in order to reach the destination. The navigation eld is computed based on a value iteration algorithm which considers the deployed sensor network as a graph, where the sensor nodes are vertices. Assume that a nite set of vertices S in the deployed network graph and a nite set of actions A the robot can take at each node. Given a subset of actions A(s) A, for every two vertices s, s S in the deployed network graph, and an action a A(s), the transition probabilities P (s | s, a) (probability of arriving at vertex s given that the robot started at vertex s and commanded an action a) for all vertices are determined. The general idea behind the value iteration is to compute the

Method Signature String getClassName() boolean getDeclare() int getScope() String getVarName() Description Returns the class name of the scripting variables coded in the tld as the <variable-class> element. Returns the value of the <declare> element coded in the tld. Returns the value of the <scope> element coded in the tld. Returns the name of the scripting variable coded in the tld as the <variable> element coded in the tld.

where is the angle of rotation of the compensator and comp is the maximum optical path difference of the compensator ( /20). The precise value of comp must be determined by calibration and is a constant in the equation. Depending on the particular compensator, retardations of /2000 can be measured under optimal conditions.

Lower motor neurone and segmental signs are unusual in multiple sclerosis. Episodes of demyelination in the spinal cord cause bre tract (upper motor neurone, posterior column, spinothalamic and autonomic) symptoms and signs below the level of the lesion. Since the length of the bre tracts in the spinal cord are physically longer for leg function than for arm function, there is a greater likelihood of plaques in the spinal cord interfering with the legs than the arms. Episodes of spinal cord demyelination may cause: heaviness, dragging or weakness of the arms, trunk or legs; loss of pain and temperature sensation in the arms, trunk or legs; tingling, numbness, sense of coldness, sense of skin wetness, sense of skin tightness, or a sensation like that which follows a local anaesthetic or a nettle-sting, in the arms, trunk or legs; clumsiness of a hand due to loss of position sense and stereognosis; bladder, bowel or sexual malfunction.

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