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radio) shows the highest cost among the other components when it is transmitting or listening to any signal from the network Thus, this module could not be put in sleep mode in order to save energy GPS devices of small size and low power consumption have been developed and are now commercially available The Leadtek 9546 low-power GPS receiver [65] was recently integrated into a sensor board of the Crossbow Mote [52] These devices consume a current of 60 mA at 33 V in full operation Despite the fact that such a consumption pro le is higher compared to the micro-controller current draw of 8 mA, it is comparable to the energy consumption pro le of the radio module However, such sensor nodes are optimized to operate in sleep mode most of the time.

coordinates are particularly useful from a human factors standpoint for representing the attitude of the host vehicle and for representing local directions. They include (i) east north up (ENU), shown in Fig. C.7; (ii) north east down (NED), which can be simpler to relate to vehicle coordinates; and (iii) alpha wander, rotated from ENU coordinates through an angle a about the local vertical (see Fig. C.8). 3. Vehicle- xed coordinates: (a) Roll pitch yaw (RPY) (axes shown in Fig. C.9). Transformations between these different coordinate systems are important for representing vehicle attitudes, for resolving inertial sensor outputs into inertial navigation coordinates, and for GPS=INS integration. Methods used for representing and implementing coordinate transformations are also presented in Appendix C, Section C.4.

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Example 5.5: Kidney Disease Progression: Bayes Predictive Density. We illustrate the computation of (5.7) using Gibbs sampling with the kidney series owgraph of Figure 2.2 introduced in Example 2.1. Example 3.1 presented the exact inversion of the MGF for the waiting time distribution for the failure of both kidneys. Example 3.8 presented the maximum likelihood estimated density for this model. Y0 is the random waiting time in state 0 until state 1 is reached, and Y1 is the random waiting time in state 1 until state 2 is reached. Recall that Y0 Exp(2 0 ) is independent of Y1 Exp( 1 ). Suppose that we have complete observations on n kidney patients so that our data consist of transition time from 0 1 and 1 2 for each person. Let y0i and y1i denote the observations on the individual waiting times for patient i and summarize the observations on n patients as y0 = (y01 , y02 , . . . , y0n ) and y1 = (y11 , y12 , . . . , y1n ). We have the additional constraint on the problem from Gross et al. (1971) that 0 1 . This compensates for the additional burden on the second kidney after the rst kidney fails. For purposes of illustration, we will assume a uniform prior on = ( 0 , 1 ) given by ( 0 , 1 ) = 1 2.5 for 0 < 0 1, 0 1 3, (5.39)

If the GPS is con gured to occasionally operate to acquire the time signal, its energy consumption may not be an issue Although adding this particular GPS module to a sensor board increases its cost by 50% today, we believe that the cost will drop signi cantly because of economy of scale In contrast, this is also the case for the current cost of sensor nodes, which are similar to the cost of a sensor board equipped with GPS We list below some open issues with time synchronization in large-scale distributed COs and WSNs: The characteristics of the network topology in ad hoc WSNs may introduce severe delays because of disconnected parts of the network To compensate for such uncertainties, the time synchronization protocol needs to be tolerant to the message delay irrespective of its dominant source (eg processing, transmission).

E1.4 Following the steps in Section 1.5, write the expressions for the even- and odd-mode capacitances for a system of ve coupled conductors and solve them for the self and mutual capacitances for the ve conductors. Comment on the accuracy of your results. E1.5 Suggest situations where it will be preferable to model an interconnection as a lumped circuit or as a transmission line.

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