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(90, 0). In addition, there are three MSs depicted in the gure. The MS1 remains stationary at location ( 45, 45) during the 5-s duration of the simulation from t0 to t5. MS2 s location at t0 is at (20, 55) and moves in the x direction at 5 m/s. MS3 s location at t0 is at (75, 87) and moves in the negative y direction at 3 m/s. The goal of the DCOP algorithm is to dynamically assess the MS associations with the APs at time tk based on the estimate of the state of the MSs at time tk+1 where tH tk 1 tk is the xed time required to handoff an IEEE 802.11 MS from one AP to a neighboring AP. For simplicity, for the simulation shown below, the handoff latency, tH, is an expected value, and tH = 350 ms corresponding to the expected handoff latency associated with the standard IEEE 802.11 protocol [54]. For the purpose of this example, the state of the MSs within a WLAN is de ned by two parameters: the AP utilization and the link quality between the MS and AP. The AP utilization is based on the number of MSs associated with it during time interval tH. Based on the current IEEE 802.11 protocol, each MS can only be associated with a single AP. Therefore, the association between APj and MSi at time tk is given by ( rij tk 1 0 APj and MSi are associated at time tk APj and MSi are not associated at time tk

These requirements immediately rule out some of the proposed localization algorithms for sensor networks. The rest of this chapter is organized as follows. In Section 14.2, we brie y discuss what information is available to the nodes whose location is unknown, and then we discuss the methods that use this information to nd the location of the object. In Section 14.3 the localization problem is introduced and methods to solve it are discussed. Similarly in Section 14.4 the target-tracking problem is introduced and methods to solve it are discussed. Section 14.5 reviews some experimental location and tracking systems, and nally Section 14.6 concludes the chapter.

The New Zealand Psychological Society was founded, having previously been a branch of the British Psychological Society.

not preferable to have resistor elements placed in series with the CMOS receiver MOSFET gate structures Resistor elements were added to minimize CMOS receiver ESD concerns caused by charged device model (CDM) failures of the MOSFET gate structure In digital CMOS, these receivers became more complex to provide good digital signal transfer In LNA receiver design, to achieve good matching and power transfer, it is not desirable to have only the MOSFET gate as the receiving signal that is purely capacitive Hence, it is preferable to have a physical element that serves an equivalency of a resistor shunt during an ac response Moreover, a load resistor is added between the output and the VDD power supply to provide voltage ampli cation This unfortunately leads to a high Miller effect as well as resistor noise generation.

Instead of using resistor elements in LNA designs, the utilization of MOSFETs in series cascode and the utilization of load inductors is preferred As a result, a preferred network that can use inductive load devices on the output is evolved On the output stage, it is considered better to place a degeneration inductor element between the MOSFET source and the ground rail This element is visible to the input network as it is in series con guration with the MOSFET gate-to-source capacitance Besides, as stated above, it is not desirable to have a purely capacitive element, hence an input network that is equivalent to a pure resistive load is preferred Hence, a design practice is to provide a resonant state at the operation frequency where the load and degeneration inductors are codesigned with the MOSFET gate-to-source capacitance.

where Sx is the ( p p) variance-covariance matrix among the p predictors, and srxy is the ( p 1) vector of covariances between the predictors and the ranks of Y. When predicting from the difference between ranks, we use aihj = (qixj qhxj ), and the weights are w2 = R 1 rsxy , sx (25.44)

Suppose that a gambler is asked to gamble sequentially on sequences x {0, 1} . He has no idea of the origin of the sequence. He is given fair odds (2-for-1) on each bit. How should he gamble If he knew the distribution of the elements of the string, he might use proportional betting because of its optimal growth-rate properties, as shown in 6. If he believes that the string occurred naturally, it seems intuitive that simpler strings are more likely than complex ones. Hence, if he were to extend the idea of proportional betting, he might bet according to the universal probability of the string. For reference, note that if the gambler knows the string x in advance, he can increase his wealth by a factor of 2l(x) simply by betting all his wealth each time on the next symbol of x. Let the wealth S(x) associated with betting scheme b(x), b(x) = 1, be given by S(x) = 2l(x) b(x). (14.76)