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AAC audio, 392 Absolute location, 442 Access, generally control, security services, 374 Internet service providers (ISPs), 109 of ine, 144 145, 265 points, wireless LAN network, 142 workload characterization, 144 145 Acknowledgments (ACKs) negative, 95 98, 346 positive, 93, 95, 345 ACME (architecture for content delivery in the mobile environment) de ned, 5 6 overview of, 183 185 performance analysis in CDMA networks, 191, 196 197 slotted ALOHA system, 191 196 system description, 191 in radio resource management, 201 terminal power consumption in, 185 186, 198 user interest correlation: algorithm, 197 198 simulations, 198 200 traces, 198 ACME Director characteristics of, 190 192, 197 198

1381 Tracking Objects Without Data Aggregation We now discuss problems associated with tracking an object, possibly moving This section considers problem aspects when data aggregation is not involved Each report therefore needs to be sent directly to the base station The problem is to send a suf cient number of reports so that the position of the object can be reliably determined by the base station, while minimizing the number of sensors that send the report We assume that each sensor knows its own location, and location of its neighboring sensors Although in reality the reliability of sensor observation depends on the distance to the object, we consider a simpli ed model, assuming that all sensors located within the sensing radius from the object can reliably detect it After detecting the object, sensors can measure either the distance to it, or the direction (angle) toward it.

The case of distance is similar to the position determination problem, discussed in 9 We therefore now study the case of measuring an angle toward an object, without knowing the distance to it All reports will be assumed accurate, although in reality some reports may be false, and security and report reliability issues need to be studied as well Estrin, Govindan, Heidemann, and Kumar [2] were the rst to investigate this problem In their solution, the sensor network is clustered rst CHs for which all neighboring CHs lie on the same side of a line drawn toward the object elect themselves to participate in object location The goal is to elect sensors that form the longest baseline for triangulation There are several problems with this approach In general, there are two such nodes, which are tangent nodes from the object to the convex hull of CHs.

is the extent to which they have developed certain kinds of abilities, and little purpose is served by rigid distinctions between these types of measures (see Anastasi & Urbina, 1997, chap 17) With this in mind, the discussion that follows traces brie y the development of four measures of achievement/aptitude and interest that have deep roots in the history of assessment psychology and enjoy continued widespread use: the Wide-Range Achievement Test, the Strong Interest Inventory, the Kuder Occupational Interest Survey, and the Holland Self-Directed Search Wide-Range Achievement Test In the United States, formal achievement testing began in the schools during the early 1920s Tests of speci c competencies (eg, spelling) had been developed prior to that time, but group-administered batteries for assessing a broad range of academic skills began with the 1923 publication of the Stanford Achievement Test (SAT), which was designed for use with elementary school students.

If one of them for any reason does not see the object (because of obstacles),.

Tb / 2

TABLE 20.7 Predicted Probabilities of Intense Male Violence, Common Couple Violence, and Nonviolence for Unit Changes in the Number of Children and for Minority vs. Nonminority Couples, Based on Multinomial Logit Model Estimates in Table 20.5 Number of Children 1 0 1 0 1 0 3 2 3 2 3 2 1.178 1.178 0 0 2.456 2.456 Minority Status of Couple nonminority nonminority nonminority nonminority nonminority nonminority nonminority nonminority nonminority nonminority nonminority nonminority minority nonminority minority nonminority minority nonminority Values of Other Predictorsa mean mean mean 1 SD mean 1 SD mean + 1 SD mean + 1 SD mean mean mean 1 SD mean 1 SD mean + 1 SD mean + 1 SD mean mean mean 1 SD mean 1 SD mean + 1 SD mean + 1 SD Probability of Intense Male Violence .023 .022 .080 .074 .006 .006 .026 .025 .092 .086 .007 .007 .031 .024 .136 .074 .006 .007 Probability of Common Couple Violence .062 .078 .115 .143 .031 .039 .039 .049 .073 .092 .019 .024 .065 .059 .196 .143 .017 .022 Probability of Nonviolence .915 .900 .805 .783 .963 .955 .935 .926 .835 .822 .974 .969 .904 .917 .668 .783 .977 .971