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Diffusion arises from a very simple idea: the most likely position of a node is at the centroid of its neighbors positions Diffusion algorithms require only radio connectivity data We describe two different variants below Bulusu et al [11] localize unknown nodes by simply averaging the positions of all beacons with which the node has radio connectivity Thus, Bulusu et al assume that nodes have no way of ranging to beacons This method is attractive in its blinding simplicity; however, the resulting positions are not very accurate, particularly when beacon density is low, or nodes fall outside the convex hull of their audible beacons Fitzpatrick and Meertens [12] describe a more sophisticated variant: each node is at the centroid of its neighbors, including nonbeacons The algorithm is as follows: Step 1 Initialize the position of all nonbeacon nodes to (0, 0) Step 2.



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were classified as at risk (71% versus 31%) and symptomatic (686% versus 588%), whereas more women than men were asymptomatic (382% versus 242%) These results suggest that (a) although the prevalence of actual dependence is low, a large number of individuals are subclinical and may either be at risk for the development of more problems in the future or experience associated disturbances in current psychological and physical functioning; and (b) men, more than women, experience exercisedependent symptoms, and thus may be more likely to report associated psychological disturbances, such as higher levels of perfectionism Furthermore, exercise-dependent athletes may be at increased risk for disturbed eating (Blaydon & Linder, 2002; Pasman & Thompson, 1988; Slay, Hayaki, Napolitano, & Brownell, 1998).





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Repeat the following until positions converge: Step 2a Set the position of each nonbeacon node to the average of all its neighbors positions This variant requires fewer beacons than Bulusu et al s algorithm; nevertheless, its accuracy is poor when node density is low, nodes are outside the convex hull of the beacons, or node density varies across the network In all of these cases, a more sophisticated algorithm would improve accuracy dramatically Fitzpatrick and Meertens s variant also uses substantially more computation than Bulusu et al s approach, since positions must be exchanged between adjacent nodes during step 2 However, this algorithm is quite useful in networks where nodes are capable of very little computation, but the network topology can be selectively changed to improve localization In particular, Savvides et al [13] recommend placing some beacons around the edges of the sensor network eld.

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For example, research has revealed that obligatory exercisers, be they runners or weight lifters, report higher levels of eating disturbances than nonexercising or nonobligatory exercising controls (Pasman & Thompson, 1988; Slay et al, 1998) Further, Kiernan, Rodin, Brownell, Wilmore, and Crandall (1992) found that exercise level, defined as weekly mileage, was significantly and positively associated with excessive eating and weight concerns among male, but not female, runners Muscle Dysmorphia Muscle dysmorphia is a form of Body Dysmorphic Disorder involving a preoccupation with a perceived lack of muscularity (Olivardia, 2001; Pope, Gruber, Choi, Olivardia, & Phillips, 1997) that results in impaired cognitive and social functioning For example, similar to exercise dependence, individuals with muscle dysmorphia often give their exercise routines priority over other important social or occupational activities.

The parameter settings of the simulation are as follows. In IEEE 802.1x, the maximum authentication message is 4096 bytes, the transmission delay per hop is about 20 ms provided with 2 Mbps link capacity [25]. The transmission delay per hop with different message sizes for each scheme is listed in Table 22.2. We evaluate the delay of cryptographic operations on a 3.0-GHz Intel Pentium machine with 1-GB RAM running Fedora Core 4 based on the cryptographic library MIRACL [28] except the pairing operations. As reported in [29], by most ef cient software optimization and hardware acceleration, the pairing calculation can be accomplished within 1:3 ms. Therefore, we can estimate all of the pairing-based public key operations shown in Table 22.3. Note that since

Selectively adding additional beacons can also help resolve pathologies in the diffusion estimates Bulusu et al [11] describe an approach for adaptive beacon placement to improve diffusionbased localization..

Because muscle dysmorphia involves an intense self-consciousness about one s physique, individuals may avoid situations in which their body will be on display The overwhelming concern among individuals with muscle dysmorphia is being too small (as opposed to being too fat), which manifests itself as a drive to attain greater muscularity (Maida & Armstrong, 2005; Pope et al, 1997) Muscle dysmorphia may be associated with increased risk of eating disorders (Olivardia, Pope, Borowiecki, & Cohane, 2004; Olivardia, Pope, & Hudson, 2000; Pope, Phillips, & Olivardia, 2000) Olivardia et al (2004), for example, found that men who reported muscle belittlement (ie, the perception of being less muscular than one is) also reported disordered eating attitudes In addition, the strong drive for muscularity associated with muscle dysmorphia.

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Obviously free is ideal. Something open source that I can debug myself is even better. But if I do have to pay for a “premium” library. I'm looking ...

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Mar 2, 2011 · This article, by Scott Mitchell, shows how to programmatically populate PDF form fields using ASP.NET and the free, open source iTextSharp ...












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