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The simple ROT scheme presented here can be used to eliminate all primary collisions. While it is possible to eliminate both primary and secondary collisions using more sophisticated time division multiple access (TDMA) schemes, nodes will suffer more serious ET problems as more time slots are generally needed. As noted above, secondary collisions can be resolved using one retransmission in most cases; the network throughput by using the simple ROT scheme is expected to be close to that by using sophisticated TDMA schemes. Transmission latency caused by retransmissions in ROT is (at least partially) compensated by the fact that less time slots are needed compared with other sophisticated TDMA schemes. Compared with a collision-free TDMA scheme, retransmissions in ROT will consume more network resource such as frequency bandwidth and energy. But fortunately, a node does not need to retransmit twice for most collisions, which may prove to be a
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Essential Windows CE Application Programming by Robert Burdick Wiley Computer Publishing, John Wiley & Sons, Inc. ISBN: 0471327476 Pub Date: 03/01/99
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(1) A (m x m) Hermitian (or real symmetric): (a) A is positive (semi) definite positive (nonnegative). (b) A is negative (semi) definite negative (nonpositive).
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Two of the most common preservative conditions employed in meat processing are low temperatures and high salt concentrations. L. sakei is remarkably well equipped to cope with these conditions. It contains several transporters for osmoprotective substances and has more cold stress proteins than other lactobacilli. L. sakei has psychrotrophic and osmotolerant properties, and is able to grow at low temperatures and in the presence of up to 10% sodium chloride (NaCl). These physiological features are associated with the presence in its genome of a higher number of genes coding for stressresponse proteins, such as cold shock and osmotolerance proteins, than found in other lactobacilli. L. sakei lacks proteins involved in adhesion to intestinal mucous, but its genome codes for numerous proteins that may be involved in adhesion to the meat surface (e.g., to collagen), aggregation, and bio lm formation. Thus, the bacterium seems well equipped to adhere to and spread on a meat surface (Eijsink and Axelsson 2005). Sanz and Toldr (2002) reported an arginine-speci c aminopeptidase activity in L. sakei that is important for the release of the free amino acid, since it could be further channeled into the arginine deiminase pathway. The genes encoding the proteins required for arginine catabolism in L. sakei are organized in a cluster (Z r ga et al. 2002), and their transcription is repressed by glucose and induced by arginine. Arginine, in particular, is an essential amino acid for L. sakei and speci cally promotes its growth in meat; it is used as an energy source in the absence of glucose (Champomier-Verges et al. 1999). The concentration of free arginine in raw meat is low, although it is relatively abundant in muscle myo brillar proteins. Moreover, the genome analysis has shown that L. sakei harbors a second putative arginine deaminase pathway, containing two peptydil-arginine deaminases, enzymes that can contribute to the metabolism of arginine (Chaillou et al.
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2.4 DECENTRALIZED WLAN RESOURCE MANAGEMENT USING MULTIAGENT SYSTEMS The centralized architecture often has the single-point failure and scalability problem. In order to achieve managing resources fairly among multiple WLANs through a fully decentralized way, a MAS-based approach is proposed to achieve information sharing and decision distribution among multiple WLANs in a distributed manner. WLAN providers may set up service-level agreements among themselves on how much data can be exchanged among agents. Compared to using a centralized controller, a MAS-based approach is more scalable. 2.4.1 Multiagent-Based Architecture
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FIGURE 16-3: Tiny Wi-Fi Linux on a Roomba with RoombaNet
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result does not deal only with mean differences, but rather includes both mean and covariance differences, and it may be worth pursuing. Multiple-group growth models have been used in all prior analyses described here to t the complete and incomplete subsets of the Bradway-McArdle data (Figure 18.6). We compared the numerical results for the complete data (Figure 18.6, Panel A) versus the complete and incomplete data together (Figure 18.6, Panel A plus Panel B), and the parameters remain the same. As a statistical test for parameter invariance over these groups, we calculated from the difference in the model likelihoods, and these differences were trivial ( 2 < 20 on df = 20). This suggests that selective dropout or subject attrition can be considered random with respect to the nonverbal abilities. This last result allows us to combine the complete and incomplete data sets in the hopes for a more accurate, powerful, and unbiased analysis. In our nal set of multiple-group models, we used the latent mixture approach to estimate latent groupings of models results for the verbal scores, and some results are graphed in Figure 18.11. The latent growth model using all the data was tted with free basis coef cients and the same ts as were reported earlier (M2). In a rst latent mixture model, we allowed the additional possibility of two latent classes (C = 2) with different parameters for the latent means and variance but assuming the same growth basis. The two-class growth model (Figure 18.11, Panel A) assumed the same free basis coef cients as previously, smaller latent variances, and an estimated class threshold (z = 2.48) separating (a) Class 1 with 92% of the people with high latent means ( 0 = 25, s = 58), from (b) Class 2 with 8% of the people with lower latent means ( 0 = 16, s = 53). This two-class model yielded an likelihood that (assuming these two models are nested) represents a substantial change in t ( 2 = 30 on df = 3). This result suggests that a small group of the Bradway persons may have started at a lower average score with a smaller change. A sequence of parameters were compared under the assumption of two classes, and the nal result is presented in Figure 18.11, Panel B. The two-class growth model yielded an estimated class threshold (z = 0.72) separating two classes with 33% and 67% of the people. The rst class seems to have a higher starting point and lower variability, but the plots of Figure 18.11b seem to show the two curves converge in adulthood. Although this is an interesting possibility, this complete two-class growth-mixture model yielded only a small improvement in t (L = 1628, 2 = 34, on df = 12), so we conclude that only one class of persons is needed to account for the basic growth curves underlying these data.
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