By Ciza Thomas
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Extra info for Sensor Fusion - Foundation and Applications
Location information of a user), because we can not directly sense the higher-level contexts. , 2010a). Therefore, we introduce some context reasoning approaches such as Fuzzy logic, Probabilistic logic, Bayesian Networks (BNs), Hidden Markov Models (HMMs), Kalman Filtering Models (KFMs), Dynamic Bayesian Networks (DBNs) and Dempster-Shafer Theory (DST) of the evidence in order to compare them with our context reasoning approach. 1 Puzzy logic, probabilistic logic and BNs In fuzzy logic, a degree of membership represented by a pair (A:m) where A is a set and m is a possibility distribution in real unit interval [0,1] is used to show an imprecise notion such as conﬁdence values (Lemmi & Betti, 2006; Zadeh, 1999).
2008a). A conﬂict resolution architecture for the comfort of occupants in intelligent ofﬁce, Proc. of the 4th IET Intl. Conf. on Intelligent Environments, pp. ; Elmasri R. (2009). Sensor Data Fusion using DSm Theory for Activity Recognition under Uncertainty in Home-based Care, Proc. of the 23th IEEE Intl. Conf. on Advanced Information Networking and Applications, pp. ; Elmasri R. (2010). Fusion Techniques for Reliable Information: A Survey, International Journal of Digital Content Technology and its Applications, Vol.
13. 5 reduces the total GPT level. The GPT level of DSmT with static weighting factor is higher than that of DBNs as shown in Figure 12. 5. 2 Comparison with different weighting factors In order to compare the GPT level of DSmT with that of DBNs with different weighting factors, ﬁrst, we apply different static weights to each context attribute based on "Ps" and "Rs" as shown in Table 6. As shown in Figure 13(a), the GPT levels of eight cases have different paired observation results. When we compare the case 1 and case 5, the conﬁdence interval includes zero so it is impossible to distinguish which one is better than the other.
Sensor Fusion - Foundation and Applications by Ciza Thomas