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Gancho Vachkov has long-term research interests, works, and achievements in the broad area of computational intelligence, including fuzzy and neural modeling and identi cation, intelligent control algorithms and applications, supervised and unsupervised learning algorithms for information compression, intelligent fault diagnosis and performance evaluation of complex machines and systems, and evolving models for performance analysis and evaluation He has achieved original research results in solving such problems as reducing the dimensionality problem in fuzzy modeling, simpli ed local learning of fuzzy models, dynamic cause effect relation-based modeling, fuzzy model-based predictive control, learning algorithms for radial basis function neural networks (RBF NN), growing and adaptive neural models for process identi cation, and data analysis and performance evaluation of machines and systems.

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Gancho Vachkov is a member of IEEE, the Computational Intelligence Society and SMC Society, and of SOFT, the Japanese Society for Fuzzy Theory and Intelligent Informatics, as well as a member of several other Japanese scienti c societies He is also a regular program committee member and active reviewer of many worldwide international conferences and symposia on computational intelligence, soft computing, and system modeling He has published more than 130 papers on the above topics in different journals and proceedings of various international conferences His most recent research results are in the area of learning algorithms for information compression and image similarity analysis, with applications for predictive maintenance and performance evaluation of complex machines and industrial systems..

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When you fine-tune and work the bugs out of your ActionScript, it s often necessary to view the properties of other objects while using an ActionScript in a different keyframe for reference. Click the Pin Current Script button, and Flash locks the current script in the Script window while you navigate to other frames or view different objects. Click the Pin Current Script button again to unlock the script.

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In summary, these network-assisted RM proposals maintain session state and transport-layer functionality in the network layer. A legitimate concern is, of course, its scalability and ef ciency impact on layer 3 routing and packet forwarding. The network layer traditionally has worked better in providing the stateless and best-effort datagram service. Maintaining session state and reliability mechanisms into layer 3 is essentially solving a transport layer problem (the problem of scalable reliable multicast) by pushing the problem into the lower layer. Therefore, whether the router-assisted approach (or how much of it) will actually succeed going forward remains to be seen. Later, we will introduce application layer multicast, which is the direct opposite of router-assisted reliable multicast. Application layer multicast solves the problem of scalable reliable multicast by pushing the problem into the upper layer the application layer.

(1) X (m x m) lower triangular:

which career direction the psychologist chooses (APA, 1997b; Kilburg, 1991; Sternberg, 1997b). Strategies for academic and clinical positions will be highlighted next.

The primary goal of IP is to provide the basic algorithm for transfer of data to and from a network. In order to achieve this, it implements two functions: addressing and fragmentation. It provides a connectionless delivery service for the upper layer protocols. This means that IP does not set up a session (a virtual link) between the transmitting station and the receiving station prior to submitting the data to the receiving station. It encapsulates the data handed to it and delivers it on a best effort basis. IP does not inform the sender or receiver of the status of the packet; it merely

3 B N Clark, C J Colbourn, and D S Johnson, Unit disk graphs, Discrete Mathematics, 86: 165 167, 1990 4 J Flynn D Baker, A, Ephremedes The design and simulation of a mobile radio network with distributed control, IEEE Journal on Selected Areas in Communications, SAC-2: 226 237, 1999 5 A Ephremedis and T Truong, A distributed algorithm for efficient and interference free broadcasting in radio networks, In Proceedings IEEE INFOCOM, 1988 6 S Even, O Goldreich, S Moran, and P Tong, On the NP-completeness of certain network testing problems, Networks, 14: 1 24, 1984 7 N Funabiki and J Kitamichi, A gradual neural network algorithm for broadcast scheduling problems in packet radio networks, IEICE Trans Fundamentals, E82-A: 815 825, 1999 8 M R Garey and D S Johnson, Computers and Intractability A Guide to the Theory of NPCompleteness, W H Freeman, San Francisco, 1979 9 W.

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