10 resultados para Architecture and Complexity
em Bulgarian Digital Mathematics Library at IMI-BAS
Resumo:
This article describes architecture and implementation of subsystem intended for working with queries and reports in adaptive dynamically extended information systems able to dynamically extending. The main features of developed approach are application universality, user orientation and opportunity to integrate with external information systems. Software implementation is based on multilevel metadata approach.
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The popular technologies Wi-Fi and WiMAX for realization of WLAN and WMAN respectively are much different, but they could compliment each other providing competitive wireless access for voice traffic. The article develops the idea of WLAN/WMAN (Wi-Fi/WiMAX) integration. WiMAX is offering a backup for the traffic overflowing from Wi-Fi cells located into the WiMAX cell. Overflow process is improved by proposed rearrangement control algorithm applied to the Wi-Fi voice calls. There are also proposed analytical models for system throughput evaluation and verification of the effectiveness using WMAN as a backup for WLAN overflow traffic and the proposed call rearrangement algorithm as well.
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In the paper learning algorithm for adjusting weight coefficients of the Cascade Neo-Fuzzy Neural Network (CNFNN) in sequential mode is introduced. Concerned architecture has the similar structure with the Cascade-Correlation Learning Architecture proposed by S.E. Fahlman and C. Lebiere, but differs from it in type of artificial neurons. CNFNN consists of neo-fuzzy neurons, which can be adjusted using high-speed linear learning procedures. Proposed CNFNN is characterized by high learning rate, low size of learning sample and its operations can be described by fuzzy linguistic “if-then” rules providing “transparency” of received results, as compared with conventional neural networks. Using of online learning algorithm allows to process input data sequentially in real time mode.
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We present quasi-Monte Carlo analogs of Monte Carlo methods for some linear algebra problems: solving systems of linear equations, computing extreme eigenvalues, and matrix inversion. Reformulating the problems as solving integral equations with a special kernels and domains permits us to analyze the quasi-Monte Carlo methods with bounds from numerical integration. Standard Monte Carlo methods for integration provide a convergence rate of O(N^(−1/2)) using N samples. Quasi-Monte Carlo methods use quasirandom sequences with the resulting convergence rate for numerical integration as good as O((logN)^k)N^(−1)). We have shown theoretically and through numerical tests that the use of quasirandom sequences improves both the magnitude of the error and the convergence rate of the considered Monte Carlo methods. We also analyze the complexity of considered quasi-Monte Carlo algorithms and compare them to the complexity of the analogous Monte Carlo and deterministic algorithms.
Resumo:
This article presents the principal results of the doctoral thesis “Semantic-oriented Architecture and Models for Personalized and Adaptive Access to the Knowledge in Multimedia Digital Library” by Desislava Ivanova Paneva-Marinova (Institute of Mathematics and Informatics), successfully defended before the Specialised Academic Council for Informatics and Mathematical Modelling on 27 October, 2008.
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∗ Thematic Harmonisation in Electrical and Information EngineeRing in Europe,Project Nr. 10063-CP-1-2000-1-PT-ERASMUS-ETNE.
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This is an extended version of an article presented at the Second International Conference on Software, Services and Semantic Technologies, Sofia, Bulgaria, 11–12 September 2010.
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The software architecture and development consideration for open metadata extraction and processing framework are outlined. Special attention is paid to the aspects of reliability and fault tolerance. Grid infrastructure is shown as useful backend for general-purpose task.
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Problems for intellectualisation for man-machine interface and methods of self-organization for network control in multi-agent infotelecommunication systems have been discussed. Architecture and principles for construction of network and neural agents for telecommunication systems of new generation have been suggested. Methods for adaptive and multi-agent routing for information flows by requests of external agents- users of global telecommunication systems and computer networks have been described.
Resumo:
This paper describes a PC-based mainframe computer emulator called VisibleZ and its use in teaching mainframe Computer Organization and Assembly Programming classes. VisibleZ models IBM’s z/Architecture and allows direct interpretation of mainframe assembly language object code in a graphical user interface environment that was developed in Java. The VisibleZ emulator acts as an interactive visualization tool to simulate enterprise computer architecture. The provided architectural components include main storage, CPU, registers, Program Status Word (PSW), and I/O Channels. Particular attention is given to providing visual clues to the user by color-coding screen components, machine instruction execution, and animation of the machine architecture components. Students interact with VisibleZ by executing machine instructions in a step-by-step mode, simultaneously observing the contents of memory, registers, and changes in the PSW during the fetch-decode-execute machine instruction cycle. The object-oriented design and implementation of VisibleZ allows students to develop their own instruction semantics by coding Java for existing specific z/Architecture machine instructions or design and implement new machine instructions. The use of VisibleZ in lectures, labs, and assignments is described in the paper and supported by a website that hosts an extensive collection of related materials. VisibleZ has been proven a useful tool in mainframe Assembly Language Programming and Computer Organization classes. Using VisibleZ, students develop a better understanding of mainframe concepts, components, and how the mainframe computer works. ACM Computing Classification System (1998): C.0, K.3.2.