899 resultados para Synchronization algorithms
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Includes bibliographical references.
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Includes bibliographies.
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Thesis - University of Illinois at Urbana-Champaign.
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"January 1982."
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"November 1982."
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"PB 82-190455."
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Mode of access: Internet.
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Mode of access: Internet.
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An important aspect in manufacturing design is the distribution of geometrical tolerances so that an assembly functions with given probability, while minimising the manufacturing cost. This requires a complex search over a multidimensional domain, much of which leads to infeasible solutions and which can have many local minima. As well, Monte-Carlo methods are often required to determine the probability that the assembly functions as designed. This paper describes a genetic algorithm for carrying out this search and successfully applies it to two specific mechanical designs, enabling comparisons of a new statistical tolerancing design method with existing methods. (C) 2003 Elsevier Ltd. All rights reserved.
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Spatio-temporal maps of the occipital cortex of macaque monkeys were analyzed using optical imaging of intrinsic signals. The images obtained during localized visual stimulation (IS) were compared with the images obtained on presentation of a blank screen (IB). We first investigated spontaneous variations of the intrinsic signals by analyzing the 100 IBs for each of the three cortical areas. Slow periodical activation was observed in alternation over the cortical areas. Cross-correlation analysis indicated that synchronization of spontaneous activation only took place within each cortical area, but not between them. When a small, drifting grating (2degreesX2degrees) was presented on the fovea. a dark spot appeared in the optical image at the cortical representation of this retinal location. It spread bilaterally along the border between V1 and V2, continuing as a number of parallel dark bands covering a large area of the lateral surface of V1. Cross-correlation analysis showed that during visual stimulation the intrinsic signals over all of the three cortical areas were synchronized, with in-phase activation of V1 and V2 and anti-phase activation of V4 and V1/V2. The significance of these extensive synergistic and antagonistic interactions between different cortical areas is discussed. (C) 2003 Elsevier B.V. All rights reserved.
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Genetic algorithms (GAs) are known to locate the global optimal solution provided sufficient population and/or generation is used. Practically, a near-optimal satisfactory result can be found by Gas with a limited number of generations. In wireless communications, the exhaustive searching approach is widely applied to many techniques, such as maximum likelihood decoding (MLD) and distance spectrum (DS) techniques. The complexity of the exhaustive searching approach in the MLD or the DS technique is exponential in the number of transmit antennas and the size of the signal constellation for the multiple-input multiple-output (MIMO) communication systems. If a large number of antennas and a large size of signal constellations, e.g. PSK and QAM, are employed in the MIMO systems, the exhaustive searching approach becomes impractical and time consuming. In this paper, the GAs are applied to the MLD and DS techniques to provide a near-optimal performance with a reduced computational complexity for the MIMO systems. Two different GA-based efficient searching approaches are proposed for the MLD and DS techniques, respectively. The first proposed approach is based on a GA with sharing function method, which is employed to locate the multiple solutions of the distance spectrum for the Space-time Trellis Coded Orthogonal Frequency Division Multiplexing (STTC-OFDM) systems. The second approach is the GA-based MLD that attempts to find the closest point to the transmitted signal. The proposed approach can return a satisfactory result with a good initial signal vector provided to the GA. Through simulation results, it is shown that the proposed GA-based efficient searching approaches can achieve near-optimal performance, but with a lower searching complexity comparing with the original MLD and DS techniques for the MIMO systems.
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Foreign exchange trading has emerged recently as a significant activity in many countries. As with most forms of trading, the activity is influenced by many random parameters so that the creation of a system that effectively emulates the trading process will be very helpful. A major issue for traders in the deregulated Foreign Exchange Market is when to sell and when to buy a particular currency in order to maximize profit. This paper presents novel trading strategies based on the machine learning methods of genetic algorithms and reinforcement learning.