3,952 research outputs found

    Emitter Location Finding using Particle Swarm Optimization

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    Using several spatially separated receivers, nowadays positioning techniques, which are implemented to determine the location of the transmitter, are often required for several important disciplines such as military, security, medical, and commercial applications. In this study, localization is carried out by particle swarm optimization using time difference of arrival. In order to increase the positioning accuracy, time difference of arrival averaging based two new methods are proposed. Results are compared with classical algorithms and Cramer-Rao lower bound which is the theoretical limit of the estimation error

    CMF-DFE Based Adaptive Blind Equalization Using Particle Swarm Optimization

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    The channel matched filter (CMF) is the optimum receiver providing the maximum signal to noise ratio (SNR) for the frequency selective channels. The output intersymbol interference (ISI) profile of the CMF convolved by the channel can be blindly obtained by using the autocorrelation of the received signal. Therefore, the inverse of the autocorrelation function can be used to equalize the channel passed through its own CMF. The only missing part to complete the proposed blind operation is the CMF coefficients. Therefore, in this work, the best training algorithm investigation is subjected for blind estimation of the CMF coefficients. The proposed method allows using more effective training algorithms for blind equalizations. However, the expected high performance training is obtained when the swarm intelligence is used. Unlike the stochastic gradient algorithms, the particle swarm optimization (PSO) is known to have fast convergence because its performance is independent of the characteristics of the systems used. The obtained mean square error (MSE) and bit error rate (BER) performances are promising for high performance real-time systems as an alternative to non-blind equalization techniques

    A practical probabilistic earthquake hazard analysis tool: case study Marmara region

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    Earthquakes have a damaging impact on the economic welfare and resilience of communities, particularly in developing countries. Seismic hazard assessment is the first step towards performing prevention, preparedness, and response or recovery actions to reduce seismic risk. This paper presents a computation tool for predicting the seismic hazard at the macro level as a part of a comprehensive multi-hazard framework on earthquake risk assessment. The probabilistic seismic hazard analysis (PSHA) procedure is based on the Monte-Carlo approach, and particular attention is paid to the definition of source zones assigned in the study area. Both Poisson and time dependent (renewal) models are adopted to quantify the effect of temporal dependencies between seismic events, while near-field rupture directivity effects are also taken into account. Marmara region in Turkey is selected as a case study area to perform a new seismic hazard analysis and verify the accuracy of the proposed tool. The results show good agreement with results from the recent SHARE project and the latest Turkish Earthquake Design code hazard maps. This confirms that the proposed PSHA method can be an attractive alternative to the direct integration based methods due to its practicality and powerful handling of uncertainties

    Search for a Standard Model Higgs Boson in CMS via Vector Boson Fusion in the H->WW->l\nu l\nu Channel

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    We present the potential for discovering the Standard Model Higgs boson produced by the vector-boson fusion mechanism. We considered the decay of Higgs bosons into the W+W- final state, with both W-bosons subsequently decaying leptonically. The main background is ttbar with one or more jets produced. This study is based on a full simulation of the CMS detector, and up-to-date reconstruction codes. The result is that a signal of 5 sigma significance can be obtained with an integrated luminosity of 12-72 1/fb for Higgs boson masses between 130-200 GeV. In addition, the major background can be measured directly to 7% from the data with an integrated luminosity of 30 1/fb. In this study, we also suggested a method to obtain information in Higgs mass using the transverse mass distributions.Comment: 26 pages, 22 figure

    The challenges of statistical patterns of language: the case of Menzerath's law in genomes

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    The importance of statistical patterns of language has been debated over decades. Although Zipf's law is perhaps the most popular case, recently, Menzerath's law has begun to be involved. Menzerath's law manifests in language, music and genomes as a tendency of the mean size of the parts to decrease as the number of parts increases in many situations. This statistical regularity emerges also in the context of genomes, for instance, as a tendency of species with more chromosomes to have a smaller mean chromosome size. It has been argued that the instantiation of this law in genomes is not indicative of any parallel between language and genomes because (a) the law is inevitable and (b) non-coding DNA dominates genomes. Here mathematical, statistical and conceptual challenges of these criticisms are discussed. Two major conclusions are drawn: the law is not inevitable and languages also have a correlate of non-coding DNA. However, the wide range of manifestations of the law in and outside genomes suggests that the striking similarities between non-coding DNA and certain linguistics units could be anecdotal for understanding the recurrence of that statistical law.Comment: Title changed, abstract and introduction improved and little corrections on the statistical argument
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