53 research outputs found

    Rapid acquisition of PN signals for DS/SS systems using a phase estimator

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    We propose a new scheme for rapid acquisition of PN signals in direct-sequence spread spectrum (DS/SS) systems by estimating the phase of the received PN signal with the use of an auxiliary signal. The auxiliary signal can be generated by a sum of the phase shifted PN signals. The phase of the incoming PN signal is estimated using the properties of cross correlation between the PN signal and the auxiliary signal. True phase alignment is detected using a conventional serial search scheme, where the initial phase of the local PN generators is set to a value obtained by the phase estimator. The performance of the proposed acquisition scheme is analytically evaluated in terms of the mean acquisition time. Numerical results show that the proposed scheme can achieve acquisition at least two times faster than the conventional scheme in nominal operating condition

    Language Identification and Morphosyntactic Tagging: The Second VarDial Evaluation Campaign

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    We present the results and the findings of the Second VarDial Evaluation Campaign on Natural Language Processing (NLP) for Similar Languages, Varieties and Dialects. The campaign was organized as part of the fifth edition of the VarDial workshop, collocated with COLING’2018. This year, the campaign included five shared tasks, including two task re-runs – Arabic Dialect Identification (ADI) and German Dialect Identification (GDI) –, and three new tasks – Morphosyntactic Tagging of Tweets (MTT), Discriminating between Dutch and Flemish in Subtitles (DFS), and Indo-Aryan Language Identification (ILI). A total of 24 teams submitted runs across the five shared tasks, and contributed 22 system description papers, which were included in the VarDial workshop proceedings and are referred to in this report.Non peer reviewe

    A Generative Verification Framework on Statistical Stability for Data-Driven Controllers

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    This study proposes a novel framework for evaluating the stability of data-driven controllers and the concept of statistical stability. The proposed framework can be used when it is challenging to show stability through conventional control theory. The novelty of this paper lies in that it provides a method for scientifically analyzing the stability of data-driven controllers, thereby improving the quality of data-driven controllers. The proposed framework consists of three parts: the generative model, controller optimizer, and verification model. A variational autoencoder is used to classify and randomly generate data, and the generated data are used to train the controller. A support vector machine is used to classify areas where the controller is statistically stable. The statistical stability of an optimal controller designed using a deep neural network structure is analyzed using the proposed framework
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