Browsing by Subject "Nonuniform sampling"

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    The effect of distribution of information on recovery of propagating signals
    (2015-09) Karabulut, Özgecan
    Interpolation is one of the fundamental concepts in signal processing. The analysis of the di fficulty of interpolation of propagating waves is the subject of this thesis. It is known that the information contained in a propagating wave fi eld can be fully described by its uniform samples taken on a planar surface transversal to the propagation direction, so the eld can be found anywhere in space by using the wave propagation equations. However in some cases, the sample locations may be irregular and/or nonuniform. We are concerned with interpolation from such samples. To be able to reduce the problem to a pure mathematical form, the fractional Fourier transform is used thanks to the direct analogy between wave propagation and fractional Fourier transformation. The linear relationship between each sample and the unknown field distribution is established this way. These relationships, which constitute a signal recovery problem based on multiple partial fractional Fourier transform information, are analyzed. Recoverability of the fi eld is examined by comparing the condition numbers of the constructed matrices corresponding to di fferent distributions of the available samples.
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    Nonuniformly sampled data processing using LSTM networks
    (Institute of Electrical and Electronics Engineers, 2019) Şahin, Safa Onur; Kozat, Süleyman Serdar
    We investigate classification and regression for nonuniformly sampled variable length sequential data and introduce a novel long short-term memory (LSTM) architecture. In particular, we extend the classical LSTM network with additional time gates, which incorporate the time information as a nonlinear scaling factor on the conventional gates. We also provide forward-pass and backward-pass update equations for the proposed LSTM architecture. We show that our approach is superior to the classical LSTM architecture when there is correlation between time samples. In our experiments, we achieve significant performance gains with respect to the classical LSTM and phased-LSTM architectures. In this sense, the proposed LSTM architecture is highly appealing for the applications involving nonuniformly sampled sequential data. IEEE