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Can Deep Learning be used to augment DSP techniques? Algorithms in DSP are typically developed starting from a mathematical model of an application. In some cases however, simplicity of the model can result in deterioration of performance when there is a severe modeling mis-match. This paper explores the idea of implementing a DSP technique as a computational graph, so that hundreds of parameters can jointly be trained to adapt to any given dataset. Using the specific example of period estimation by Ramanujan Subspaces, significant improvement in estimation accuracies under high noise and very short datalengths is demonstrated.more » « less
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Absence seizures are a type of generalized seizures characterized by a 3 Hz periodic spike and wave discharge pattern in the Electroencephalogram (EEG). The most common way to diagnose them is by detecting such periodic patterns in a patient’s EEG. Recently, a new method known as Ramanujan Filter Bank (RFB) was proposed for identifying, estimating and localizing periodicities in data. The RFB was shown to offer important advantages over traditional period estimation techniques in DSP. In this work, we demonstrate that the RFB offers very useful diagnostic information when applied to EEG signals from absence-seizure patients.more » « less
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This paper addresses a fundamental question in the context of multi-dimensional periodicity. Namely, to distinguish between two N-dimensional periodic patterns, what is the least number of (possibly non-contiguous) samples that need to be observed? This question was only recently addressed for onedimensional signals. This paper generalizes those results to Ndimensional signals. It will be shown that the optimal sampling pattern takes the form of sparse and uniformly separated bunches. Apart from new theoretical insights, this paper’s results may provide the foundation for fast N-dimensional period recognition algorithms that use minimal number of samples.more » « less