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Full-Text Articles in Electrical and Computer Engineering
Machine Learning Classification Of Digitally Modulated Signals, James A. Latshaw
Machine Learning Classification Of Digitally Modulated Signals, James A. Latshaw
Electrical & Computer Engineering Theses & Dissertations
Automatic classification of digitally modulated signals is a challenging problem that has traditionally been approached using signal processing tools such as log-likelihood algorithms for signal classification or cyclostationary signal analysis. These approaches are computationally intensive and cumbersome in general, and in recent years alternative approaches that use machine learning have been presented in the literature for automatic classification of digitally modulated signals. This thesis studies deep learning approaches for classifying digitally modulated signals that use deep artificial neural networks in conjunction with the canonical representation of digitally modulated signals in terms of in-phase and quadrature components. Specifically, capsule networks are …
Representations, Approximations, And Algorithms For Mathematical Speech Processing, Laura R. Suzuki
Representations, Approximations, And Algorithms For Mathematical Speech Processing, Laura R. Suzuki
Theses and Dissertations
Representing speech signals such that specific characteristics of speech are included is essential in many Air Force and DoD signal processing applications. A mathematical construct called a frame is presented which captures the important time-varying characteristic of speech. Roughly speaking, frames generalize the idea of an orthogonal basis in a Hilbert space, Specific spaces applicable to speech are L2(R) and the Hardy spaces Hp(D) for p> 1 where D is the unit disk in the complex plane. Results are given for representations in the Hardy spaces involving Carleson's inequalities (and its extensions), …
Reducing Uncertainty, Richard C. Heyser
Reducing Uncertainty, Richard C. Heyser
Unpublished Writings
Intended for audio engineers, Richard C. Heyser meant for this paper to bring attention to the misapplication of the theoretical concept, the Uncertainty Principle. Heyser argues that this concept has been "freely applied without regard to the errors which may result due to lack of understanding of its derivation."