Search for dissertations about: "signal statistics."
Showing result 1 - 5 of 156 swedish dissertations containing the words signal statistics..
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1. Inverse problems in signal processing : Functional optimization, parameter estimation and machine learning
Abstract : Inverse problems arise in any scientific endeavor. Indeed, it is seldom the case that our senses or basic instruments, i.e., the data, provide the answer we seek. READ MORE
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2. Group-Sparse Regression : With Applications in Spectral Analysis and Audio Signal Processing
Abstract : This doctorate thesis focuses on sparse regression, a statistical modeling tool for selecting valuable predictors in underdetermined linear models. By imposing different constraints on the structure of the variable vector in the regression problem, one obtains estimates which have sparse supports, i.e. READ MORE
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3. The PET sampling puzzle : intelligent data sampling methods for positron emission tomography
Abstract : Much like a backwards computed Sudoku puzzle, starting from the completed number grid and working ones way down to a partially completed grid without damaging the route back to the full unique solution, this thesis tackles the challenges behind setting up a number puzzle in the context of biomedical imaging. By leveraging sparse signal processing theory, we study the means of practical undersampling of positron emission tomography (PET) measurements, an imaging modality in nuclear medicine that visualises functional processes within the body using radioactive tracers. READ MORE
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4. Signal Separation - II
Abstract : Signal separation is a signal processing technique which enables the separation of superimposed signals. There are several practical situations were signal separation can be used, for example in noise reduction for cellular phones subject to background noise. READ MORE
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5. Statistical inference and time-frequency estimation for non-stationary signal classification
Abstract : This thesis focuses on statistical methods for non-stationary signals. The methods considered or developed address problems of stochastic modeling, inference, spectral analysis, time-frequency analysis, and deep learning for classification. READ MORE