Search for dissertations about: "Noisy Information"
Showing result 1 - 5 of 158 swedish dissertations containing the words Noisy Information.
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1. Dynamic Resampling for Preference-based Evolutionary Multi-objective Optimization of Stochastic Systems : Improving the efficiency of time-constrained optimization
Abstract : In preference-based Evolutionary Multi-objective Optimization (EMO), the decision maker is looking for a diverse, but locally focused non-dominated front in a preferred area of the objective space, as close as possible to the true Pareto-front. Since solutions found outside the area of interest are considered less important or even irrelevant, the optimization can focus its efforts on the preferred area and find the solutions that the decision maker is looking for more quickly, i. READ MORE
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2. Computational driver behavior models for vehicle safety applications
Abstract : The aim of this thesis is to investigate how human driving behaviors can be formally described in mathematical models intended for online personalization of advanced driver assistance systems (ADAS) or offline virtual safety evaluations. Both longitudinal (braking) and lateral (steering) behaviors in routine driving and emergencies are addressed. READ MORE
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3. Perceptually motivated speech recognition and mispronunciation detection
Abstract : This doctoral thesis is the result of a research effort performed in two fields of speech technology, i.e., speech recognition and mispronunciation detection. Although the two areas are clearly distinguishable, the proposed approaches share a common hypothesis based on psychoacoustic processing of speech signals. READ MORE
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4. mm-Wave Data Transmission and Measurement Techniques: A Holistic Approach
Abstract : The ever-increasing demand on data services places unprecedented technical requirements on networks capacity. With wireless systems having significant roles in broadband delivery, innovative approaches to their development are imperative. READ MORE
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5. Machine learning for quantum information and computing
Abstract : This compilation thesis explores the merger of machine learning, quantum information, and computing. Inspired by the successes of neural networks and gradient-based learning, the thesis explores how such ideas can be adapted to tackle complex problems that arise during the modeling and control of quantum systems, such as quantum tomography with noisy data or optimizing quantum operations, by incorporating physics-based constraints. READ MORE