Sökning: "Jun Yu"
Visar resultat 1 - 5 av 9 avhandlingar innehållade orden Jun Yu.
1. Nearest neighbor probability density estimators
Sammanfattning : .... LÄS MER
2. Statistical methods in medical image estimation and sparse signal recovery
Sammanfattning : This thesis presents work on methods for the estimation of computed tomography (CT) images from magnetic resonance (MR) images for a number of diagnostic and therapeutic workflows. The study also demonstrates sparse signal recovery method, which is an intermediate method for magnetic resonance image reconstruction. LÄS MER
3. Data-driven quality management using explainable machine learning and adaptive control limits
Sammanfattning : In industrial applications, the objective of statistical quality management is to achieve quality guarantees through the efficient and effective application of statistical methods. Historically, quality management has been characterized by a systematic monitoring of critical quality characteristics, accompanied by manual and experience-based root cause analysis in case of an observed decline in quality. LÄS MER
4. Biotic resistance in freshwater fish communities
Sammanfattning : Invasions of non-native species cause problems in ecosystems worldwide, and despite the extensive effort that has been put into research about invasions, we still lack a good understanding for why some, but not other, communities resist these invasions. In this doctoral thesis I test hypotheses on biotic resistance using a large dataset of more than 1000 both failed and successful introductions of freshwater fish into Swedish lakes. LÄS MER
5. The PET sampling puzzle : intelligent data sampling methods for positron emission tomography
Sammanfattning : 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. LÄS MER