Global ETD Search

621	Investigation of Interface Diffusion on the Reliability of AlGaN/GaN High Electron Mobility Transistor by Thermodynamic Modeling Ucci, Russell 14 August 2012 (has links) No description available. Chemical Engineering Electrical Engineering Engineering Materials Science Mechanical Engineering Nanoscience Nanotechnology AlGaN GaN HEMT Diffusion CALPHAD Phase Diagram DICTRA ThermoCalc Transistor
622	Advanced Channel Engineering in III-Nitride HEMTs for High Frequency Performance Park, Pil Sung January 2013 (has links) No description available. Electrical Engineering Solid State Physics Quantum Physics
623	Design of Power-Scalable Gallium Nitride Class E Power Amplifiers Connor, Mark Anthony 26 August 2014 (has links) No description available. Electrical Engineering gallium nitride GaN HEMT power amplifier class E transistor switch reconfigurable MMIC power scaling impedance matching load-pull RF microwave integrated circuit
624	Investigation of electrically-active defects in AlGaN/GaN high electron mobility transistors by spatially-resolved spectroscopic scanned probe techniques. Cardwell, Drew 16 September 2013 (has links) No description available. Solid State Physics Electrical Engineering
625	Cryogenic Irradiation and Low Temperature Annealing of Semiconductor and Optical Materials Reinke, Benjamin T. 09 June 2016 (has links) No description available. Nuclear Engineering nuclear radiation irradiation semiconductor GaN silica damage cryogenic LHe defect space NASA Ohio State optical fiber single-mode multi-mode
626	Optical and Electrical Study of the Rare Earth Doped III-nitride Semiconductor Materials Wang, Jingzhou January 2016 (has links) No description available. Electrical Engineering Materials Science Nanotechnology Optics III-nitride GaN InGaN Europium Ytterbium defect deep level spectroscopy DLTS Laplace-DLTS MCTS gettering
627	Development of MOKE Spectrometer for Magneto-optical Studies of Novel Magnetic Materials and Quantum Structures Tanaka, Hiroki 29 December 2008 (has links) No description available. Electrical Engineering MOKE polar Longitudinal Kerr rotation Kerr ellipticity Co Pt Fe GaN LabVIEW Setup calibration Lorentz Magneto optical Kerr effect
628	Investigation of AlGaN films and nickel/AlGaN Schottky diodes using depth-dependent cathodoluminescence spectroscopy and secondary ion mass spectrometry Bradley, Shawn Todd 04 March 2004 (has links) No description available. GaN AlN AlGaN gallium nitride nitride LEEN cathodoluminescence CL IPE internal photoemission spectroscopy secondary ion mass spectrometry SIMS Schottky doping HFET HEMT UHV
629	Quantitative defect spectroscopy on operating AlGaN/GaN high electron mobility transistors Malonis, Andrew C. January 2009 (has links) No description available. Electrical Engineering Materials Science GaN AlGaN HEMT defect trap deep level spectroscopy DLTS DLOS ID-DLTS ID-DLOS CID-DLTS CID-DLOS
630	Reduction of streak artifacts in radial MRI using CycleGAN / Reducering av streak-artefakter i radiell MRT med CycleGAN Ullvin, Amanda January 2020 (has links) One way of reducing the examination time in magnetic resonance imaging (MRI) is to reduce the amount of raw data acquired, by performing so-called undersampling. Conventionally, MRI data is acquired line-by-line on a Cartesian grid. In the field of Cardiovascular Magnetic Resonance (CMR), however, radial k-space sampling is seen as a promising emerging technique for rapid image acquisitions, mainly due to its robustness against motion disturbances occurring from the beating heart. Whereas Cartesian undersampling will result in image aliasing, radial undersampling will introduce streak artifacts. The objective of this work was to train the deep learning architecture, CycleGAN, to reduce streak artifacts in radially undersampled CMR images, and to evaluate the model performance. A benefit of using CycleGAN over other deep learning techniques for this application is that it can be trained on unpaired data. In this work, CycleGAN network was trained on 3060 radial and 2775 Cartesian unpaired CMR images acquired in human subjects to learn a mapping between the two image domains. The model was evaluated in comparison to images reconstructed using another emerging technique called GRASP. Whereas more investigation is warranted, the results are promising, suggesting that CycleGAN could be a viable method for effective streak-reduction in clinical applications. MRI Cardiovascular CMR Medical Imaging Radial k-space sampling Sub-Nyquist sampling Golden Angle GRASP Compressed Sensing Artificial Intelligence Neural Network Deep Learning GAN CycleGAN Medical Engineering Medicinteknik

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