Master of Science

thesisConsistency analysis and data collaboration is a relatively new scientific area. It deals with quantifying how well scientific models approximate empirical reality. Consistency analysis is based on methodically comparing model predictions with experimental measurements, but this task is made more difficult by the fact that both models and experiments have their own inherent uncertainties. Computational fluid dynamics (CFD) models are numerical methods able to solve complicated discrete fluid dynamics problems. They are used thoroughly in mechanical, aerospace and energy science. As CFD models are being applied to more and more critical systems, there is a growing need to improve the reliability of CFD model predictions. This work addresses this need by presenting consistency analysis results for a simple CFD model and an experiment in which the concentration field of a buoyant helium plume had been studied by holographic interferometry. A detailed procedure is presented for carrying out data collaboration between simulation and experimental data. This work is novel in a sense that it is the first to present the specific difficulties of collaborating interferometric data. These difficulties arise from the encoded nature of information being present in interferometric fringe images

Explant Analysis of Total Disc Replacement

Explant analysis of human disc prostheses allow early evaluation of the host response to the prosthesis and the response of the prosthesis from the host. Furthermore, early predictions of failure and wear can be obtained. Thus far, about 2-3% of disc prostheses have been removed. Observed wear patterns are similar to that of appendicular prostheses including abrasions/scratching, burnishing, surface deformation, fatigue, and embedded debris. Chemically the polymeric components have shown little degradation in short-term implantation. In metal on metal prostheses the histologic responses consist of large numbers of metallic particles with occasional macrophages and giant cells. Only rare cases of significant inflammatory response from polymeric debris have been seen

A Renormalisation group for TCSA

We discuss the errors introduced by level truncation in the study of boundary renormalisation group flows by the Truncated Conformal Space Approach. We show that the TCSA results can have the qualitative form of a sequence of RG flows between different conformal boundary conditions. In the case of a perturbation by the field phi(13), we propose a renormalisation group equation for the coupling constant which predicts a fixed point at a finite value of the TCSA coupling constant and we compare the predictions with data obtained using TBA equations.Comment: 11 pages, 7 figures, talk presented by G Watts at the workshop "Integrable Models and Applications: from Strings to Condensed Matter", Santiago de Compostela, Spain, 12-16 September 200

Deep Interference Mitigation and Denoising of Real-World FMCW Radar Signals

Radar sensors are crucial for environment perception of driver assistance systems as well as autonomous cars. Key performance factors are a fine range resolution and the possibility to directly measure velocity. With a rising number of radar sensors and the so far unregulated automotive radar frequency band, mutual interference is inevitable and must be dealt with. Sensors must be capable of detecting, or even mitigating the harmful effects of interference, which include a decreased detection sensitivity. In this paper, we evaluate a Convolutional Neural Network (CNN)-based approach for interference mitigation on real-world radar measurements. We combine real measurements with simulated interference in order to create input-output data suitable for training the model. We analyze the performance to model complexity relation on simulated and measurement data, based on an extensive parameter search. Further, a finite sample size performance comparison shows the effectiveness of the model trained on either simulated or real data as well as for transfer learning. A comparative performance analysis with the state of the art emphasizes the potential of CNN-based models for interference mitigation and denoising of real-world measurements, also considering resource constraints of the hardware.Comment: 2020 IEEE International Radar Conference (RADAR

Letters to the Editor

Dear Sirs: I have recently received a brochure advertising the so-called Major Authors Edition of your anthology of English literature. The blurb absurdly advertises it as the essential works of the essential authors. More properly, it should be described as Some Major (and Some Minor) Works by Thirty White Male British Authors ..

Methane observations from the Greenhouse Gases Observing SATellite: Comparison to ground‐based TCCON data and model calculations

We report new short-wave infrared (SWIR) column retrievals of atmospheric methane (X_(CH4)) from the Japanese Greenhouse Gases Observing SATellite (GOSAT) and compare observed spatial and temporal variations with correlative ground-based measurements from the Total Carbon Column Observing Network (TCCON) and with the global 3-D GEOS-Chem chemistry transport model. GOSAT X_(CH4) retrievals are compared with daily TCCON observations at six sites between April 2009 and July 2010 (Bialystok, Park Falls, Lamont, Orleans, Darwin and Wollongong). GOSAT reproduces the site-dependent seasonal cycles as observed by TCCON with correlations typically between 0.5 and 0.7 with an estimated single-sounding precision between 0.4–0.8%. We find a latitudinal-dependent difference between the X_(CH4) retrievals from GOSAT and TCCON which ranges from 17.9 ppb at the most northerly site (Bialystok) to −14.6 ppb at the site with the lowest latitude (Darwin). We estimate that the mean smoothing error difference included in the GOSAT to TCCON comparisons can account for 15.7 to 17.4 ppb for the northerly sites and for 1.1 ppb at the lowest latitude site. The GOSAT X_(CH4) retrievals agree well with the GEOS-Chem model on annual (August 2009 – July 2010) and monthly timescales, capturing over 80% of the zonal variability. Differences between model and observed X_(CH4) are found over key source regions such as Southeast Asia and central Africa which will be further investigated using a formal inverse model analysis