Laserskanning har blivit en vanlig metod för dokumentation, övervakning, underhåll och ut­veckling av olika industrimiljöer. Särskilt för inmätning och visualisering av komplexa rörledningar på industrianläggningars tak är punktmoln från laserskanning ett viktigt verktyg för att på ett enkelt sätt hitta potentiella platser för installation av nya rörledningar. Detta examensarbete genomfördes i samarbete med konsultbolaget Swecos mätningsgrupp i Karlstad och undersökte om det är möj­ligt att ersätta punktmoln från terrester laserskanning med punktmoln skapade med flygbilder tagna med UAS (Unmanned Aerial System) för dokumentering av komplexa rördragningar på industritak. Studien genomfördes på ett mindre område (5x25 m) på reningsverket i stadsdelen Sjöstad (Sjö­stadsverket) i Karlstad. Området innehöll omfattande rörledningar i olika dimensioner och andra detaljer såsom rattar, flänsar och gallerluckor. Detta område ansågs därför vara lämpligt att använda för studiens syfte och tillträdet förutsatte dessutom inte omfattande och dyra säkerhetsutbildningar. Undersökningen genomfördes genom markering av ett sextiotal kontrollpunkter som sedan mättes in med totalstation i ett lokalt referenssystem. Bakåtobjekten användes även som fästpunkter för sfäriska måltavlor under laserskanningen. Dessutom mättes även målade markstödsignaler på bet­ongen in i samband med detta för att möjliggöra en georeferering av flygbilderna. Därefter genom­fördes terrester laserskanning inom undersökningsområdet från nio uppställningar med varierande instrumenthöjder samt två UAS-flygningar med flygfotografering från tio respektive 22 m flyghöjd. Efterbearbetningarna började med att etablera ett lokaltreferenssystem, vilket användes för geo­referering av både laserskanningspunkmolnet samt respektive flygfotograferingspunktmoln. De er­hållna lokala koordinaterna för kontrollpunkterna i respektive punktmoln jämfördes gentemot de totalstationsinmätta koordinaterna för att analysera lägesosäkerheten. Punktmolnet från terrester laserskanning innehöll 55 tydligt identifierbara kontrollpunkter medan punktmolnen från UAS-flygningen visade 53 respektive 22 identifierbara kontrollpunkter. Den kvadratiska medelavvikelsen (RMS) i 3D för dessa punkter uppgick till 8 mm i laserskann­ingspunktmolnet respektive 25 mm för båda flygbildspunktmoln. Efter detta analysmoment valdes punktmolnet från flygningen på 22 m höjd bort inför de fortsatta analyserna då cirka två tredjedelar av kontrollpunkterna inte var identifierbara. Även mät- och lägesosäkerheten från flygbildspunktmolnet från tio meters höjd visade sig dock i början vara otillräckligt för att kunna ersätta terrester laserskanning med flygfotografering med UAS. Ändå tillät detaljeringsgraden en identifiering av ett stort antal kontrollpunkter och vidare analyser visade att den stora lägesosäkerheten främst berodde på kontrollpunkter kopplade till vissa detaljtyper (dolda stödben och omarkerade bultar). En nyberäkning av lägesosäkerheten utan dessa kontrollpunkter gav betydligt bättre värden för lägesosäkerhet inom flygbildspunktmolnet från tio meters höjd, ett RMS i 3D på 12 mm. Eftersom användningarna där Sweco skulle vilja ersätta terrester laserskanning med flygfoto­graf­ering inte kräver en detaljnivå på bultstorlek visade sig därmed flygfotografering med UAS som en lämplig alternativ metod för att dokumentera komplexa rördragningar på industritak. / This study explored the possibility of using aerial photography from Unmanned Aerial Systems (UAS) as a replacement for terrestrial laser scanning in documenting complex pipeline systems on industrial roofs. The research, conducted in collaboration with Sweco's survey group in Karlstad, focused on visual qualities and positional uncertainty in point clouds generated by terrestrial laser scanning and aerial photography. Control points were marked and surveyed using a total station, then terrestrial laser scanning and UAS-aerial photography was performed to generate point clouds. Analysis revealed that the aerial photography at 22 m altitude was not suitable due to unrecognizable control points. However, the aerial photography at 10 m altitude, after excluding certain types of control points, showed improved positional uncertainty. As the desired applications did not require fine-level detail, UAS aerial photography proved to be a suitable alternative for documenting complex pipeline systems on industrial roofs.

Volumetric clouds are often used in video games in order to improve the realism or graphical quality of the game. However, in order to achieve real-time rendered clouds, optimizations have to be implemented as part of the rendering algorithm. These kinds of optimizations improve the performance, but can also have a negative impact on the visual quality of the clouds. This thesis investigates the use of bilinear interpolation for the purpose of improving the performance of a volumetric cloud renderer, while trying to avoid any substantial reduction in visual quality. This is extended by looking at the effect of adaptively sampling the pixel colors. The renderer itself is created in Unity3D using a ray marching algorithm. As part of the literature study, this research also explores different ways of measuring visual quality within real-time rendering. As a result of this, the thesis uses the Structural Similarity Index Measure to measure the visual quality. The research found that utilizing bilinear interpolation to ray march every eighth pixel results in a performance gain of 45%. However, it also reduces the visual quality of the volumetric clouds. This is counteracted by using adaptive sampling to interpolate only where the standard deviation of pixel colors is below a threshold. We cannot, however, determine the optimal value of this parameter, since it depends on the requirements of the renderer. Instead, it has to be determined on a case-by-case basis. / Volymetriska moln används i spel för att uppnå realism och förbättra den grafiska kvaliteten. Men för att uppnå realtidsrendering så måste optimeringar göras. Dessa typer av optimeringar förbättrar prestandan av programmet, men kan också försämra den visuella kvalteten. Den här studien undersöker hur en optimering baserad på bilinjär interpolering kan användas för att förbättra prestandan av volymetriska moln, utan att försämra den visuella kvaliteten i någon större utsträckning. Studien tittar även på hur adaptiv sampling av pixlarna påverkar optimeringen. För att utföra detta renderas molnen i Unity3D med hjälp av en ray marching-algoritm. Som del av litteraturstudien utforskas även olika sätt att evaluera visuell kvalitet inom realtidsrendering. Utifrån denna använder studien måttet Structural Similarity Index Measure för att mäta visuell kvalitet. Studien fann att den bilinjära interpoleringen resulterade i att prestandan ökade med 45% när endast var åttonde pixel är beräknad med ray marching, och resten interpoleras. Dock reduceras även den visuella kvaliteten av molnen. Detta kan motverkas med hjälp av adaptiv sampling. Då interpoleras endast pixlar där standardavvikelsen av de kringliggande pixlarna är under ett fördefinierat värde. Vi kan däremot inte definiera ett universiellt optimalt värde på detta värde. Det beror på att det optimala värdet beror på kraven vi har på programmet. Dessa kan variera från program till program. Därför måste detta bestämmas individuellt för varje program.

Automatic bin picking is a well-known problem in industrial automation and computer vision, where a robot picks an object from a bin and places it somewhere else. There is continuous ongoing research for many years to improve the contemporary solution. With camera technology advancing rapidly and available fast computation resources, solving this problem with deep learning has become a current interest for several researchers. This thesis intends to leverage the current state-of-the-art deep learning based methods of 3D instance segmentation and point cloud registration and combine them to improve the bin picking solution by improving the performance and make them robust. The problem of bin picking becomes complex when the bin contains identical objects with heavy occlusion. To solve this problem, a 3D instance segmentation is performed with Fast Point Cloud Clustering (FPCC) method to detect and locate the objects in the bin. Further, an extraction strategy is proposed to choose one predicted instance at a time. Inthe next step, a point cloud registration technique is implemented based on PointNetLK method to estimate the pose of the selected object from the bin. The above implementation is trained, tested, and evaluated on synthetically generated datasets. The synthetic dataset also contains several noisy point clouds to imitate a real situation. The real data captured at the company ’SICK IVP’ is also tested with the implemented model. It is observed that the 3D instance segmentation can detect and locate the objects available in the bin. In a noisy environment, the performance degrades as the noise level increase. However, the decrease in the performance is found to be not so significant. Point cloud registration is observed to register best with the full point cloud of the object, when compared to point cloud with missing points.

In dieser Arbeit wird der auf maschinellem Lernen basierende Algorithmus zur Erkennung von unterkühlten Flüssigwasserschichten in Mischphasenwolken (MPCs) jenseits der Lidarattenuation VOODOO (reVealing supercOOled liquiD beyOnd lidar attenuatiOn) vorgestellt. Beobachtungen von zwei Langzeitmesskampagnen bilden die Datengrundlage. Für die nördliche Hemisphäre wurden die Daten mittels der mobilen bodengebundenen Fernerkundungsanlage des Leipziger Instituts für Meteorologie (LIM) in Leipzig (Deutschland, 51.3°N, 12. 4°E) erhoben. Für die südliche Hemisphäre wurde ein 9-monatiger Teil der dreijährigen Feldkampagne DACAPO-PESO (Dynamics Aerosol Clouds And Precipitation Observation in the Pristine Environment of the Southern Ocean; Beobachtung von Dynamik, Aerosol, Wolken und Niederschlag in der unverschmutzten Umgebung des Südozeans) aus Punta Arenas (Chile, 53.1°S, 70.9°W) verwendet. Dieser Datensatz wurde mittels des 94GHz Wolkenradars des LIM in Kombination mit LACROS (Leipzig Aerosol and Cloud Remote Observations System; Leipziger Aerosol- und Wolken-Fernerkundungssystem)- Instrumenten erhoben. Datensätze von vertikal ausgerichteten Doppler-Wolkenradaren und Lidarsystemen liefern Erkenntnisse über Wolkeneigenschaften mit hoher zeitlicher und räumlicher Auflösung. Die Identifizierung von flüssigen Wolkentröpfchen ist jedoch aufgrund der Abschwächung des Lidarsignals oft eingeschränkt. Wolkenradare sind andererseits in der Lage, mehrere Flüssigwassersschichten zu durchdringen, und können potenziell eingesetzt werden, um die Identifizierung der thermodynamischen Wolkenphase auf die gesamte vertikale Säule jenseits der Lidar-Signalabschwächungshöhe auszudehnen. Dazu werden morphologische Merkmale in Wolkenradar-Doppler-Spektren extrahiert, um auf das Vorhandensein von Flüssigwasser zu schließen. Das wichtigste Ergebnis dieser Arbeit ist die Implementierung des Open-Source-Retrievals VOODOO. Für beide Langzeitdatensätze zeigt die Wolken- und Flüssigwasseridentifikation von VOODOO hervorragende zeitliche und räumliche Übereinstimmungen mit der weit verbreitenten Cloudnet-Klassifizierung. Vergleiche des vom Mikrowellenradiometers (MWR) gemessenen Flüssigwasserpfads (LWP) mit modelliertem adiabatischen LWP zeigen jedoch die Überlegenheit von VOODOO in der Detektion hochreichender und mehrschichtiger MPCs im Vergleich zu Cloudnet. Der Einfluss von Turbulenzen auf die Vorhersageleistung von VOODOO wurde analysiert und als gering eingestuft. Darüber hinaus bestätigen weltraumgestützte Lidar-Beobachtungen die VOODOO-basierten Vorhersagen von Oberkanten von Flüssigwasserschichten für ausgewählte Satellitenüberflüge über Punta Arenas. Das Endresultat zeigt für ein Fallbeispiel die resultierende Reduktion der Fehler zwischen kurzwelliger solarer Strahlung am Erdboden, sowie des Strahlungseffekts von Wolken zwischen Beobachtungen und Strahlungstransfersimulationen um den Faktor 2, bei der Verwendung der VOODOO-Flüssigwasseridentifikationen.:i Mixed-Phase Clouds 1 Introduction 3 2 Remote-sensing of mixed-phase clouds 7 2.1 Definition, occurrence, and impact . . . . . . . . . . . . . . . . . . . . . . . 7 2.2 Instrumentation and operating principles . . . . . . . . . . . . . . . . . . . 9 2.2.1 Doppler cloud radar . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.2.2 Lidar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.2.3 Microwave radiometer . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.2.4 Numerical weather forecast model . . . . . . . . . . . . . . . . . . . 18 2.2.5 Additional data sources . . . . . . . . . . . . . . . . . . . . . . . . . 18 3 Datasets 19 3.1 Punta Arenas, Chile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3.2 Leipzig, Germany . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 ii Methodology 4 Identifying the thermodynamic phase of hydrometeors 25 4.1 Multisensor-based approaches . . . . . . . . . . . . . . . . . . . . . . . . . . 25 4.1.1 Cloudnet: Illingworth et al., 2007 . . . . . . . . . . . . . . . . . . . . 25 4.1.2 Shupe, 2007 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 4.2 Radar-moment-based approaches . . . . . . . . . . . . . . . . . . . . . . . . 27 4.2.1 Silber et al., 2020 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 4.2.2 Kalogeras et al., 2021 . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 4.3 Doppler-Spectrum-based approaches . . . . . . . . . . . . . . . . . . . . . . 28 4.3.1 Yu et al., 2014 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 4.3.2 PEAKO + peakTree: Kalesse et al., 2019; Radenz et al., 2019 . . . . 28 4.3.3 Luke et al., 2010 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 4.4 Challenges in cloud-phase classification . . . . . . . . . . . . . . . . . . . . 30 5 Machine learning model 33 5.1 Mathematical basics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 5.2 Pre-processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 5.3 Training and validation dataset . . . . . . . . . . . . . . . . . . . . . . . . . 36 5.4 Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 5.4.1 Convolution layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 5.4.2 Perceptron layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 5.4.3 Output layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 5.5 Training process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 5.6 Post-processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 5.7 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 5.7.1 Confusion matrix and binary classification metrics . . . . . . . . . 43 5.7.2 Correlation with independent measurements . . . . . . . . . . . . . 45 5.7.3 Influence of LWP and turbulence on the performance . . . . . . . . 45 5.7.4 Probability density functions . . . . . . . . . . . . . . . . . . . . . . 46 5.7.5 Validation via space-borne lidar satellite CALIPSO . . . . . . . . . 46 5.7.6 Radiative closure study . . . . . . . . . . . . . . . . . . . . . . . . . 46 iii Results 6 Results 51 6.1 Training results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 6.2 Case study: 1. August 2019, Punta Arenas, Chile . . . . . . . . . . . . . . . 53 6.3 Case study: 30 December 2020, Leipzig, Germany . . . . . . . . . . . . . . 57 6.4 Performance analysis for larger data sets . . . . . . . . . . . . . . . . . . . . 60 6.5 Probability density functions . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 6.6 Case study for satellite-based cloud phase validation . . . . . . . . . . . . . 66 6.7 Radiative closure study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 iv Outcome 7 Summary and Conclusion 77 8 Outlook 79 Publication record 83 List of Figures 85 List of Tables 88 List of Symbols 93 Bibliography 99 / This work presents a data driven retrieval algorithm for reVealing supercOOled liquiD beyOnd lidar attenuatiOn (VOODOO) in mixed-phase clouds (MPCs), which is based on deep convolutional neural networks (CNNs). Observations of two long-term field campaigns of mobile ground-based remote-sensing instrument deployments from both hemispheres are utilized. For the northern hemisphere, the data set was obtained by the mobile ground-based remote-sensing suite of the Leipzig Institute for Meteorology (LIM) in Leipzig (Germany, 51.3°N, 12.4°E) and for the southern hemisphere, 9-months of the three-year-long data set of the Dynamics Aerosol Clouds And Precipitation Observation in the Pristine Environment of the Southern Ocean (DACAPO-PESO) field campaign in Punta Arenas (Chile, 53.1°S, 70.9°W), collected by the supersite LACROS (Leipzig Aerosol and Cloud Remote Observations System). Data sets of vertically pointing Doppler cloud radars and lidars provide insights into cloud properties at high temporal and spatial resolution. However, the identification of liquid cloud droplets is often limited due to the attenuation of the lidar signal. On the contrary, cloud radars are able to penetrate multiple liquid layers and can potentially be used to expand the identification of cloud phase to the entire vertical column beyond the lidar signal attenuation height, by exploiting morphological features in cloud radar Doppler spectra that relate to the existence of supercooled liquid. The most important result of this work is the the open-source implementation of the VOODOO retrieval, predicting excellent temporal and spatial agreement in cloud-droplet bearing pixels detected by the widely-used Cloudnet atmospheric target classification. Comparisons of the liquid water path (LWP) measured by the microwave radiometer (MWR), with modeled adiabatic LWP show the superiority of VOODOO in detecting liquid in deep and multilayer MPCs compared to Cloudnet. The influence of turbulence on VOODOO’s predictive performance was analyzed and found to be minor. Additionally, space-borne lidar observations confirm liquid cloud top predictions of MPCs tops for selected satellite overpasses over Punta Arenas, Chile. The final results of this work is the demonstration of the ability to reduce the shortwave downward radiation bias and the bias in cloud radiative effect between ground-based pyranometer observations and radiative transfer simulations by a factor of 2 for a case study. This highlights the fact that from a measurement perspective, advanced cloud thermodynamic phase retrievals based on sophisticated remote-sensing observations can be a way to reduce the Southern Ocean radiation bias.:i Mixed-Phase Clouds 1 Introduction 3 2 Remote-sensing of mixed-phase clouds 7 2.1 Definition, occurrence, and impact . . . . . . . . . . . . . . . . . . . . . . . 7 2.2 Instrumentation and operating principles . . . . . . . . . . . . . . . . . . . 9 2.2.1 Doppler cloud radar . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.2.2 Lidar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.2.3 Microwave radiometer . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.2.4 Numerical weather forecast model . . . . . . . . . . . . . . . . . . . 18 2.2.5 Additional data sources . . . . . . . . . . . . . . . . . . . . . . . . . 18 3 Datasets 19 3.1 Punta Arenas, Chile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3.2 Leipzig, Germany . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 ii Methodology 4 Identifying the thermodynamic phase of hydrometeors 25 4.1 Multisensor-based approaches . . . . . . . . . . . . . . . . . . . . . . . . . . 25 4.1.1 Cloudnet: Illingworth et al., 2007 . . . . . . . . . . . . . . . . . . . . 25 4.1.2 Shupe, 2007 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 4.2 Radar-moment-based approaches . . . . . . . . . . . . . . . . . . . . . . . . 27 4.2.1 Silber et al., 2020 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 4.2.2 Kalogeras et al., 2021 . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 4.3 Doppler-Spectrum-based approaches . . . . . . . . . . . . . . . . . . . . . . 28 4.3.1 Yu et al., 2014 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 4.3.2 PEAKO + peakTree: Kalesse et al., 2019; Radenz et al., 2019 . . . . 28 4.3.3 Luke et al., 2010 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 4.4 Challenges in cloud-phase classification . . . . . . . . . . . . . . . . . . . . 30 5 Machine learning model 33 5.1 Mathematical basics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 5.2 Pre-processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 5.3 Training and validation dataset . . . . . . . . . . . . . . . . . . . . . . . . . 36 5.4 Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 5.4.1 Convolution layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 5.4.2 Perceptron layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 5.4.3 Output layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 5.5 Training process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 5.6 Post-processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 5.7 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 5.7.1 Confusion matrix and binary classification metrics . . . . . . . . . 43 5.7.2 Correlation with independent measurements . . . . . . . . . . . . . 45 5.7.3 Influence of LWP and turbulence on the performance . . . . . . . . 45 5.7.4 Probability density functions . . . . . . . . . . . . . . . . . . . . . . 46 5.7.5 Validation via space-borne lidar satellite CALIPSO . . . . . . . . . 46 5.7.6 Radiative closure study . . . . . . . . . . . . . . . . . . . . . . . . . 46 iii Results 6 Results 51 6.1 Training results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 6.2 Case study: 1. August 2019, Punta Arenas, Chile . . . . . . . . . . . . . . . 53 6.3 Case study: 30 December 2020, Leipzig, Germany . . . . . . . . . . . . . . 57 6.4 Performance analysis for larger data sets . . . . . . . . . . . . . . . . . . . . 60 6.5 Probability density functions . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 6.6 Case study for satellite-based cloud phase validation . . . . . . . . . . . . . 66 6.7 Radiative closure study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 iv Outcome 7 Summary and Conclusion 77 8 Outlook 79 Publication record 83 List of Figures 85 List of Tables 88 List of Symbols 93 Bibliography 99

In this thesis we present cloud-scale ALMA observations of two local mergers, NGC 3256 and NGC 4038/9 (the "Antennae"), in CO J=2-1. Through a pixel-based analysis of NGC 3256 we measure molecular-gas properties and compare to nearby spiral galaxies from the PHANGS-ALMA survey. NGC 3256 exhibits high mass surface densities, velocity dispersions, peak brightness temperatures, virial parameters, and internal turbulent pressures. High surface densities are expected to accompany its high star-formation rate, and high brightness temperatures may indicate warmer gas, heated by the vigorous star formation. Large virial parameters and internal pressures imply the molecular gas is not bound by self-gravity, but we explore how material external to clouds could alter this. We argue the molecular gas in NGC 3256 is smoother than in nearby spiral galaxies down to 55 pc. We also perform a cloud analysis of our NGC 3256 observations, identifying 185 clouds, and find similar results to the pixel analysis. We calculate additional cloud properties including eccentricity, CO luminosity, CO-estimated mass, virial mass, size-linewidth coefficient, and free-fall time. Properties in NGC 3256 are extreme compared to clouds from PHANGS-ALMA, including slightly larger clouds and shorter free-fall times. Cloud eccentricities in NGC 3256 are similar to those in PHANGS-ALMA galaxies, possibly indicating similar average cloud dynamical states. The shape of the cloud mass function in NGC 3256 is similar to many PHANGS-ALMA galaxies. Finally, we analyse our NGC 4038/9 observations using the same pixel methods as used in NGC 3256. NGC 4038/9 also harbours extreme molecular-gas properties and potentially smoother emission compared to spiral galaxies, but not as extreme as NGC 3256. We find the most-massive spiral galaxies have central molecular-gas properties similar to the mergers. Virial parameters in NGC 4038/9 are similar to many spiral galaxies, making it quite different from NGC 3256, potentially due to their different merger stages. Comparison of the overlap region of NGC 4038/9 in CO (2-1) to CO (3-2) shows general agreement. / Thesis / Doctor of Philosophy (PhD)

No description available.

<p>Stars form in clusters amidst complex and coupled physical phenomena. Among the most important of these is radiative feedback, which heats the surrounding gas to suppress the formation of many low-mass stars. In simulations of star formation, pre-main-sequence modeling has often been neglected and stars are assumed to have the radii and luminosities of zero-age main sequence stars. We challenge this approach by developing and integrating a one-zone protostellar evolution model for FLASH and using it to regulate the radiation output of forming stars. The impact of accurate pre-main-sequence models is less ionizing radiation and less heating during the early stages of star formation. For stars modeled in isolation, the effect of protostellar modeling resulted in ultracompact HII regions that formed slower than in the ZAMS case, but also responded to transitions in the star itself. The HII region was seen to collapse and subsequently be rebuilt as the star underwent a swelling of its radius in response to changes in stellar structure and nuclear burning. This is an important effect that has been missed in previous simulations. It implies that observed variations in HII regions may signal changes in the stars themselves, if these variation can be disentangled from other environmental effects seen in the chaotic cluster environment.</p> / Master of Science (MSc)

Potholes are caused by erosion and as such always emerging on our roadnetwork. Potholes may not only cause great damages to vehicles, but can alsocause road accidents, which in the worst case are fatal. Today, the detection ofpotholes is usually based on citizen reports or ocular inspection by vehicle,where a loose description of the potholes properties and location can be given.Recent research has explored the possibility of aerial inspection of paved roadswith the new, cost effective, Structure-from-Motion (SfM) technique, whichcan produce 3D point clouds from photogrammetric data. SfM point cloudshave then been used in conjunction with processing algorithms toautomatically detect and extract potholes from paved surfaces. However, theresults have not been optimal for practical use. The purpose of this study is,therefore, to explore the possibility of using UAV LiDAR for potholedetection in paved roads as a better alternative to the currently popularStructure-from-Motion (SfM) technique. A LiDAR point cloud is derived by alaser scanner and may have several advantages over SfM, for instance, theinsensitivity to poor light conditions and modelling errors. This study is setout to answer how point clouds derived from UAV SfM and UAV LiDARcompare to each other regarding detecting potholes of different sizes, wheredetected potholes will be compared to ground truth data. An elevation check,consisting of 126 height control points along the paved road, will also be usedto evaluate the height accuracy in the clouds. Data collection is done with theUAV system mdLiDAR3000DL aaS containing a RIEGL miniVUX-1DLlaser scanner for LiDAR data and Sony RX1R II 42.4 megapixel camera forSfM data. The data for both methods are collected during the same flight. Theproposed method automatically detects and extracts potholes from a pavedsurface based on the vertical distance to local reference planes which representthe undamaged road surface. The point clouds are filtered in CloudComparebefore imported to TerraScan for detection and extraction of potholes. Theextraction results are then controlled by a set of terrestrial measurements bytotal station. The results show that potholes with a smaller width of at least16.5 cm and a depth of at least 2.7 cm can be detected and extracted frompoint clouds derived by UAV LiDAR at a flight altitude of 30 m. Theextracted potholes had a standard deviation of 1.40 cm in width and 6.7 mmin depth. Shadows on the road caused height anomalies in the point cloudproduced by Structure-from-Motion (SfM), which made pothole detectionimpossible with the proposed methodology. / Potthål skapas genom erosion i vägar och uppstår varje år i vägnätet. Skadornapåverkar inte bara fordonens skick, utan kan även vara orsaken till olyckorsom i vissa fall är dödliga. I dagsläget detekteras potthål genom ockulärt frånfordon av kommunala arbetare eller så rapporteras de in av medborgare via etjänst där en lös beskrivning kan ges angående potthålens egenskaper ochposition.På senare tid har studier utforskat möjligheterna för flygburen inspektion avasfalterade vägar med den nya, kostnadseffektiva, Structure-from-Motion(SfM) tekniken som kan producera 3D-punktmoln från fotogrammetrisk data.Punktmolnen som är framtagna genom denna metod har vidare använtstillsammans med bearbetningsalgoritmer för att detektion och extraktion avpotthål i asfalterade vägar. Dock har resultaten inte varit optimala för attmetoden ska fungera i praktiken. Syftet med den här studien är därför attutforska möjligheten för att använda UAV LiDAR som en bättre metod fördenna process. Punktmoln framtagna genom LiDAR-teknik, mer känt somlaserskanning, kan ha ett flertal potentiella fördelar över SfM som okänslighetmot modelleringsfel och dåliga ljusförhållanden.Denna studie ger svar på hur punktmoln framtagna genom UAV LiDAR ochUAV SfM förhåller sig till varandra när det gäller detektion av potthål i olikastorlekar från asfalterade vägar, där potthålens dimensioner kommer attjämföras mot markbundna kontrollmätningar. Vidare görs en höjdkontrollmot 126 höjdstöd i båda punktmolnen för att jämföra kvaliteten förhöjdmätningar på den asfalterade vägen genom respektive metod.Insamlingen av data gjordes samtidigt under samma flygning för bådametoderna. Drönaren som användes var Microdrones mdLiDAR3000DL aaSmed en RIEGL miniVUX-1DL laserskanner och en Sony RX1R II 42,4megapixelkamera monterad. Mjukvarorna som har använts för bearbetning ärCloudCompare för filtrering av brus med mera och TerraScan för självadetektions -och extraktionsprocessen.Resultatet visar att det är möjligt att extrahera potthål från LiDAR-baseradepunktmoln med en mindre bredd på minst 16,5 cm och ett djup på 2,7 cm.Standardavvikelsen för potthålens bredd är 1,4 cm och 6,7 mm i djup.Grupper av avvikande punkter skapades på vägen i det SfM-baseradepunktmolnen som en följd av ett modelleringsfel i skuggområden på vägen,vilket vidare gjorde detektion -och extraktionsprocessen omöjlig med denframtagna metoden.

This study probes selected stories from Bessie Head's The Collector of Treasures (1977) in order to elicit instances of contiguity and disjuncture between orality and literacy, to establish Head's complex identity configurations which are often manifested in the interactions between aesthetic form and content, authorial consciousness, character delineation, and narrative voice. At the same time, the dissertation explores her portrayal of the proscribed condition of women, the subversive consciousness that undercuts women's subjugation by patriarchy, and her vision for the liberatory possibilities for women from the exigencies of patriarchal domination. I also examine Head's (re-)vision of culture within the framework of hybridity and creolity and determine how some of these perspectives are crystallized in discourses such as When Rain Clouds Gather (1968), Maru (1971) and A Question of Power (1973). I juxtapose my reading of Head with other African writers such as Bâ, Emecheta and Nwapa to draw references in instances where the context permits. The dominant critical approach adopted in this thesis is a contextual approach. I consider this approach useful for my purposes because of its flexibility, the attention it pays to the formal properties of literary texts and, its cognizance of the socio-historical genesis of texts and its demonstration of literature's timeless value. / English Studies / M.A. (English)

This thesis primarily uses submillimetre molecular line data from HARP, a heterodyne array on the James Clerk Maxwell Telescope (JCMT), to further investigate star formation in the Ophiuchus L1688 cloud. HARP was used to observe CO J = 3-2 isotopologues: 12CO, 13CO and C18O; and the dense gas tracer HCO+ J = 4-3. A method for calculating molecular line contamination in the SCUBA-2 450 and 850 μm dust continuum data was developed, which can be used to convert 12CO J =6-5and J =3-2 maps of integrated intensity (K km s−1) to molecular line flux (mJy beam−1) contaminating the continuum emission. Using HARP maps of 12CO J = 3-2, I quantified the amount of molecular line contamination found in the SCUBA-2 850 μm maps of three different regions, including NGC 1333 of Perseus and NGC 2071 and NGC 2024 of Orion B. Regions with ‘significant’ (i.e. > 20%) molecular line contamination correspond to molecular outflows. This method is now being used to remove molecular line contamination from regions with both SCUBA-2 dust continuum and HARP 12CO map coverage in the Gould Belt Legacy Survey (GBS). The Ophiuchus L1688 cloud was observed in all three CO J = 3-2 isotopologues. I carried out a molecular outflow analysis in the region on a list of 30 sources from the Spitzer ‘c2d’ survey [Evans et al., 2009]. Out of the 30 sources, 8 had confirmed bipolar outflows, 20 sources had ‘confused’ outflow detections and 2 sources did not have outflow detections. The Ophiuchus cloud was found to be gravitationally bound with the turbulent kinetic energy a factor of 7 lower than the gravitational binding energy. The high-velocity outflowing gas was found to be only 21% of the turbulence in the cloud, suggesting outflows are significant but not the dominant source of turbulence in the region. Other factors were found to influence the global high-velocity outflowing gas in addition to molecular outflows, including hot dust from nearby B-type stars, outflow remnants from less embedded sources and stellar winds from the Upper Scorpius OB association. To trace high density gas in the Ophiuchus L1688 cloud, HCO+ J = 4-3 was observed to further investigate the relationship between high column density and high density in the molecular cloud. Non-LTE codes RADEX and TORUS were used to develop density models corresponding to the HCO+ emission. The models involved both constant density and peaked density profiles. RADEX [van der Tak et al., 2007] models used a constant density model along the line-of-sight and indicated the HCO+ traced densities that were predominantly subthermally excited with den- sities ranging from 10^3–10^5 cm^−3. Line-of-sight estimates ranged from several parsecs to 90 pc, which was unrealistic for the Ophiuchus cloud. This lead to the implementation of peaked density profiles using the TORUS non-LTE radiative transfer code. Initial models used a ‘triangle’ density profile and a more complicated log-normal density probability density function (PDF) profile was subsequently implemented. Peaked density models were relatively successful at fitting the HCO+ data. Triangle models had density fits ranging from 0.2–2.0×10^6 cm^−3 and 0.1–0.3×10^6 cm^−3 for the 0.2 and 0.3 pc cloud length models re- spectively. Log-normal density models with constant-σ had peak density ranges from 0.2–1.0 ×10^5 cm^−3 and 0.6–2.0×10^5 cm^−3 for 0.2 and 0.3 pc models respectively. Similarly, log-normal models with varying-σ had lower and upper density limits corresponding to the range of FWHM velocities. Densities (lower and upper limits) ranged from 0.1–1.0 ×10^6 and 0.5–3.0 ×10^5 cm^-3 for the 0.2 and 0.3 pc models respectively. The result of the HCO+ density modelling indicated the distributions of starless, prestellar and protostellar cores do not have a preference for higher densities with respect to the rest of the cloud. This is contrary to past research suggesting the probability of finding a submillimetre core steeply rises as a function of column density (i.e. density; Belloche et al. 2011; Hatchell et al. 2005). Since the majority of sources are less embedded (i.e Class II/III), it is possible the evolutionary state of Ophiuchus is the main reason the small sample of Class 0/I protostars do not appear to have a preference for higher densities in the cloud.