[pt] A dramaturgia de Tennessee Williams (1911-1983) é reconhecida por seu acentuado lirismo que se revela pela abundante presença de símbolos e de imagens poéticas. Este trabalho tem como objetivo demonstrar que esse recurso é utilizado por Williams para potencializar a experiência cênica. Esta dissertação é dividida em dois ensaios principais que tratam da memória e do desejo a partir do close reading, respectivamente, das peças O Zoológico de Vidro e Um Bonde Chamado Desejo. Por fim, este estudo visa demonstrar que a presença da obra do poeta Hart Crane na dramaturgia de Williams vai além das epígrafes e citações, alcançando o aspecto visionário da experiência poética e possibilitando uma nova abordagem da dramaturgia de Tennessee Williams. / [en] Tennessee Williams: the dramatic power of the poetic imagery. Tennessee Williams plays are generally recognized by their expressive lyricism, which is revealed by its large presence of symbols and imageries. This study aims to demonstrate how this resource is used by Williams to increase stage experience. This dissertation is divided into two essays that deal with memory and desire through the close reading, respectively, of the plays The Glass Menagerie and A Streetcar Named Desire. Finally, this work is going to demonstrate that the presence of Hart Crane s poetry in Williams plays goes beyond epigraphs and citations, achieving the visionary aspect of poetic experience and enabling a new approach of Williams dramaturgy.

This thesis aims to examine how the usage of different-sized models can improve consumer loyalty to a brand of female customers in Sweden. Previous research has examined how using non-skinny models appeals to female customers and what feelings are evoked, but there is little research on how this approach can increase consumer loyalty as a whole. The authors of this thesis hypothesize that brands using different-sized models will lead to female customers feeling higher levels of the concepts of Awareness and Safety concerning the brand. In turn, the authors hypothesize that a brand achieving high levels of Awareness and Safety within the context of using different-sized models will improve consumer loyalty toward their female customers in Sweden. To examine whether different-sized models have an impact on consumer loyalty, a deductive approach has been used where this thesis relies on existing theories to answer the research question. Data was collected through a survey consisting of multi-choice questions with closed answers in order to conduct a quantitative analysis of the data. The questions were operalized, and the internal validity of each concept was tested through Cronbach’s Alpha to ensure that the questions measured what they were supposed to measure. The data was analyzed through Spearman’s Rank Correlation test, where the dependence between the concepts was measured. In conclusion, the authors found that usage of different-sized models had a positive correlation with consumer loyalty through both aspects of Awareness and Safety. Based on the results and the collected literature, the authors believe that usage of different-sized models in a company’s online business activities can help build a stronger relationship with their customers.

Fibromyalgia syndrome (FMS) is a debilitating and chronic condition with an array of symptoms, the most distinguishable being widespread pain. FMS patients experience a marked decrease in quality of life related to intensity of symptoms. Current treatment options and pharmaceuticals do not provide adequate relief. This thesis examines integrative and complementary therapy options for symptom management and improvement of quality of life for FMS patients. A literature review was conducted of English current research using multiple databases. Findings indicate mindful movement therapies (MMT) such as yoga and tai chi, mindfulness, sensory-related relaxation techniques with guided imagery, and cognitive behavioral therapy (CBT) provided some relief and increased in perceived quality of life (QoL).

Change detection (CD) has, in recent years, shown promising results in remote sensing (RS). The development of deep learning CD (DLCD) has, in even more recent years, taken change detection to another level and it has become more widely researched. However, the research depends on publicly available datasets that have been manually annotated for the task of CD. This method is cumbersome and the resulting datasets do not often include all types of change. In this thesis, the generalizability to different areas and different change types of a model trained on a widely used public dataset is analyzed. Also, the thesis investigates how 3D information from Maxar Technologies 3D models can be used to automatically create new more general datasets for CD with both binary or non-binary outputs. The access to large amounts of satellite images together with 3D information enables the creation of more general datasets that can capture more types of change.The thesis concludes that a model trained on the publicly available dataset does not generalize to other areas or other types of change. Models trained on the automatically generated datasets yield relatively good results which indicates that using 3D information to automatically create large datasets is a valid method for CD. Even non-binary approaches show promising results which enable using to gain more practical information on the change of an area. While the thesis presents encouraging results, work can definitely be done to further improve the generalization of the models and improve the dataset generation.

No description available.

Expansion of Pinus L. (pinyon) and Juniperus L. (juniper) (P-J) trees into sagebrush (Artemisia L.) steppe communities can lead to negative effects on hydrology, loss of wildlife habitat, and a decrease in desirable understory vegetation. Tree reduction treatments are often implemented to mitigate these negative effects. In order to prioritize and effectively plan these treatments, rapid, accurate, and inexpensive methods are needed to estimate tree canopy cover at the landscape scale. We used object based image analysis (OBIA) software (Feature AnalystTM for ArcMap 10.1®, ENVI Feature Extraction®, and Trimble eCognition Developer 8.2®) to extract tree canopy cover using NAIP (National Agricultural Imagery Program) imagery. We then compared our extractions with ground measured tree canopy cover (crown diameter and line point) on 309 subplots across 44 sites in Utah. Extraction methods did not consistently over- or under-estimate ground measured P-J canopy cover except where tree cover was > 45%. Estimates of tree canopy cover using OBIA techniques were strongly correlated with estimates using the crown diameter method (r = 0.93 for ENVI, 0.91 for Feature Analyst, and 0.92 for eCognition). Tree cover estimates using OBIA techniques had lower correlations with tree cover measurements using the line-point method (r = 0.85 for ENVI, 0.83 for Feature Analyst, and 0.83 for eCognition). Results from this study suggest that OBIA techniques may be used to extract P-J tree canopy cover accurately and inexpensively. All software packages accurately evaluated accurately extracted P-J canopy cover from NAIP imagery when imagery was not blurred and when P-J cover was not mixed with Amelanchier alnifolia (Utah serviceberry) and Quercus gambelii (Gambel's oak), which are shrubs with similar spectral values as P-J.

No description available.

Miniature Aerial Vehicles (MAVs) are quickly gaining acceptance as a platform for performing remote sensing or surveillance of remote areas. However, because MAVs are typically flown close to the ground (1000 feet or less in altitude), their field of view for any one image is relatively small. In addition, the context of the video (where and at what orientation are the objects being observed, the relationship between images) is unclear from any one image. To overcome these problems, we propose a geo-referenced mosaicing method that creates a mosaic from the captured images and geo-references the mosaic using information from the MAV IMU/GPS unit. Our method utilizes bundle adjustment within a constrained optimization framework and topology refinement. Using real MAV video, we have demonstrated our mosaic creation process on over 900 frames. Our method has been shown to produce the high quality mosaics to within 7m using tightly synchronized MAV telemetry data and to within 30m using only GPS information (i.e. no roll and pitch information).

Analysis of overhead imagery has a great potential to produce real-time data cost-effectively. This can be an important foundation for decision-making for businesses and politics. Every day a massive amount of new satellite imagery is produced. To fully take advantage of these data volumes a computationally efficient pipeline is required for the analysis. This thesis proposes a pipeline which outperforms the Segment Before you Detect network [6] and different types of fast region based convolutional neural networks [61] with a large margin in a fraction of the time. The model obtains a prediction error for counting cars of 1.67% on the Potsdam dataset and increases the vehiclewise F1 score on the VEDAI dataset from 0.305 reported by [61] to 0.542. This thesis also shows that it is possible to outperform the Segment Before you Detect network in less than 1% of the time on car counting and vehicle detection while also using less than half of the resolution. This makes the proposed model a viable solution for large-scale satellite imagery analysis. / Analys av flyg- och satellitbilder har stor potential att kostnadseffektivt producera data i realtid för beslutsfattande för företag och politik. Varje dag produceras massiva mängder nya satellitbilder. För att fullt kunna utnyttja dessa datamängder krävs ett beräkningseffektivt nätverk för analysen. Denna avhandling föreslår ett nätverk som överträffar Segment Before you Detect-nätverket [6] och olika typer av snabbt regionsbaserade faltningsnätverk [61]  med en stor marginal på en bråkdel av tiden. Den föreslagna modellen erhåller ett prediktionsfel för att räkna bilar på 1,67% på Potsdam-datasetet och ökar F1- poängen for fordons detektion på VEDAI-datasetet från 0.305 rapporterat av [61]  till 0.542. Denna avhandling visar också att det är möjligt att överträffa Segment Before you Detect-nätverket på mindre än 1% av tiden på bilräkning och fordonsdetektering samtidigt som den föreslagna modellen använder mindre än hälften av upplösningen. Detta gör den föreslagna modellen till en attraktiv lösning för storskalig satellitbildanalys.

Due to the changing climate and inherent atypically occurring meteorological events in the Arctic regions, more accurate snow quality predictions are needed in order to support the Sámi reindeer herding communities in northern Sweden that struggle to adapt to the rapidly changing Arctic climate. Spatial snow depth distribution is a crucial parameter not only to assess snow quality but also for multiple environmental research and social land use purposes. This contrasts with the current availability of affordable and efficient snow monitoring methods to estimate such an extremely variable parameter in both space and time. In this thesis, a novel approach to determine spatial snow depth distribution in challenging alpine terrain is presented and tested during a field campaign performed in Tarfala, Sweden in April 2019. A multispectral camera capturing five spectral bands in wavelengths between 470 and 860 nanometers on board of a small Unmanned Aerial Vehicle is deployed to derive 3D snow surface models via photogrammetric image processing techniques. The main advantage over conventional photogrammetric surveys is the utilization of accurate RTK positioning technology that enables direct georeferencing of the images, and thus eliminates the need for ground control points and dangerous and time-consuming fieldwork. The continuous snow depth distribution is retrieved by differencing two digital surface models corresponding to the snow-free and snow-covered study areas. An extensive error assessment based on ground measurements is performed including an analysis of the impact of multispectral imagery. Uncertainties and non-transparencies due to a black-box environment in the photogrammetric processing are, however, present, but accounted for during the error source analysis. The results of this project demonstrate that the proposed methodology is capable of producing high-resolution 3D snow-covered surface models (< 7 cm/pixel) of alpine areas up to 8 hectares in a fast, reliable and cost-efficient way. The overall RMSE of the snow depth estimates is 7.5 cm for data acquired in ideal survey conditions. The proposed method furthermore assists in closing the scale gap between discrete point measurements and regional-scale remote sensing, and in complementing large-scale remote sensing data by providing an adequate validation source. As part of the Swedish cooperation project ’Snow4all’, the findings of this project are used to support and validate large-scale snow models for improved snow quality prediction in northern Sweden. / På grund av klimatförändringar och naturliga meteorologiska händelser i arktis behövs mer exakta snökvalitetsprognoser för att stödja samernas rensköttsamhällen i norra Sverige som har problem med att anpassa sig till det snabbt föränderliga arktiska klimatet. Rumslig snödjupsfördelning är en avgörande parameter för att inte bara bedöma snökvaliteten utan även för flera miljöforskning och sociala markanvändningsändamål. Detta står i motsats till den nuvarande tillgången till överkomliga och effektiva metoder för snöövervakning för att uppskatta sådan extremt varierande parameter i tid och rum. I detta arbete presenteras och testas en ny metod för att bestämma rumslig snödjupssdistribution i utmanande alpin terräng under en fältstudie som genomfördes i Tarfala i norra Sverige i april 2019. Via fotogrammetrisk bildbehandlingsteknik hämtades snöytemodeller i 3D med hjälp av en multispektral kamera monterad på en liten obemannad drönare. En viktig fördel, i jämförelse med konventionella fotogrammetriska undersökningar, är användningen av exakt RTK-positioneringsteknik som möjliggör direkt georeferencing och eliminerar behovet av markkontrollpunkter. Den kontinuerliga snödjupfördelningen hämtas genom att ytmodellerna delas upp i snöfria respektive snötäckta undersökningsområden. En omfattande felsökning som baseras på markmätningar utförs, inklusive en analys av effekten av multispektrala bilder. Resultaten från denna studie visar att den famtagna metoden kan producera högupplösta snötäckta höjdmodeller i 3D (< 7 cm/pixel) av alpina områden på upp till 8 hektar på ett snabbt, pålitligt och kostnadseffektivt sätt. Den övergripande RMSE för det beräknade snödjupet är 7,5 cm för data som förvärvats under idealiska undersökningsförhållanden. Som ett led i det svenska projektet “Snow4all” används resultaten från projektet för att förbättra och validera storskaliga snömodeller för att bättre förutse snökvaliteten i norra Sverige.