It has long been known that ship wakes are observable by synthetic aperture radar. However, incomplete physical understanding has prevented the development of simulation tools that can predict both the structure and persistence of wakes in the ocean environment. It is the focus of this work to develop an end-to-end multi-scale modeling-and-simulation methodology that captures the known physics between the source of disturbance and the sensor. This includes turbulent hydrodynamics, free-surface effects, environmental forcing through Langmuir-type circulations, generation of surface currents and redistribution of surface-active substances, surface-roughness modification, and simulation of the signature generated by reflection and scattering of electromagnetic waves from the ocean surface. The end-to-end methodology is based upon several customized computational fluid dynamics solvers and empirical models which are linked together. The unsteady Reynolds-averaged Navier-Stokes equations, including models for the Craik-Leibovich vortex force and near surface Reynolds-stress anisotropy, are solved at full-scale Reynolds and Froude numbers on domains that extend tens of kilometers behind the ship. A parametric study is undertaken to explore the effects of ship heading, ship propulsion, ocean-wave amplitude and wavelength, and the relative importance of Langmuir-type circulations vs. near-surface Reynolds-stress anisotropy on the generation of surface currents that are transverse to the wake centerline. Due to the vortex force, the structure of the persistent wake is shown to be a function of the relative angle between the ambient long-wavelength swell and the ship heading. Ships operating in head seas observe 1-3 streaks, while ships operating in following seas observe 2 symmetric streaks. Ships operating in calm seas generate similar wakes to those in following seas, but with reduced wake width and persistence. In addition to the structure of the persistent wake, the far wake is shown to be dominated by ship-induced turbulence and surface-current gradients generating a wide center wake. The redistribution of surface-active substances by surface currents is simulated using a scalar-transport model on the ocean surface. Simulation of surface-roughness modification is accomplished by solving a wave-action balance model which accounts for the relative change in the ambient wave-spectrum by the surface currents and the damping-effects of surface-active substances and turbulence. Simulated returns from synthetic aperture radar are generated with two methods implemented. The first method generates a perfect SAR image where the instrument and platform based errors are neglected, but the impact of a randomized ocean field on the radar cross section is considered. The second method simulates the full SAR process including signal detection and processing. Comparisons are made to full-scale field experiments with good agreement between the structure of the persistent wake and observed SAR imagery. / 1 / It has long been known that ship wakes are observable by synthetic aperture radar. However, incomplete physical understanding has prevented the development of simulation tools that can predict both the structure and persistence of wakes in the ocean environment, which is critical to understanding both the design and operation of maritime remote sensors as well as providing tactically relevant operational guidance and awareness of the maritime domain. It is the focus of this work to develop an end-to-end multi-scale modeling-and simulation methodology that captures the known physics between the source of disturbance and the sensor. This includes turbulent hydrodynamics, free-surface effects, environmental forcing, generation of surface currents and redistribution of surface-active substances, surface-roughness modification, and simulation of the signature from the ocean surface. The end-to-end methodology is based upon several customized computational fluid dynamics solvers and empirical models. The unsteady Reynolds-averaged Navier-Stokes equations, including models to account for environmental effects and near-surface turbulence, are solved at full-scale on domains that extend tens of kilometers behind the ship. A parametric study is undertaken to explore the effects of ship heading, ship propulsion, ocean-wave amplitude and wavelength, and the relative importance of environmental forcing vs. near-surface turbulence on the generation of surface currents that are transverse to the wake centerline. Due to the environmental forcing, the structure of the persistent wake is shown to be a function of the relative angle between the ambient long-wavelength swell and the ship heading. Ships operating in head seas observe 1-3 streaks, while ships operating in following seas observe 2 symmetric streaks. Ships operating in calm seas generate similar wakes to those in following seas, but with reduced wake width and persistence. In addition to the structure of the persistent wake, the far wake is shown to be dominated by ship-induced turbulence and surface-current gradients generating a wide center wake. The redistribution of surface films by surface currents is simulated using a scalar-transport model on the ocean surface. Simulation of surface-roughness modification is accomplished by solving a wave-action-balance model which accounts for the relative change in the ambient surface profile by the surface currents and the damping-effects of surface-active substances and turbulence. Simulated returns from synthetic aperture radar are generated with two methods implemented. The first method generates a perfect SAR image where the instrument and platform based errors are neglected, but the impact of a randomized ocean field on the radar cross section is considered. The second method simulates the full SAR process including signal detection and processing. Comparisons are made to full-scale field experiments with good agreement between the structure of the persistent wake and observed SAR imagery.

This study examined the effects of different imagery strategies and the cognitive style field dependence on the learning of different levels of instructional objectives. One hundred thirteen (113) college students from six (6) intact college classes participated. All students were given the Group Embedded Figures Test to determine their level of field dependence-independence. One of three treatments, mental images recreated from a previously presented visual, self-generated imagery from an audio presentation; and a control group, which received no instructions to use imagery, was randomly assigned to each intact group. The content of the lesson consisted of the Dwyer (1967) Experimental Instructional Materials. The dependent measures were five criterion tests designed by Dwyer (1967) to measure different levels of instructional objectives. Data was analyzed using a series of two-way Analysis of Variance procedures with type of imagery and cognitive style as independent variables and the five criterion tests as dependent variables. The results of this study indicate that there was no difference in the amount of learning when imagery was used as a rehearsal strategy for four of the five dependent measures; however, on the fifth test, the Identification Test, the use of self-generated imagery was less effective as a rehearsal strategy than either the recreated imagery strategy or the control group strategy. On four of the five dependent measures those students who were identified as field-independent demonstrated the anticipated higher level of learning when compared to the field-dependent students. However, on the fifth test, the Identification Test, field-dependent students performed as well as field-independent students. Imagery and cognitive style did not interact. / Ed. D.

Drought is a climatic event regarding prolonged "drier than normal" conditions. Precipitation deficits, crop-moisture stress, soil-water unbalance, sudden stream flow cut-offs and low carrying capacity of ecosystems are responses to drought. Drought can occur in humid to arid climates, however, drought is more severe in arid and semi-arid areas due to the fact that in those distinctive areas, water resources are extremely limited and restricted. Additionally, local ecologies and ecosystems in arid regions are very fragile. Once a water competition occurs, critical services of ecosystems such as pure water, recreation, and land productivity will be threatened. This research focuses on prolonged drought events that have been occurring more frequently in a coastal province of South Central Vietnam – named Binh Thuan. The study area is distinctive because its climate is characterized as one of the driest provinces in Vietnam. Annual rainfall in the North and near the coast of the province is less than 800 mm per year. During 6 months of dry season, there is almost no rain, or less than 50 mm. Due to precipitation deficits and high surface temperatures in recent years, meteorological droughts have occurred more frequently, and lasted longer, thereby stressing water resources for vegetation, wildlife, households, and industry. The occurrence of prolonged droughts has constrained economic activities in the coastal areas, especially agriculture and aquaculture. Furthermore, a long duration of dry conditions coupled with unsustainable land management (such as overgrazing), "drought-sensitive" soils in areas with sand and barren lands may introduce and accelerate risks of desertification processes (land productivity deterioration and unable to recover). This research uses geospatial technologies to monitor drought severity and drought impacts on land use and land cover. Chapter 1 is a brief introduction and literature review of the drought context in Binh Thuan Province to place the research questions and objectives in content. Chapter 2 discusses the occurrence of meteorological droughts in Binh Thuan Province, then proposes climatic indices able to monitor this type of drought. Chapter 3 focuses on explaining and assessing uneven dry conditions that stressed vegetation health in the study area. This chapter investigates spatiotemporal distributions and frequencies of prolonged agricultural droughts using remotely sensed data and anomalies of precipitation distribution. Results indicate that coastal areas in the North of Binh Thuan are subject to severe droughts. Chapter 4 assesses human impacts on land management and practices in the study area during drought periods. Results show that in recent years (2010 to present), local governments and residents have implemented strategies to prevent sand dominance and to adapt to water shortages during dry seasons, such as vegetative cover, crop rotation with drought-tolerant plants and wind breaks. Accuracy was assessed using field data collected in the summer of 2016, in conjunction with Google Earth imagery. In summary, this dissertation enhances understanding of drought events and impacts in Binh Thuan Province by considering different types of drought - meteorological and agricultural drought, and interactions of drought and human impacts upon land management and land practices during dry periods. Furthermore, findings and results of this research have demonstrated the effectiveness of remotely sensed datasets, and other geospatial technologies, such as geographic information systems, in modeling drought severity and in examining efforts and drought-adaptive practices of local residents. This work is a valuable foundation on which further studies can build to support policy development to protect and reserve soil-land productivity in Binh Thuan and other coastal regions of Vietnam affected by prolonged droughts. / Doctor of Philosophy / Drought is a temporal climatic event with "drier than normal" conditions. While drought can occur in any climates, it can be more extreme in arid and semi-arid areas where annual rainfall and water resources are limited. Depending on types of drought, its presences and impacts may differ: (1) meteorological drought relates to a decrease of average rainfall/snowfall may resulting in moisture stress, (2) hydrological drought leads to a reduction of streamflow and groundwater, and (3) agricultural drought influences soil-water-crop balance or vegetation health. Prolonged drought – abnormally long duration of dry conditions, coupled with unsustainable management in water and land practice may cause losses of land productivity, promote soil erosion, and result in sand dominance in coastal areas. These land degradation processes can lead to "a desert-like condition" in impacted areas. This research concerns drought and its impacts in a coastal province in South central Vietnam, Binh Thuan. The study area is distinctive because its climate is characterized as one of the driest provinces in Vietnam. Annual rainfall in the North and near the coast is less than 800 mm per year, and during the 6 months of the dry season, there is almost no rain, or less than 50 mm. Due to precipitation deficits and high surface temperatures in recent years, meteorological droughts have occurred more frequently and lasted longer, stressing water resources for vegetation, wildlife, households, and industry. Additionally, unsustainable land management, such as overgrazing, coupled with movements of sand and barren lands from the coast inland, have accelerated the risks of land degradation. This research applies an integration of geospatial technologies for monitoring drought severity and impacts on land management and illustrates how local people have adapted to droughts.

This study explores the potential of vehicle trajectory data obtained from Wide Area Motion Imagery for modeling and analyzing traffic characteristics. The data in question is collected by PV Labs and also known as persistent wide-area video. This video, in combination with PVLab's integrated Tactical Content Management System's spatiotemporal capability, automatically identifies and captures every vehicle in the video view frame, storing each vehicle with a discrete ID, track ID, and time-stamped location. This unique data capture provides comprehensive vehicle trajectory information. This thesis explores the use of data collected by the PVLab's system for an approximate area of 4 square kilometers area in the CBD area of Hamilton, Canada for use in understanding traffic characteristics. The data was collected for two three-hour continuous periods, one in the morning and one in the evening of the same day. Like any other computer vision algorithm, this data suffers from false detection, no detection, and other inaccuracies caused by faulty image registration. Data filtering requirements to remove noisy trajectories and reduce error is developed and presented. A methodology for extracting microscopic traffic data (gap, relative velocity, acceleration, speed) from the vehicle trajectories is presented in details. This study includes the development of a data model for storing this type of large-scale spatiotemporal data. The proposed data model is a combination of two efficient trajectory data storing techniques, the 3-D schema and the network schema and was developed to store trajectory information along with associated microscopic traffic information. The data model is designed to run fast queries on trajectory information. A 15-minute sample of tracks was validated using manual extraction from imagery frames from the video. Microscopic traffic data is extracted from this trajectory data using customized GIS analysis. Resulting tracks were map-matched to roads and individual lanes to support macro and microscopic traffic characteristic extraction. The final processed dataset includes vehicles and their trajectories for an area of approximately 4-square miles that includes a dense and complex urban network of roads over two continuous three-hour periods. Two subsets of the data were extracted, cleaned, and processed for use in calibrating car-following sub-models used in microscopic simulations. The car-following model is one of the cornerstones of any simulation based traffic analysis. Calibrating and validating these models is essential for enhancing the ability of the model's capability of representing local traffic. Calibration efforts have previously been limited by the availability and accuracy of microscopic traffic data. Even datasets like the NGSIM data are restricted in either time or space. Trajectory data of all vehicles over a wide area during an extended period of time can provide new insight into microscopic models. Persistent wide-area imagery provides a source for this data. This study explores data smoothing required to handle measurement error and to prepare model input for calibration. Three car-following models : the GHR model, the linear Helly model, and the Intelligent Driver model are calibrated using this new data source. Two approaches were taken for calibrating model parameters. First, a least square method is used to estimate the best fit value for the model parameter that minimizes the global error between the observed and predicted values. The calibration results outline the limitation of both the WAMI data source and the models themselves. Existing model structures impose limitations on the parameter values. Models become unstable beyond these parameter values and these values may not be near global optima. Most of the car-following models were developed based upon some kinematic relation between driver reaction and expected stimuli of that response. For this reason, models in their current form are ill-suited for calibration with noisy microscopic data. On the other hand, the limitation of the WAMI data is the inability of obtaining an estimate of the measurement errors. With unknown measurement errors, any model development or calibration becomes questionable irrespective of the data smoothing or filtering technique undertaken. These findings indicate requirements for development of a new generation of car-following model that can accommodate noisy trajectory data for calibration of its parameters. / MS / The decision making process undertaken by transportation agencies for planning, evaluating, and operating transportation facilities relies on analyzing traffic and driver behavior in both aggregated and disaggregated manner. Different computational tools relying on representative models of aggregate traffic flow measures and/or driver behavior are used in the decision support system tools. Field data is used not only as an input for the computational tools but also to develop, calibrate, and validate the models representing a particular aspect of traffic and driver behavior. Different approaches have been undertaken to collect the data required for analyzing traffic and driver behavior. One of the applied approach is to collect trajectory (i.e. position, speed, acceleration) information of vehicles in the analysis zone. However, this data collection approach is often limited to relatively small stretch of a roadway for short duration due to high cost of collection and limitation of technology. As a result, the models developed and calibrated using these data often lack generalization power for different situation. This study explores the potential of a new data source to address the aforementioned limitations. The data used in this study collects the trajectory information for the whole population of vehicles in the study area by collecting wide-area (WAMI) video data. The data is collected by Canada based imaging solution company PV Labs. The collection area is relatively large to cover wide range of roadway types and traffic operation system. A framework has been developed to extract traffic flow measures from the trajectory data. The extracted traffic flow measures are then applied to calibrate the car-following model. The car-following model attempts to mimic the longitudinal movement of real-world drivers following another vehicle in front of them. The calibration results outline the limitations of the WAMI data. Although, this dataset is capable of capturing traffic measures for different driving condition, the lack of information about measurement error imposes limits on the direct application of the data for model calibration. Findings of this study can be applied for refinement of the video data capture technology and subsequent application in modelling traffic characteristics as well as development of new and calibration of existing driver behavior model.

This work aims to develop a deep learning model for land cover quantification through hyperspectral unmixing using an unsupervised autoencoder. Land cover identification and classification is instrumental in urban planning, environmental monitoring and land management. With the technological advancements in remote sensing, hyperspectral imagery which captures high resolution images of the earth's surface across hundreds of wavelength bands, is becoming increasingly popular. The high spectral information in these images can be analyzed to identify the various target materials present in the image scene based on their unique reflectance patterns. An autoencoder is a deep learning model that can perform spectral unmixing by decomposing the complex image spectra into its constituent materials and estimating their abundance compositions. The advantage of using this technique for land cover quantification is that it is completely unsupervised and eliminates the need for labelled data which generally requires years of field survey and formulation of detailed maps. We evaluate the performance of the autoencoder on various synthetic and real hyperspectral images consisting of different land covers using similarity metrics and abundance maps. The scalability of the technique with respect to landscapes is assessed by evaluating its performance on hyperspectral images spanning across 100m x 100m, 200m x 200m, 1000m x 1000m, 4000m x 4000m and 5000m x 5000m regions. Finally, we analyze the performance of this technique by comparing it to several supervised learning methods like Support Vector Machine (SVM), Random Forest (RF) and multilayer perceptron using F1-score, Precision and Recall metrics and other unsupervised techniques like K-Means, N-Findr, and VCA using cosine similarity, mean square error and estimated abundances. The land cover classification obtained using this technique is compared to the existing United States National Land Cover Database (NLCD) classification standard. / Master of Science / This work aims to develop an automated deep learning model for identifying and estimating the composition of the different land covers in a region using hyperspectral remote sensing imagery. With the technological advancements in remote sensing, hyperspectral imagery which captures high resolution images of the earth's surface across hundreds of wavelength bands, is becoming increasingly popular. As every surface has a unique reflectance pattern, the high spectral information contained in these images can be analyzed to identify the various target materials present in the image scene. An autoencoder is a deep learning model that can perform spectral unmixing by decomposing the complex image spectra into its constituent materials and estimate their percent compositions. The advantage of this method in land cover quantification is that it is an unsupervised technique which does not require labelled data which generally requires years of field survey and formulation of detailed maps. The performance of this technique is evaluated on various synthetic and real hyperspectral datasets consisting of different land covers. We assess the scalability of the model by evaluating its performance on images of different sizes spanning over a few hundred square meters to thousands of square meters. Finally, we compare the performance of the autoencoder based approach with other supervised and unsupervised deep learning techniques and with the current land cover classification standard.

Sjuksköterskors erfarenheter av att utföra Guided Imagery på barn- "Wow alltså, det är en väldig power i den här metoden". Syfte. Syftet med den här studien var att beskriva sjuksköterskors erfarenheter av att använda Guided Imagery [GI] på barn. Bakgrund. Rädsla är vanligt förekommande inom barnsjukvården vilken kan tendera i ökad rädsla vid nästa sjukvårdskontakt. Distraktionssätt såsom GI kan vara effektivt för att reducera dessa känslor och stärka barnen så de får ökat självförtroende och ökad känsla av kontroll. Design. Semi strukturerade intervjuer användes för datainsamling och kvalitativ innehållsanalys användes till dataanalys. Metod. Sju sjuksköterskor som hade genomgått utbildning i GI och utfört det på barn intervjuades under våren 2016 med semistrukturerade intervjuer som spelades in och transkriberades ordagrant. Sjuksköterskorna valdes ut med hjälp av snöbollsurval och materialet analyserades med Kvalitativ innehållsanalys. Resultat. Analysen av sjuksköterskornas erfarenheter av att använda GI på barn resulterade i två kategorier: att kunna hjälpa barnen och att inte komma till skott, vilka huvudsakligen handlar om sjuksköterskornas positiva erfarenheter av att kunna hjälpa barnen samt deras erfarna faktorer som försvårar möjligheten att inte komma till skott. Konklusion. GI är enligt sjuksköterskorna tillämpbar inom barnsjukvården med gott resultat och erfors ge barnen kontroll, självförtroende och redskap att ta sig igenom en svår situation. Däremot finns faktorer som försvårar implementeringen av GI på sjuksköterskornas arbetsplatser och framtida forskning kan fokusera på att hitta instrument som kan vara tillämpbara att övervinna barriärerna. Nyckelord: barn, distraktionssätt, erfarenheter, Guided Imagery, kvalitativ innehållsanalys, omvårdnad, sjuksköterskor.

This study will argue that visual art and the making of images share much With other languages. If writing can be deoonstructed, visual Imagery can be deconfigured, for figuring an image is much like structuring a sentence. The process of deconfiguration however relies on repetition. DeconflguratiOn therefore denies any claim of a primary creator. It will be argued though that deconfiguratlon remains creative as it engages the imagination in a process of transference and through association. Moreover, deconfiguration shows how binary opposites are essential In the making of artworks. The repetitive process takes place when the artwork Is made and continues during the appreciation and/or interpretation of the artwork. For the interpretation to really deconfigure, it would mean that the image constituted by the artist has metaphorical, allegorical and even symbolical implications. The interpreter will thus always remain a partidpant in the creative process suggested by the artwork. / Art History, Visual Arts & Musicology / M.A. (Visual Arts)

This study deals with weather symbolism in DBZ Ntuli's literature. Chapter one describes the aim, biography of DBZ Ntuli, definition of important literary concepts, the scope and the methodology. Chapter two considers the symbolic use of mist, fog, overcast weather and clouds. Each of these aspects is defined and is studied under each genre, i.e. in terms of its use by Ntuli in prose, drama and poetry. Chapter three explores the symbolic usage of rain, thunder and the rainbow in all the genres in which Ntuli writes. Chapter four deals with the imagery of the sun. The sun is shown as exhibiting three distinct levels of heat: mild, hot and extremely hot. Chapter five deals with the symbol of cold weather. Its aspects can be perceived on two levels: cold weather and extremely cold weather. Chapter six is a general conclusion that reveals the outcome of the research, observations and the recommendations. / African Languages / M.A. (African Languages)

The bulk of research in digital 3D animation is focused on problem solving and the development of new techniques and innovations for the 3D animation software of the future. However, little consideration is given for the underlying reasons why problems arise from a psychological perspective, and if 3D animation is to establish itself as a discipline in academia, strides must be taken to strengthen its foundations with existing academic disciplines.This undergraduate thesis examines the possible causes of joint deformation problems inherent to digital human character rigs in Autodesk Maya using cognitive psychology, specifically theories of perception, with additional considerations for the roles of the Uncanny Valley effect and suspension of disbelief. An experiment was devised to evaluate the presence of joint deformation problems on a basic human character rig and in two approaches of solution.The results supported the presented hypothesis on what causes the viewer to notice joint deformation problems, but further investigation is required for test it definitively. The study also implied that whilst joint deformation problems may be noticed by the viewer and cause distraction from the content of a digital film, other factors also strongly affect the viewer’s experience in a similar manner. The results of further studies could help digital artists better understand how the audience may respond to the presence of joint deformation problems and optimise their workflow. / Den övergripande forskningen kring digital 3D-animation är främst fokuserad på problemlösning samt utvecklingen av ny teknik och innovation för framtida programvaror utformade för 3D-animation. Dock har det sällan tagits någon hänsyn till de underliggande anledningarna om varför problemen uppstår, och inte heller genom ett psykologiskt perspektiv. Om 3D-animation ska bli mer etablerat inom den akademiska disciplinen så behöver framsteg göras för att skapa en starkare grund.Denna vetenskapliga uppsats undersöker möjliga anledningar till varför joint-deformationsproblem upplevs med riggen för digitala mänskliga karaktärer i Autodesk Maya. Detta utforskas genom att använda kognitiv psykologi, speciellt teorier kring perception, med ytterligare överväganden om att ”the Uncanny Valley” och ”Suspension of disbelief” skulle vara avgörande faktorer. Ett experiment var utformat för att utvärdera närvarandet av joint-deformationsproblem gällande en grundläggande mänsklig karaktär samt två möjliga lösningar till problemet.Den presenterade hypotesen, om vad som påverkar att en betraktare noterar joint-deformationsproblem, stärktes av undersökningens givna resultat. Dock är fortsatta forskningar kring ämnet nödvändiga för att definitivt bekräfta hypotesens reliabilitet och validitet. Undersökningen visade att problem med joint-deformationer kan noteras av betraktaren och därmed skapa en distraktion för filmens handling, men att andra faktorer kan vara mer avgörande för samma innebörd. Framtida forskningar kan resultera i att hjälpa digitala artister att underlätta deras förståelse om hur en publik kan tänkas reagera på närvarandet av joint-deformationsproblem och att effektivisera deras arbetsprocess.

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