Rock-a-Buy Baby: Consumerism by New, First-Time Mothers, is the first known sociological exploration of need-based consumption for babies, despite the baby gear industry being a $6-billion-dollar business (whattoexpect.com). Data stemmed from qualitative, semistructured interviews with new, first-time mothers (3 months – 1 year postpartum) conducted within participants‘ households. The insights gained from the present study tell us a great deal about the ―needs‖ that predominantly white, middle-class mothers socially constructed in anticipation of their first child, and the consumptive behaviors used to accomplish these "needs." Respondents had turned to similar resources (other mothers, online forums, consumer reports, books, magazines, etc.) to help them construct ―need‖ and formulate decisions among commodities. Provided they were relying on comparable, if not overlapping, bodies of knowledge, mothers‘ narratives about consumer ―need‖ were often congruent. Additionally, the ways expectant mothers accumulated items are ritualized and made tradition. The baby shower and gift registration process (which all of my respondents participated in to some variation) are social constructions; these practices, which are so strongly tied to consumption, also constituted reality for mothers, and inevitably, their babies.

This dissertation is concerned with the relationship between the trumpet and organ in twentieth-century music for this ensemble and how that relationship effects performance with regard to organ registration and synchronization. The compositions discussed include "The Other Voices of the Trumpet," by Daniel Pinkham (1971); "Jericho: Battle Music," by William Albright (1976); "Three Pictures of Satan," by Jere Hutcheson (1975); and "Okna," by Petr Eben (1980). The theoretical writings of Pierre Boulez, Robert Erickson, and Donald Cogan deal with developing a contemporary concept of texture. This dissertation applies their theory that texture exists in two dimensions: vertical and horizontal. Stratification and blending of timbres comprise the vertical dimension. The succession of textures, governed by tempo, creates the second dimension. Chapter I provides an historical setting for the genre, introduces the theory of Boulez, Erickson, and Cogan, and supplies the programmatic content of the four works chosen for analysis. In Chapter II , the vertical elements of texture in these four works are isolated and examined. Chapter III deals with Pierre Boulez's theory that the succession of textures, governed by tempo, shapes the work. Each work is examined with regard to tempo, either mobile (fluctuating) or fixed. In Chapter IV the analysis is related to performance. Stratified textures, fused ensemble timbres, and their horizontal progression present problems for the ensemble in organ registration and synchronization. There are general guidelines given for registration as well as specific registration problems encountered in stratified textures and fused ensemble timbres. Synchronization, or coordination of events is the second challenge presented by the horizontal progression of textures.

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Corneal diseases are one of the major causes of visual impairment and blindness worldwide. Used for diagnoses, a laser confocal microscope provides a sequence of images, at incremental depths, of the various corneal layers and structures. From these, ophthalmologists can extract clinical information on the state of health of a patient’s cornea. However, many factors impede ophthalmologists in forming diagnoses starting with the large number and variable quality of the individual images (blurring, non-uniform illumination within images, variable illumination between images and noise), and there are also difficulties posed for automatic processing caused by eye movements in both lateral and axial directions during the scanning process. Aiding ophthalmologists working with long sequences of corneal image requires the development of new algorithms which enhance, correctly order and register the corneal images within a sequence. The novel algorithms devised for this purpose and presented in this thesis are divided into four main categories. The first is enhancement to reduce the problems within individual images. The second is automatic image classification to identify which part of the cornea each image belongs to, when they may not be in the correct sequence. The third is automatic reordering of the images to place the images in the right sequence. The fourth is automatic registration of the images with each other. A flexible application called CORNEASYS has been developed and implemented using MATLAB and the C language to provide and run all the algorithms and methods presented in this thesis. CORNEASYS offers users a collection of all the proposed approaches and algorithms in this thesis in one platform package. CORNEASYS also provides a facility to help the research team and Ophthalmologists, who are in discussions to determine future system requirements which meet clinicians’ needs. / The data and image files accompanying this thesis are not available online.

Point Cloud Registration with data measured from a photon-counting LIDAR sensor from a large distance (500 m - 1.5 km) is an expanding field. Data measuredfrom far is sparse and have low detail, which can make the registration processdifficult, and registering this type of data is fairly unexplored. In recent years,machine learning for point cloud registration has been explored with promisingresults. This work compares the performance of the point cloud registration algorithm Iterative Closest Point with state-of-the-art algorithms, with data froma photon-counting LIDAR sensor. The data was provided by the Swedish Defense Research Agency (FOI). The chosen state-of-the-art algorithms were thenon-learning-based Fast Global Registration and learning-based D3Feat and SpinNet. The results indicated that all state-of-the-art algorithms achieve a substantial increase in performance compared to the Iterative Closest Point method. Allthe state-of-the-art algorithms utilize their calculated features to obtain bettercorrespondence points and therefore, can achieve higher performance in pointcloud registration. D3Feat performed point cloud registration with the highestaccuracy of all the state-of-the-art algorithms and ICP.

Autonomous vehicles are currently under study and one of the critical parts is the localization of the vehicle in the environment. Different localization methods have been studied over the years, such as the GPS sensor, commonly fused with other sensors such as the IMU. However, situations where the vehicle crosses a tunnel, a bridge, or there is simply traffic congestion, can cause the vehicle to get lost. Therefore, other methods such as point cloud registration have been used, where two point clouds are aligned, thus finding the pose of the vehicle on a precomputed map. Point cloud alignment, although a useful and functional method, is not free from errors that can lead to vehicle mislocalization. The intention of this work is to develop and compare different metrics capable of measuring in real time the performance of the point cloud alignment algorithm used, in this case Normal Distribution Transform (NDT). Therefore, it is important first of all to know if the position obtained meets the minimum requirements defined, just by knowing the input and output parameters of the algorithm. In addition to classifying the positioning as good or bad, the objective is to have a quality parameter that allows estimating the error committed in a complex environment where the uncertainty is very high. In addition, the influence of vehicle speed on the error made by the point cloud alignment algorithm will also be studied to determine whether there is any significant correlation between them. For this purpose, four different metrics have been studied, two of them being new contributions to this algorithm, called Error Propagation and CorAl, while the ones called Hessian and Score are obtained from the alignment algorithm itself. Data used was previously recorded and corrected, therefore obtaining ground truth data. Once the metrics were implemented, all of them were subjected to the same experiments, thus obtaining for each instant a quality measure that allowed a fair comparison to be made. These experiments were carried out on two different routes, being simulated 5 times each. In addition, from these simulations the speed was recorded, allowing the influence study to be carried out. The results show that the best performing metrics in terms of classification and estimation were the Error Propagation and the Hessian, while being impossible to determine a threshold value for the case of CorAl. Furthermore, they show that despite being functional, the error estimation is still far from perfect. It has also been shown that the error estimation of the lateral axis of the vehicle is more complex than in the case of the longitudinal axis. Finally, a strong and positive relationship between the vehicle speed and the error made by the alignment algorithm has been found. / Autonoma fordon studeras för närvarande och en av de kritiska delarna är lokaliseringen av fordonet i omgivningen. Olika lokaliseringsmetoder har studerats genom åren, t.ex. GPS-sensorn som ofta kombineras med andra sensorer, t.ex. IMU. Situationer där fordonet korsar en tunnel, en bro eller där det helt enkelt är trafikstockningar kan leda till att fordonet tappar uppfattningen om sin position. Därför har andra metoder utvecklats, t.ex. registrering av punktmoln, där två punktmoln justeras för att hitta fordonets position på en förinställd karta. Även om punktmolnsjustering är en användbar och funktionell metod, är den inte fri från fel som kan leda till felaktig lokalisering av fordonet. Syftet med detta arbete är att utveckla och jämföra olika mätmetoder som i realtid kan mätaprestandan hos den algoritm för punktmolnsjustering som används, i detta fall Normal DistributionTransform (NDT). Därför är det viktigt att först och främst veta om den erhållna tjänsten uppfyllerde fastställda minimikraven, bara genom att känna till algoritmens in- och utgångsparametrar.Förutom att klassificera positioneringen som bra eller dålig är målet att ha en kvalitetsparametersom gör det möjligt att uppskatta det fel som begåtts i en komplex miljö där osäkerheten är myckethög. Dessutom kommer fordonshastighetens inverkan på felet som görs av algoritmen för justeringav punktmoln också att studeras för att avgöra om det finns någon signifikant korrelation mellandem. För detta ändamål har fyra olika mått studerats, varav två är nya bidrag till denna algoritm, kallade Error Propagation och CorAl, medan de som kallas Hessian och Score erhålls från själva anpassningsalgoritmen. Data har tidigare registrerats och korrigerats, vilket ger sanningsdata. När mätvärdena hade implementerats utsattes de alla för samma experiment, så att man för varje ögonblick fick ett kvalitetsmått som gjorde det möjligt att göra en rättvis jämförelse. Dessa experiment utfördes på två olika rutter, som simulerades 5 gånger vardera. Dessutom registrerades hastigheten från dessa simuleringar, vilket gjorde det möjligt att genomföra en påverkansstudie. Resultaten visar att de bäst presterande mätvärdena när det gäller klassificering och uppskattning var Error Propagation och Hessian. Dessutom visar de att feluppskattningen fortfarande är långt ifrån perfekt. Det har också visats att feluppskattningen av fordonets sidoaxel är mer komplex än i fallet med den längsgående axeln. Slutligen har ett starkt och positivt samband mellan fordonshastigheten och felet som görs av inriktningsalgoritmen hittats.

Introduction: Trainee pharmacists are required to undertake a work-based pre-registration training placement (PRTP) in order to qualify. Literature exploring how this placement influences the development of students’ professionalism is sparse, however it is acknowledged that placements offer learning that can not be replicated in an academic environment. Following recent recommendations for the PRTP to be split into two six-month placements, the “sandwich” Master of Pharmacy (MPharm) programme at the University of Bradford offers a unique opportunity to study the impact of an early PRTP. This project aimed to understand the experiences of “sandwich” students during their early PRTP and generate a theory explaining how professionalism develops during this time. Methods: A constructivist grounded theory approach was taken. Fourteen students who had recently completed their early PRTP were interviewed using semi-structured, face-to-face interviews. A constant comparative approach to analysis was taken. Findings: The process developing a professional identity emerged as the core category. This consisted of four interlinking stages; reflection, selection of attributes, professional socialisation and perception of role. Developing a professional identity occurred under the conditions of realising the reality of the profession, developing practical knowledge and skills and learning from mentors. The consequence of developing a professional identity was that participants felt they were now a trainee professional. Discussion and conclusion: The theory demonstrates that developing a professional identity was the main process that occurred whilst MPharm students were on their early PRTP. Regulatory, funding and educational organisations should consider this when reviewing pharmacists’ training and students’ approach on return to university.

<p> </p> <p>Modern remote sensing platforms such as unmanned aerial vehicles (UAVs) that can carry a variety of sensors including RGB frame cameras, hyperspectral (HS) line cameras, and LiDAR sensors are commonly used in several application domains. In order to derive accurate products such as point clouds and orthophotos, sensors’ interior and exterior orientation parameters (IOP and EOP) must be established. These parameters are derived/refined in a triangulation framework through minimizing the discrepancy between conjugate features extracted from involved datasets. Existing triangulation approaches are not general enough to deal with varying nature of data from different sensors/platforms acquired in diverse environmental conditions. This research develops a generic triangulation framework that can handle different types of primitives (e.g., point, linear, and/or planar features), and sensing modalities (e.g., RGB cameras, HS cameras, and/or LiDAR sensors) for delivering accurate products under challenging conditions with a primary focus on digital agriculture and stockpile monitoring application domains. </p>

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