Advanced display design, such as Ecological Interface Design (EID), makes extensive use of complex graphical objects. Research has shown that by following EID methodologies, supervisory operators have better performance with the EID displays (Pawlak and Vicente, 1996). However, past research does not consider the visual aspects of the graphical objects used in EID. Of particular interest is how different design decisions of graphical objects affect the performance of the objects used within that design. This thesis examines the visual sensitivity of dynamic graphical objects by examining features that make certain graphical objects visually superior for certain monitoring tasks. Previous research into the visual aspects of supervisory control with respect to emergent features, psychophysics and attention were considered in the investigation of the visual sensitivities of the dynamic graphical objects used. Research into static graphical objects, combined with prior work on emergent features has been merged to find emergent features that best show changes in dynamic graphical objects for the monitoring tasks investigated. It was found that for simple dynamic objects such as bars and polygon objects, a line changing in angle was the most noticeable emergent feature to show a departure from ?normal? state. For complex graphical objects, those target-indicator displays that mimic a ?bull?s eye? when at the target value should be used for displays that show observers when a target value has been reached. Abrupt changes in shape should be used in trend meters to show when variables or processes have changed direction. Finally, ?solid objects? that make use of vertical lines and shading should be used for comparison meters that compare two values and keep them in a particular ratio. These findings provide guidance for designers of dynamic advanced graphical displays by encouraging the consideration of visual aspects of graphical objects, as well as prescribing graphical objects that should be used in the types of tasks investigated.

Clinical data systems continue to grow as a result of the proliferation of features that are collected and stored. Demands for accurate and well-organized clinical data have intensified due to the increased focus on cost-effectiveness, and continuous quality improvement for better clinical diagnosis and prognosis. Clinical organizations have opportunities to use the information they collect and their oversight role to enhance health safety. Due to the continuous growth in the number of parameters that are accumulated in large databases, the capability of interactively mining patient clinical information is an increasingly urgent need to the clinical domain for providing accurate and efficient health care. Simple database queries fail to address this concern for several problems like the lack of the use of knowledge contained in these extremely complex databases. Data mining addresses this problem by analyzing the databases and making decisions based on the hidden patterns. The collection of data from multiple locations in clinical organizations leads to the loss of data in data warehouses. Data preprocessing is the part of knowledge discovery where the data is cleaned and transformed to perform accurate and efficient data mining results. Missing values in the databases result in the loss of useful data. Handling missing values and reducing noise in the data is necessary to acquire better quality mining results. This thesis explores the idea of either rejecting inappropriate values during the data entry level or suggesting various methods of handling missing values in the databases. E-Intelligence form is designed to perform the data preprocessing tasks at different levels of the knowledge discovery process. Here the minimum data set of mental health and the breast cancer data set are used as case studies. Once the missing values are handled, decision trees are used as the data mining tool to perform the classification of the diagnosis of the databases for analyzing the results. Due to the ever increasing mobile devices and internet in health care, the analysis here also addresses issues relevant hand-held computers and communicational devices or web based applications for quick and better access.

This project was about develop a concept for next generation weather station and was assigned by Bushnell, Kansas U.S. The project was appropriate for the students since it would involve product development with both visual design and engineering. For the reason that the initiator was located in another country, the students got an offer of being positioned at the consultant firm; Epsilon in Gothenburg. This project is about developing an innovative product as the next step of their evolution; a portable Wi-Fi household appliance that keeps the user up-dated with weather forecasts as well as news and other desirable info. This next generation weather station shall be transboundary by offering more than traditional weather forecasts. Through its direct connection to Internet it shall display and capture customizable data such as news, traffic, sports (scores, news and highlights), photo and video content to name a few. The user shall be able to get “data at-a-glance” in a fun user-friendly, low maintenance approach while keeping the simplicity of the older products.

The task given to us was to develop an industrial wheel with new thinking in terms of fresh design and construction, with the ability to brake. While the produced wheels of today only own the property of total braking, a wish was made to integrate the ability of directional locking. The time spent working with the project has been filled with multiple sketches of ideas and function analysis. Choices of material and ways of construction have been verified by calculations, tests and prototype building. The goal of this thesis has been to develop a unique caster with well thought-out design that would be adaptable to different areas of applications. The result was a hubless wheel with the ability of directional locking before total braking. A prototype of the wheel has been made to show the design and functions.

Vi har i samarbete med HemoCue AB utfört ett exemensarbete inom konstruktionsteknik.  HemoCue AB utvecklar, tillverkar och marknadsför patientnära medicinteknisk utrustning. De erbjuder testning av patienter på ”plats” med samma  noggrannhet och precision som om testet skulle ske på ett laboratorium med ett laboratorium instrument.  Syftet med detta projekt var att ta fram en laddstation till ett mätinstrument. De krav som vi hade att följa var många, de mest utslagsgivande var dock att laddstationen skall vara placerad liggandes på bordet och att användning av instrumentet under laddnings tiden skall vara möjlig utan några som helst problem. De metoder som vi valt att arbeta efter är dels Brainstorming och dels Fredy Olssons metoder för Princip- och primär konstruktion(1976). Vi har även tittat en del på Arne Åkessons tillverkningskonstruktion(1981). Att vi valt att arbeta med dessa är för att få med all viktig dokumentation, från idé till färdigt konstruktions underlag. Detta examensarbete har resulterat i en prototyp av en laddstation som uppfyller samtliga krav och önskemål som vi haft att arbeta efter.  Det underlag som vi tagit fram kommer HemoCue AB troligtvis att utveckla ännu lite längre innan laddstationen kommer att i bruk.

Statistical models for the joint statistics of image pixels are of central importance in many image processing applications. However the high dimensionality stemming from large problem size and the long-range spatial interactions make statistical image modeling particularly challenging. Commonly this modeling is simplified by a change of basis, mostly using a wavelet transform. Indeed, the wavelet transform has widely been used as an approximate whitener of statistical time series. It has, however, long been recognized that the wavelet coefficients are neither Gaussian, in terms of the marginal statistics, nor white, in terms of the joint statistics. The question of wavelet joint models is complicated and admits for possibilities, with statistical structures within subbands, across orientations, and scales. Although a variety of joint models have been proposed and tested, few models appear to be directly based on empirical studies of wavelet coefficient cross-statistics. Rather, they are based on intuitive or heuristic notions of wavelet neighborhood structures. Without an examination of the underlying statistics, such heuristic approaches necessarily leave unanswered questions of neighborhood sufficiency and necessity. This thesis presents an empirical study of joint wavelet statistics for textures and other imagery including dependencies across scale, space, and orientation. There is a growing realization that modeling wavelet coefficients as independent, or at best correlated only across scales, may be a poor assumption. While recent developments in wavelet-domain Hidden Markov Models (notably HMT-3S) account for within-scale dependencies, we find that wavelet spatial statistics are strongly orientation dependent, structures which are surprisingly not considered by state-of-the-art wavelet modeling techniques. To demonstrate the effectiveness of the studied wavelet correlation models a novel non-linear correlated empirical Bayesian shrinkage algorithm based on the wavelet joint statistics is proposed. In comparison with popular nonlinear shrinkage algorithms, it improves the denoising results.

Advanced display design, such as Ecological Interface Design (EID), makes extensive use of complex graphical objects. Research has shown that by following EID methodologies, supervisory operators have better performance with the EID displays (Pawlak and Vicente, 1996). However, past research does not consider the visual aspects of the graphical objects used in EID. Of particular interest is how different design decisions of graphical objects affect the performance of the objects used within that design. This thesis examines the visual sensitivity of dynamic graphical objects by examining features that make certain graphical objects visually superior for certain monitoring tasks. Previous research into the visual aspects of supervisory control with respect to emergent features, psychophysics and attention were considered in the investigation of the visual sensitivities of the dynamic graphical objects used. Research into static graphical objects, combined with prior work on emergent features has been merged to find emergent features that best show changes in dynamic graphical objects for the monitoring tasks investigated. It was found that for simple dynamic objects such as bars and polygon objects, a line changing in angle was the most noticeable emergent feature to show a departure from ?normal? state. For complex graphical objects, those target-indicator displays that mimic a ?bull?s eye? when at the target value should be used for displays that show observers when a target value has been reached. Abrupt changes in shape should be used in trend meters to show when variables or processes have changed direction. Finally, ?solid objects? that make use of vertical lines and shading should be used for comparison meters that compare two values and keep them in a particular ratio. These findings provide guidance for designers of dynamic advanced graphical displays by encouraging the consideration of visual aspects of graphical objects, as well as prescribing graphical objects that should be used in the types of tasks investigated.

Clinical data systems continue to grow as a result of the proliferation of features that are collected and stored. Demands for accurate and well-organized clinical data have intensified due to the increased focus on cost-effectiveness, and continuous quality improvement for better clinical diagnosis and prognosis. Clinical organizations have opportunities to use the information they collect and their oversight role to enhance health safety. Due to the continuous growth in the number of parameters that are accumulated in large databases, the capability of interactively mining patient clinical information is an increasingly urgent need to the clinical domain for providing accurate and efficient health care. Simple database queries fail to address this concern for several problems like the lack of the use of knowledge contained in these extremely complex databases. Data mining addresses this problem by analyzing the databases and making decisions based on the hidden patterns. The collection of data from multiple locations in clinical organizations leads to the loss of data in data warehouses. Data preprocessing is the part of knowledge discovery where the data is cleaned and transformed to perform accurate and efficient data mining results. Missing values in the databases result in the loss of useful data. Handling missing values and reducing noise in the data is necessary to acquire better quality mining results. This thesis explores the idea of either rejecting inappropriate values during the data entry level or suggesting various methods of handling missing values in the databases. E-Intelligence form is designed to perform the data preprocessing tasks at different levels of the knowledge discovery process. Here the minimum data set of mental health and the breast cancer data set are used as case studies. Once the missing values are handled, decision trees are used as the data mining tool to perform the classification of the diagnosis of the databases for analyzing the results. Due to the ever increasing mobile devices and internet in health care, the analysis here also addresses issues relevant hand-held computers and communicational devices or web based applications for quick and better access.

Vid hållplatser i städer finns ofta en busskur i glas med metallstomme. Tillsammans bildar de ett enhetligt uttryck i stadsbilden och bidrar till att resenärerna skyddas mot väder och vind. På landsbygden däremot är det glesare mellan kurerna och ofta ser de olika ut vid varje hållplats. För att nå det nationella mål med fördubblad kollektivtrafik fram till år 2020 kan en satsning för att skydda resenärerna vara på sin plats. Syftet med detta examensarbete är att utveckla en modern busskur för landsbygdsresenären som passar in i omgivningen. Kuren ska vara av trä och uppbyggt i delar som förenklar byggandet och minimerar byggtiderna. De metoder som tillämpats i arbetet är intervjuer, muntliga och skriftliga, en mindre enkätundersökning och en jämförelse mellan tre befintliga träkurer på landsbygden. Inspirationsbilder och litteraturstudier har också bidragit med idéer och kunskap. Resultatet av arbetet kom att bli fyra varianter av en modern kur i trä och stål, med influenser av nymodernism, inspirerad av fyra svenska landskap.

Thermal membrane distillation is one of the novel separation methods in the process industry. It involves the simultaneous heat and mass transfer through a hydrophobic semipermeable membrane through the use of thermal energy to bring about the separation of a feed mixture into two streams- a permeate and a retentate stream. Traditionally, studies on this technology have focused on the performance of individual modules as a function of material of the membrane and also configuration of the membrane. However, an investigation into the performance of a network of these modules has not been conducted in the past. A hierarchical parametric programming technique for synthesis of an optimal network of these modules is presented. A global mass allocation representation involving sources and sinks was used to solve the problem and derive criteria for optimality in specific regions of the parametric space. Two case studies have been presented to illustrate the applicability of the presented methodology.