Part I: Various image reconstruction algorithms used for subsurface targets are reviewed. It is shown how some approximate wavefield inversion techniques: Stripmap Synthetic Aperture Radar (SAR), Kirchhoff Migration (KM) and Frequency-Wavenumber (FK) migration are developed from various models for wavefield scattering. The similarities of these techniques are delineated both from a theoretical and practical perspective and it is shown that Stripmap SAR is, computationally, almost identical to FK migration. A plane wave interpretation of both Stripmap SAR and FK migration is used to show why they are so similar. The electromagnetic assumptions made in the image reconstruction algorithms are highlighted. In addition, it is shown that, theoretically, FK and KM are identical. Image reconstruction results for KM, Stripmap SAR and FK are shown for both synthetic and experimental Ground Penetrating Radar (GPR) data. Subjectively the reconstructed images show little difference, but computationally, Stripmap SAR (and therefore, FK migration) are much more efficient. Part II: A preliminary investigation into the use of the Singularity Expansion Method (SEM) for use in identifying landmines is completed using a Finite-Difference Time-Domain code to simulate a simplified GPR system. The Total Least Squares Matrix Pencil Method (TLS-MPM) is used to determine the complex poles from an arbitrary late-time signal. Both dielectric and metallic targets buried in lossless and lossy half-spaces are considered. Complex poles (resonances) of targets change significantly when the objects are buried in an external medium, and perturbation formulae for Perfect Electric Conductor (PEC) and dielectric targets are highlighted and used. These perturbation formulae are developed for homogenous surrounding media, and their utilization for the half-space (layered medium) GPR problem causes inaccuracies in their predictions. The results show that the decay rate (real part) of the complex poles is not suitable for identification in this problem, but that with further research, the resonant frequency (imaginary part) of the complex poles shows promise as an identification feature. / February 2005

Conventional Magnetic Resonance Imaging (MRI) is a qualitative method for obtaining images of softtissues in patients. Conventional MRI is the standard method used today and it results in gray-scaleimages in which the different magnetic properties of biological tissues determine the image contrast. However, the magnitude of the measured signal is only relative and therefore not directlycomparable between images. Synthetic MRI is a relatively new technique which can be used to postsynthesizedifferent images based on absolute measurement of several magnetic properties oftissues. Synthetic MRI can therefore provide quantitative information together with the contrastimages. In order to use synthetic MRI clinically an evaluation of the image quality and diagnostic ability isrequired. The purpose of this thesis is to evaluate if synthetic MRI and conventional MRI produceimages with equal contrast. A study was designed and conducted for statistical evaluation of contrast and Contrast-to-Noise Ratio(CNR) generated with different imaging methods. A total of 22 patients were examined using bothconventional MRI and synthetic MRI and the results were pairwise analyzed. The contrast and CNR could not be stated as equal for the imaging methods. Typically the contrastwas higher in the synthetic images for the T1 and T2 weighted images. This was not observed withCNR which suggests that the noise is higher in the synthetic images. The higher contrast obtained insynthetic images resulted in a better separation of different tissues using synthetic MRI. Thesynthetic T2 FLAIR images contained artifacts that are not good for clinical use. However the fact thatthe different imaging methods produce different image quality is not proven to be clinically decisive.

Part I: Various image reconstruction algorithms used for subsurface targets are reviewed. It is shown how some approximate wavefield inversion techniques: Stripmap Synthetic Aperture Radar (SAR), Kirchhoff Migration (KM) and Frequency-Wavenumber (FK) migration are developed from various models for wavefield scattering. The similarities of these techniques are delineated both from a theoretical and practical perspective and it is shown that Stripmap SAR is, computationally, almost identical to FK migration. A plane wave interpretation of both Stripmap SAR and FK migration is used to show why they are so similar. The electromagnetic assumptions made in the image reconstruction algorithms are highlighted. In addition, it is shown that, theoretically, FK and KM are identical. Image reconstruction results for KM, Stripmap SAR and FK are shown for both synthetic and experimental Ground Penetrating Radar (GPR) data. Subjectively the reconstructed images show little difference, but computationally, Stripmap SAR (and therefore, FK migration) are much more efficient. Part II: A preliminary investigation into the use of the Singularity Expansion Method (SEM) for use in identifying landmines is completed using a Finite-Difference Time-Domain code to simulate a simplified GPR system. The Total Least Squares Matrix Pencil Method (TLS-MPM) is used to determine the complex poles from an arbitrary late-time signal. Both dielectric and metallic targets buried in lossless and lossy half-spaces are considered. Complex poles (resonances) of targets change significantly when the objects are buried in an external medium, and perturbation formulae for Perfect Electric Conductor (PEC) and dielectric targets are highlighted and used. These perturbation formulae are developed for homogenous surrounding media, and their utilization for the half-space (layered medium) GPR problem causes inaccuracies in their predictions. The results show that the decay rate (real part) of the complex poles is not suitable for identification in this problem, but that with further research, the resonant frequency (imaginary part) of the complex poles shows promise as an identification feature.

Part I: Various image reconstruction algorithms used for subsurface targets are reviewed. It is shown how some approximate wavefield inversion techniques: Stripmap Synthetic Aperture Radar (SAR), Kirchhoff Migration (KM) and Frequency-Wavenumber (FK) migration are developed from various models for wavefield scattering. The similarities of these techniques are delineated both from a theoretical and practical perspective and it is shown that Stripmap SAR is, computationally, almost identical to FK migration. A plane wave interpretation of both Stripmap SAR and FK migration is used to show why they are so similar. The electromagnetic assumptions made in the image reconstruction algorithms are highlighted. In addition, it is shown that, theoretically, FK and KM are identical. Image reconstruction results for KM, Stripmap SAR and FK are shown for both synthetic and experimental Ground Penetrating Radar (GPR) data. Subjectively the reconstructed images show little difference, but computationally, Stripmap SAR (and therefore, FK migration) are much more efficient. Part II: A preliminary investigation into the use of the Singularity Expansion Method (SEM) for use in identifying landmines is completed using a Finite-Difference Time-Domain code to simulate a simplified GPR system. The Total Least Squares Matrix Pencil Method (TLS-MPM) is used to determine the complex poles from an arbitrary late-time signal. Both dielectric and metallic targets buried in lossless and lossy half-spaces are considered. Complex poles (resonances) of targets change significantly when the objects are buried in an external medium, and perturbation formulae for Perfect Electric Conductor (PEC) and dielectric targets are highlighted and used. These perturbation formulae are developed for homogenous surrounding media, and their utilization for the half-space (layered medium) GPR problem causes inaccuracies in their predictions. The results show that the decay rate (real part) of the complex poles is not suitable for identification in this problem, but that with further research, the resonant frequency (imaginary part) of the complex poles shows promise as an identification feature.

During the recent years, a new area called Cloud Networked Robotics (CNR) has evolved from conventional robotics, thanks to the increasing availability of cheap robot systems and steady improvements in the area of cloud computing. Cloud networked robots refers to robots with the ability to offload computation heavy modules to a cloud, in order to make use of storage, scalable computation power, and other functionalities enabled by a cloud such as shared knowledge between robots on a global level. However, these cloud robots face a problem with reachability and QoS of crucial modules that are offloaded to the cloud, when operating in unstable network environments. Under such conditions, the robots might lose the connection to the cloud at any moment; in worst case, leaving the robots “brain-dead”. This thesis project proposes a machine learning-based network aware framework for a cloud robot, that can choose the most efficient module placement based on location, task, and the network condition. The proposed solution was implemented upon a cloud robot prototype based on the TurtleBot 2 robot development kit, running Robot Operating System (ROS). A continuous experiment was conducted where the cloud robot was ordered to execute a simple task in the laboratory corridor under various network conditions. The proposed solution was evaluated by comparing the results from the continuous experiment with measurements taken from the same robot, with all modules placed locally, doing the same task. The results show that the proposed framework can potentially decrease the battery consumption by 10% while improving the efficiency of the task by 2.4 seconds (2.8%). However, there is an inherent bottleneck in the proposed solution where each new robot would need 2 months to accumulate enough data for the training set, in order to show good performance. The proposed solution can potentially benefit the area of CNR if connected and integrated with a shared-knowledge platform which can enable new robots to skip the training phase, by downloading the existing knowledge from the cloud. / Under de senaste åren har ett nytt forskningsområde kallat Cloud Networked Robotics (CNR) växt fram inom den konventionella robottekniken, tack vare den ökade tillgången på billiga robotsystem och stadiga framsteg inom området cloud computing. Molnrobotar syftar på robotar med förmågan att flytta resurstunga moduler till ett moln för att ta del av lagringskapaciteten, den skalbara processorkraften och andra tjänster som ett moln kan tillhandahålla, t.ex. en kunskapsdatabas för robotar över hela världen. Det finns dock ett problem med dessa sorters robotar gällande nåbarhet och QoS för kritiska moduler placerade på ett moln, när dessa robotar verkar i instabila nätverksmiljöer. I ett sådant scenario kan robotarna när som helst förlora anslutningen till molnet, vilket i värsta fall lämnar robotarna hjärndöda. Den här rapporten föreslår en maskininlärningsbaserad nätverksmedveten ramverkslösning för en molnrobot, som kan välja de mest effektiva modulplaceringarna baserat på robotens position, den givna uppgiften och de rådande nätverksförhållanderna. Ramverkslösningen implementerades på en molnrobotsprototyp, baserad på ett robot development kit kallat TurtleBot 2, som använder sig av ett middleware som heter Robot Operating System (ROS). Ett fortskridande experiment utfördes där molnroboten fick i uppgift att utföra ett enkelt uppdrag i laboratoriets korridor, under varierande nätverksförhållanden. Ramverkslösningen utvärderades genom att jämföra resultaten från det fortskridrande experimentet med mätningar som gjordes med samma robot som utförde samma uppgift, fast med alla moduler placerade lokalt på roboten. Resultaten visar att den föreslagna ramverkslösningen kan potentiellt minska batterikonsumptionen med 10%, samtidigt som tiden för att utföra en uppgift kan minskas med 2.4 sekunder (2.8%). Däremot uppstår en flaskhals i framtagna lösningen där varje ny robot kräver 2 månader för att samla ihop nog med data för att maskinilärningsalgoritmen ska visa bra prestanda. Den förlsagna lösningen kan dock vara fördelaktig för CNR om man integrerar den med en kunskapsdatabas för robotar, som kan möjliggöra för varje ny robot att kringå den 2 månader långa träningsperioden, genom att ladda ner existerande kunskap från molnet.

Antal datortomografiundersökningar har ökat under flera år i Sverige tack vare tekniska utvecklingar och ökad tillgänglighet på sjukvård. Södertälje sjukhus röntgenavdelningen är i behov av att byta ut en utdaterad datortomograf (eng: Computed tomography, CT) och avdelningen för medicinsk teknik har föreslagit ett inköp av en CT med fotonräknande-detektor. Bilddata framställdes genom en fantomstudie för att påvisa förhållandet mellanstråldosparametern CTDIvol och kontrast-brus-förhållandet (CNR) hos CT-systemen: SOMATOM Drive och NAEOTOM Alpha. Den genererade datan påvisade att det finns en väsentlig skillnad i CNR-CTDIvol-förhållandet mellan SOMATOM Drive och NAEOTOM Alpha. Resultaten tydliggör att NAEOTOM Alpha kan producera bilder med betydligt mindre brus vid lägre stråldoser. Ett inköp av en fotonräknande detektor CT skulle kunna rättfärdigas utifrån bildkvalitéförbättringen som systemet kan erbjuda. / The number of computed tomography (CT) scans has increased during the past years in Sweden due to technical advancements and increased availability of healthcare. The x-ray department at Södertälje hospital is in need of replacing an outdated computed tomography and the departmentof clinical engineering has proposed a purchase of a photon-counting detector CT. Image data was produced through a phantom study to demonstrate the relationship between the parameter CTDIvol radiation dose and the contrast-to-noise ratio (CNR) of the CT systems: SOMATOM Drive and NAEOTOM Alpha. The generated data demonstrated that there is a substantial difference in the CNR-CTDIvol relationship between SOMATOM Drive and NAEOTOM Alpha. The results entail that NAEOTOM Alpha can produce images with considerably less noise at lower radiation doses. The purchase of a photon-counting CT could be justified by the improved image quality it can offer.

The inhomogeneous B1 field at higher main fields (B0) becomes more serious, leading to unsatisfactory MR image quality. To improve the signal homogeneity of routinely used T1-weighted image, usually acquired by a well-known sequence, Magnetization Prepared Rapid Acquisition Gradient Echo (MPRAGE), a new pulse sequence, Magnetization Prepared 2 Rapid Acquisition Gradient Echoes (MP2RAGE), was proposed in 2009. This technique acquires two sets of high-resolution three- dimentional images at different inversion times after a series of inversion pulses. After any of two simple calculations of the raw images (Ratio or MP2RAGE reconstruction), the output volume was obtained with dramatically reduced spatial inhomogenuity of MR signal. In this study, the contrast-to-noise ratio (CNR) optimation at 3 T was implemented independently to reproduce the previous results of other group. After that, the simulation of scanning parameters was done to optimize CNR of brain tissue at 1.5 T according to different encoding methods, different pulse sequences, and different reconstruction algorithms. Phantom and human experiments were carried on a 1.5 T scanner for further validation. The results of phantom experiment showed that both MP2RAGE and Ratio reconstructions can achiever better B1 homogeneity than MPRAGE, even with the vendor-equipped correction packages, SCIC and PURE. In addition, the agreement was made between simulation and in-vivo imaging that MP2RAGE provides higher CNR than Ratio when centric encoding also outduels linear encoding.

This thesis examines the characteristics of railway infrastructure development and associated issues in Manitoba, Canada. The period under consideration dates from when the first tracks were laid in 1878 through to the completion of the Hudson Bay Railway in 1929. Setting the scene is a template for railway development in general, one that allows hypotheses to be drawn that are specific to Manitoba. In order to test those hypotheses it is necessary to first provide a comprehensive overview of the historical evolution of the railway network. Next, aspects of graph theory are reviewed, identifying the methodology most appropriate for a spatial analysis of railway networks. This analysis attempts to draw conclusions about the relationship between the railway companies and the governments, people and geography that they were compelled to deal with. The testing of these forms revealed that while the Manitoba railway network is very complex, it never arrived at the maximum possible complexity.

This thesis examines the characteristics of railway infrastructure development and associated issues in Manitoba, Canada. The period under consideration dates from when the first tracks were laid in 1878 through to the completion of the Hudson Bay Railway in 1929. Setting the scene is a template for railway development in general, one that allows hypotheses to be drawn that are specific to Manitoba. In order to test those hypotheses it is necessary to first provide a comprehensive overview of the historical evolution of the railway network. Next, aspects of graph theory are reviewed, identifying the methodology most appropriate for a spatial analysis of railway networks. This analysis attempts to draw conclusions about the relationship between the railway companies and the governments, people and geography that they were compelled to deal with. The testing of these forms revealed that while the Manitoba railway network is very complex, it never arrived at the maximum possible complexity.

While the increase in shear strength of Steel Fibre Reinforced Concrete (SFRC) is well recognized, it has yet to be found common application of this material in building structures and there is no existing national standard that treats SFRC in a systematic manner. The aim of the diploma work is to investigate the shear strength of fibre reinforced concrete beams and the available test data and analyse the latter against the mostpromising equations available in the literature. The equations investigated are:Narayanan and Darwish’s formula, the German, the RILEM and the Italian guidelines. Thirty articles, selected among over one hundred articles taken from literature, have been used to create the database that contains almost 600 beams tested in shear. This large number of beams has been decreased to 371 excluding all those beams and test that do not fall within the limitation stated for this thesis. Narayanan and Darwish’s formula can be utilized every time that the fibre percentage, the type of fibres, the beam dimensions, the flexural reinforcement and the concrete strength class have been defined. On the opposite, the parameters introduced in the German, the RILEM and the Italian guidelines always require a further characterization of the concrete (with bending test) in order to describe the post‐cracking behaviour. The parameters involved in the guidelines are the residual flexural tensile strengths according to the different test set‐ups. A method for predicting the residual flexural tensile strength from the knowledge of the fibre properties, the cylindrical compressive strength of the concrete and the amount of fibres percentage is suggested. The predictions of the shear strength, obtained using the proposed method for the residual flexural tensile strength, showed to be satisfactory when compared with the experimental results. A comparison among the aforementioned equations corroborate the validity of the empirical formulations proposed by Narayanan and Darwish nevertheless only the other equations provide a realistic assessments of the strength, toughness and ductility of structural elements subjected to shear loading. Over the three investigated equations, which work with the post‐cracking characterization of the material, the Italian guideline proposal is the one that, due to its wide domain of validity and the results obtained for the gathered database of beams, has been selected as the most reliable equation.