Ultrasound-based fetal biometry is used to derive important clinical information for identifying IUGR (intra-uterine growth restriction) and managing risk in pregnancy. Accurate and reproducible biometric measurement relies heavily on a good standard image plane. However, qualitative visual assessment, which includes the visual identification of certain anatomical landmarks in the image is prone to inter- and intra-reviewer variability and is also time-consuming to perform. Automated anatomical structure detection is the first step towards the development of a fast and reproducible quality assessment of fetal biometry images. This thesis deals specifically with abdominal scans in the development and evaluation of methods to automatically detect the stomach and the umbilical vein within them. First, an original method for detecting the stomach and the umbilical vein in fetal abdominal scans was developed using a machine learning framework. A classifier solution was designed with AdaBoost learning algorithm with Haar features extracted from the intensity image. The performance of the new method was compared on different clinically relevant gestational age groups. Speckle and the low contrast nature of ultrasound images motivated the idea of introducing features extracted from local phase images. Local phase is contrast invariant and has proven to be useful in other ultrasound image analysis application compared with intensity. Nevertheless, it has never been implemented in a machine learning environment before. In our second experiment, local phase features were proven to have higher discriminative power than intensity features which enabled them to be selected as the first weak classifiers with large classifier weight. Third, a novel approach to improving the speed of the detection was developed using a global feature symmetry map based on local phase to select the candidate locations for the stomach and the umbilical vein. It was coupled with a local intensity-based classifier to form a “hybrid” detector. A nine-fold increase in the average computational speed was recorded along with higher accuracy in the detection of both the anatomical structures. Quantitative and qualitative evaluations of all the algorithms were presented using 2384 fetal abdominal images retrieved from the image database study of the Oxford Ultrasound Quality Control Unit of the INTERGROWTH-21st project. Finally, the “hybrid” detection method was evaluated in two potential application scenarios. The first application was clinical scoring in which both the computer algorithm and four experts were asked to record presence or absence of the stomach and the umbilical vein in 400 ultrasound images. The computer-experts agreement was found to be comparable with the inter-expert agreement. The second application concerned selecting the standard image plane from 3D abdominal ultrasound volume. The algorithm was successful in selecting 93.36% of the images plane defined by the expert in 30 ultrasound volumes.

In this thesis I study constraint satisfaction problems or CSPs. These require determining whether values can be assigned to variables so that all constraints are satisfied. An important challenge is to identify tractable CSPs which can be solved efficiently. CSP instances have usually been grouped together by restricting either the allowed combinations of values, or the way the variables are allowed to interact. Such restrictions sometimes yield tractable CSPs. A weakness of this method is that it cannot explain why all-different constraints form a tractable CSP. In this common type of constraint, all variables must be assigned values that are different from each other. New techniques are therefore needed to explain why such CSPs can be solved efficiently. My main contribution is an investigation of such hybrid CSPs which cannot be defined with either one of these kinds of restrictions. The main technique I use is a transformation of a CSP instance to the microstructure representation. This represents an instance as a collection of sets, and a solution of the instance corresponds to an independent set in the clause structure. For the common case where all constraints involve only two variables, I show how the microstructure can be used to define CSPs that are tractable because their clause structures fall within classes of graphs for which an independent set of specified size can be found efficiently. Such tractable hereditary classes are defined by using the technique of excluded induced subgraphs, such as classes of graphs that contain neither odd cycles with five or more vertices, nor their complements. I also develop finer grained techniques, by allowing vertices of the microstructure representation to be assigned colours, and the variables to be ordered. I show that these techniques define a new tractable CSP that forbids an ordered vertex-coloured subgraph in the microstructure representation.

Indoor tracking and navigation is a fundamental need for pervasive and context-aware applications. However, no practical and reliable indoor positioning solution is available at present. The major challenge of a practical solution lies in the fact that only the existing devices and infrastructure can be utilized to achieve high positioning accuracy. This thesis presents a robust indoor positioning system with the lifelong learning ability. The typical features of the proposed solution is low-cost, accurate, robust, and scalable. This system only takes the floor plan and the existing devices, e.g. phones, pads, etc. and infrastructure such as WiFi/BLE access points for the sake of practicality. This system has four closely correlated components including, non-line-of-sight identification and mitigation (NIMIT), robust pedestrian dead reckoning (R-PDR), lightweight map matching (MapCraft), and lifelong learning. NIMIT projects the received signal strength (RSS) from WiFi/BLE to locations. The R-PDR component converts the data from inertial measurement unit (IMU) sensors ubiquitous in mobile devices and wearables to the trajectories of the user. Then MapCraft fuses trajectories estimated from the R-PDR and the coarse location information from NIMIT with the floor plan and provides accurate location estimations. The lifelong learning component then learns the various parameters used in all other three components in an unsupervised manner, which continuously improves the the positioning accuracy of the system. Extensive real world experiments in multiple sites show how the proposed system outperforms state-of-the art approaches, demonstrating excellent sub-meter positioning accuracy and accurate reconstruction of tortuous trajectories with zero training effort. As proof of its robustness, we also demonstrate how it is able to accurately track the position regardless of the users, devices, attachments, and environments. We believe that such an accurate and robust approach will enable always-on background localization, enabling a new era of location-aware applications to be developed.

Collecting data from sensor nodes to designated sinks is a common and challenging task in a wide variety of wireless sensor network (WSN) applications, ranging from animal monitoring to security surveillance. A number of approaches exploiting sink mobility have been proposed in recent years: some are proactive, in that sensor nodes push their read- ings to storage nodes from where they are collected by roaming mobile sinks, whereas others are reactive, in that mobile sinks pull readings from nearby sensor nodes as they traverse the sensor network. In this thesis, we point out that deciding which data collection approach is more energy-efficient depends on application characteristics, includ- ing the mobility patterns of sinks and the desired latency of collected data. We introduce novel adaptive data collection schemes that are able to automatically adjust to changing sink visiting patterns or data requirements, thereby significantly easing the deployment of a WSN. We illustrate cases where combining proactive and reactive modes of data collection is particularly beneficial. This motivates the design of TwinRoute, a novel hybrid algorithm that can flexibly mix the two col- lection modes at appropriate levels depending on the application sce- nario. Our extensive experimental evaluation, which uses synthetic and real-world sink traces, allows us to identify scenario characteristics that suit proactive, reactive or hybrid data collection schemes. It shows that TwinRoute outperforms the pure approaches in most scenarios, achiev- ing desirable tradeoffs between communication cost and timely delivery of sensor data.

In this thesis, we develop a motion correction method to overcome the degradation of image quality introduced by respiratory motion in positron emission tomography (PET), so that diagnostic performance for lung cancer can be improved. Lung cancer is currently the most common cause of cancer death both in the UK and in the world. PET/CT, which is a combination of PET and CT, providing clinicians with both functional and anatomical information, is routinely used as a non-invasive imaging technique to diagnose and stage lung cancer. However, since a PET scan normally takes 15-30 minutes, respiration is inevitable in data acquisition. As a result, thoracic PET images are substantially degraded by respiratory motion, not only by being blurred, but also by being inaccurately attenuation corrected due to the mismatch between PET and CT. If these challenges are not addressed, the diagnosis of lung cancer may be misled. The main contribution of this thesis is to propose a novel process for respiratory motion correction, in which non-attenuation corrected PET images (PET-NAC) are registered to a reference position for motion correction and then multiplied by a voxel-wise attenuation correction factor (ACF) image for attenuation correction. The ACF image is derived from a CT image which matches the reference position, so that no attenuation correction artefacts would occur. In experiments, the motion corrected PET images show significant improvements over the uncorrected images, which represent the acquisitions typical of current clinical practice. The enhanced image quality means that our method has the potential to improve diagnostic performance for lung cancer. We also develop an automatic lesion detection method based on motion corrected images. A small lung lesion is only 2 or 3 voxels in diameter and of marginal contrast. It could easily be missed by human observers. Our method aims to provide radiologists with a map of potential lesions for decision so that diagnostic efficiency can be improved. It utilises both PET and CT images. The CT image provides a lung mask, to which lesion detection is confined, whereas the PET image provides distribution of glucose metabolism, according to which lung lesions are detected. Experimental results show that respiratory motion correction significantly increases the success of lesion detection, especially for small lesions, and most of the lung lesions can be detected by our method. The method can serve as a useful computer-aided image analysing tool to help radiologists read images and find malignant lung lesions. Finally, we explore the possibility of incorporating temporal information into respiratory motion correction. Conventionally, respiratory gated PET images are individually registered to the reference position. Temporal continuity across the respiratory period is not considered. We propose a spatio-temporal registration algorithm, which models temporally smooth deformation in order to improve the registration performance. However, we discover that the improvement introduced by temporal information is relatively small at the cost of a much longer computation time. Spatial registration with regularisation yields similar results but is superior in speed. Therefore, it is preferable for respiratory motion correction.

In this thesis, I have developed a new methodology for the accurate determination of mass-dependent variations in nickel (Ni) isotope compositions. Nickel is initially separated in a three-column ion-exchange procedure, and the purified solutions are analysed by multi-collector inductively coupled plasma mass spectrometry (MCICPMS) using a double-spike technique. Using this methodology, I have measured the first Ni isotope ratios for a wide variety of natural geological samples. Significant Ni isotope variations were observed, with an overall spread in delta 60Ni-values of -0.9 to 2.5 permil. In igneous rocks Ni isotopes appear to be largely homogeneous, with only small variations (0.2 permil) between different rock types. Weathering of silicate rocks does on the other hand appear to cause significant fractionation of Ni isotopes, probably producing an isotopically heavy riverine input to the ocean. A heavy isotope signature is also visible in hydrogenetic ferromanganese crusts, with surface scrapings from globally distributed crusts show an average delta 60Ni-value of 1.65 permil. However, the variation in these samples is over 1.5 permil, likely reflecting local sources or biological processes, or alternatively indicating a heterogeneous Ni isotopic composition of the ocean. Organic-rich sediments also show heavy isotopic compositions, which are possibly transferred to the crude oils originating in these types of sediments. The only significant reservoir of light Ni isotopes found during this project are sulphides from magmatic systems. Overall, this thesis demonstrates the potential of this system as a powerful new tracer for a variety of geochemical processes.

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Typescript (photocopy). / Digitized by Kansas Correctional Industries

This study used cinematography to determine differences in velocity, acceleration, moments of force, and body centers of gravity in four different positions of two techniques of the long-hang kip. Three female gymnasts performed five attempts of each technique: the traditional method, with an arch in the lower back at the end of the forward swing, and approximate shoulder angle of 180 degrees or more; and the newer method, with no arch in the lower back and approximate shoulder angle of 90 degrees or less. Three. USGF-rated judges scored the kips, and due to inability to distinguish between the two techniques, two subjects were eliminated. Major differences occurred in the swing extension, with the newer technique producing more velocity and a higher center of gravity throughout the movement.

The intensification of agricultural production in the Mekong Delta has faced serious challenges with respect to increased use of agrochemicals and especially pesticides. The indiscriminate use of pesticide could potentially impact on the long-term food production, environmental and human health in the delta. The aim of this thesis was to investigate the negative side effects of the current use of pesticides on climbing perch (Anabas testudineus) in rice fields using brain acetylcholinesterase (hereafter referred to as AChE) activity as a biomarker. The empirical work, on which this thesis is based, includes structured questionnaires, laboratory and field experiments. First, a field survey using questionnaires was carried out to gain a better understanding of the current state of rice farming systems, the use of pesticides and attitude to pest management strategies among rice and rice-fish farmers, as well as to provide basic information for the set-up of the laboratory and field experiments. Secondly, laboratory studies were conducted to clarify if the selected insecticides applied alone and in mixtures caused negative side effects on climbing perch fingerlings. Thirdly, further toxicity studies were carried out, under rice field conditions, to further investigate the toxicity effects of the insecticides, applied alone, in mixtures and under sequential applications, on climbing perch fingerlings. The results showed that although there were a more selective use of pesticides and an increased awareness among farmers of the negative side effects of pesticides in 2007 as compared to 1999, the current use of pesticide in the Mekong Delta still cause many problems to the environment and human health. Chlorpyrifos ethyl (hereafter referred to as CPF) was found to cause a significant and more prolonged inhibition on the brain AChE activity in climbing perch than fenobucarb (hereafter referred to as F). The inhibition by the mixture of CPF and F were significantly higher than the inhibition by only F, but less prolonged and significant lower than the inhibition by only CPF. The results suggest that the combined effect from a mixture of F and CPF can create both additive effects initially and later antagonistic effects. CPF and F applied at concentrations used by farmers, either as separate doses, in a mixture or in sequential doses, decreased the brain AChE activity, growth and survival rates in climbing perch. The results demonstrate that brain AChE activity in climbing perch is a relevant biomarker for monitoring of exposure to, and sub-lethal impacts from organophosphates and carbamates under tropical conditions. The result also shows that 2-PAM re-activate the brain AChE activity, and can be used as an alternative method to assess the AChE inhibition level in organisms recently exposed to OP’s, in situation where it may be difficult to find unexposed individuals as controls. In conclusion, this thesis shows that the current use of pesticides in the Mekong Delta has a negative effect on climbing perch living in rice fields. It indicates that a sustained long-term food production in the Mekong Delta must be based on ecological principles, taking advantages of ecosystem biodiversity and productivity, and not through intensified use of pesticides. / <p>At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 3: Submitted. Paper 5: Submitted.</p>