Natural ventilation design attributes application effect on, indoor natural ventilation performance of a double storey, single unit residential building

In establishing a good indoor thermal condition, air movement is one of the important parameter to be considered to provide indoor fresh air for occupants. Due to the public awareness on environment impact, people has been increasingly attentive to passive design in achieving good condition of indoor building ventilation. Throughout case studies, significant building attributes were found giving effect on building indoor natural ventilation performance. The studies were categorized under vernacular houses, contemporary houses with vernacular element and contemporary houses. The indoor air movement of every each spaces in the houses were compared with the outdoor air movement surrounding the houses to indicate the space’s indoor natural ventilation performance. Analysis found the wind catcher element appears to be the most significant attribute to contribute most to indoor natural ventilation. Wide opening was also found to be significant especially those with louvers. Whereas it is also interesting to find indoor layout design is also significantly giving impact on the performance. The finding indicates that a good indoor natural ventilation is not only dictated by having proper openings at proper location of a building, but also on how the incoming air movement is managed throughout the interior spaces by proper layout. Understanding on the air pressure distribution caused by indoor windward and leeward side is important in directing the air flow to desired spaces in producing an overall good indoor natural ventilation performance

Intelligent Imaging of Perfusion Using Arterial Spin Labelling

Arterial spin labelling (ASL) is a powerful magnetic resonance imaging technique, which can be used to noninvasively measure perfusion in the brain and other organs of the body. Promising research results show how ASL might be used in stroke, tumours, dementia and paediatric medicine, in addition to many other areas. However, significant obstacles remain to prevent widespread use: ASL images have an inherently low signal to noise ratio, and are susceptible to corrupting artifacts from motion and other sources. The objective of the work in this thesis is to move towards an "intelligent imaging" paradigm: one in which the image acquisition, reconstruction and processing are mutually coupled, and tailored to the individual patient. This thesis explores how ASL images may be improved at several stages of the imaging pipeline. We review the relevant ASL literature, exploring details of ASL acquisitions, parameter inference and artifact post-processing. We subsequently present original work: we use the framework of Bayesian experimental design to generate optimised ASL acquisitions, we present original methods to improve parameter inference through anatomically-driven modelling of spatial correlation, and we describe a novel deep learning approach for simultaneous denoising and artifact filtering. Using a mixture of theoretical derivation, simulation results and imaging experiments, the work in this thesis presents several new approaches for ASL, and hopefully will shape future research and future ASL usage

Variable Splitting as a Key to Efficient Image Reconstruction

The problem of reconstruction of digital images from their degraded measurements has always been a problem of central importance in numerous applications of imaging sciences. In real life, acquired imaging data is typically contaminated by various types of degradation phenomena which are usually related to the imperfections of image acquisition devices and/or environmental effects. Accordingly, given the degraded measurements of an image of interest, the fundamental goal of image reconstruction is to recover its close approximation, thereby "reversing" the effect of image degradation. Moreover, the massive production and proliferation of digital data across different fields of applied sciences creates the need for methods of image restoration which would be both accurate and computationally efficient. Developing such methods, however, has never been a trivial task, as improving the accuracy of image reconstruction is generally achieved at the expense of an elevated computational burden. Accordingly, the main goal of this thesis has been to develop an analytical framework which allows one to tackle a wide scope of image reconstruction problems in a computationally efficient manner. To this end, we generalize the concept of variable splitting, as a tool for simplifying complex reconstruction problems through their replacement by a sequence of simpler and therefore easily solvable ones. Moreover, we consider two different types of variable splitting and demonstrate their connection to a number of existing approaches which are currently used to solve various inverse problems. In particular, we refer to the first type of variable splitting as Bregman Type Splitting (BTS) and demonstrate its applicability to the solution of complex reconstruction problems with composite, cross-domain constraints. As specific applications of practical importance, we consider the problem of reconstruction of diffusion MRI signals from sub-critically sampled, incomplete data as well as the problem of blind deconvolution of medical ultrasound images. Further, we refer to the second type of variable splitting as Fuzzy Clustering Splitting (FCS) and show its application to the problem of image denoising. Specifically, we demonstrate how this splitting technique allows us to generalize the concept of neighbourhood operation as well as to derive a unifying approach to denoising of imaging data under a variety of different noise scenarios

The Developing of a Smart Elbow Prosthesis for Loosening Detection

Total Elbow Arthroplasty (TEA) is an effective surgical procedure for restoring elbow joint function and improve a patient's quality of life by relieving pain suffered from various musculoskeletal disorders. Despite new designs for prostheses and improved surgical procedures, TEA still suffers today from mid to-long-term complications such as aseptic loosening, infection, dislocation, and pre-prosthetic fractures. With aseptic loosening followed by infection being the most persistent reason for TEA revision, investigating methods for early diagnosis of implant loosening and differentiating between the infection and aseptic loosening is necessary to address this problem. This thesis aims to develop a novel diagnostic tool that can be embedded into the prosthetic and provide a quantitative measurement for early signs of the implant loosening without any usage of radiographs or any contact with the implant. In this study, three types of sensor configurations along with detection algorithms were developed, designed, and tested along with a functional prototype to detect the migration of the elbow prosthesis (Aseptic loosening). The detection system was validated under realistic conditions through experiments with a custom-designed mechanical testing rig. Finally, for infection detection, a biocompatible chemical sensor (Hydrogel) was synthesised and was linked with the aseptic loosening detection system to investigate the early signs of infection. Among the three sensor configurations, the single sensor configuration detected the implant migration at a resolution of 0.3 mm with a detection error of less than 3 %. The configuration was able to detect angular motion up to 3 degrees with a detection error of 5 %. The quad sensor configuration, an arrangement of four closely packed sensors, enhanced the overall detection performance by increasing system resolution to 0.15 mm in multiple axes along with increasing the signal to noise ratio, reducing root mean square error, and compensating the tilt effect of the single sensor. While the dual sensor configuration, two sensors arranged in-line but 42 mm apart, downgraded the detection performance by introducing a detection error of 30 %. The detection system showed negligible effect on the biomaterial used in TEA and was able to differentiate between different migrations types (Linear, Angular, Static and Dynamic). The difference in three fixation scenarios (grossly loose, partially loose, and fully fixed) was identified evidently by the detection system with the grossly loose fixation showed a displacement of 0.187 ± 0.061 mm on the x-axis and 0.387 ± 0.059 mm on the y-axis. The chemical sensor (Hydrogel) was able to detect the change in its surrounding pH level (highlighting the potential to detect infection) and by the amalgamation with the detection system, pH change was detected without the use of an imaging technique. Further improvement in the synthesis of the hydrogel and the optimisation of the detection system has also been suggested. The quad sensor system implies that it has the potential to be used to continually or intermittently monitor implant behaviour without hospital visitation or x-ray exposure. This could be applied more widely to other major joints such as the hips and knees, giving in-situ biomechanical insight into joint replacement behaviour over time