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Stroke or “brain attack” occurs when a blood clot carried by the blood vessels from other part of the body blocks the cerebral artery in the brain or when a blood vessel breaks and interrupts the blood flow to parts of the brain. Depending on which part of the brain is being damaged functional abilities controlled by that region of the brain is lost. By interpreting the patient’s symptoms it is possible to make a coarse estimate of the location of the stroke, e.g. if it is on the left or right hemisphere of the brain. The aim of this study was to evaluate if microwave technology can be used to estimate the location of haemorrhagic stroke. In the first part of the thesis, CT images of the patients for whom the microwave measurement are taken is analysed and are used as a reference to know the location of bleeding in the brain. The X, Y and Z coordinates are calculated from the target slice (where the bleeding is more prominent). Based on the bleeding coordinated the datasets are divided into classes. Under supervised learning method the ISC algorithm is trained to classify stroke in the left and right hemispheres; stroke in the anterior and posterior part of the brain and the stroke in the inferior and superior region of the brain. The second part of the thesis is to analyse the classification result in order to identify the patients that were being misclassified. The classification results to classify the location of bleeding were promising with a high sensitivity and specificity that are indicated by the area under the ROC curve (AUC). AUC of 0.86 was obtained for bleedings in the left and right brain and an AUC of 0.94 was obtained for bleeding in the inferior and superior brain. The main constraint was the small size of the dataset and few availability of dataset with bleeding in the front brain that leads to imbalance between classes. After analysis it was found that bleedings that were close to the skull and few small bleedings that are deep inside the brain are being misclassified. Many factors can be responsible for misclassification like the antenna position, head size, amount of hair etc. The overall results indicate that SDD using ISC algorithm has high potential to distinguish bleedings in different locations. It is expected that the results will be more stable with increased patient dataset for training.

The work presented within this dissertation focusses on the development of affinite element method (FEM) model for the microwave absorption and heat transfer within a microwave combination washer dryer (MCWD). FEM will be used to aid in the implementation of more advanced fluid dynamics such as laminar or turbulent flow, that may be present within the system. The intended use of the model is to aid a South African based company in the development of a control system for the MCWD. The model development presented focusses on the washing cycle of the MCWD and will therefore not take into account the drying cycle of the system. The target of the microwave heating within the model will be distilled water as the dielectric constant of water is a know quantity. Various literature sources on microwave absorption and heat transfer models can be found, but none specific to the topic of the combination washer dryer. By reviewing literature from various sources, the finite element method was selected as the modelling technique and the COMSOL® software package was selected as the tool for developing the model. A model for the MCWD will be developed within the COMSOL® environment which in turn implements FEM as a technique to solve the model. The model development is broken into nine stages. Stage one start by modelling the heat transfer within the washing drum. Each consecutive stage expands the model by adding features or model domains. Model verification takes place in parallel to the development by verifying each stage before moving to the next stage. The stage eight and nine models, which represent a full three dimensional model of the system, are selected to be validated as the final models. Stage eight models the system without an enclosure and makes use of convective cooling boundary conditions on the boundary of the air enclosed within the system enclosure. Stage stage nine models the system with the aluminium enclosure of the system and also implements convective cooling boundary conditions on the outer boundary of the aluminium. The boundary between the enclosed air and aluminium enclosure is implemented as a normal convective heat transfer boundary between a gas and solid. Data capturing is done using the dSpace® platform. Sensors to log the microwave power and system temperature are selected and optimal placement of the sensors is evaluated. The capturing platform is interfaced to the sensors by an in-house developed signal conditioning board. Model validation is completed by comparing the response of the model to the practical system. Numerous simulations are completed to select the optimal configuration of the model that provides the optimal response. The stage eight model was found to be more accurate then the stage nine model with respect to the difference between the simulated and expected response over the whole domain of the transient temperature response. A further method implemented to easily compare the results of various simulations is by comparing the average absolute temperature of the response over the whole domain of the transient response. The average absolute temperature is calculated by taking absolute difference between the expected results and the model response at each time step within the response domain and then to average the absolute difference. This enables the comparison of two responses using two values. Needles to say this method should not be used alone and should be used in conjunction with a comparison over the full response domain. Use of the average absolute temperature difference is aimed at filtering the results from a selection of results which warrants a more in depth investigation. Using a comparison of the average absolute temperature difference of the target in the 500 W model, it was found that their respective values are 2:92 °C and 11:36 °C. The stage eight model computation time was far less than the stage nine model and is therefore recommended for further development. The final conclusion was made that the stage eight model represents the system fairly accurately at this stage and warrants further development by expanding the model to account for the drying cycle of the MCWD. The term fairy accurate is used to describe the results as further improvement of the model is definitely possible with regards to the accuracy of the transient response of the system. Further improvement of the model response may be possible by implementing a smaller mesh size or launching an in depth study on the effect of the various material thermal properties on the response of the system during various stages. For instance below a certain temperature the response closely represents the expected response and above that temperature the response various greatly from the expected response. Future work on the model include, to change the target from distilled water to an actual representation of the textiles intended to be washed within the MCWD. This will require a study into how the various parameters such as the density and dielectric constant, of the heterogeneous mixtures of textiles and water, can be combined for use into the model. As a next step in the expansion of the model, the model can be configured to account for the drying cycle of the system which will require the model to account for the phase changes that the water will undergo. / Thesis (MIng (Computer and Electronic Engineering))--North-West University, Potchefstroom Campus, 2013.

The work presented within this dissertation focusses on the development of affinite element method (FEM) model for the microwave absorption and heat transfer within a microwave combination washer dryer (MCWD). FEM will be used to aid in the implementation of more advanced fluid dynamics such as laminar or turbulent flow, that may be present within the system. The intended use of the model is to aid a South African based company in the development of a control system for the MCWD. The model development presented focusses on the washing cycle of the MCWD and will therefore not take into account the drying cycle of the system. The target of the microwave heating within the model will be distilled water as the dielectric constant of water is a know quantity. Various literature sources on microwave absorption and heat transfer models can be found, but none specific to the topic of the combination washer dryer. By reviewing literature from various sources, the finite element method was selected as the modelling technique and the COMSOL® software package was selected as the tool for developing the model. A model for the MCWD will be developed within the COMSOL® environment which in turn implements FEM as a technique to solve the model. The model development is broken into nine stages. Stage one start by modelling the heat transfer within the washing drum. Each consecutive stage expands the model by adding features or model domains. Model verification takes place in parallel to the development by verifying each stage before moving to the next stage. The stage eight and nine models, which represent a full three dimensional model of the system, are selected to be validated as the final models. Stage eight models the system without an enclosure and makes use of convective cooling boundary conditions on the boundary of the air enclosed within the system enclosure. Stage stage nine models the system with the aluminium enclosure of the system and also implements convective cooling boundary conditions on the outer boundary of the aluminium. The boundary between the enclosed air and aluminium enclosure is implemented as a normal convective heat transfer boundary between a gas and solid. Data capturing is done using the dSpace® platform. Sensors to log the microwave power and system temperature are selected and optimal placement of the sensors is evaluated. The capturing platform is interfaced to the sensors by an in-house developed signal conditioning board. Model validation is completed by comparing the response of the model to the practical system. Numerous simulations are completed to select the optimal configuration of the model that provides the optimal response. The stage eight model was found to be more accurate then the stage nine model with respect to the difference between the simulated and expected response over the whole domain of the transient temperature response. A further method implemented to easily compare the results of various simulations is by comparing the average absolute temperature of the response over the whole domain of the transient response. The average absolute temperature is calculated by taking absolute difference between the expected results and the model response at each time step within the response domain and then to average the absolute difference. This enables the comparison of two responses using two values. Needles to say this method should not be used alone and should be used in conjunction with a comparison over the full response domain. Use of the average absolute temperature difference is aimed at filtering the results from a selection of results which warrants a more in depth investigation. Using a comparison of the average absolute temperature difference of the target in the 500 W model, it was found that their respective values are 2:92 °C and 11:36 °C. The stage eight model computation time was far less than the stage nine model and is therefore recommended for further development. The final conclusion was made that the stage eight model represents the system fairly accurately at this stage and warrants further development by expanding the model to account for the drying cycle of the MCWD. The term fairy accurate is used to describe the results as further improvement of the model is definitely possible with regards to the accuracy of the transient response of the system. Further improvement of the model response may be possible by implementing a smaller mesh size or launching an in depth study on the effect of the various material thermal properties on the response of the system during various stages. For instance below a certain temperature the response closely represents the expected response and above that temperature the response various greatly from the expected response. Future work on the model include, to change the target from distilled water to an actual representation of the textiles intended to be washed within the MCWD. This will require a study into how the various parameters such as the density and dielectric constant, of the heterogeneous mixtures of textiles and water, can be combined for use into the model. As a next step in the expansion of the model, the model can be configured to account for the drying cycle of the system which will require the model to account for the phase changes that the water will undergo. / Thesis (MIng (Computer and Electronic Engineering))--North-West University, Potchefstroom Campus, 2013.