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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.

This thesis has examined the relationship between recognition tasks and recall tasks, more specifically, the superiority of recognition over recall. Although this supperiority was still found to exist, evidence suggested that as discriminability among alternatives in the recognition task decreased, the superiority of recognition, over recall also decreased. Also investigated was the effect recognition and recall have on one another. It was found that recognition enhanced the ensuing recalls however, evidence suggested that as discriminability among alternatives in the recognition task became more difficult, performance on subsequent recall decreased.

As people across the globe are becoming more interested in watching their weight, eating more healthily, and avoiding obesity, a system that can measure calories and nutrition in everyday meals can be very useful. Recently, there has been an increase in the usage of personal mobile technology such as smartphones or tablets, which users carry with them practically all the time. In this paper, we proposed a food calorie and nutrition measurement system that can help patients and dieticians to measure and manage daily food intake. Our system is built on food image processing and uses nutritional fact tables. Via a special calibration technique, our system uses the built-in camera of such mobile devices and records a photo of the food before and after eating it in order to measure the consumption of calorie and nutrient components. The proposed algorithm used color, texture and contour segmentation and extracted important features such as shape, color, size and texture. Using various combinations of these features and applying a support vector machine as a classifier, a good classification was achieved and simulation results show that the algorithm recognizes food categories with an accuracy rate of 92.2%, on average.

Canada’s Truth and Reconciliation Commission (TRC) was established as one of the responses seeking to address the harm done by the Indian residential school system. While the main goals of the TRC include discovering truth and promoting healing and reconciliation, it is necessary to critically interrogate its design and activities in order to gain insight into its potential to allow Canada to move beyond trauma and build a just future. To accomplish this challenging task, my thesis employs qualitative research design and applies the conceptual framework of restorative justice, Charles Taylor’s theory of recognition, and Nancy Fraser’s tripartite theory of social justice in an attempt to assess the TRC’s restorative and recognitive potential. The main finding of this thesis is that the TRC is not fully restorative and possesses limited potential to contribute to the decolonization of Canada.