The Modelling of Photovoltaic, Solar Thermal, and Photovoltaic/Thermal Domestic Hot Water Systems

Solar heating of potable water has traditionally been accomplished through the use of solar thermal (ST) collectors. With the recent increases in availability and lower cost of photovoltaic (PV) panels, the potential of coupling PV solar arrays to electrically heated domestic hot water (DHW) tanks has been considered. Additionally, innovations in the SDHW industry have led to the creation of photovoltaic/thermal (PV/T) collectors, which heat water using both electrical and thermal energy. The current work compared the performance and cost-effectiveness of a traditional solar thermal (ST) DHW system to PV-solar-electric DHW systems and a PV/T DHW system. To accomplish this, a detailed TRNSYS model of the solar hot water systems was created and annual simulations were performed for 250 L/day and 325 L/day loads in Toronto, Vancouver, Montreal, Halifax, and Calgary. It was shown that when considering thermal performance, PV-DHW systems were not competitive when compared to ST-DHW and PVT-DHW systems. As an example, for Toronto the simulated annual solar fractions of PV-DHW systems were approximately 30%, while the ST-DHW and PVT-DHW systems achieved 65% and 71% respectively. With current manufacturing and system costs, the PV-DHW system was the most cost-effective system for domestic purposes. The capital cost of the PV-DHW systems were approximately $1,923-$2,178 depending on the system configuration, and the ST-DHW and PVT system were estimated to have a capital cost of $2,288 and $2,373 respectively. Although the capital cost of the PVT-DHW system was higher than the other systems, a Present Worth analysis for a 20-year period showed that for a 250 L/day load in Toronto the Present Worth of the PV/T system was approximately $4,597, with PV-DHW systems costing approximately $7,683-$7,816 and the ST-DHW system costing $5,238.

Detection and Grading of Prostate Cancer in Temporal Enhanced Ultrasound using Convolutional Neural Networks

Prostate cancer is the most common non-dermatological cancer amongst men in the developed world. The current definitive diagnosis is core needle biopsy guided by transrectal ultrasound. However, this method suffers from low sensitivity and specificity in detecting cancer. Recently, a new ultrasound based tissue typing approach has been proposed, known as temporal enhanced ultrasound (TeUS). In this approach, a set of temporal ultrasound frames is collected from a stationary tissue location without any intentional mechanical excitation. The main aim of this thesis is to implement a deep learning-based solution for prostate cancer detection and grading using TeUS data. In the proposed solution, convolutional neural networks are trained to extract high-level features from time domain TeUS data in temporally and spatially adjacent frames in nine in vivo prostatectomy cases. This approach avoids information loss due to feature extraction and also improves cancer detection rate. The output likelihoods of two TeUS arrangements are then combined to form our novel decision support system. This deep learning-based approach results in the area under the receiver operating characteristic curve (AUC) of 0.80 and 0.73 for prostate cancer detection and grading, respectively, in leave-one-patient-out cross-validation. Recently, multi-parametric magnetic resonance imaging (mp-MRI) has been utilized to improve detection rate of aggressive prostate cancer. In this thesis, for the first time, we present the fusion of mp-MRI and TeUS for characterization of prostate cancer to compensates the deficiencies of each image modalities and improve cancer detection rate. The results obtained using TeUS are fused with those attained using consolidated mp-MRI maps from multiple MR modalities and cancer delineations on those by multiple clinicians. The proposed fusion approach yields the AUC of 0.86 in prostate cancer detection. The outcomes of this thesis emphasize the viable potential of TeUS as a tissue typing method. Employing this ultrasound-based intervention, which is non-invasive and inexpensive, can be a valuable and practical addition to enhance the current prostate cancer detection.