The Winner-Take-All (WTA) is an essential neural network operation for locating the most active neuron. Such a procedure has been extensively used in larger application areas. The Winner-Take-All circuit selects the maximum of the inputs inhibiting all other nodes. The efficiency of the analog circuits may well be considerably higher than the digital circuits. Also, analog circuits’ design footprint and processing time can be significantly small. A current-based Winner-Take-All circuit for analog neural networks is presented in this research. A compare and pass (CAP) mechanism has been used, where each input pair is compared, and the winner is selected and passed to another level. The inputs are compared by a sense amplifier which generates high and low voltage signals at the output node. The voltage signal of the sense amplifier is used to select the winner and passed to another level using logic gates. Also, each winner follows a sequence of digital bits to be selected. The findings of the SPICE simulation are also presented. The simulation results on the MNIST, Fashion-MNIST, and CIFAR10 datasets for the memristive deep neural network model show the significantly accurate result of the winner class with an average difference of input and selected winner output current of 0.00795uA, 0.01076uA and 0.02364uA respectively. The experimental result with transient noise analysis is also presented.

High-speed transmission over contemporary communication networks has drawn many research efforts. Traffic scheduling schemes which play a critical role in managing network transmission have been pervasively studied and widely implemented in various practical communication networks. In a sophisticated communication system, a variety of applications co-exist and require differentiated Quality-of-Service (QoS). Innovative scheduling schemes and hybrid scheduling disciplines which integrate multiple traditional scheduling mechanisms have emerged for QoS differentiation. This study aims to develop novel analytical models for commonly interested scheduling schemes in communication systems under more realistic network traffic and use the models to investigate the issues of design and development of traffic scheduling schemes. In the open literature, it is commonly recognized that network traffic exhibits self-similar nature, which has serious impact on the performance of communication networks and protocols. To have a deep study of self-similar traffic, the real-world traffic datasets are measured and evaluated in this study. The results reveal that selfsimilar traffic is a ubiquitous phenomenon in high-speed communication networks and highlight the importance of the developed analytical models under self-similar traffic. The original analytical models are then developed for the centralized scheduling schemes including the Deficit Round Robin, the hybrid PQGPS which integrates the traditional Priority Queueing (PQ) and Generalized Processor Sharing (GPS) schemes, and the Automatic Repeat reQuest (ARQ) forward error control discipline in the presence of self-similar traffic. Most recently, research on the innovative Cognitive Radio (CR) techniques in wireless networks is popular. However, most of the existing analytical models still employ the traditional Poisson traffic to examine the performance of CR involved systems. In addition, few studies have been reported for estimating the residual service left by primary users. Instead, extensive existing studies use an ON/OFF source to model the residual service regardless of the primary traffic. In this thesis, a PQ theory is adopted to investigate and model the possible service left by selfsimilar primary traffic and derive the queue length distribution of individual secondary users under the distributed spectrum random access protocol.

This thesis details a higher-fidelity, scalable simulation tool and model for message response time and bus utilization rate analysis for the Controller Area Network (CAN) protocol stack. This tool achieves higher fidelity than existing commercial and academic simulation tools by including details of the stack implementation that are often neglected, such as receive and transmit hardware buffer availability and usage policy (i.e., which messages are able to be copied to which buffer resources), and the buffer polling or queueing policies. Key details of these features have been identified by a thorough examination of CAN stack behaviour, taking into account the physical considerations of commercial CAN implementations. Inclusion of these details in the simulation can produce better accuracy by exposing certain priority inversion scenarios. Scalability is achieved by using a transaction-based modelling approach and modelling transmissions at the protocol level rather than the physical/bit level. The tool requires minimal user interaction, and system level model generation is automated using an AUTOSAR XML system description file (ARXML format) to specify network topology and message information (transmitter, receiver(s), period, length, etc.), and an Excel spreadsheet file (XLS or XLSX format) to specify node hardware/software implementation details (buffer resource details, polling loop rates, main control loop rates, etc.) as inputs. / Thesis / Master of Applied Science (MASc)

Water resources managers rely on information collected by hydrometric networks without a quantitative way to assess their efficiency, and most Canadian water monitoring networks still do not meet the minimum density requirements. There is also no established way to quantify the importance of each existing station in a hydrometric network. This research examines the properties of Combined Regionalization Dual Entropy Multi-Objective Optimization (CR-DEMO), a robust network design technique which combines the merits of information theory and multi-objective optimization. Another information theory based method called transinformation (TI) which can rank the contribution of unique information from each specific hydrometric station in the network is tested for use with CR-DEMO. When used in conjunction, these methods can not only provide an objective measure of network efficiency and the relative importance of each station, but also allow the user to make recommendations to improve existing hydrometric networks across Canada. The Ottawa River Basin, a major Canadian watershed in Ontario and Quebec, was selected for analysis. Various regionalization methods which could be used in CR-DEMO such as distance weighting and a rainfall runoff model were compared in a leave one out cross validation. The effect of removing stations with regulated and unnatural flow regimes from the regionalization process is also tested. The analysis is repeated on a smaller tributary of the Ottawa River Basin, the Madawaska Watershed, to examine scale effects in TI analysis and CR-DEMO application. In this study, tests were conducted to determine whether to include stations outside of the river basin in order to provide more context to the basin boundaries. It was found that the TI analysis complemented CR-DEMO well and it provided a detailed station ranking which was supported by CR-DEMO results. The inverse distance weighting drainage area ratio method was found to provide more accurate regionalization results compared to the rainfall-runoff model, and was thus chosen for CR-DEMO. Regionalization was shown to be more accurate when the regulated basins were omitted using leave one out cross validation. It was discovered that CR-DEMO is sensitive to scaling because some sub-basins which are relatively “well-equipped” compared to others in dire conditions may be penalized. The TI analysis was not as sensitive to scaling. Including stations outside of the Ottawa River Basin improved the information density and regionalization accuracy in the Madawaska Watershed because they provided context to sparse areas. Finally, Pareto optimal network solutions for both the Ottawa River Basin and the Madawaska Watershed were presented and analyzed. A number of optimal networks are proposed for each watershed along with “hot-spots” where new stations should be added whatever the end users’ choice of network. / Thesis / Master of Applied Science (MASc)

PhD Thesis / On congested North American urban road networks, driver concerns over increased travel time play a major role in whether or not cycling infrastructure is built. This fact is recognized by transportation planning agencies in Canada and the United States, including the Ministry of Transportation Ontario and the Federal Highway Administration. However, specific frameworks to address such driver concerns do not exist within the practice of urban planning nor the academic literature. One potentially fruitful avenue is to explore the methods and tools of critical link analysis. One such avenue is provided by the Network Robustness Index (NRI) and the Network Robustness Index Calculator, as this method and tool indexes critical links through traffic simulation from least to most critical. The specific information that can be used to address driver concerns is found in the least critical links as these roadways have additional capacity, and therefore may be considered underutilized. This thesis explores the use of the NRI as a framework for urban cycling infrastructure planning. Experiments on the utility of the NRI against common traffic and cycling planning tools are explored. The NRI Calculator’s ability to perform full network scans for potential bike lane locations, least cost corridors, and full cycling networks consisting of different designs is tested throughout the chapters of this manuscript. / Thesis / Doctor of Philosophy (PhD) / This thesis aids in the planning of urban bike lanes by addressing driver concerns through traffic simulation.

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