Ryerson University

About: Ryerson University is a education organization based out in Toronto, Ontario, Canada. It is known for research contribution in the topics: Computer science & Population. The organization has 7671 authors who have published 20164 publications receiving 394976 citations. The organization is also known as: Ryerson Polytechnical Institute & Ryerson Institute of Technology.

Modelling greenhouse gas emissions for municipal solid waste management strategies in Ottawa, Ontario, Canada

Abstract: Human-induced climate change, through the emission of greenhouse gases, may result in a significant negative impact on Earth. Canada is one of the largest per capita emitters of greenhouse gas, generating 720 megatonnes (Mt) carbon dioxide equivalents (CO 2 e), or per capita emissions of 23.2 t CO 2 e. The solid waste sector in Canada was directly responsible for 25 Mt CO 2 e in 2001, of which 23 Mt CO 2 e were produced by landfill gas (LFG). A modelling exercise was undertaken to determine greenhouse gas (GHG) emissions from the waste sector using the waste disposal, recycling, and composting data from Ottawa, Ontario, Canada for the year 2003, as well as the results of an audit of residential units performed in the same year. This evaluation determined that, among the options examined, waste incineration, further source separation of recyclables, and anaerobic digestion of an organic wastes have the greatest benefits for reducing GHG emissions in the City of Ottawa's waste sector. Challenges surrounding the installation of incineration facilities in Canada suggest that improved diversion of recyclable materials and anaerobic digestion of organic materials are the optimal options for the City of Ottawa to pursue.

A Fine-Grained Performance Model of Cloud Computing Centers

Abstract: Accurate performance evaluation of cloud computing resources is a necessary prerequisite for ensuring that quality of service parameters remain within agreed limits. In this paper, we employ both the analytical and simulation modeling to addresses the complexity of cloud computing systems. Analytical model is comprised of distinct functional submodels, the results of which are combined in an iterative manner to obtain the solution with required accuracy. Our models incorporate the important features of cloud centers such as batch arrival of user requests, resource virtualization, and realistic servicing steps, to obtain important performance metrics such as task blocking probability and total waiting time incurred on user requests. Also, our results reveal important insights for capacity planning to control delay of servicing users requests.

Automatic lung segmentation in CT images using watershed transform

Abstract: The preprocessing step of most computer-aided diagnosis (CAD) systems for identifying the lung diseases is lung segmentation. We present a novel lung segmentation technique based on watershed transform, which is fast and accurate. Lung region is precisely marked with internal and external markers. The markers are combined with the gradient image of the original data and watershed transform is applied on the combined data to find the lung borders. Rolling ball filter is used to smooth the contour and fill the cavities while preserving the original borders. The proposed method eliminates the tasks of finding an optimal threshold and separating the attached left and right lungs, which are two common practices in most lung segmentation methods and require a significant amount of time. We have applied our new approach on several pulmonary CT images and the results reveal the speed, robustness and accuracy of this method.

DeepNOMA: A Unified Framework for NOMA Using Deep Multi-Task Learning

Abstract: Non-orthogonal multiple access (NOMA) will provide massive connectivity for future Internet of Things. However, the intrinsic non-orthogonality in NOMA makes it non-trivial to approach the performance limit with only conventional communication-theoretic tools. In this paper, we resort to deep multi-task learning for end-to-end optimization of NOMA, by regarding the overlapped transmissions as multiple distinctive but correlated learning tasks. First of all, we establish a unified multi-task deep neural network (DNN) framework for NOMA, namely DeepNOMA, which consists of a channel module, a multiple access signature mapping module, namely DeepMAS, and a multi-user detection module, namely DeepMUD. DeepMAS and DeepMUD are automatically trained in a data-driven fashion, and a multi-task balancing technique is then proposed to guarantee fairness among tasks as well as to avoid local optima. To further exploit the benefits of communication-domain expertise, we introduce constellation shape prior and inter-task interference cancellation structure into DeepMAS and DeepMUD, respectively. These sophisticated designs help to reduce the implementation complexity without sacrificing DNN’s universal function approximation property, which makes DeepNOMA a universal transceiver optimization approach. Detailed experiments and link-level simulations show that higher transmission accuracy and lower computational complexity can be simultaneously achieved by DeepNOMA under various channel models, compared with state-of-the-art.

Compressive strength, abrasion resistance and energy absorption capacity of rubberized concretes with and without slag

Abstract: It has been estimated that around one billion tires are withdrawn from use in the world every year. Therefore, the development of new techniques for recycling waste tires is necessary. A number of innovative solutions that meet the challenge of the tire disposal problem involve using waste as an additive to cement-based materials. In this study, an experimental program was carried out to determine the compressive strength, abrasion resistance, and energy absorption capacity of rubberized concretes with and without ground granulated blast furnace slag (GGBFS). For this purpose, a water–binder ratio (0.4), four designated levels of crumb rubber (CR) contents (0, 5, 15 and 25% by fine aggregate volume), and three levels of GGBFS content (0, 20, and 40%) were considered as experimental parameters. In total, 12 concrete mixtures were cast and tested for compressive strength, abrasion resistance, and energy absorption capacity. Test results indicate that using CR aggregate decreases compressive strength and abrasion resistance of the concretes, but increases energy absorption capacity significantly.