This project is part of a larger research project which investigates the applicability of the peer-to-peer (P2P) computing paradigm in designing large-scale content distribution systems. To develop an efficient content distribution system, it is essential to understand the workload that will be distributed, the behavior of content consumers and the environment in which the system will operate.
This project aims at performance modelling of athletes and involves the collection of detailed data that affects rider performance in professional cycling. This data is utilized for assessment of training and performance and for supporting individual training schedules through modelling and profiling of individual athletes. The methodology is based on pattern discovery and recognition using Self Organizing Maps, an exploratory data analysis model of demonstrated success in automated monitoring tasks involving multiple parameters.
Behavioural detectors for intrusion detection require training in order to correctly characterize the operation of a service – protocol combination. Implicit in this is the assumption that the learning algorithm will scale to large datasets and provide simple solutions. This work will address both requirements under a Genetic Programming context through the use of a combined multi-objective, host-parasite model. It has already been demonstrated that both schemes are appropriate independently.
The objective of the project is to determine the impact of various game features on VLT game play. These features are based on the current strategies adopted by SPIELO for its North American market. This data will be collected from players in a real life setting, then analyzed, and used for player profiling and as an input for improving existing strategies and measure their effectiveness. The analyzed data, the derived player profiles and the inference from the study will be used in developing a software simulator of original gaming environment.
Currently, EMS paramedics record patient information such as history, medical assessment, and treatments rendered onsite with pen and paper. They then convert this information to a paper-based call report and hand it over to the hospital along with the patient. This method is time-consuming and error prone. It causes delay for data analysis and lengthens paramedic turn-around time. This project will produce a data collection tool based on the mobile IP infrastructure which will reduce clerical errors, improve data analysis and medical care.
Distributed Denial of Service (DDoS) attacks are widely regarded as a major threat to the Internet because of their ability to make a service unavailable and create a huge volume of unwanted traffic. Unwanted traffic control is one of the most important challenges of Bell Canada. Current countermeasures cannot assure higher quality of service under a tremendous increase in unwanted traffic.
This project is about content-based networks. In content-based networks, the decisions regarding the forwarding of data are done by inspecting the actual information contents. The forwarding process is guided by properties of the payload data. This is in contrast with traditional networks where forwarding is guided only by the destination addresses contained in packets.
D-Wave Systems is currently involved in the research and development of quantum computing technology. Quantum computers allow for not just a fast computer, but potentially, a change in the computational complexity of problems. Graph theory plays a crucial role in the development and understanding of the capabilities and behaviour of the quantum computing hardware being developed, both from an operational and an applications perspective.
Medical image registration is the task of bringing two images into spatial alignment. Automatic and accurate 3D co-registration of nuclear medicine 3D image data with 3D anatomical data is crucial for improving the functional image reconstruction through anatomy-based attenuation correction. Co-registration is also important for the fusion of anatomical data with functional information. Most registration methods involve optimizing an intensity-based similarity metric that is defined by the transformation parameters.
Typically, movement of the patient during any medical imaging scan will result in a degraded final image if this motion is not accounted for. This research project aims to use the video from two cameras, recording from different angles, to estimate the motion of a patient’s head during a PET (positron emission tomography) scan. By using the video from two cameras to track features of the patient’s head, motion in all three dimensions can be estimated.