Many hospitals use image-guided cone-beam computed tomography (CT) to provide qualitative treatment for cancer patients. Although cone-beam CT provides good volumetric images, it takes a while to reconstruct them, which limits its applications because in many cases, real-time spatial information about patient’s internal structures is needed.

During the last few years, Breast Microwave imaging (BMI) has shown its potential as an alternative technique for breast cancer detection. BMI offers a variety of features, such as a high contrast between cancer and breast tissue and non-compressive image acquisition procedures which would make its use desirable in a clinical environment. Due to the fact that target reflections present different travel times and the collected data is a function of the arrival time and scan location, the recorded responses must be reconstructed in order to be properly visualized and interpreted.

One of the problems in identifying trends in time series or spatial data is that there are usually so many irregularities or random fluctuations that the underlying trend is difficult to discern. Smoothing techniques can be used to reduce local irregularities (local in the sense of being close in time or geography) so that the underlying trend becomes clear. Although these techniques are widely used in the physical sciences, they are seldom used in the social sciences or in spatial (GIS) applications.

NeuroRx is a Montreal, QC-based company which provides professional management of MRI-related study activities and delivers precise MRI outcome measurements that are performed in a regulatory compliant environment. The intern’s project will involve examining the computational issues associated with multi-dimensional analysis, namely, the computational efficiency and memory utilization, while creating an N-dimensional algorithm for segmenting and classifying the different tissue types in the brain (white matter, grey matter and cerebral spinal fluid) as well as white matter lesions.

The objective of the internship is to develop better thresholds for minimum connection times between flights. These thresholds must be large enough to reduce the risk of passengers missing their connecting flights due to flight delays. On the other hand, lengthier connection times lead to increased costs as planes and crews must wait longer between flights. The best compromises will be found by using historical data on flight delays to evaluate the effect of varying thresholds on the plane and crew costs using optimization models.

The objective of the intern’s research is to develop a computer program based on a panel-free method to predict the second-order wave loads on floating offshore structures which are critical for coupled mooring analysis. Integration of the mooring analysis computer program with a second-order wave loading analysis tool will allow for the coupled mooring line and vessel dynamics computation. Validation studies will be carried out for various platforms.

This internship project has been initiated to support an emerging technology for hydrogen production that is being developed by Atlantic Hydrogen, a Fredericton, NB-based company conducting research into a technology to produce hydrogen from natural gas without the generation of greenhouse gases. This emerging technology produces nanoscale carbon as a mixture of carbon nanotubes, Fullerenes and amorphous carbon. This research will develop methods to separate the highly valuable nanotubes and Fullerenes from the amorphous carbon.

Olympus NDT is a world-leading manufacturer of innovative, nondestructive testing instruments that are used in industrial and research applications ranging from aerospace, energy and automotive to consumer products. The primary focus of this internship research is to sharpen images of cracks and imperfections that have developed in oil pipelines to within + 0.5mm using an ultrasonic phased array. Ultrasonic phased arrays are a relatively new technology for industrial testing and currently can size defects and cracks to within + 2mm.

Complex Systems focuses on developing the next generation of sensor networks. Detection and location of microwave emitter/reflector using sensor networks have attracted much attention in the sensor and communication communities. Thus, the intern’s research project addresses some important issues in system design for target detection and localization. Detection and localization schemes will be developed and exploited in the designed sensor network. Testing will also be carried out to validate the proposed sensor network for wireless detection and localization.

The goal of this internship project is to optimize Bell Canada’s network engineering processes to better support the complexity of the company’s network technologies. In this project, to maximize network flexibility and scalability and to minimize the costs, the intern proposes to explore an innovative modular approach for network engineering. Each module will have specific network functions and configurations with the objective of minimizing design and network management costs. It would be then possible to design custom networks for specific clients rapidly with standardized modules.