Lumentum produces high-performance optical devices and test equipment for fiber optics communications systems. One such device is the Wavelength Selective Switch (WSS) that is used to switch optical signals between different optical fibers, depending on the wavelength of the light carrying the signal. Although these devices work very well and Lumentum is a leader in the design and production of such devices, their performance is currently somewhat limited by relatively slow fluctuations in the amount of light scattered into the desired optical fiber for a chosen wavelength.
The proposal focuses on the design and synthesis of novel bio-based, biodegradable materials to be used in packaging and protective coatings (varnishes), and targeted at replacing the nonrenewable, non- biodegradable materials currently used to manufacture these products. Our new process allows us to obtain a desired combination of material properties, and also eliminates the need for using VOCs (Volatile Organic Compounds). We achieve this using a new type of solvent whose properties can be “switched” simply by introducing or removing CO2 into or from the mixture.
This research project will investigate how modern Remotely Piloted Aircraft Systems (also referred to as RPAS, drones, UAVs or UASs) can optimize surveillance missions and target characterization through the integration of Full Motion Video (FMV) cameras. FMV data will be processed and analyzed including the use of Artificial Intelligence (AI) algorithms. The large amount of data which results from such surveillance missions must be analyzed via semi-automatic and automatic methods, as manual analysis is neither fast enough nor economical.
Among all chronic diseases, mental health issues have the highest burden on health care systems. However, unlike other chronic diseases, like Diabetes or hypertension, no monitoring procedures exist to monitor patients’ mental health status to prevent relapse and crisis situations. It is therefore necessary to develop cheap, convenient and accessible monitoring systems that could be used outside clinical setting. Most mental health diseases demonstrate a range of physical and behavioral symptoms (e.g.
This project/research is based on creating AI models that assist in determining blood glucose levels in individuals that have been diagnosed with Diabetes and classify changes in their risk-levels over a period of time. This research will be carried out by Ian Ho, student at Queen’s University pursuing his Bachelor’s in Applied Science – BASc, Applied Mathematics and Computer Engineering. He will research and develop predictive algorithms and analytical models for early diagnosis and risk assessment for Diabetes, under the supervision and advisory of QMind and Dr.
Artificial Intelligence (AI) is transforming our lives in the same way as the advent of the Internet and cellular phones has done. However, it takes thousands of CPUs and GPUs, and many weeks to train the neural networks in AI hardware. Traditional CPUs, GPUs, and brain-inspired electronics will not be powerful enough to train the neural networks of the near future. To radically impact the next generation of AI hardware, I propose to develop a fundamental technology: a photonic cognitive processor that uses light (instead of electrons).
The rate of discovery of new, large mineral deposits has slowed, yet significant opportunity exists in many world-class belts where post-mineral cover obscures bedrock and can potentially hide world-class deposits beyond the reach of traditional geochemical tools. However, locating mineral deposits in areas of thick or transported overburden is challenging.
The proposed research project has two aims: 1) to understand the experiences of Canadian Military Veterans with lower extremity pain when participating in aquatic therapy; 2) To determine the effectiveness of aquatic therapy versus land-based therapy on Canadian military veterans with chronic lower-extremity pain.
Gallium Nitride (GaN) semiconductors are more and more being used in switching power devices and the GaN transistors are the promising candidate of next-generation power devices that can substitute Silicon (Si) devices.
However, this young technology suffers from reliability difficulties. The aim of this research work is to contribute to the understanding of the properties of GaN devices. These studies give an understanding of the more complex dynamic instability and static reliability issues of GaN devices which helps manufactures to improve the reliability of GaN transistors.
The proposed research program aims to better enable the provision of digitally based self-management support programs for Canadians who live with chronic conditions that were further complicated by COVID-19. In order to accomplish this objective, this study will evaluate existing self-management programs for adults with chronic conditions and curate condition specific evidence-based content that will be provided through the Curatio platform.