Metal-based direct energy deposition processes, such as robotic welding and laser powder fed additive manufacturing, ideally require feedback sensing of the deposition quality using vision detectors. Image processing algorithms are challenging to develop due to changing process operating conditions. Despite challenges, implementing in-process image processing algorithms is beneficial for traceability and quality assurance, for calibrating […]

Climate change, land development, invasive species, and other disturbances can alter the composition, structure, and functions of native vegetation across landscapes. These disturbances also impact insect parasitoids, which are a key, and often overlooked, component of biodiversity. By their ability to control other insect populations, they are integral for fostering resilient and functional forests. Understanding […]

Wetlands provide critical habitat and valuable ecosystem services. Land use conversion in Ontario, however, has led to substantial wetland loss. The restoration of wetlands on agricultural properties has the potential to offset wetland loss, yet these wetlands are also susceptible to contamination by pesticides. Our research will therefore establish: (1) to what degree restored wetlands […]

Machine learning (ML) has recently achieved impressive success in many applications. As ML starts to penetrate into safety-critical domains, security/robustness concerns on ML systems have received lots of attention lately. Very surprisingly, recent work has shown that current ML models are vulnerable to adversarial attacks, e.g. by perturbing the input slightly ML models can be […]

As a result of climate change and other pressures that result in “extreme events” like wildfire and flooding, many drinking water utilities are at risk of potentially catastrophic failure and need treatment adaptation strategies to prepare for increasingly variable and potentially rapid deterioration in source water quality. Currently there are no recognized tools for evaluating the anticipated impacts […]

Contemporary machine learning has been very successfully applied to processing static images and words in consumer applications, resulting in billions of dollars in recent acquisitions of machine learning companies by Microsoft, Amazon, Facebook, and Google. However, applications to dynamic information (e.g. movies, controlling robotics) has been less well-developed. In this project, will develop and apply […]

The research project will use a case study approach at the proposed Transit Hub for the City of Kitchener. In its objectives, the research will focus on the opportunities for a high quality green public space/square to better integrate a new LRT line and a new GO/VIA rail station into the surrounding city. It will […]

Recently, due to stringent emission regulations such as US EPA (Environmental Protection Agency) and CARB (California Air Resource Board), improvement in fuel economy and reduction in the exhaust gas emissions have become the two major challenges for engine manufacturers. To meet the new emission standards, new innovative technologies are needed to improve the performance of […]

A new aqueous rechargeable battery combining an intercalation cathode with a metal anode has been developed recently. The energy density for a prototype battery is comparable or superior to commercial 2 V rechargeable batteries. There is a need to further improve the cycle performance and to reduce self-discharge effects of this battery. In this proposed […]

Research into understanding and controlling microscopic quantum mechanical phenomena has led to revolutionary new quantum devices, including quantum sensors and actuators that have unprecedented levels of sensitivity, efficiency, and functionality for a wide variety of tasks. A particularly compelling example is high quality factor (high-Q) superconducting resonators for magnetic resonance. These new devices will be […]

The research will entail modeling of diesel engine emissions to correlate with varying states of diesel engine operations so as to determine normal operating parameters. Using machine learning techniques, develop methods to analyze, alert and report on abnormal operating conditions when the vehicle is monitored in real time. The research will provide an important first […]

This project is a collaboration between MMSENSE Technologies and the Centre for Intelligent Antenna and Radio Systems (CIARS) at the University of Waterloo to research, investigate, and design an integrated radar module at millimeter-waves. These sensors, which will be utilized in automotive applications such as forward collision alert, rear traffic crossing alert, blind spot detection, […]