Innovative Projects Realized

Modern Plug-and-Play Image Priors for RAW Image Enhancement

Modern mobile phones have become the dominant photography device in recent years. However, due to form factor constraints both the camera sensor and lens have to remain compact, which in turn has a negative effect on the resulting image quality such as decreased resolution and noise. Image restoration attempts to recover a clean latent image from degraded input image(s) in a computational manner. To yield a natural image, one must impose additional constraints on the resulting optimization problem. In this project we explore some modern techniques for imposing such constraints, such as diffusion models and implicit image representation. We hope that this will not only result in an improved image quality but an easy plug and play tool to improve quality for any future imaging devices and methods. This is of major interest to Samsung as one of the leaders in the mobile phone industry.

Optimizing the Digital Conveyancing Software

Specialized legal technology aims to improve productivity by speeding up routine tasks. Unfortunately, current legal software suffers from unstable performance, steep learning curves, and a lack of user-friendliness. Innobridge challenges these issues with Smart Estate, a no-frills but smart application for legal conveyancing. This is the processing of property transactions, which requires the completion of many forms. Smart Estate automates these forms and communications, saving time and efforts for lawyers, while keeping costs low. Innobridge’s goal is to work closely with lawyers and researchers to realize this “digital conveyancer” concept. The research team will carefully learn the entire conveyancing process. By finding hidden opportunities to enhance the workflow, researchers will help Innobridge to advance the digital conveyancer software. Successful development of this project will drive Innobridge to the frontier of legal technology innovation in Canada.

Demonstration of TCAD simulations of exchange coupling in silicon double quantum dots

Nanoacademic has recently released a commercial computer-aided design software package called QTCAD for quantum hardware based on spin qubits in semiconductors, one of the leading candidates for the implementation of a quantum computer. While several demonstrations of the tool in realistic operation conditions have already been completed with academic and industrial partners, these demonstrations were limited to the case of a single qubit. In this project, the intern will generalize these demonstrations to the two-qubit case by calculating the exchange interaction in a double quantum dot. The exchange interaction determines the speed at which two-qubit quantum logic gates can be implemented in spin qubits. By completing this demonstration and writing a corresponding tutorial, the partner organization will be able to provide its customers from academic, government, and industrial research labs with an additional simulation feature which is required for the design of large-scale quantum computers.

3D printing of chiral gold nanoparticles

This project involves the 3D printing of amino-acid and peptide-directed synthesis of chiral plasmonic gold nanoparticles. The
chirality of these nanoparticles is important due to their exceptional light polarization control, negative refractive index and chiral
sensing. Previous research on this topic look at the synthesis of these nanoparticles using amino acids and peptides which can
control the optical activity, handedness and chiral plasmonic resonance of the nanoparticles. Due to the polarization potential of
these nanoparticles, there has been much research that looks at the colour filter and display technologies that can be used to
replace traditional dye and pigment systems.

Statistical learning and its application in energy liability management

Develop a statistical model to predict the frequency and severity of contamination on oil and gas sites. Advanced statistical learning techniques will be employed to fully utilize the existing data and the data to be collected. The intern will work closely with colleagues in 360 to integrate domain knowledge into the model. The model will be used to generate cost estimates to remediate the contamination to ensure that oil and gas companies allocate sufficient funds to clean up their sites and reduce the number of Orphan wells across Western Canada. The project will benefit the partner organization 360 by bringing enhanced ability to predict the budget required for the oil and gas companies.

Exploring the Uptake and Impact of Using the LEADS in a Caring Environment Frameworkto Enhance Leadership Capacity in Health Systems

The health system in Canada is increasingly recognized as a complex adaptive environment requiring system wide perspectives and approaches for improving outcomes. The World Health Organization has identified leadership as a key building block of systems change, yet evaluating the value of investment in leadership development is challenging. LEADS is an evidence-based leadership development framework offered through the Canadian College of Health Leaders (CCHL), complemented by four 360 LEADS© self-assessment tools. Working with a team including one of the original developers, Fenwick Leadership Explorations, Dr. Vilches will use action research with key partners and feedback data from prior LEADS implementation, exploring how complexity in health care delivery systems impact and influence leadership development outcomes. The immediate outcome will be a better understanding of how to apply the broadly adopted LEADS framework, as well as a better understanding of leadership development in complex adaptive environments, specifically in the Canadian health system.

Design and characterization of novel chemosensors for the detection of biomarkers in biofluids

Development of cementitious materials with recycled combustion slag

Research on the Face Stability for Shield Tunnelling

The role of the cerebellum for feedback processing

Fiber optical sensors and modelling for lithium-ion batteries in the aspect of battery safety

Robotics and Deep Learning – How to Cooperate via Emergent Language