Innovative Projects Realized

Graphene Oxide Langmuir Blodgett (GO-LB) Functional Thin Films

The aim of this project is to utilize the Langmuir Blodgett (LB) deposition technique at the University of Waterloo to form uniform thin films layers of PAni nanocomposites layers. Successful production of thin film PAni nanofibers will allow research into laser writing and the fabrication and testing of simple proof-of-principle flexible sensor devices. This will facilitate the development of a direct patterning method for producing high-resolution features, that is both simple and inexpensive, and, realization of a new class of polymer printed devices and soft robotics.

Wireless charging based on capacitive-inductive resonance

This project is about a novel chip-based wireless power transfer system. Its integrated and flexible design eliminates the need for power cables for any DC device in homes, offices, parkades and power generating facilities. Unlike existing solutions, the system operates simultaneously with multi electricity requirement devices, has extended range, generates no heat, can transfer power through solid material, while increasing the effective utilization of harvested electricity. It does so at a lower cost per power density than its competitors.

Biosensor for Detecting Pharmaceutical Residues in Water

The intention of this research project is to detect pharmaceutical agents in water systems, specifically carbamazepine, ciprofloxacin, and diclofenac. These substances present an issue as they compromise the water quality and have low removal rates from conventional water treatments. The use of nanotechnology in biosensors holds the promise of high sensitivity and miniaturization. Here the detection of these agents is based on a single DNA biochip for the parallelized analysis of the mobility of DNA tethered particles. A drug molecule is grafted to the DNAs at a precise location and the recognition element is coupled to the particles. In a competition assay, the particles initially linked to the DNA are released after the binding of a free drug to the recognition element. The binding event leading to an increase of the amplitude of motion of the particle is monitored by videoimaging. The biosensor would be tested in a synthetic water solution similar to river water with varying concentrations of each drug. DNA molecule biosensors have a wide range of applications for future water monitoring, and is an area of research that must be further explored.

Ships arrival times prediction using deep learning

The objective of this research project is to use Deep Learning in predicting ships’ arrival times of a medium size multi-commodity bulk port. The outcome of this project will serve dynamically in updating berths schedules to minimize ports turnaround time and build models closer to reality. We will be using ships trajectories data or AIS data combined with some exogenous data such as weather data, physical structure of ships, Machine conditions, unforeseen events and seasonality. Also, we will focus more on the real time estimation of arrival times by building flexible model for short-term and long-term prediction horizons. A first objective will be to prepare properly the data that will be used in building the predictive model, as the problem may arise in real-time data management in terms of computing and storage resources. Also, the use of multiple data sources will require a specific treatment related to data handling and feature engineering. The second objective will be to build models based on Artificial Neural Networks, train them and select the best one. Then, the last objective will be to deploy the chosen model in order to make prediction on production data.

Mobile video stitching, navigation, sharing, and efficient shipping to the cloud

The project includes three subprojects that are all of interest to Nokia to inform them of

latest research in the area. The first subproject is to improve the features of the Ztitch

mobile application that we developed, which has been downloaded more than 80,000

times. We will implement some algorithms to improve the stitching performance, and

allow users to stitch multiple videos according to their camera locations and

orientations, so that users can navigate in the 3D scene, and share the results with

other users. The second subproject is to study how a complex computational

functionality, such as the video coding and the computation for Ztitch, can be efficient

and securely implemented with the assistance of remote cloud, given the limits of

computational power and the lack of available information on personal mobile devices

that users interact with. In the third subproject, we will conduct some preliminary

research and prototype development on several long-term topics.

Selection for antimicrobial resistance by plant protection products – analysis of established experimental field sites

Antibiotics are applied to agricultural plants and soils as plant protection products (PPPs) to combat bacterial disease. Their use in agriculture has the potential to cause development and spread of antibiotic resistance genes. Non antibiotic PPPs such as herbicides and insecticides are also applied to agricultural soils. There has been limited research into whether non antibiotic PPPs can cause co-selection for antibiotic resistance. Laboratory experiments and a variety of culture based and molecular microbiology methods will be used to determine if exposing soil bacterial communities to non-antibiotic PPPs results in increased levels of antibiotic resistance. Soil samples will be obtained from well-established experiment field plots, which are treated with PPPs annually. The findings from this novel research may be useful for influencing regulation of PPPs, food safety policy and human health.

Circulations impériales : les Indiens de Birmanie et d’Indochine française au travail, 1919-1940

Mes recherches de maîtrise se concentrent sur les migrations internes à l’Asie orientale dans l’entre-deux-guerres et plus particulièrement dans le contexte des grands empires coloniaux de la région. À partir d’une étude comparative, il serait question d’analyser les flux migratoires de l’Inde vers le Viêt Nam (Indochine française) et la Birmanie. En analysant le parcours de ces migrants indiens, dont le flux était essentiellement généré par leurs rôles de commerçants et de banquiers, au travers des discours des autorités coloniales véhiculés sur eux, il serait question de comparer leurs statuts dans ces deux colonies. En ce sens, cette recherche viserait à étayer l’hypothèse que, bien que moins nombreux en Indochine qu’en Birmanie, les migrants indiens y avaient un meilleur statut qu’au sein de l’Empire britannique, et ce, d’une part par l’accès éventuel et facilité à la citoyenneté française, d’autre part par le fait qu’on leur permettait plus facilement de s’y installer à long terme et donc de jouir d’une plus grande émancipation de la société de castes qui formait traditionnellement l’Inde.

Slope-failure mechanisms, catastrophic mass flow processes and public perception of risk in British Columbia and Southern Patagonia

Mountainous environments present a variety of risks to human populations in the surrounding communities. Landslides in mountainous regions are a particularly deadly form of natural hazard due to their unpredictability and potential scale, resulting in thousands of deaths every year. Due to current climate warming as a result of human actions, the risk of these natural disasters is increasing in both frequency and magnitude. The Mount Meager Massif presents a contemporary case study of how climate warming is resulting in increased landslides that have the capability to severely impact local communities. This research intends to use fieldwork to map and quantify this risk to local communities, as well as interviews with the first-nation communities in the area to determine their perception of risk in a warming climate. The outcomes of this research will be a chapter within my PhD thesis, and a research paper in an academic journal.

Generalization of chest x-ray disease prediction by learning feature representations agnostic to clinical domains

Chest radiography is a common and essential examination tool in medical practice for the diagnosis of lung diseases. Recent approaches in artificial intelligence have demonstrated that transfer learning of deep learning models was able to provide performance gains at the level of practicing radiologists. These techniques transfer the features learned on ImageNet to medical data through fine-tuning pre-trained deep convolutional networks. They have used public chest x-ray datasets with train and test inputs of the same distributions. Unfortunately, the performance of deep learning models degrades significantly on test data coming from a different domain. In clinical applications, there is a significant shift between train (source) and test (target) domains. Also, the target data are not always present during training. Building a diagnostic model transferable to different clinical domains and populations is still an open challenge. This project focuses on exploring novel techniques of transfer learning, explicitly domain generalization, and on developing a novel model capable of learning feature representations agnostic to domains shift. The originality of this work will allow the intern to submit a research paper at the end of the program. The company will then exploit the developed algorithms to validate the diagnostic model on two clinical sites.

Modeling semi-batch reactive crystallization processes for particle sizecontrol and process scale-up

Reactive crystallization is a process commonly used for the production of chemical compounds including pharmaceutical ingredients. In a stirred-tank setup, one reagent solution stream is injected into the reactor where it meets a sitting solution of the other reagent. Reaction occurs as soon as the reagents meet, forming an insoluble product which quickly precipitates in the form of crystal particles. Distribution of the crystal particle size is controlled by the coupling between fluid mixing, reaction, and crystal formation and growth, which is of vital importance to product quality control. Simphilia is a technical consulting company interested in strengthening its capability of modeling such processes and predicting their outcomes, in order to better help its clients to achieve consistent particle size control. The proposed work will contribute to this goal by developing a comprehensive model that combines first principles with a data-driven approach.

Machine learning model development for blast furnaces

Iron and Steel are the backbone of major industrial sectors, like automobile, building and construction, heavy machine manufacturing etc. and Blast Furnace (BF) Ironmaking is the chief production route of iron throughout the world. The objective of this project is to develop novel models for blast furnaces to enhance their productivity in a sustainable way with no further pollution related risks. Industrial data has been collected from different steel plants. This project concentrates on using Artificial Neural Network and Support Vector Machine techniques to develop a novel model keeping those data as supporting reference. The model will be unique since it will consider the effect of almost all process variables in BF ironmaking and will indicate optimum values of the variables to cost effectively achieve maximum productivity of the BF. The enhanced sustainable productivity will positively affect the afore-mentioned giant industrial sectors and thereby, the outcome of this project can play a pivotal role in Industry 4.0.

Development of Cost-Effective Self-Consolidating Concrete ContainingSilica Breccia

The proposed project will focus on the development of low-cost, self-consolidating concrete

(SCC) mixtures containing a high volume of silica breccia. The workability, fresh properties

and local unit cost of SCC containing 0-70% silica breccia (as a partial replacement for

cement) will be investigated and compared with the control mixture and with other SCC

mixtures containing fly ash, slag and metakaolin.