Projets novateurs réalisés

Développement d’un système de détection automatique des erreurs de parallaxe dans les modèles stéréoscopiques

Le projet proposé vise à développer un système de détection des erreurs de parallaxe présentes sur les modèles stéréoscopiques par intelligence artificielle, et plus particulièrement par l’utilisation des réseaux de neurones convolutifs. Ces derniers sont reconnus pour leur performance en vision par ordinateur, notamment dans la détection automatisée d’éléments fournis à travers des exemples d’entrainement. Le système de détection proposé sera composé d’une architecture principale pour répondre aux problématiques rencontrées par la compagnie dans le processus création des modèles stéréoscopiques : la présence d’une erreur de parallaxe. Les algorithmes développés seront intégrés dans un système opérationnel qui permettra d’automatiser une bonne partie de la phase d’analyse et ainsi de réduire le temps et les coûts de cette opération pour la compagnie.

Design and fabrication of hybridized nanophotocatalyst for waste water treatment using natural sunlight.

Sustainable method of treating waste water is one of the major issues across the world. Different techniques have been developed by various scientific communities but each possess some limitations. So, keeping in the mind of these major limitations such as high cost , external power source requirement, huge setups , non recyclable etc, design of novel solar light driven nanomaterials are proposed. Since, the use of solar energy is only ~5% in the water industry which needs to be further extend more by using solar driven nanomaterial for water treatment. Thus, our proposed research direct towards to harvest full solar spectrum (UV-Vis-NIR). Hence, the idea for developing novel hybrid nanophotocatalyst for getting the desired properties in a single composite WS2/hexa-WO3/g-C3N4 are proposed. Here, WS2 acts as light harvester, g-C3N4 provides the high conductive surface, wide space tunnelling structure that allows fast mobility of charge carriers will be provided by hexa-WO3.

Expected Outcome:
1. High efficiency in the waste water treatment.
2. Effective utilisation of solar light.
3. Promoting sustainable energy.

Towards Developing an Artificial Intelligence-based System for Detection of Cyber Attacks in Modern Industrial Control Systems

Modern Industrial Control Systems (ICS) are increasingly getting connected to the Internet to facilitate operations. To ensure safety on the internet, the ICS communications are being encrypted. This poses a challenge for the traditional Intrusion Detection Systems that used to rely on visible messages and control data communication for detecting the presence of known attacks or anomalies in system behavior. In this work, we aim to develop an AI-based system for intrusion detection in a modern ICS by modeling the encrypted network communications across an ICS. This is particularly challenging due to the following reasons: (1) a modern ICS is a multi-vendor system, consisting of devices with proprietary hardware and software; (2) collecting and analyzing the traffic for time-sensitive applications becomes difficult due to the geographically distributed nature of modern ICS networks; and, (3) the ICS components use proprietary communication protocols and encryption algorithms. Designing an IDS that scales across all these factors is a complex data modeling problem. The expected outcome of this project is an AI-based framework for analysis of encrypted ICS communications for early intrusion detection.

Caractérisation fonctionnelle et valorisation des peptides de défense des arbres de la famille des Salicacées

L’agriculture moderne recherche des solutions pour diminuer l’utilisation de pesticides chimiques. Dans ce contexte, les acteurs de la recherche et de l’innovation ont un rôle à jouer pour développer des pesticides non-chimiques : les biopesticides. Le projet s’inscrit dans ce contexte, en caractérisant une famille de peptides antimicrobiens (appelés RISPs – Rust Induced Secreted Protein) des arbres de la famille des Salicacées (peuplier et saule) et en évaluant la valorisation de ces peptides comme biopesticides. Les peptides RISPs présentent une activité antimicrobienne ciblée envers les champignons de type Pucciniales, principaux agents des maladies des rouilles des cultures, et une activité élicitrice du système immunitaire de la plante. RISP est le premier exemple de peptide de défense multifonctionnel chez les plantes. La caractérisation de la famille RISP représente à la fois une opportunité de recherche scientifique fondamentale – celle d’effectuer un apport conceptuel majeur en biologie – ainsi qu’une opportunité de démarche appliquée – celle de développer des biopesticides à partir de certains membres de la famille.

Using Artificial Intelligence to Characterize the Dynamics of Seismic Risk

The Pacific Northwest has the potential to experience large earthquakes (magnitude ~9) along the Cascadia Subduction Zone (CSZ) fault, which is located approximately 120 km off the city of Vancouver. The last CSZ earthquake occurred in 1700, and it is expected to happen every 500 years. An occurrence of this event could lead to large economic and social consequences, which raises an importance of seismic risk assessment in this region. However, seismic risk is subject to change over time due to variations in the built environment (e.g., aging infrastructure) and population shifts. The Covid-19 pandemic is an ongoing issue in British Columbia (and the world) that has changed population distribution dramatically across the region. Under such circumstances, our understanding of seismic risk and its consequences might be substantially different from what is anticipated per the existing risk models In Canada. This research aims to utilize Artificial Intelligence (AI) methods to characterize the dynamics of risk associated with changes in population distribution due to pandemics, which will provide a dynamic setup to continuously evaluate seismic risk. This framework will also serve as an adaptive decision-making tool to evaluate policy measures and their implications on community resilience.

Low cost Microchip for Point of Care high Sensitive Assays

We have developed microfluidic polymer chips, and their associated reader for rapid differential diagnosis of disease states2. Each polymer chip is 2?2 cm2, and needs only 10 ?l of whole blood sample of the patient for diagnoses. The turnaround time from introducing the blood sample to get the results is 15-30 min. The chip contains all the reagents, and thus it is disposable and can be used in doctors’ office/emergency rooms of the hospitals without any need for central laboratory equipment. The device is also fairly sensitive and we wish to extend the limit of detection for measuring disease biomarkers in blood down to femtogram/ml.

Applications of Wearable Data and AI to Augment the i-Share Platform

i-Share is a project being conducted by the Université de Bordeaux. It is one of the largest mental health studies in the world, with the goal of collecting self-report information on mental health (e.g., stress, sleep, physical exercise, depression) from over 20,000 students from French universities.
Currently, students have access to personal devices, such as smartphones and smartwatches, that continuously collect health data in real-time (e.g., physical activity, sleep, heart rate). These objective data can be integrated with i-Share’s previously existing information and allow researchers to better understand and improve mental health of students.
The goal of this project is to explore the integration of sensors embedded in smart technologies with i-Share. More specifically, we will explore the integration of data from Apple Health (Apple’s health data repository) into i-Share’s capabilities. We will also develop Artificial Intelligence algorithms in order to generate insight into these datasets.

Ensemble Application of Symbolic and Subsymbolic AI for Sentiment Analysis

Deep learning has unlocked new paths towards the emulation of the peculiarly-human capability of learning from examples. While this kind of bottom-up learning works well for tasks such as image classification or object detection, it is not as effective when it comes to natural language processing. Communication is much more than learning a sequence of letters and words: it requires a basic understanding of the world and social norms, cultural awareness, commonsense knowledge, etc.; all things that we mostly learn in a top-down manner. In this project we will integrate top-down and bottom-up learning via an ensemble of symbolic and subsymbolic AI tools, which we will apply to the interesting problem of polarity detection from text. In particular, we will integrate logical reasoning within deep learning architectures to build a commonsense knowledge base for sentiment analysis.

Feasibility of physiological assessment for objective confirmation of non-invasive electrical recruitment of the saphenous nerve

Overactive bladder, urinary urgency, affects 14-18% of the Canadian population and costs our health care system over $350,000,000 annually. Most current treatments require ongoing in-person support or have low adherence rates, enhancing greater strain on our healthcare system and economy. Non-invasive saphenous nerve stimulation (nSNS) overcomes these issues however there is currently no objective method of confirming whether patients can activate the saphenous nerve during each treatment session. This research project will investigate the feasibility of measuring electrically evoked neural activity generated by nSNS. . We hypothesize that nSNS in humans can be quantitatively measured in a non-invasive manner. This will be the first-ever human feasibility study aimed at using electrically evoked neural signals to potentially screen OAB patients that can benefit from nSNS therapy.

Microbial Biocontrol agents as source of Plant Biostimulants

Plant biostimulants are substance(s) and/or micro-organisms whose function when applied to plants or the rhizosphere is to stimulate natural processes to enhance/benefit nutrient uptake, nutrient efficiency, tolerance to abiotic stresses, and crop quality (EBIC). A number of bacterial and fungal biocontrol agents that are used for the management of plant diseases and insect pest are known secrete compounds that exhibit plant biostimulant properties. In this project we propose to develop plant biostimulants from metabolites secreted by unique strains of fungal and bacterial biocontrol agents. These microbe plant biostimulants will be an important crop input to mitigate abiotic stresses caused due to climate change and to reduce chemical inputs like fertilizer and pesticides in agriculture thus promoting sustainability of agriculture.

Exploring the utility of digital voice assistants for spatial navigation, in enhancing human cognition

In today’s technology-centred world, we have become increasingly reliant upon artificial intelligence (AI) devices and software. To aid with navigation, many people use SIRI or other digital voice assistants to help them navigate from point A to point B. While successful in helping humans arrive at intended locations, there is a downside. Recent studies have shown that human cognition, specifically spatial memory, suffers when we offload basic human skills to an AI device. The goal of this proposal is to measure route knowledge and navigation ability following a variety of ways of delivering directions to humans, that rely differentially on AI devices.
Recent work in human cognitive sciences suggests that memory is enhanced when people actively explore routes, within virtual reality, compared to when they are simply passively guided to a destination. In this current project, we have two aims: 1) We will design and conduct a study comparing the relative benefits and costs of having a voice assistant provide navigation and directions to humans, to without the help of an AI device. 2) We will then implement and evaluate an alternative means for how AI apps can provide such instructions

Characterization of thermophilic ?-Mannanase of Thermomyces lanuginosus for production of health promoting prebiotics from agro-waste products.

This research proposal deals with characterization of thermophilic ?-Mannanase of Thermomyces lanuginosus and using this enzyme for hydrolysis of natural mannan to produce prebiotic MOS (Mannooligosaccharides). Alternative routes for the utilization of agricultural by-products are of interest because the economic value of these by-products as animal feed compounds is decreasing. MOS are non-digestible sugar oligomers made up of mannose units. The growing commercial importance of these non-digestible oligosaccharides is based on their beneficial health properties, particularly the prebiotic activity. The high cost of MOS, is a big hurdle in incorporating them into food products and this can be prevented by some efficient ?-Mannanase and economical substrates. The agro-waste products rich in mannan content can be utilized for producing mannan and, its further hydrolysis with a potent ?-mannanase will generate health promoting mannooligosaccharides. The exploitation of agro-waste as a source of mannan will not only control the issue of waste disposal but also promote the generation of the value-added products. Thermophiles producing thermophilic enzymes are always a considerable product of interest as biocatalysts for large-scale applications. Mannooligosaccharides shows tremendous potential as prebiotics by ameliorating the beneficial gut microflora and thus, can be applied as functional food ingredients.