Generation of a remote sensing methodology for the identification and variable chemical control of weeds with ground equipment in sugarcane

The proposed project intends to generate a method for identifying weeds and develop site-specific weed control prescriptions for sugarcane cultivations in Costa Rica using Remote Sensing techniques. Remotely sensed data will be captured using multispectral camera and LiDAR sensors attached to Remotely Piloted Aircraft Systems (RPAS). Images will be processed using machine learning algorithms to […]

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Advancing Generative Models for Vision and Language: A Collaborative Study with ServiceNow Research and ÉTS Montréal

This research project, a collaboration between ServiceNow and ÉTS Montréal, aims to improve generative AI (e.g. artificial intelligence models that learn to generate data), which can impact various creative and knowledge-based industries like graphic design, content creation, and research. The project aims to create advanced generative models that can generate a variety of data types, […]

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Development of AI-based decision system for facility safety using computer vision and non-destructive technology

This project is a collaboration between Korea’s Soongsil University NDT Lab and Canada’s Waterloo University Computer Vision for Smart Structure Lab (CViSS), aiming to promote new research at each institute and build smart construction and infrastructure monitoring. NDT Lab focuses on developing non-destructive engineering technologies for concrete internal crack width, reinforcing bar diameter and internal […]

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Development of an AI Assisted Denoising Technique for Optical Coherence Tomography Imaging for Breast Cancer Margin Assessment

Breast cancer is the most common cancer (excluding non-melanoma skin cancer) in 109 countries including Canada. Approximately 70% of the breast cancer surgeries are breast-conserving lumpectomy procedures. Histopathology analysis typically require 2-4 days, resulting in the need for a second surgery if a margin is positive. Perimeter Medical is developing advanced Optical Coherence Tomography (OCT) […]

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Development of simulation program for industry with AR/VR

There is a growing demand for training simulation programs using Augmented/Virtual Reality (AR/VR) in various industries such as nursing and construction worldwide due to safety and high cost issues. Additionally, the need for collaboration among workers within a country or across multiple countries for practical training and education is expanding in certain industries. However, there […]

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Amino Acid Electronic Structures and Interactions

Quantum chemistry techniques can only be used in small-scale systems and the structure of biomolecules like proteins cannot be effectively examined by them. So, it is essential to develop a workable solution that could be applied to larger systems. Finding such a solution will then enable us to investigate the structure of larger molecules like […]

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Towards AI Assisted Training Tool: Automated Identification of Calcifications in Optical Coherence Tomography Breast Tissue Images.

Breast cancer is the most common cancer (excluding non-melanoma skin cancer) in 109 countries including Canada. Approximately 70% of the breast cancer surgeries are breast-conserving lumpectomy procedures. Histopathology analysis typically require 2-4 days, resulting in the need for a second surgery if a margin is positive. Perimeter Medical is developing advanced Optical Coherence Tomography (OCT) […]

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Towards Fully Automated Tumor and Organ-at-Risk Detection and Segmentation from PSMA PET and SPECT Scans of Prostate Cancer Patients

Prostate cancer is the third deadliest cancer in men and early detection is crucial. PSMA is a protein that is highly present in prostate cells, making it a promising target for imaging and treatment. Total metabolic tumor volume (TMTV) is a measure of tumors’ characteristics, but it is currently not measured in clinical settings due […]

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Development of a distributed framework for deep learning models

Layer 6 powers the AI use cases for a variety of banking and financial applications at TD Bank. The goal of the research project is to improve the AI engine by having the training more efficient and distributed among a variety of clusters. The AI engine will allow models to be trained faster and with […]

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Out of Distribution Detection in Deep Generative Models

As generative models become increasingly prominent in machine learning, the need for accurately detecting out-of-distribution data has become crucial. The primary objective of this research is to develop an approach that can identify when the program encounters data that is vastly different from what it was trained on. In machine learning, programs may make errors […]

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