Adaptive radiotherapy (ART) consists of adjusting irradiation at each treatment phase in response to changes in the patient’s body (such as weight loss) or from the patient’s change in position. Indeed, since initial dose plans are determined from standard CT, the initial dose distribution may vary and yield sub-optimal dose delivery. Intra-procedural imaging such as cone-beam CT (CBCT) can be used to adapt plans on a daily basis, but requires real-time performance with the patient on the table.
The latest cybersecurity incidents (e.g., Desjardinds, CapitalOne) have shown that the current cybersecurity solutions are not always effective in capturing and preventing attacks, especially when it comes to insider attacks that originate from within the targeted organization. In this project which will be conducted in collaboration with Xpertics, we aim to build a new solution for extracting and analyzing users’ access and entitlement data within a cloud environment, in order to detect suspicious and abnormal access activities and permissions.
The ability of the health system to manage a massive influx of patients is based on the combination of four factors: the personnel, the equipment, the physical spaces and the system in place. A combination better known in jargon as the 4 "S" (staff, stuff, structure / space, system). A fifth factor that is often misunderstood is synchronicity.
Blockchain is a well-known immutable, transparent and anti-counterfeit platform. As such it can be appropriate for submitting certificates for validation and safe-keeping. However, because of its requirements for complex and heavy computations for enhanced security and verification of each submission in the network, in this proposal, we have decided to conduct an experimental study to firstly evaluate several possible Blockchain candidate technologies to determine the ones that are the most cost-efficient and have a better performance for our case study.
With this project, Nuvoola will propose a whole new process of detection, identification and monitoring of people in its entirety from embedded algorithms within an "EDGE" computer system, with a view to performance, and reliability and precision.
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.
The proposed project proposes design solutions for a Multi-camera vehicle identification, tracking and geo-localization system. The systems is constrained to use video streams from inexpensive surveillance cameras, and leverages image recognition software trained to detect and track vehicles in a controlled area. This will allow apps to pinpoint vehicles easily and reliably. Such technology works well in controlled environments but faces serious challenges when deployed in the real world.
The result of this project (which will be demonstrated by a use case) can make health equipments to be used outside of hospitals. This is achieved by reducing the computation cost of running Deep Learning models by 3rd party tools and use our accelerator solution to run the size reduced and optimized model. This greatly helps to lower the barrier for using costly equipments and make them more affordable and reachable to people in need of these equipments.
L’entreprise Rheinmetall Canada développe un véhicule militaire autonome qui a la faculté de se déplacer dans des environnements jugés dangereux et accidentés, sans l’intervention d’un pilote. Ce véhicule peut accomplir des missions telles que la protection des soldats et le ravitaillement des troupes.
