Detection of financial fraud is a priority for financial institutions. There are a variety of techniques and models that can be used to address the problem of financial fraud. However, as fraudsters are becoming more inventive and adaptive, they have been able to penetrate the conventional protective methods. This is one of the main reasons for the growth in financial fraud activity, regardless of the efforts of financial institutions and government and law enforcement agencies.
As the 5G Network will be capable of being reconfigured and optimized on-the-fly, they will also be more automated, requiring less manual effort to provision resources and make the most efficient use of bandwidth. The Ciena Analytics as a Service is a suite of tools to assist network operator in pro-active discovery on their network operations. This project will look at integrating new advanced intrepretable Artificial Intelligence and Machine Learning techniques to tackle different challenging networking tasks (e.g. traffic prediction, anomaly detection, topology discovery, etc.).
The main goal of this project is to develop data-driven approaches to reduce energy consumption and cost when operating commercial building’s cooling systems. Indeed, according to recent studies the building sector is one of the largest energy-consuming entities (almost 40% of global energy consumption) and this consumption is predicted to increase by 50% by 2050. Thus, there is an urgent need to provide solutions to reduce energy consumption taking into account the importance to improve environmental sustainability and the increase of electricity prices.
With the increasing popularity of digital assets such as cryptocurrencies, many financial technology (FinTech) systems have become safety critical. However, current FinTech system development approaches often lack the rigorous safety practices found in the aerospace, nuclear, automotive, and military industries.
During interplanetary travels, space radiations are always crucial to spacecraft design due to their extreme hazards to human beings and electronics. Conventional materials, such as aluminum and other heavy metals, have been widely used on spacecraft. The cost of launching overweighed items to space is still high even reusable rockets are available now. Reducing weight of shielding structures on spacecraft will largely benefit future space missions. Nanocomposites with extreme low density but high radiation shielding properties are proposed in this project.
Reinforcement learning (RL) is the problem of designing an agent that interacts with its environment and adaptively improves its long-term performance. Many complex real-world industrial decision-making problems can be formulated as an RL problem. RL is at the core of artificial intelligence and has the potential of having a huge impact on our economy and society, perhaps more so than any other area of machine learning.
Deep learning in computer vision has set new standards in mobile and web-based applications. The power of learning-based computer vision has also tremendous potential in machine vision. Traditionally, machine vision in manufacturing employs analytic solutions often resulting in excellent accuracy but poor robustness. The goal of this project is to increase robustness of a vision-based measurement process in sheet metal manufacturing using deep learning.
The main objective of this project is to design and test millimeter-wave RF front end components for applications in 5G new radio communication systems utilizing the thin-film multilayer LTCC fabrication process developed at ACAMP. With these fabricated and characterized prototypes, ACAMP will be able to showcase its specialized LTCC fabrication process and provide support to potential Canadian technology clients and companies looking to develop technology for the upcoming 5G mobile communications market.
Information is everywhere, especially in the commercial vehicle industry. Vehicles may be classified by number of axles/tires. There are several text- and label-based classification systems: for dangerous goods transport (HAZMAT); vehicle safety code compliance (CVSA); and general identification and tracking (license plates, USDOT numbers). Employing humans to perform simple classification and recognition tasks can be impractical. However, explicitly programming these tasks can be challenging.
The current non-destructive and fast method of hyperspectral imaging technology for the chicken egg-related problems in agri-food processing suffers from the quality of acquired hyperspectral images. Therefore, the results of all existing attempts to deal with those egg-related problems (e.g., freshness, grading eggs, fertility detection and distinguishing abnormal eggs) can be improved since they are highly related to the quality of hyperspectral images taken over eggs.