The popularity of autonomous survey vessels (ASVs) are rapidly increasing due to their low-cost, efficiency, ease of operations, and safety benefits. These systems are applied to ocean mapping survey operations that would have traditionally been completed by small, crewed vessels using either single beam or multibeam echosounder systems. To maintain the simplicity of the ASV, the remote-controlled propulsion system is often separate from the onboard sonar and positioning hardware system.
As financial networks grow limiting the spread of illicit funds becomes an increasingly important aspect of security. We model this spread within a network-like structure called a graph and examine how best to defend against the spread of these illicit funds. This problem has been examined in the past and we further that knowledge by restricting the defender’s ability to defend the network, thus allowing our models to behave more similarly to the real world in certain applications.
An advanced video encoder (e.g., HEVC, AVC), has many encoding configuration parameters. Encoding “presets” set the values of certain codec parameters and thus facilitated configuring the encoder. The goal of this project is to develop a computationally efficient learning-based approach to make a run-time decision on the encoder’s optimal preset configuration to achieve the best quality for a given bit rate.
Transportation services, ranging from public buses to private logistics fleets, could benefit significant from the introduction of Rogers’ 5G wireless networks. This project has a primary objective to support UBC’s efforts to achieve its 2050 emissions reductions goals, with an emphasis on a reduction of GHG and air pollutant emissions from transportation services.
The field of responsible investing is rapidly expanding, with even greater attention on the importance of responsible investment with each passing year, as seen most recently in the aftermath of the impactful 2021 COP26 summit, where responsible investment was key focal point. Directing our financial resources in a sustainable direction has the potential to have a massive impact on helping us meet the Sustainable Development Goals set forth by the UN.
In land-based aquaculture being able to estimate the mass of a fish is critically important in monitoring their health as they move through their lifecycle as well as knowing when to harvest. Current methods are invasive to the fish in its environment and result in mortality during sampling. We aim to use two cameras placed closely together underwater to take simultaneous pictures of fish and enable the estimation of their biomass.
The intern will research quantitative techniques to identify biases that influence traders’ performance. By developing software tools that utilize statistical as well as machine learning methods in order to identify these biases and integrate into a product it will enable traders and financial managers to get feedback on how they can avoid these biases and improve their performance in the future.
Six million people in rural and Indigenous communities in Canada face water insecurity and associated risks to ecosystem functioning and biodiversity loss. Many of these communities could benefit from the implementation of nature-based solutions (NBCS). One challenge in the widespread implementation of these solutions is appropriate knowledge-sharing pathways, particularly in rural and Indigenous communities. This project will use the principles of reconciliation to develop strategies for knowledge dissemination in local communities.
This research will determine which combinations of metrics sets and machine learning algorithms
provide the most accurate outcomes when analyzing the data produced in the CSE-IDC-IDS2018 dataset.
Conversational artificial intelligence (AI) chatbots, also known as Virtual Assistants (VAs), combine the power of machine learning (ML) and natural language processing (NLP) to understand the intent of a user's utterance, and to formulate a useful, human-like response.
