The proposed research project is focused on customizable medical tattoo electrodes used on patients in different clinical settings. These electrodes are placed in specific places on the body and are connected to medical signal acquisition systems to collect different medical signals. The signals can include ECG (heart) signals and EMG (muscle) signals, and these signals can be used to diagnose or simply monitor the respective systems (cardiac and skeletomuscular) in the body. This project is part of the Lab2Market program.
Machine learning applications in healthcare have shown excellent inroads in medical imaging sciences in recent years. Our research aims to improve upon and open up doors into several different pathology diagnosis applications using artificial intelligence. Contemporary research into some of these applications has shown better diagnostic capacity than expert-level clinicians. Our research into artificial intelligence applications in healthcare include autonomous polyp and bone metastasis pixel-level detection, along with pathology detection in chest x-rays without explicitly labeled data.
As brain computer interface (BCI) is an emerging technology, this project attempts to understand the functionality of this technology from the user's perspective. To achieve this goal, a BCI-based application is developed, and users' ideas are collected from pre- and post-experience surveys. The previous study dealt with functionality of BCI in technical level and this project is more about the user experience.
In the past decade, deep learning models have demonstrated their highest performance for a variety of tasks. These models outperformed classical machine learning models and even humans in terms of performance and accuracy. However, previous research indicated that these models are vulnerable to out-of-distribution and adversarial inputs. Ideally, these inputs should be rejected by the deep learning model, but the deep learning model generates confident outcomes for it.
Tiresias is a client private solution to malware protection and threat intelligence. Tiresias allows a user to put all their incoming files in a cryptographically secure Data Chest locally. After sending the Data Chest to our cloud environment, our AI scans and infers if it is malicious without seeing the actual file content from the Data Chest. This method protects the client data privacy and confidentiality. The Data Chest is a novel research outcome at the Queen’s School of Computing.
People are switching from traditional shopping to internet commerce as Internet access increases quickly. So nowadays people are becoming more dependent on e-commerce-based websites. On the other hand, instead of robbing businesses like banks and stores, modern thieves now use the anonymous internet architecture to track down their victims online. Hackers are employing new strategies, such as phishing, to deceive their victims by creating fake websites to collect sensitive data, such as account numbers, usernames, and passwords.
Age-verification mandatory procedure for delivering certain services and products. Traditionally, identification documents have been a common mechanism of age-verification. However, this current strategy is subject to certain risks regarding privacy protection and online forgery. This demonstrates the value in anonymous age verification schemes using biometrics. Considering its age-dependent attributes, Electrocardiogram (ECG) is a potential solution. Preliminary experiments have been conducted regarding age estimation- classification from ECG obtained in clinical settings.
The aim of this project is to develop an application that can proactively protect users from identity theft and create awareness around safe digital practices. For this, we will be developing novel ways of extracting utility out of data and helping users to maintain a least-risk profile score. Most of the computations will be done on edge devices and computations will be prioritized based on value extractions. This research will help to bridge the gap between the accuracy and efficiency of the models to preserve privacy.
With millions of lithium batteries in the marked and billions yet to be, the question is can we produce a battery that can last 100 years, cannot catch fire, work in freezing conditions, charge 10 times faster and yet made from ecofriendly materials like graphite, rubber, wallpaper paste and paper? The answer is yes. Our goal is to attempt to build this novel eco-friendly battery and benchmark it with its equivalent traditionally produced counterpart. We anticipate the ecofriendly battery to perform on par but at a much lower cost of production.
In this project, we are trying to expose our existing core system functionalities to a public user interface. We will design, implement and test a research prototype user interface that can provide simple and flexible content sharing over a web-based user interface (UI) for organizations such as healthcare or surveillance systems that require a mechanism for (patrial-)sharing content between users in a simple way while providing a chain of custody.