This research will focus on the design of 5G networks to provide for future wireless services include the use cases of Enhanced Mobile Broadband (eMBB), Massive Machine Type Communication (mMTC), and Ultra-reliable and Low Latency Communications (URLLC), and application area use cases such as Smart City, Smart Home/In-building, Augmented Reality, Self-Driving Cars, etc. 5G Technology has been standardized according to a broad framework in terms of the format of the transmitted wireless signals and the basic protocols including its compatibility with LTE networks.
A risk-based approach to anonymization includes an assessment of the risk that an attack to reveal or uncover personal information will be realized, known as threat modelling, against the risk that an attack on the data will be successful (e.g., a re-identification). We wish to incorporate the provable guarantees of differential privacy into this assessment of risk, to produce safe data in context of the environment in which it will be used. We also need adapt the methods of statistical disclosure control to such an updated approach.
As part of this proposal the intern will be working with DME to develop and examine the viability of data-driven point-of-care system for the assessment of suicide risk. DME is a Canadian start-up company in the business of developing cloud-based point-of-care monitoring systems for the management of psychiatric illnesses. DME has developed algorithms to diagnose and predict optimal treatment for major depression disorder and schizophrenia, and has been allowed patents describing its technology in Canada, the USA, and Australia. If found acceptable the algorithms
In the near future the way that we encrypt and authenticate information online may not be safe. For this reason, we need to create new tools that will enable secure communication for many coming years. The proposed research is to create such tools from a certain algebraic object called isogenies. These are functions that take one elliptic curve to another. Breaking isogeny-based encryption is thought to be difficult, and so we will be able to create other cryptographic tools from them besides encryption.
The main goal of this project is to develop machine learning and natural language processing approaches to help customers to communicate their preferred brands and/or retailers via Heyday solutions. These approaches will automate answers and help to humanely engage with customers. In order to reach these objectives, some challenges will be tackled such as automatically recognizing the users intent and replying to frequently asked questions. Recognizing ambiguous words is another challenging task to provide accurate answers.
The emerging edge computing (EC) paradigm promises to deliver superior user experience and enable a wide range of Internet of Things (IoT) applications by bringing storage and computing facilities closer to the end users. Virtualization technologies such as Software-Defined Networking (SDN) and Network Function Virtualization (NFV) will allow sellers and buyers to access the open EC ecosystem.
The project is to develop a middleware system for improving drinking water management system. The middleware integrates multiple data sources in addition to the real-time network data, including information of weather from satellite/ radar and water quality of surface water from remote sensing and then analyze them. It's smart algorithms will predict and prioritizes events depending on the severity of the problem.
Source code is what programmers write as instructions to the computer to execute to complete a desired task. All operating systems and applications on a computer or a mobile device is a runnable version of a compiled source code. Experienced programmers can easily browse and understand source code in different programming languages because they have the necessary technical background that is not available for every-day users.
Novel view synthesis is the process of generating new images from an unseen perspective, given at least one image of a scene. There may be more than one probable novel view associated with each unseen perspective, an assumption made by existing methods. This simplifying assumption prevents these methods from being applied to more difficult novel views where the set of probable novel views is highly varied. This project proposes to investigate a new approach to generate a wide variety novel views from a single image, and can produce multiple probable outputs.
The latest artificial intelligence (AI) technologies have effectively leveraged the wealth of data from cyber-physical systems (CPSs) to automate intelligent decisions. However, for safety-critical CPS like smart grids and smart cities, the conversion of massive data into actionable information by the AI must be not only effective but also reliable.
