Designing a Driving Simulator in an Immersive Virtual Reality Environment as an Engaging Driving Game for Older Adults

In this project a driving simulator in virtual reality will be designed and developed, in which a user can drive a virtual vehicle in a country road with incoming cars and traffic lights and possibly some animals crossing the road. The users will learn the path to reach a destination through the trial and then they are supposed to drive the virtual vehicle in the same pathway and by doing so, strengthen their spatial navigation skills. The game will be played by a physical steering wheel and two pedals for acceleration and brake like a real car.

A Web-Based Traffic Steering and Orchestration Platform for NFV Services

Traditionally a Service Function Chain (SFC) consists of a set of dedicated network service boxes such as firewall, load balancers, and application delivery controllers that are concatenated together to support a specific service. With a new service, new devices must be installed and interconnected in certain order. This can be a very complex, time-consuming, and error-prone process, requiring careful planning of topology changes and network outages and incurring high OPEX.

A generic microgrid controller with rule-based dispatch

In order to provide more reliable electricity, facilitate clean enery integration and supply energy to remote communitites, part of the power grid may be required to operate autonomously. Microgrids which can be islanded from teh main grid, are deployed for this purpose. However, the compositions and objectives of microgrids vary in different applications and operating modes. This project aims to design a generic microgrid controller with modular rule-based dispatch to address these challenges.

Unsupervised dimension reduction for data clustering and improving signal-to-noise-ratio

Recently, machine learning has been used in every possible field to leverage its amazing power. In this project we employ and advance machine learning algorithms for analysis of networks log data due to extraction of informative features. These data, which are recorded every millisecond, are usually high dimensional and imbalanced where no class label is assigned to them. We propose to realize data analysis through simultaneous performing of dimensionality reduction and data clustering incorporating local characteristics of the sample space to handle data imbalancity and variations.

High Power Density DC-DC Converter for Electric Vehicle Fast Charging Stations

Limited driving range coupled with limited availability of fast charging facilities is a major obstacle for electric vehicle (EV) adoption. As battery technology improves for EVs to have fast charging capabilities, the demand for EV fast charging facilities is also increasing. However, fast charging stations do impose challenges to the electricity grid due to high power demand and supplying such power is not even possible at locations with low short circuit power. Therefore, fast charging stations with an intermediate buffer energy storage system (ESS) are considered in this project.

Shipping Noise Characterization in Shallow Water Environment

Underwater acoustic propagation modeling was largely advanced by the world’s Navies from WWII until the early 2000’s. Growing evidence of the effects of sounds from human activities on marine life has made propagation modeling relevant to a much broader community including marine biologists, ecologists, regulators and environmental non-governmental organizations.

Cryptographic filesystem for video integrity

When storing and retrieving large quantities of aerial surveillance video to be used as evidence, it must be possible to validate video as authentic without relying on secret knowledge. Part of the solution to this problem involves a novel combination of cryptography techniques used in blockchains and elsewhere together with computer filesystems, allowing data to be stored in a way that can be easily authenticated.

Cognitive and Computationally Intelligent Algorithms for the Detection of Cyber Threats

The rapid and widespread advancement of cyber-threats within the past few years has had a profound impact on virtually everyone, from ordinary people to governments to local organizations. This has caused cyber-security to be considered a global challenge, which is now requiring innovative solutions, such as incorporating human cognition based methods into the software algorithms to detect malicious activities of adversaries.

Efficacy of Radiant Energy Veils in Multi-glazing Fenestration

Today buildings, which account for 40% of the total global energy consumption, constitute large glazing surfaces given that windows provide the necessary spacious feel and direct daylighting for occupants. However, 60% of the heat loss through its exterior surface is attributable to glazed surfaces (windows). Therefore, it is not surprising that advanced fenestration products have enormous potential to realize large energy savings and contribute toward the vision of net/near-zero energy buildings.

Detecting deception, disinformation, and crowd manipulation on social media through machine learning, natural language processing and artificial intelligence

Recently, due to the widespread effects of “fake news”, a form of propaganda that is intentionally designed to mislead the reader, there has been a significant research effort to automate the process of detection of misinformation in social media. Although existing methods for automatic fake news detection are promising, distinguishing between true and false news is a hard task even for a human, and there is considerable scope for performance improvement.