The population age distribution is undergoing an “inversion” and the dependency ratio, i.e., the proportion of older adults who are not working over the number of adults who are working, is increasing. This has an effect on a society’s ability to deliver community-care services and the underlying national economic capacity to pay for their needs. Advances in ICT (information and communications technologies) promise to provide support for affordable systems of care that enable human resources to be used more effectively.

Millions of people post information on social media sites about their interests, preferences, opinions etc. on a daily basis. LeadSift mines this data stream in real-time to generate incredibly accurate and targeted sales leads. Given the short text and ambiguity around a social post, it gets very difficult to accurately identify intent. This project will explore different Natural Language Processing techniques to analyze the inherent semantic structure of social posts. Due the real-time nature of the social data, the algorithms need to be extremely efficient and scalable.

The main objective of the project is to develop a software system to expertly capture an electronic health profile of a client with physical and/or cognitive disabilities to determine their level of functional ability, mobility and cognitive ability. Using the profile, the system will expertly assess the client’s ability to live either independently or with the assistance of a caregiver, and recommend the necessary changes. The proposed system requires integration of multiple software modules, such as from Functional Independence Measure (FIM), DGIClinical, VIDex, Invacare, etc.

Operating Centre control rooms are large rooms where trained operators remotely supervise data centre equipment. Operators aim to maintain the connectivity and computer services modern society relies on. They provide services to their organizations or government or industry clients including forming and monitoring tactical teams that solve time-critical computing issues. Their daily activities include monitoring and responding to alerts, while simultaneously participating in multiple distributed teams where the role of the operator is to ensure progress towards a problem's resolution.

Enormous quantities of machine-readable data, measured in many terabytes, are readily available. The sources include the world-wide web, publicly available databases, raw experimental data, unannotated genomic sequences generated by biochemical tools, and so on. However, data does not necessarily equal useful information. More often than not, the data has to be intelligently processed, interpreted, transformed, and then integrated into unified repositories.

Cloud computing is one of the pillars of the modern computing infrastructure mainly because it allows the procurement of computing services on a pay-as-you-go basis. However, despite the many benefits offered by cloud computing, it has several significant drawbacks such as data lock-in, lack of universal geographic proximity, risk of service outages, and variable cost structures.

When a user makes an input to a computer, there is a necessary delay before the computer is able to respond. For mouse-based computers, responding within 100ms was sufficient to appear ‘immediate’. In touch-based systems, latency is more apparent, represented as a physical separation between the finger and the object moved onscreen. Technologies capable of immediate (1ms) reporting of input to the computer are being developed by Tactual Labs. Such sensors hold the key to the elimination of lag in the user interface.

We propose to tackle a challenging problem in computer graphics called 'skinning'. A skinning technique consists in animating the skin of a 3D virtual character. In our context we seek a method specifically targeted to computer games. Therefore our goal is to develop a skinning method as visually plausible as possible with real-time performance. We recently introduced a method called implicit skinning, this technique produces the effect of skin contact and fold at joints in real-time.

In recent years, machining with robots has become a trend in the manufacturing industry. The concept offers an economical solution for medium to low accuracy machining applications. However, due to the complexity of the robot kinematics, planning for these paths is challenging. Jabez Technologies has developed a semi-graphical approach that can program large robot-paths. This approach has been very well received by the industry and has proven to be extremely robust in practice. However, this approach is semi-automatic and cannot work without user input.

This project uses machine learning algorithms to better understand back movement and low back pain. We apply supervised learning time series algorithms to data collected from Backtracks’ wearable de-vice — which consists of a malleable think curve that reads data collected from the participants’ spine movements. At each time step, such movements are represented as a curve; the dynamic evolution of this curve in time represents an individual’s spinal movements.