Thematic Programme

In response to the Smart Nation programme, a SPIRIT (Smart Platform Infrastructure Research on Integrative Technology) Centre funded by NRF is being established in the College of Engineering to pursue translational research including fast solutioning and prototyping of applications for the national vision.

Led by Prof Lam Kwok Yan from the School of Computer Science and Engineering and in collaboration with PIs from the NTU Rapid-Rich Object Search (ROSE) Lab as well as the School of Electrical & Electronic Engineering, the SPIRIT Centre will serve as a one-stop-shop with range of engineering competencies including in systems research and artificial intelligence to develop a resilient and scalable smart platform infrastructure with video analytic capabilities and wireless sensor networks. 

For the start, the SPIRIT Infrastructure will focus on Improved Government Operations & Service Delivery; and Enhanced Mobility.  Progressively, the Centre will build up a partner eco-system to support its rapid prototyping efforts, by collaborating with other research labs, public sector agencies and industry partners to translate those prototypes into full-scale Smart Nation solutions.

 

Existing railway inspection technologies focus mainly on the mechanical and structural soundness of the rail network.

However, the train’s electrification system - the power delivery path from the substation to the electric drive on the train, is equally important. Any defects associated with the electrification system will lead to service interruptions.

With funding from NRF, Assoc Prof See Kye of the School of Electrical and Electronic Engineering and team are working on a novel patented inductive coupling technology that enables real-time condition monitoring of a train’s electrification system.

The technology requires no direct electrical contact to the high-voltage electrification system and therefore can be fixed on the train easily without major retrofitting work. Each in-service train equipped with this technology doubles up as an inspection vehicle, where real-time monitoring of the conditions of the train’s electrical drive, the third rail, the railway track and all associated electrical parts becomes possible. Any early signs of defects of the electrification system can be detected timely for immediate remedial actions so as to enhance service reliability.

A working prototype has been developed with promising trial results collected. Besides condition monitoring of railway system, the technology can be extended to critical high-voltage infrastructure, such as high-voltage transformers in power grid and process control equipment in petrochemical plant, thus minimising monetary loss to companies in event of breakdowns.

Insects are impressive natural flyers.  They fly with high agility and maneuverability by flapping their wings. Emulating their flight capability and flight mechanisms may provide a good start in the design of a micro air vehicle (MAV).

In this project led by Professor Lau Gih Keong from the School of Mechanical and Aerospace Engineering, wing flappers are designed and developed with reference to the blueprint of the flight thorax of insects. The developed wing flappers consist of a thoracic frame structure as a flapping mechanism and a vibration motor as a driver.  The bio-inspired thorax design is evaluated and its performances are compared with those of the flapping wing insects. The initial prototype demonstrates that the wing flappers are comparable to the insects in terms of the wingbeat frequency and body mass. The initial wing flappers can flap at a flapping angle of 30° as shown in Figure below. In addition, simplified analytic model of the wing flappers is derived to optimise the design. Upon re-design, an improved wing flapper can flap at a large flapping angle of 75°.

 

Energy storage is the outstanding issue to be resolved in enabling decentralised power generation. A durable, rechargeable lithium ion batteries would be an ideal medium to couple with intermittent renewable energy harnessing sources (viz, solar, wind) enabling 24/7 power on demand and grid-independence. Lithium ion batteries technology offers highest energy and power density as compared to other rechargeable battery and has been the forerunner in portable and mobile application. However, safety, reliability, cost and cycle life durability of the present day lithium ion batteries precludes its scale up and directs application to solar energy storage.

In the project led by Professor Madhavi Srinivasan from  the School of Materials Science and Engineering and in collaboration with other schools in NTU, Ecole Polytechnique Federale de Lausanne, Switzerland and NUS, the team aims to develop low cost cathodes, high cycle life anodes, safe electrolytes that would lead to next generation environment friendly, durable (10-20 years’ lifetime) and reliable lithium ion batteries with moderate emphasis on energy and power density.

The project which paves way for a paradigm shift in the constituent electrode and electrolyte material combinations in LIB necessary for it to be a viable solution for solar energy storage entails employing novel synthesis methods including electro spinning, carbothermal polyol methods that are scalable to fabricate nanowires and hollow nanostructured materials. Non-fluorinated salts, solvent, polymer matrix combinations are also being synthesised and optimised for improved gel electrolytes.

Since project commencement, the team had developed electrospun carbon nanofiber olivine composite cathodes with high conductivity (10-1S/cm) and significant capacity improvement (25%), fabricated high capacity anodes (650mAh/g) based on sulphides, MoO2 and ZnS (700mAh/g) and polymer gel electrolytes P(VdF-co-HFP) and PAN spun by electrospinning with inorganic fillers.

 

Operation research is a scientific method to analyse and solve the problems in the systematic way. It has been successfully applied to many areas e.g. power industry, supply chain, finance, and natural resource management.

In this project led by Professor Niyato Dusit from the School of Computer Science and Engineering and in collaboration with A*STAR Institute for Infocomm Research, the research will use the operation research approach to address problems in new research and business domain of cloud computing for data value chain by improving the efficiency and productivity of resources used in the data value chain.

Various problems in cloud computing environment will be studied and mathematical models will be developed to generate solutions for decision making. Software that can automatically operate to obtain optimal decision given the dynamics of cloud computing environment and application demand will also be implemented.

With the pervasiveness of wireless network and mobility of users, the project will also consider mobile cloud computing to support seamless applications for mobile users.  A green cloud computing with minimum power consumption and prototype systems for applications in healthcare (e.g. elderly care service), transportation (vehicular telematic service) and business (collaboration service) will be developed 

Led by Professor Lee Bu Sung, Francis from the School of Computer Science and Engineering, this project in partnership with A*STAR Institute of High Performance Computing aims to develop a new scalable distributed data analytics framework in the cloud. This is to complement users of analytics by retrieval, integration and summarisation/visualisation of relevant heterogeneous information from external sources and facilitates user interpretation, interaction and collaboration to achieve domain-specific solutions.

The framework is divided into two modules. The first module, the Data Infrastructure Module is the module responsible for the organising and storage of the data. It also provides the raw computational power required by the services sitting on top of this module. The technology here mainly involves Data Preprocessing and Processing using Hadoop, Data Service Brokering and Cloud-based Data Storage Management.

The second module, the Data Analytics Module, helps users to execute their initial analytics requests, but at the same time fetches useful services from the cloud, packages and presents them to users as both recommendation and information to provoke the users to formulate better analytics problems and approaches.  The technology used in this module includes Intelligent Crawler, Transfer Learning, Cloud-based meta-learning, Usage Mining and Visual Analytics.

The resulting framework would help further what has already been achieved in this field. Analytics will be more accessible by non experts, as new services leveraging relevant secondary data and usage patterns are designed to assist the design of analytics workflows. Customised and domain-specific solutions will be easier to achieve with improved interactivity and cross-domain analyses support. Much larger scale analytics than what we have currently will be accomplished by harnessing the power of distributed computation and storage resources.

Due to longer life expectancy and slower birth rate, the proportion of elderly people in the society is increasing in developed countries including Singapore. This trend may lead to problems such as insufficient manpower for caretaking and assisting elderly people. There are also social issues which underline the need for developing technology which would make life easier for them and enable them to live independently. Along this line, the demands for technological aid for elderly, such as smart home and home service robot are increasing over the time.

However, elderly people are not familiar with state-of-the-art technologies. To efficiently design robotic systems, understanding and appreciating needs of elderly people should be considered primarily. Although the current research trend has focused more on the functional needs, meeting the emotional needs is also quite important especially for the acceptance of the new technology. Especially for the home service robot, elderly people need to accept it as a companion. This project aims to compile the emotional needs of elderly and identify which requirements can be fulfilled feasibly by means of providing a specially designed home service robot. Through a multi-step analysis, the design specifications of the robot for elderly will be proposed and a prototype model will be built based on the specifications.

Currently, the theoretical framework has been established to capture how elderly people perceive robots, and the compilation of functional and emotional needs is in progress.

This project which commenced in March 2010 is led by Professor Taezoon Park from the School of Mechanical and Aerospace Engineering.

Currently, the identification of Cryptosporidium oocysts in environmental samples is time consuming (can take days), requiring a large laboratory setup and skillful personnel to interpret the results. In addition, it does not impose any procedure to differentiate live and dead oocysts. In the event of an outbreak, the current methods cannot respond rapidly.

To overcome the existing problems, Professor Gong Haiqing, Thomas from the School of Mechanical and Aerospace Engineering led a team to embark on research to develop a field deployable integrated genetic analysis instrument that is highly capable of replacing current methods and techniques used for Cryptosporidium detection from tap water samples, operated by unskilled personnel and installed at various sites in the water distribution system to locate the source of contamination and monitor the contamination levels.

The instrument capable of processing and analysing 1,000L water sample in less than 1 hour, with the system modules that can bring about high flux filtration for 99% recovery of trapped pathogens, sample concentration, rapid and innovative cell viability assay, PCR inhibitor removal and rapid real-time PCR will be the first in the world to achieve on-line and portable integrated Cryptosporidium monitoring based on high performance Cryptosporidium sample preparation and genetic testing.

 

Hepatitis C virus (HCV) affects over 170 million people worldwide, and current treatment options are inadequate. In particular, the virus is learning how to evade current drugs on the market and new drugs must be developed, especially new drugs which stop the virus from replicating inside the human body. However, stopping this process is complicated because the virus replicates on membranes found in your cells. With current technology, it is very difficult to study virus activities that occur on membranes, and this difficulty has not only limited our scientific understanding but also precluded the discovery of new drugs.

To address this issue, the team led by Nanyang Associate Professor Nam-Joon Cho from the School of Materials Science and Engineering together with Professor Jeffrey S. Glenn of the Stanford University School of Medicine and collaborators in the Virology Discovery team at Roche engineered artificial cell membranes to serve as a platform for functional analysis of membrane-associated viral proteins in the native state that when combined with state-of-the-art biosensor tools, enabled the study of virus genome replication in a non-living system for the first time.

This system provides a quicker and more cost-effective method to new drugs compared to existing cell-based systems that are widely used in the pharmaceutical industry. In the short-term, the team;s technology platform can help accelerate the development of promising drug candidates by validating their mechanisms of action in a biologically relevant model system. In the longer term, it is envisioned that the technology developed in the research will find broad application for studying the assembly and function of a wide range of viruses.