STAFF-IN-CONFIDENCE
DSO-NUS PhD Research Award
New Topics Proposed for Academic Year 2012/2013
RESEARCH TOPIC/ SUPERVISORS
AREA
RESEARCH OBJECTIVE/COMMENT
Manifold learning
for computer
vision applications
The ubiquitous presence of sensors in the present-day world has led to a
plethora of high-dimensional data available. In order to efficiently understand
and analyze such data, it is of utmost importance to develop the correct
mathematical tools. More precisely, the aim of this project is to develop
algorithms for the purpose of joint dimensionality reduction and classification of
the data.
DSO Supervisors
Dr Alvina Goh Siew
Wee, SPL
Dr Pang Sze Kim, SRP4
University Supervisor
Assistant Professor Yan
Shuicheng, ECE, NUS
Most of the existing frameworks operate by treating dimensionality reduction
and classification as separate problems. They first perform dimensionality
reduction and use the low-dimensional space as the input feature space for
classification algorithms. However, given that dimensionality reduction and
classification are intertwined with each other and therefore not independent,
there is a need for a joint framework that takes into consideration the two
processes simultaneously.
In this project, the student will develop novel algorithms for different types of
data arrangements. In addition, these newly developed algorithms will be
applied to a variety of computer vision problems, from motion segmentation,
activity recognition to hyperspectral data analysis. The student is expected to
publish this research in top-tier conferences and journals.
The research will be supervised by Dr Alvina Goh Siew Wee and Dr Pang Sze
Kim in DSO.
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RESEARCH
TOPIC/AREA
SUPERVISORS
RESEARCH OBJECTIVE/COMMENT
Physical
Principles-based
EM modelling
DSO Supervisor
Dr Chia Tse Tong
The challenging task we are facing now for computational electromagnetics
(CEM) is how to solve electromagnetic problems with practical configurations
that are electrically large in size and complex in structure. These practical
configurations normally consist of one very bigger main body or platform with
some other smaller sub-structures that are removable or changeable. To
efficiently model this kind of configurations, the whole structure of the problem
to be solve is divided into smaller ones (i.e. sub-domains) and solve the subdomain problems to produce the solution of the whole domain problem, which is
the main idea of domain decomposition methods (DDMs) developed and being
developed. The restrictions of the DDMs include:
University Supervisor
Dr Wang Chao-Fu
(TL@NUS)
(1) Most DDMs are strongly dependent on the equations (IE or PDE with
FEM) adopted for modelling problems. This cannot provide more flexibility to
hybridize with other methods, such as other numerical techniques and high
frequency techniques.
(2) There is no way to combine solutions for sub-structures obtained
using different techniques to produce a solution of the whole problem.
(3) It is not easy to provide local modification of the configuration to be
modelled for modelling removable and changeable substructures.
To overcome the restrictions mentioned above, it is necessary to develop some
physical principles to combine solution components obtained using different
techniques according to the natural characteristic of the sub-structures of the
configuration to be modelled. Some suggestions on this direction are:
(1) To investigate equivalence principle algorithm for wave interaction
with complex structures.
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(2) To investigate how to realize modular algorithm for local modification
for local design and modelling.
(3) To investigate scattering diagrams in electromagnetic theory for
combining solution components to produce a solution of the whole problem.
Fast interpolation / DSO Supervisor
extrapolation in EM Dr Chia Tse Tong
University Supervisor
Dr Wang Chao-Fu
(TL@NUS)
This research direction will focus on how to provide some possible ways to
more efficiently produce useful EM response data with the wide range of
frequency and/or angles based on the efficient EM solvers we have. To
enhance the capability and flexibility of the reduced order modeling techniques
(ROMT) to be developed, it is better to avoid following the normal fast
frequency sweep techniques, such as AWE, as they cannot be applied to
simulate large problems due to their need of performing matrix inverse. Some
suggestions on this direction are:
(1) To investigate smart interpolation/extrapolation algorithms for high
frequency techniques.
(2) To investigate smart interpolation/extrapolation algorithms to extract
characteristic information from calculated or measured results (RCS or other
EM response data) for reproducing useful data with more bandwidth.
(3) To investigate a way to avoid performing matrix inverse or to perform
matrix inverse in a smart and efficient way in normal fast sweep techniques with
consideration for hybridization with fast algorithms.
(4) To investigate other reduced order modelling techniques, such as
POD based methods, for large EM problem modelling.
(5) To investigate how to apply local approximation and global
approximation techniques to EM problems with many unknowns.
(6) To investigate how to develop efficient 2-D and 3-D sweep techniques
using less sample points.
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RESEARCH TOPIC/ SUPERVISORS
AREA
DMERI
Fatigue/ Exercise
Physiology
DSO Supervisor
Dr Jason Lee Kai Wei
SMTS, CPP, DMERI
University Supervisor
Prof Soong Tuck Wah
Department of
Physiology / Yong Loo
Lin School of Medicine
RESEARCH OBJECTIVE/COMMENT
The aetiology of fatigue appears to be complex and it is likely that several
important factors are involved. Currently, physiological changes in peripheral
mechanisms such as impaired substrate availability or utilization, accumulation
of lactate, potassium and calcium distribution or the progressive loss of body
fluids do not adequately explain the reduction in exercise performance; this
leads to the suggestion that the central nervous system might be important as a
causative factor in fatigue.
The main aim of this research is to elucidate the underlying brain mechanisms
to explain central fatigue and issues related to Mind over Body. The research
design will mimic modern combat stresses (e.g. sleep disruption, energy deficit,
environmental strain, information overload, psychological and physical exertion).
These stressors will either be studied in isolation or in combination. Together
with standard biomarkers and functional assessment (physical and cognitive
tests), we will employ methods, such as the transcranial magnetic stimulation,
EEG and other imaging techniques to link changes in cortical excitability to
changes in cerebral carbohydrate, amino acid and neurotransmitter metabolism,
as well as to metabolite and hormonal signalling between the brain and the
muscles.
Outcomes from this research will provide opportunities for targeted strategies
(training, nutrition etc.) to be proposed to alleviate fatigue during intense military
exercises. Furthermore, insights into mechanisms responsible for central
fatigue/Mind over Body could be relevant for the treatment of soldiers suffering
from diseases associated with chronic fatigue.
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