RESEARCH GROUP
FKE, UiTMPP
Advance Control System & Computing
Research Group
(ACSCRG)
Background of ACSCRG
The Advance Control System & Computing Research Group
(ACSCRG), Faculty of Electrical Engineering, UiTM Pulau Pinang
was formally established in December 2010 to spearhead
research and consultancy in Intelligent Control Technique and
Computing that related to Advanced Rehabilitation Engineering
and Medical Imaging.
The research group is actively running the research work
especially on the FES-Assisted Movement and Exercises, Hybrid
Orthosis, Brainwave Signal Using EEG, Medical Image
Segmentation, Noise Filtering, Artificial Intelligent and many more.
Team Member of ACSCRG
Research Team Member:
Chair : Dr Zakaria Hussain
Vice Chair :
Dr Siti Noraini Sulaiman
Secretary 1 :
Iza Sazanita Isa
Secretary 2 :
Saiful Zaimy Yahaya
Treasurer :
Abdul Rahim Ahmad
Active Member: Dr. Muhammad Khusairi Osman
Rozan Boudville
Mohd Faizal Abdul Rahman
Fadhil Dato’ Ahmad
Norhazimi Hamzah
Adi Izhar Che Ani
Khairul Azman Ahmad
Mohd Halim Mohd Noor
Current Research Area
Current Research Work includes :- FES-Assisted Movement
- Knee Swinging Exercise
- Elliptical Stepping Exercise
- Rowing exercise
- Body Supported Walking
- Abdominal Stimulation
- Hybrid Orthosis and Prosthesis
- Brain Signal and Images
- EEG
- MRI and fMRI
- Medical Imaging
- Noise filtering
- Image segmentation
- Artificial Intelligent
- ANN
-GA
- PSO
Research Collaboration under ACSCRG
Research Collaboration:
NO
RESEARCHER
(MAIN)
YEARS
Department of Family Medicine, Medical Faculty, UKM Medical
1 Centre Cheras, Kuala Lumpur.
2011
Rehabilitation Department, Medical Faculty, Universiti Malaya, Kuala
2 Lumpur.
2012
Department Of Neurosciences, The School of Medical Sciences of
3 Universiti Sains Malaysia (USM), Kelantan
2014
Research Grant Secured by ACSCRG
Research Grant:
NO
1
RESEARCHER
(MAIN)
Siti Noraini
Sulaiman
2 Rozan Boudville
PROJECT NAME
A Novel Random-valued Impulse
Noise Removal Based on Adaptive
Switching Filter and Localpreserving Scheme
A Novel Neuroprostheses Control
Algorithm For Stroke Patients
Lower Extremities Rehabilitation
COMPLETION
AMOUNT
CATEGORY
DATE
(RM)
1-Jul-17
FRGS
67,700
1-Jul-15
ERGS
100,000
3 Zakaria Hussain
A Novel Hybrid Orthosis: Assisted
Lower Extremities Movement
15-Apr-15
FRGS
86,760
4 Iza Sazanita Isa
An Alpha-Beta Steady-State
Correlation Of
Electroencephalographic (EEG)
Power Spectral Density (PSD)
Brain Balancing
15-Oct-14
FRGS
69,000
Research Grant Secured by ACSCRG
Research Grant:
NO
RESEARCHER
(MAIN)
Saiful Zaimy
5
Yahaya
6
Norhazimi
Hamzah
PROJECT NAME
COMPLETION
AMOUNT
CATEGORY
DATE
(RM)
A Novel Dynamic Algorithm for
Functional Electrical Abdominal
Stimulation
1-Jan-14
FRGS
64,000
Robust Dynamic Control Allocation
Algorithm of Yaw Dynamic Stability
1-Jul-13
FRGS
78,000
Postgraduate Students under ACSCRG
Postgraduate students:
NO STUDENT NAME
1
2
3
4
5
PROJECT TITLE
SUPERVISOR
Intelligent Control Technique for FESDr Zakaria
Rozan Boudville Assisted Knee Swing in Stroke
Hussain
Rehabilitation
Intelligent Control Technique for FESSaiful Zaimy
Dr Zakaria
Assisted Elliptical Stepping in Stroke
Yahaya
Hussain
Rehabilitation
Intelligent Control Technique For FESMohd Aswad
Dr Zakaria
Assisted Indoor Rowing Exercise in Stroke
Amat Mushim
Hussain
Rehabilitation
Intelligent Control Technique For FESDr Zakaria
Adi Izhar Che Ani Assisted Hybrid Orthosis Body Supported
Hussain
Walking in Stroke Rehabilitation
New Features Extraction Analysis of Small
Vessel Stroke Predisposition Based on
Iza Sazanita Isa
Dr Siti Noraini
White Matter Correlation for Image
processing
LEVEL
PhD
PhD
PhD
PhD
PhD
Postgraduate Students under ACSCRG
Postgraduate students:
NO STUDENT NAME
6 Pais Saidin
7
Abdul Rahim
Ahmad
Balkis Solehah
8
Binti Zainuddin
PROJECT TITLE
SUPERVISOR
LEVEL
Intelligent Classification of Transmission
Line Fault Location For Global Sensitivity
Power Protection Digital Relay
Dr Zakaria
Hussain
PhD
Nature Based Gel Electroforesis Image
Segmenattion
Dr Zakaria
Hussain
MSc
EEG-Based Intelligent Classification of
Stroke Patient Imaginary Movement Using
Alpha Beta Steady State Correlation
Dr Zakaria
Hussain
MSc
Current Research Area
FES-Assisted Knee Swinging Exercise
- Utilize the flexed non-paretic knee to assist extension of the
paretic knee.
- Optimize functional electrical stimulation
- Allow patient to perform repetitive FES-assisted knee
swinging exercise
Left Knee
Extension
Right Knee
Extension
Rest Position
Figure 1 Setup of the FES-assisted knee ergometer model
Current Research Area
FES-Assisted Knee Swinging Exercise
Current Research Area
FES-Assisted Knee Swinging Exercise
1
PID
d
Ref Paretic
Par Angle
q_k
PID Paretic
1
TotalMoment
vNPlant
dq_k/dt
2
Muscle Model
Par Ang Vel
Knee Ergometer
3
Non-par Angle
2
PID
Ref Non-paretic
PID Non-paretic
220
Left Ref Knee Traj
Right Ref Knee Traj
Left Act Knee Traj
Right Act Knee Traj
200
4
2
Error (degree)
180
Angle (degree)
Paretic leg
Non-paretic leg
160
140
0
-2
120
100
-4
0
1
2
3
4
Time (sec)
(a) Actual and reference knee trajectories
Figure 3.
5
0
1
2
3
4
Time (second)
(b) Knee error
Knee trajectories and error obtained from PID
controller
5
Current Research Area
FES-Assisted Elliptical Stepping Exercise
- Utilize control technique to produce smooth movement of
elliptical stepping exercise.
To implement the technique of optimizing the control parameter to
enhance the accuracy of the movement
Current Research Area
FES-Assisted Elliptical Stepping Exercise
Figure 6 Cadence speed at control gain
setting of 0.5 and 1
Figure 7 Produced knee joint torque
for control gain setting of 0.5
Figure 8 Produced knee joint torque
for control gain setting of 1
Current Research Area
Brainwave Signal using EEG
- Established the Brainwave signal
- Stroke Rehabilitation
- Stroke patient psychology – Mentally unstable.
- Determine Brainwave signal for stroke patient
- Encourage for physiotherapy/rehabilitation
Current Research Area
EEG Brainwave
Sample
Brainwave Frequency
State of
Beta
13–30 Hz



Alpha
7-12 Hz



Theta
3-7 Hz



Delta
0.1-3 Hz



Fully Awake and Alert
Concentration
Associated with left-brain thinking
activity-conscious mind
Relaxed, daydreaming
Creativity, visualization
Generally associated with right-brain
thinking activity
Deeply relaxed, dreaming
Meditation, intuition, memory
Generally associated with right-brain
thinking activity – deeper subconscious to
super conscious
Sleep, dreamless
Detached awareness, healing
Generally associated with no thinking
Current Research Area
A Novel Random-valued Impulse Noise Removal Based on
Adaptive Switching Filter and Local-preserving Scheme
The aim of this research is to establish the fundamental technique for
Random-Valued Impulse Noise removal. Hence, the objectives are as
follows:
• To investigate the characteristics or the behavior of RVIN in terms of
noise occurrence on the image histogram.
• To formulate a two phase iterative method (detect then preserve) for
detecting and removing RVIN by incorporating intelligent principles
for adaptive noise filtering and a local preserving scheme that able to
suppress high density of noise in digital images.
• To evaluate the performance of the proposed method in terms of its
efficiency to detect the noise and preserving the fine details of the
original image.
Current Research Area
A Novel Random-valued Impulse Noise Removal Based on
Adaptive Switching Filter and Local-preserving Scheme
Original Image
Noisy image Corrupted with
50% RVIN
Restored image by MED
Figure 1: Result of conventional MED filter in restoring 50% corrupted Lena image.
Current Research Area
A Novel Random-valued Impulse Noise Removal Based on
Adaptive Switching Filter and Local-preserving Scheme
Original Image
Noisy image Corrupted with
50% RVIN
Restored image by MED
Figure 2: Result of conventional MED filter in restoring 50% corrupted MRI image.
Current Research Area
A Novel Random-valued Impulse Noise Removal Based on
Adaptive Switching Filter and Local-preserving Scheme
Original Image
Noisy image Corrupted with
50% RVIN
Restored image by MED
Figure 3: Result of conventional MED filter in restoring 50% corrupted Satellite image.
Q&A
……………………………….
Thank you