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 1 Department of Family Medicine, Medical Faculty, UKM Medical Centre Cheras, Kuala Lumpur Rehabilitation Department, Medical Faculty, Universiti Malaya, Kuala Lumpur Department Of Neurosciences, The School of Medical Sciences of Universiti Sains Malaysia (USM), Kelantan 2014

Research Grant Secured by ACSCRG Research Grant: NO RESEARCHER (MAIN) PROJECT NAME COMPLETION DATE CATEGORY AMOUNT (RM) 1 Siti Noraini Sulaiman A Novel Random-valued Impulse Noise Removal Based on Adaptive Switching Filter and Local- preserving Scheme 1-Jul-17FRGS67,700 2Rozan Boudville A Novel Neuroprostheses Control Algorithm For Stroke Patients Lower Extremities Rehabilitation 1-Jul-15ERGS100,000 3Zakaria Hussain A Novel Hybrid Orthosis: Assisted Lower Extremities Movement 15-Apr-15FRGS86,760 4Iza Sazanita Isa An Alpha-Beta Steady-State Correlation Of Electroencephalographic (EEG) Power Spectral Density (PSD) Brain Balancing 15-Oct-14FRGS69,000

Research Grant Secured by ACSCRG Research Grant: NO RESEARCHER (MAIN) PROJECT NAME COMPLETION DATE CATEGORY AMOUNT (RM) 5 Saiful Zaimy Yahaya A Novel Dynamic Algorithm for Functional Electrical Abdominal Stimulation 1-Jan-14FRGS64,000 6 Norhazimi Hamzah Robust Dynamic Control Allocation Algorithm of Yaw Dynamic Stability 1-Jul-13FRGS78,000

Postgraduate Students under ACSCRG Postgraduate students: NOSTUDENT NAMEPROJECT TITLESUPERVISORLEVEL 1Rozan Boudville Intelligent Control Technique for FES- Assisted Knee Swing in Stroke Rehabilitation Dr Zakaria Hussain PhD 2 Saiful Zaimy Yahaya Intelligent Control Technique for FES- Assisted Elliptical Stepping in Stroke Rehabilitation Dr Zakaria Hussain PhD 3 Mohd Aswad Amat Mushim Intelligent Control Technique For FES- Assisted Indoor Rowing Exercise in Stroke Rehabilitation Dr Zakaria Hussain PhD 4Adi Izhar Che Ani Intelligent Control Technique For FES- Assisted Hybrid Orthosis Body Supported Walking in Stroke Rehabilitation Dr Zakaria Hussain PhD 5Iza Sazanita Isa New Features Extraction Analysis of Small Vessel Stroke Predisposition Based on White Matter Correlation for Image processing Dr Siti NorainiPhD

Postgraduate Students under ACSCRG Postgraduate students: NOSTUDENT NAMEPROJECT TITLESUPERVISORLEVEL 6Pais Saidin Intelligent Classification of Transmission Line Fault Location For Global Sensitivity Power Protection Digital Relay Dr Zakaria Hussain PhD 7 Abdul Rahim Ahmad Nature Based Gel Electroforesis Image Segmenattion Dr Zakaria Hussain MSc 8 Balkis Solehah Binti Zainuddin 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 (a) Actual and reference knee trajectories (b)Knee error Figure 3. Knee trajectories and error obtained from PID controller

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 FrequencyState of Beta 13–30 Hz  Fully Awake and Alert  Concentration  Associated with left-brain thinking activity-conscious mind Alpha 7-12 Hz  Relaxed, daydreaming  Creativity, visualization  Generally associated with right-brain thinking activity Theta 3-7 Hz  Deeply relaxed, dreaming  Meditation, intuition, memory  Generally associated with right-brain thinking activity – deeper subconscious to super conscious Delta Hz  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

Current Research Area A Novel Random-valued Impulse Noise Removal Based on Adaptive Switching Filter and Local-preserving Scheme

Current Research Area A Novel Random-valued Impulse Noise Removal Based on Adaptive Switching Filter and Local-preserving Scheme

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