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RESEARCH GROUP FKE, UiTMPP

UiTM PP's ACSCRG conducts research in Intelligent Control Technique, Advanced Rehabilitation Engineering, & Medical Imaging. Current areas include FES-Assisted Movement & more.

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RESEARCH GROUP FKE, UiTMPP

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  1. RESEARCH GROUPFKE, UiTMPP Advance Control System & Computing Research Group (ACSCRG)

  2. 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.

  3. 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

  4. 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

  5. Research Collaboration under ACSCRG Research Collaboration:

  6. Research Grant Secured by ACSCRG Research Grant:

  7. Research Grant Secured by ACSCRG Research Grant:

  8. Postgraduate Students under ACSCRG Postgraduate students:

  9. Postgraduate Students under ACSCRG Postgraduate students:

  10. 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

  11. Current Research Area FES-Assisted Knee Swinging Exercise

  12. Current Research Area FES-Assisted Knee Swinging Exercise • Knee error (a) Actual and reference knee trajectories Figure 3. Knee trajectories and error obtained from PID controller

  13. 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

  14. 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

  15. 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

  16. Current Research Area

  17. 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.

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

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

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

  21. Q & A ………………………………. Thank you

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