Browsing Abdul Hamid Adom, Prof. Dr. by Author "Paulraj, Murugesa Pandiyan, Assoc. Prof. Dr."

Now showing items 1-7 of 7

    • Adaptive boosting with SVM classifier for moving vehicle classification 

      Norasmadi, Abdul Rahim; Paulraj, Murugesa Pandiyan, Assoc. Prof. Dr.; Abdul Hamid, Adom, Prof. Dr. (Malaysian Technical Universities Network (MTUN), 2012-11-20)
      This study examines co-solvent modified supercritical carbon dioxide (SC-CO2) to extract the saturated fatty acids from palm oil. The applied pressure was ranging from 60 to 180 bar and the extraction temperatures were ...

    • Classification of acoustic sound signature of moving vehicle using artificial neural network 

      Paulraj, Murugesa Pandiyan, Assoc. Prof. Dr.; Abdul Hamid, Adom, Prof., Dr.; Sathishkumar, Sundararaj (Universiti Malaysia Perlis (UniMAP), 2012-06-18)
      The hearing impaired is afraid of walking along a street and living a life alone. Since, it is difficult for hearing impaired to hear and judge sound information and they often encounter risky situations while they are in ...

    • Classification of EEG colour imagination tasks based BMI using energy and entropy features 

      Paulraj, Murugesa Pandiyan, Assoc. Prof. Dr.; Abdul Hamid, Adom, Assoc. Prof. Dr.; Hema, Chengalvarayan Radhakrishnamurthy; Purushothaman, Divakar (Universiti Malaysia Perlis (UniMAP)School of Mechatronic Engineering, 2012-02-27)
      Electroencephalogram (EEG) signals are the electrophysiological measures of brain function and it is used to develop a brain machine interface. Brain machine interface (BMI) system is used to provide a communication and ...

    • Improving classification of EEG signals for a four-state brain machine interface 

      Hema, Chengalvarayan Radhakrishnamurthy; Paulraj, Murugesa Pandiyan, Assoc. Prof. Dr.; Abdul Hamid, Adom, Prof. Dr. (Institute of Electrical and Electronics Engineers (IEEE), 2012)
      Neural network classifiers are one among the popular modes in the design of classifiers for electroencephalograph based brain machine interfaces. This study presents algorithms to improve the classification performance of ...

    • Moving vehicle identification using artificial neural network 

      Paulraj, Murugesa Pandiyan, Assoc. Prof. Dr.; Abdul Hamid, Adom, Assoc. Prof. Dr.; Siti Marhainis, Othman; Sundararaj, Sathish Kumar (Universiti Malaysia Perlis (UniMAP)School of Mechatronic Engineering, 2012-02-27)
      Hearing impaired people cannot distinguish the sound from a moving vehicle approaching from their behind. They often face a risky situation while they are in outdoors. In this paper, a simple algorithm is proposed to ...

    • Multi-classifier system for moving vehicles classification based on spectral bands energy 

      Norasmadi, Abdul Rahim; Paulraj, Murugesa Pandiyan, Assoc. Prof. Dr.; Adom, Abdul Hamid, Prof. Dr.; Sathishkumar, Sundararaj (Universiti Malaysia Perlis (UniMAP), 2012-06-18)
      Profoundly hearing impaired community cannot moderate wisely an acoustic noise emanated from moving vehicle in outdoor. They are not able to distinguish either type or distance of moving vehicle approaching from behind. ...

    • Synchronous brain machine interface design using focused time delay networks 

      Hema, Chengalvarayan Radhakrishnamurthy; Paulraj, Murugesa Pandiyan, Assoc. Prof. Dr.; Sazali, Yaacob, Prof. Dr.; Abdul Hamid, Adom, Assoc. Prof. Dr.; Ramachandran, Nagarajan, Prof. Dr. (Universiti Malaysia Perlis (UniMAP)School of Mechatronic Engineering, 2012-02-27)
      Focused time delay neural network based design for a four-state Brain Machine Interface (BMI) to drive a wheelchair is analyzed. Motor imagery signals recorded noninvasively using two bipolar electrodes are used in the ...