Browsing Paulraj Murugesa Pandiyan, Assoc. Prof. Dr. by Title

Now showing items 11-30 of 114

    • Bimodal Rehabilitation for the Voice and Movement Impaired using Brain Signals 

      Hema, C. R.; Paulraj, M. P., Assoc. Prof. Dr.; Sazali, Yaacob, Prof.; Abdul Hamid, Adom, Assoc. Prof. Dr.; Nagarajan, Prof.; Leong, Shi Wei; Erdy Sulino, Mohd Muslim Tan; Ahmad Zulfadli, Musa; Farid, Affendi (Universiti Malaysia Perlis, 2009-01-07)
      The Bimodal Rehabilitation for the Voice and Movement Impaired using Brain Signals is a device which can help the paralyzed patients to communicate through a digital voice and also to drive a robot chair within their homes ...

    • A Biometric Authentication System Using Brain Signatures 

      Hema, C. R.; Paulraj, M. P., Assoc. Prof. Dr.; Sazali, Yaacob, Prof. Dr.; R., Nagarajan, Prof. Dr.; Abdul Hamid, Adom, Assoc. Prof. Dr.; Adzizul Adzlan, Osman; Leong, Shi Wei; Mohd Ridhwan, Rashid (Universiti Malaysia Perlis, 2010-01-07)
      The Biometric Authentication system comprises of an acquisition unit, a processing unit and a display unit. Signal Processing and Artificial Intelligence is used to process the brain signatures collected through EEG ...

    • BMI using spectral energy entropy for colour visual tasks 

      Divakar, Purushothaman; Paulraj, Murugesa Pandiyan, Prof. Madya Dr.; Abdul Hamid, Adom, Dr.; Hema, Chengalvarayan Radhakrishnamurthy (Universiti Malaysia Perlis (UniMAP)Centre for Graduate Studies, 2010-10-16)
      EEG signals are the electrophysiological measures of brain function and it is used to develop a Brain machine Interface. A Brain machine Interface (BMI) system is used to provide a communication and control technology ...

    • Brain machine interface based wheelchair control with minimal subject training 

      Hema, Chengalvarayan Radhakrishnamurthy; Paulraj, Murugesapandian; Sazali, Yaacob; Abdul Hamid, Adom; Ramachandran, Nagarajan (Universiti Malaysia Perlis, 2009-10-11)
      Wheelchair control using a Brain Machine Interface based on motor imagery requires adequate subject training. In this paper we propose a new algorithm for a brain machine interface design which is implemented in real-time ...

    • Brain machine interface for physically retarded people using colour visual tasks 

      Pandiyan, Paulraj Murugesa, Prof. Dr.; Abdul Hamid, Adom, Prof. Dr.; Hema, Chengalvarayan Radhakrishnamurthy; Purushothaman, D. (IEEE Conference Publications, 2010-05)
      A Brain Machine Interface is a communication system which connects the human brain activity to an external device bypassing the peripheral nervous system and muscular system. It provides a communication channel for the ...

    • Brain machine interface: A comparison between fuzzy and neural classifiers 

      Hema, Chengalvarayan Radhakrishnamurthy; Paulraj, Murugesapandian; Ramachandran, Nagarajan; Sazali, Yaacob; Abdul Hamid, Adom (ICIC International, 2009)
      Patients with neurodegenerative disease loose all motor movements including impairment of speech, leaving the patients totally locked-in. One possible option for rehabilitation of such patients is through a brain machine ...

    • Brain machine interface: Analysis of segmented EEG signal classification using short-time PCA and recurrent neural networks 

      Hema, Chengalvarayan Radhakrishnamurthy; Paulraj, Murugesa Pandiyan, Assoc. Prof.; Nagarajan, Ramachandran, Prof. Dr.; Sazali, Yaacob, Prof. Dr.; Abdul Hamid, Adom, Prof. Madya (University of Basrah, 2008)
      Brain machine interface provides a communication channel between the human brain and an external device. Brain interfaces are studied to provide rehabilitation to patients with neurodegenerative diseases; such patients ...

    • Brain machine interface: classification of mental tasks using short-time PCA and recurrent neural networks 

      Hema, Chengalvarayan Radhakrishnamurthy; Paulraj, Murugesapandian; Sazali, Yaacob; Abd Hamid, Adom; Ramachandran, Nagarajan (Institute of Electrical and Electronics Engineering (IEEE), 2007-11-25)
      Brain machine interface provides a communication channel between the human brain and an external device. Brain interfaces are studied to provide rehabilitation to patients with neurodegenerative diseases; such patients ...

    • Brain machine interface: motor imagery recognition with different signal length representations 

      Hema, Chengalvarayan Radhakrishnamurthy; Paulraj, Murugesapandian; Sazali, Yaacob; Abdul Hamid, Adom; Ramachandran, Nagarajan (Institute of Electrical and Electronics Engineering (IEEE), 2009-03-06)
      This work investigates how signal representations affect the performance of a motor imagery recognition system, specifically we investigate on recognition accuracy and computational time of a brain machine interface designed ...

    • Brain signatures: a modality for biometric authentication 

      Hema, Chengalvarayan Radhakrishnamurthy; Paulraj, Murugesapandian; Kaur, Harkirenjit (Institute of Electrical and Electronics Engineering (IEEE), 2008-12-01)
      In this paper we investigate the use of brain signatures as a possible biometric authentication technique. Research on brain EEG signals has shown that individuals exhibit unique brain patterns for similar tasks. In this ...

    • Car cabin interior noise classification using temporal composite features and probabilistic neural network model 

      Paulraj, Murugesa Pandiyan, Prof. Dr.; Allan Melvin, Andrew; Sazali, Yaacob, Prof. Dr. (Trans Tech Publications Inc., 2014)
      Determination of vehicle comfort is important because continuous exposure to the noise and vibration leads to health problems for the driver and passengers. In this paper, a vehicle comfort level classification system has ...

    • 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 ...

    • Classification of speaker accent using hybrid DWT-LPC features and K-nearest neighbors in ethnically diverse Malaysian English 

      Yusnita, Mohd Ali; Pandiyan, Paulraj Murugesa, Prof. Dr.; Sazali, Yaacob, Prof. Dr.; Shahriman, Abu Bakar, Dr. (IEEE Conference Publications, 2012-12)
      Accent is a major cause of variability in automatic speaker-independent speech recognition systems. Under certain circumstances, this event introduces unsatisfactory performance of the systems. In order to circumvent this ...

    • Classification of vehicle noise comfort level using feedforward neural network 

      Paulraj, Murugesa Pandiyan, Assoc. Prof. Dr.; Sazali, Yaacob, Prof. Dr.; Siti Marhainis; Andrew, Allan Melvin (Universiti Malaysia Perlis (UniMAP)School of Mechatronic Engineering, 2012-02-27)

    • Classification of vowel sounds using MFCC and feed forward neural network 

      Paulraj, Murugesa Pandiyan, Prof. Madya; Sazali, Yaacob, Prof. Dr.; Nazri, A.; Kumar, S. (Institute of Electrical and Elctronics Engineering (IEEE), 2009-03-06)
      The English language as spoken by Malaysians varies from place to place and differs from one ethnic community and its sub-group to another. Hence, it is necessary to develop an exclusive Speech to text translation system ...

    • Classroom speech intelligibility prediction using Elman neural network 

      Paulraj, Murugesa Pandiyan, Prof. Madya; Sazali, Yaacob, Prof. Dr.; Ahmad Nazri, Abdullah; M., Thagirarani; M. Ridhwan, Tamjis (Institute of Electrical and Electronics Engineers (IEEE), 2010-05-21)
      A study was conducted to develop a simple system for classrooms speech intelligibility prediction. In this study, several classrooms properties such as size, signal-to-noise ratio (SNR) and Speech Transmission Index ...

    • Color recognition algorithm using a neural network model in determining the ripeness of a Banana 

      Paulraj, Murugesapandian; Hema, Chengalvarayan Radhakrishnamurthy; R. Pranesh, Krishnan; Siti Sofiah, Mohd Radzi (Universiti Malaysia Perlis, 2009-10-11)
      This paper presents a simple color recognition algorithm using a Neural Network model and applied to determine the ripeness of a banana. The captured image of the banana is resized and its RGB color components are extracted. ...

    • Control brain machine interface for a power wheelchair 

      Hema, Chengalvarayan Radhakrishnamurthy; Murugesa Pandiyan, Paulraj, Assoc. Prof. Dr. (Springer-Verlag, 2011-06-20)
      Controlling a power wheelchair using a brain machine interface (BMI) requires sufficient subject training. A neural network based BMI design using motor imagery of four states is used to control the navigation of a power ...

    • Damage detection in steel plates using artificial neural networks 

      Paulraj, Murugesa Pandiyan, Prof. Madya; Mohd Shukri, Abdul Majid; Sazali, Yaacob, Prof. Dr.; Mohd Hafiz, Fazalul Rahiman; Krishnan, R. P. (Institute of Electrical and Electronics Engineering (IEEE), 2009-06-04)
      In this paper, a simple method for crack identification in steel plates based on frame energy based Discrete Cosine Transformation (DCT) is presented. A simple experimental procedure is also proposed to measure the vibration ...