Browsing Paulraj Murugesa Pandiyan, Assoc. Prof. Dr. by Subject "Neural network"

Now showing items 1-20 of 24

    • Automatic detection of voice disorders using self loop architecture in back propagation network 

      Murugesa Pandiyan, Paulraj, Prof. Madya Dr,; Sazali, Yaacob, Prof. Dr.; Sivanandam, S. N.; Muthusamy, Hariharan, Dr. (Anna University, 2008-01-04)
      Acoustic analysis is a non-invasive technique to detect the voice disorders and diagnose the vocal and voice disease. In the recent years, voice disease are increasing dramatically due to unhealthy social habits and voice ...

    • 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 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 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 ...

    • 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 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)

    • 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. ...

    • Diagnosis of voice disorders using band energy spectrum in wavelet domain 

      Murugesa Pandiyan, Paulraj, Prof. Madya Dr,; Sazali, Yaacob, Prof. Dr.; Muthusamy, Hariharan, Dr. (Universiti Malaysia Perlis (UniMAP), 2008-03-08)
      In the evolution of quality of speech, acoustic analyses of normal and pathological voices have become increasingly interesting to researchers in laryngology and speech pathologies. Vocal signal information plays an important ...

    • Discrimination of pathological voices using systole activated neural network 

      Murugesa Pandiyan, Paulraj, Prof. Madya Dr,; Sazali, Yaacob, Prof. Dr.; Hariharan, Muthusamy, Dr. (Noise, Vibration and Comfort Research Group, 2007-11-27)
      The discrimination of normal and pathological voices using noninvasive acoustical analysis features helps speech specialits to perform accurate diagnoses of vocal and voices disease. Acoustic analysis is a non-invasive ...

    • EEG based detection of conductive and sensorineural hearing loss using artificial neural networks 

      Pandiyan, Paulraj Murugesa , Prof. Dr.; Subramaniam, Kamalraj; Sazali, Yaacob, Prof. Dr.; Abdul Hamid, Adom, Prof. Dr.; Hema, C. R. (Advanced Institute of Convergence IT, 2013-05)
      In this paper, a simple method has been proposed to distinguish the normal and abnormal hearing subjects (conductive or sensorineural hearing loss) using acoustically stimulated EEG signals. Auditory Evoked Potential (AEP) ...

    • Estimation of mobile robot orientation using neural networks 

      Pandiyan, Paulraj Murugesa; R. Badlishah, Ahmad; Hema, Chengalvarayan Radhakrishnamurthy; Hashim, F. (Institute of Electrical and Electronics Engineering (IEEE), 2009-03-06)
      The computation of a mobile robot position and orientation is a common task in the area of computer vision and image processing. For a successful application, it is important that the position and orientation of a mobile ...

    • Feature extraction based on mel-scaled wavelet packet transform for the diagnosis of voice disorders 

      Murugesa Pandian, Paulraj, Prof. Madya Dr,; Sazali, Yaacob, Prof. Dr.; Muthusamy, Hariharan, Dr. (SpringerLink, 2008-06-25)
      Feature extraction from the vocal signal plays very important role in the area of automatic detection of voice disorders. Many feature extraction algorithms have been developed in the last three decades based on acoustic ...

    • Gesture recognition system for Kod Tangan Bahasa Melayu (KTBM) using neural network 

      Paulraj, Murugesa Pandiyan, Prof. Madya; Sazali, Yaacob, Prof. Dr.; Hazry, Desa, Prof. Madya Dr.; Majid, W. M. R. W. A. (Institute of Electrical and Elctronics Engineering (IEEE), 2009-03-06)
      This paper presents simple methods for translating Kod Tangan Bahasa Melayu (KTBM) into voice signal based on subject head and two hand gestures. Different gesture signs made by different subjects are captured using a USB ...

    • Identification of vocal and voice disorders 

      Murugesa Pandiyan, Paulraj, Prof. Madya Dr.; Sazali, Yaacob, Prof. Dr.; Mohd Rizon, Mohammed Juhari, Prof. Dr.; Sivanandam, S. N.; Muthusamy, Hariharan, Dr. (Universiti Malaysia Perlis (UniMAP), 2007-10-25)
      The discrimination of normal and pathological voices using noninvasive acoustic analysis helps to perform accurate identification of voice disorders and diagnoses of vocal and voice disease. Acoustic analysis is a non- ...

    • Image quality assessment using Elman neural network model 

      Paulraj, Murugesa Pandiyan, Assoc. Prof. Dr.; Palaniappan, Rajkumar; Mohd Shuhanaz, Zanar Azalan (Universiti Malaysia Perlis (UniMAP)School of Mechatronic Engineering, 2012-02-27)
      Measurement of visual quality is of fundamental importance for numerous image and video processing applications, where the goal of quality assessment algorithms is to automatically assess the quality of images or videos ...

    • Improved back propagation neural network for the diagnosis of pathological voices 

      Paulraj, M.P.; Sazali, Yaacob, Prof. Dr.; Sivanandam, S. N.; Hariharan, Muthusamy (Association for Advancedment of Modelling and Simulation Techniques in Entreprises (A.M.S.E), 2008)
      Most of vocal and voice diseases cause changes in the voice. ENT clinicians use acoustic voice analysis to characterize the pathological voices. Nowadays, voice diseases are increasing dramatically due to unhealthy social ...

    • Loudspeaker fault detection using artificial neural network 

      Paulraj, Murugesa Pandiyan, Prof. Madya; Sazali, Yaacob, Prof. Dr.; Saad, M. R. (Institute of Electrical and Elctronics Engineering (IEEE), 2009-03-06)
      Traditionally, loudspeaker's quality control has been done manually and inspection of loudspeaker faults is time consuming and causes error in the quality evaluation. In order to reduce the time consumption and errors in ...

    • Malaysian vowel recognition based on spectral envelope using bandwidth approach 

      Fadzilah, Siraj; Shahrul Azmi, M. Y.; Paulraj, Murugesapandian; Sazali, Yaacob (Institute of Electrical and Electronics Engineering (IEEE), 2009-05-25)
      Automatic speech recognition (ASR) has made great strides with the development of digital signal processing hardware and software especially using English as the language of choice. In this paper, a new feature extraction ...

    • A phoneme based sign language recognition system using skin color segmentation 

      Paulraj, Murugesa Pandiyan, Prof. Madya; Sazali, Yaacob, Prof. Dr.; Mohd Shuhanaz, Zanar Azalan; Palaniappan, Rajkumar (Institute of Electrical and Elctronics Engineering (IEEE), 2010-05-21)
      A sign language is a language which, instead of acoustically conveyed sound patterns, uses visually transmitted sign patterns. Sign languages are commonly developed for deaf communities, which can include interpreters, ...