Browsing by Author "Hema, Chengalvarayan Radhakrishnamurthy"

Now showing items 1-20 of 36

    • Active stereo vision based system for estimation of Mobile Robot Orientation using composition matrix 

      Paulraj, Murugesapandian; Fadzilah, Hashim; R. Badlishah, Ahmad; Hema, Chengalvarayan Radhakrishnamurthy; Abdul Hamid, Adom (Universiti Malaysia Perlis, 2009-10-11)
      The computation of a mobile robot position and orientation is a common task in the area of computing vision and image processing. For a successful application, it is important that the position and orientation of a mobile ...

    • Asynchronous brain machine interface-based control of a wheelchair 

      Hema, Chengalvarayan Radhakrishnamurthy; Murugesan Pandiyan, Paulraj, Prof. Dr.; Sazali, Yaacob, Prof. Dr.; Abdul Hamid, Adom, Dr.; Ramachandran, Nagarajan, Prof. Dr. (Springerlink, 2011)
      A brain machine interface (BMI) design for controlling the navigation of a power wheelchair is proposed. Real-time experiments with four able bodied subjects are carried out using the BMI-controlled wheelchair. The BMI is ...

    • 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 ...

    • 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 ...

    • 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 ...

    • Designing a hybrid sensor system for a housekeeping robot 

      Hema, Chengalvarayan Radhakrishnamurthy; Sim, Kwoh Fung; Poo, Tarn Shi (Institute of Electrical and Electronics Engineering (IEEE), 2009-03-06)
      Housekeeping robots are service robots specially designed to perform housekeeping tasks such as cleaning and vacuuming, our research focuses on the design of a housekeeping robot to pick up waste objects in a home or office ...

    • EEG classification using radial basis PSO neural network for brain machine interfaces 

      Paulraj, Murugesapandian; Hema, Chengalvarayan Radhakrishnamurthy; Ramachandran, Nagarajan; Sazali, Yaacob; Abdul Hamid, Adom (Institute of Electrical and Electronics Engineering (IEEE), 2007-12)
      Brain Machine Interfaces use the cognitive abilities of patients with neuromuscular disorders to restore communication and motor functions. At present, only EEG and related methods, which have relatively short time constants, ...

    • EEG signal classification using Particle Swarm Optimization (PSO) neural network for brain machine interfaces 

      Paulraj, Murugesapandian; Hema, Chengalvarayan Radhakrishnamurthy; Ramachandran, Nagarajan; Sazali, Yaacob; Abdul Hamid, Adom (Association for the Advancement of Modelling & Simulation Techniques in Enterprises (A.M.S.E.), 2008)
      The brain uses the neuromuscular channels to communicate and control its external environment, however many disorders can disrupt these channels. Amyotrophic lateral sclerosis is one such disorder which impairs the neural ...

    • EEG signal recognition for brain word interface using wavelet decomposition 

      Hema, Chengalvarayan Radhakrishnamurthy; Leong, Shi Wei; Erdy Sulino, Mohd Muslim Tan (Institute of Electrical and Electronics Engineers (IEEE), 2010-05-21)
      A simple brain word dictionary (BWD) system using wavelet decomposition to form feature sets is developed. A BWD is an essential tool in the rehabilitation of paralyzed individuals which converts the brain EEG signals ...

    • 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 ...

    • Extraction of head and hand gesture features for recognition of sign language 

      Paulraj, M. P.; Sazali, Yaacob, Prof. Dr.; Hazry, Desa, Assoc. Prof. Dr.; Hema, Chengalvarayan Radhakrishnamurthy; Wan Mohd Ridzuan, Wan Ab Majid (IEEE Conference Publications, 2008)
      Sign language is the primary communication method that impaired hearing people used in their daily life. Sign language recognition has gained a lot of attention recently by researchers in computer vision. Sign language ...

    • Functional link PSO neural network based classification of EEG mental task signals 

      Hema, Chengalvarayan Radhakrishnamurthy; Paulraj, Murugesapandian; Sazali, Yaacob; Abdul Hamid, Adom; Nagarajan, Ramachandran (Institute of Electrical and Electronics Engineering (IEEE), 2008-08-26)
      Classification of EEG mental task signals is a technique in the design of Brain machine interface [BMI]. A BMI can provide a digital channel for communication in the absence of the biological channels and are used to ...