Asha Vijayan | Amrita Vishwa Vidyapeetham (original) (raw)
Papers by Asha Vijayan
Internet-enabled technologies for robotics education are gaining importance as online platforms f... more Internet-enabled technologies for robotics education are gaining importance as online platforms facilitating and promoting skill training. Understanding the use and design of robotics is now introduced at university undergraduate levels, but in developing economies establishing usable hardware and software platforms face several challenges like cost, equipment etc. Remote labs help providing alternatives to some of the challenges. We developed an online laboratory for bioinspired robotics using a low-cost 6 degree-of-freedom robotic articulator with a neuro-inspired controller. Cerebellum-inspired neural network algorithm approximates forward and inverse kinematics for movement coordination. With over 210000 registered users, the remote lab has been perceived as an interactive online learning tool and a practice platform. Direct feedback from 60 students and 100 university teachers indicated that the remote laboratory motivated self-organized learning and was useful as teaching material to aid robotics skill education.
Internet-enabled technologies for robotics education are gaining importance as online platforms f... more Internet-enabled technologies for robotics education are gaining importance as online platforms facilitating and promoting skill training. Understanding the use and design of robotics is now introduced at university undergraduate levels, but in developing economies establishing usable hardware and software platforms face several challenges like cost, equipment etc. Remote labs help providing alternatives to some of the challenges. We developed an online laboratory for bioinspired robotics using a low-cost 6 degree-of-freedom robotic articulator with a neuro-inspired controller. Cerebellum-inspired neural network algorithm approximates forward and inverse kinematics for movement coordination. With over 210000 registered users, the remote lab has been perceived as an interactive online learning tool and a practice platform. Direct feedback from 60 students and 100 university teachers indicated that the remote laboratory motivated self-organized learning and was useful as teaching material to aid robotics skill education.
Articulation via target-oriented approaches have been commonly used in robotics. Movement of a ro... more Articulation via target-oriented approaches have been commonly used in robotics. Movement of a robotic arm can involve targeting via a forward or inverse kinematics approach to reach the target. We attempted to transform the task of controlling the motor articulation to a machine learning approach. Towards this goal, we built an online robotic arm to extract articulation datasets and have used SVM and Naïve Bayes techniques to predict multi-joint articulation. For controlling the preciseness and efficiency, we developed pick and place tasks based on pre-marked positions and extracted training datasets which were then used for learning. We have used classification as a scheme to replace prediction-correction approach as usually attempted in traditional robotics. This study reports significant classification accuracy and efficiency on real and synthetic datasets generated by the device. The study also suggests SVM and Naïve Bayes algorithms as alternatives for computational intensive prediction-correction learning schemes for articulator movement in laboratory environments.
2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2015
Spiking neural encoding models allow classification of real world tasks to suit for brain-machine... more Spiking neural encoding models allow classification of real world tasks to suit for brain-machine interfaces in addition to serving as internal models. We developed a new spike encoding model inspired from cerebellum granular layer and tested different classification techniques like SVM, Naïve Bayes, MLP for training spiking neural networks to perform pattern recognition tasks on encoded datasets. As a precursor to spiking networkbased pattern recognition, in this study, real world datasets were encoded into spike trains. The objective of this study was to encode information from datasets into spiking neuron patterns that were relevant for spiking neural networks and for conventional machine learning algorithms. In this initial study, we present a new approach similar to cerebellum granular layer encoding and compared it with BSA encoding techniques. We have also compared the efficiency of the encoded dataset with different datasets and with standard machine learning algorithms.
2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2015
Precise fine-tuning of motor movements has been known to be a vital function of cerebellum, which... more Precise fine-tuning of motor movements has been known to be a vital function of cerebellum, which is critical for maintaining posture and balance. Purkinje cell (PC) plays a prominent role in this fine-tuning through association of inputs and output alongside learning through error correction. Several classical studies showed that PC follows perceptron like behavior, which can be used to develop cerebellum like neural circuits to address the association and learning. With respect to the input, the PC learns the motor movement through update of synaptic weights. In order to understand how cerebellar circuits associate spiking information during learning, we developed a spiking neural network using adaptive exponential integrate and fire neuron model (AdEx) based on cerebellar molecular layer perceptron-Iike architecture and estimated the maximal storage capacity at parallel fiber-PC synapse. In this study, we explored information storage in cerebellar microcircuits using this abstraction. Our simulations suggest that perceptron mimicking PC behavior was capable of learning the output through modification via finite precision algorithm. The study evaluates the pattern processing in cerebellar Purkinje neurons via a mathematical model estimating the storage capacity based on input patterns and indicates the role of sparse encoding of granular layer neurons in such circuits.
2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW), 2010
Abstract Microarray data has been widely used to predict different disease condition. But the pro... more Abstract Microarray data has been widely used to predict different disease condition. But the problem has been the high dimensionality of microarray data, because of very few samples compared to a huge number of genes. To tackle this necessity we have developed EVOL ...
Proceedings of the International Conference on Neural Computation Theory and Applications, 2014
ABSTRACT There have been significant advancements in brain computer interface (BCI) techniques us... more ABSTRACT There have been significant advancements in brain computer interface (BCI) techniques using EEG-like methods. EEG can serve as non-invasive BMI technique, to control devices like wheelchairs, cursors and robotic arm. In this paper, we discuss the use of EEG recordings to control low-cost robotic arms by extracting motor task patterns and indicate where such control algorithms may show promise towards the humanitarian challenge. Studies have shown robotic arm movement solutions using kinematics and machine learning methods. With iterative processes for trajectory making, EEG signals have been known to be used to control robotic arms. The paper also showcases a case-study developed towards this challenge in order to test such algorithmic approaches. Non-traditional approaches using neuro-inspired processing techniques without implicit kinematics have also shown potential applications. Use of EEG to resolve temporal information may, indeed, help understand movement coordination in robotic arm.
Third International Conference on Innovative Computing Technology (INTECH 2013), 2013
ABSTRACT Target-oriented approaches have been commonly used in robotics. In 3D space, movement of... more ABSTRACT Target-oriented approaches have been commonly used in robotics. In 3D space, movement of a robotic arm depends on the target position which can either follow a forward or inverse kinematics approach to reach the target. Predicting the movement of a robotic arm requires prior learning through methods such as transformation matrices or other machine learning techniques. In this paper, we built an online robotic arm to extract movement datasets and have used machine learning algorithms to predict robotic arm articulation. For efficient training, small training datasets were used for learning purpose. Classification is used as a scheme to replace prediction-correction approach and to test whether the method can function as a replacement of usual forward kinematics schemes or predictor-corrector methods in directing a remotely controlled robotic articulator. This study reports significant classification accuracy and efficiency on real and synthetic datasets generated by the device. The study also suggests linear SVM and Naïve Bayes algorithms as alternatives for computational intensive learning schemes while predicting articulator movement in laboratory environments.
Proceedings of the 2014 International Conference on Interdisciplinary Advances in Applied Computing - ICONIAAC '14, 2014
Simple spiking models have been known to replicate detailed mathematical models firing properties... more Simple spiking models have been known to replicate detailed mathematical models firing properties with reliable accuracy in spike timing. We modified the adaptive exponential integrate and fire mathematical model to reconstruct different cerebellar neuronal firing patterns. We were able to reconstruct the firing dynamics of various types of cerebellar neurons and validated with previously published experimental studies. To model the neurons, we exploited particle swarm optimization to fit the parameters. The study showcases the match of electro-responsiveness of the neuronal models to data from biological neurons. Results suggest that models are close reconstructions of the biological data since frequency and spike-timing closely matched known values and were similar to those in previously published detailed computationally intensive biophysical models. Such spiking models have a number of applications including design of largescale circuit models in order to understand physiological dysfunction and for various computational advantages.
IEEE International Conference on Bioinformatics and Biomedicine Workshops, 2009
Notice of Violation of IEEE Publication Principles"PathMapper-An Integrative Approach for On... more Notice of Violation of IEEE Publication Principles"PathMapper-An Integrative Approach for Oncogene Pathway Identification"by Asha Vijayan, Bessey Elen Skariah, Bipin G. Nair, Gerald H. Lushington, Sabarinath Subramanian, Mahesh Visvanathanin the Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine Workshop, 2009. BIBMW 2009, November 2009, pp. 267-271After careful and considered review of the content and authorship of this paper by
Classifying Movement Articulation for Robotic Arms via Machine Learning, Sep 1, 2013
Internet-enabled technologies for robotics education are gaining importance as online platforms f... more Internet-enabled technologies for robotics education are gaining importance as online platforms facilitating and promoting skill training. Understanding the use and design of robotics is now introduced at university undergraduate levels, but in developing economies establishing usable hardware and software platforms face several challenges like cost, equipment etc. Remote labs help providing alternatives to some of the challenges. We developed an online laboratory for bioinspired robotics using a low-cost 6 degree-of-freedom robotic articulator with a neuro-inspired controller. Cerebellum-inspired neural network algorithm approximates forward and inverse kinematics for movement coordination. With over 210000 registered users, the remote lab has been perceived as an interactive online learning tool and a practice platform. Direct feedback from 60 students and 100 university teachers indicated that the remote laboratory motivated self-organized learning and was useful as teaching material to aid robotics skill education.
Internet-enabled technologies for robotics education are gaining importance as online platforms f... more Internet-enabled technologies for robotics education are gaining importance as online platforms facilitating and promoting skill training. Understanding the use and design of robotics is now introduced at university undergraduate levels, but in developing economies establishing usable hardware and software platforms face several challenges like cost, equipment etc. Remote labs help providing alternatives to some of the challenges. We developed an online laboratory for bioinspired robotics using a low-cost 6 degree-of-freedom robotic articulator with a neuro-inspired controller. Cerebellum-inspired neural network algorithm approximates forward and inverse kinematics for movement coordination. With over 210000 registered users, the remote lab has been perceived as an interactive online learning tool and a practice platform. Direct feedback from 60 students and 100 university teachers indicated that the remote laboratory motivated self-organized learning and was useful as teaching material to aid robotics skill education.
Articulation via target-oriented approaches have been commonly used in robotics. Movement of a ro... more Articulation via target-oriented approaches have been commonly used in robotics. Movement of a robotic arm can involve targeting via a forward or inverse kinematics approach to reach the target. We attempted to transform the task of controlling the motor articulation to a machine learning approach. Towards this goal, we built an online robotic arm to extract articulation datasets and have used SVM and Naïve Bayes techniques to predict multi-joint articulation. For controlling the preciseness and efficiency, we developed pick and place tasks based on pre-marked positions and extracted training datasets which were then used for learning. We have used classification as a scheme to replace prediction-correction approach as usually attempted in traditional robotics. This study reports significant classification accuracy and efficiency on real and synthetic datasets generated by the device. The study also suggests SVM and Naïve Bayes algorithms as alternatives for computational intensive prediction-correction learning schemes for articulator movement in laboratory environments.
2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2015
Spiking neural encoding models allow classification of real world tasks to suit for brain-machine... more Spiking neural encoding models allow classification of real world tasks to suit for brain-machine interfaces in addition to serving as internal models. We developed a new spike encoding model inspired from cerebellum granular layer and tested different classification techniques like SVM, Naïve Bayes, MLP for training spiking neural networks to perform pattern recognition tasks on encoded datasets. As a precursor to spiking networkbased pattern recognition, in this study, real world datasets were encoded into spike trains. The objective of this study was to encode information from datasets into spiking neuron patterns that were relevant for spiking neural networks and for conventional machine learning algorithms. In this initial study, we present a new approach similar to cerebellum granular layer encoding and compared it with BSA encoding techniques. We have also compared the efficiency of the encoded dataset with different datasets and with standard machine learning algorithms.
2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2015
Precise fine-tuning of motor movements has been known to be a vital function of cerebellum, which... more Precise fine-tuning of motor movements has been known to be a vital function of cerebellum, which is critical for maintaining posture and balance. Purkinje cell (PC) plays a prominent role in this fine-tuning through association of inputs and output alongside learning through error correction. Several classical studies showed that PC follows perceptron like behavior, which can be used to develop cerebellum like neural circuits to address the association and learning. With respect to the input, the PC learns the motor movement through update of synaptic weights. In order to understand how cerebellar circuits associate spiking information during learning, we developed a spiking neural network using adaptive exponential integrate and fire neuron model (AdEx) based on cerebellar molecular layer perceptron-Iike architecture and estimated the maximal storage capacity at parallel fiber-PC synapse. In this study, we explored information storage in cerebellar microcircuits using this abstraction. Our simulations suggest that perceptron mimicking PC behavior was capable of learning the output through modification via finite precision algorithm. The study evaluates the pattern processing in cerebellar Purkinje neurons via a mathematical model estimating the storage capacity based on input patterns and indicates the role of sparse encoding of granular layer neurons in such circuits.
2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW), 2010
Abstract Microarray data has been widely used to predict different disease condition. But the pro... more Abstract Microarray data has been widely used to predict different disease condition. But the problem has been the high dimensionality of microarray data, because of very few samples compared to a huge number of genes. To tackle this necessity we have developed EVOL ...
Proceedings of the International Conference on Neural Computation Theory and Applications, 2014
ABSTRACT There have been significant advancements in brain computer interface (BCI) techniques us... more ABSTRACT There have been significant advancements in brain computer interface (BCI) techniques using EEG-like methods. EEG can serve as non-invasive BMI technique, to control devices like wheelchairs, cursors and robotic arm. In this paper, we discuss the use of EEG recordings to control low-cost robotic arms by extracting motor task patterns and indicate where such control algorithms may show promise towards the humanitarian challenge. Studies have shown robotic arm movement solutions using kinematics and machine learning methods. With iterative processes for trajectory making, EEG signals have been known to be used to control robotic arms. The paper also showcases a case-study developed towards this challenge in order to test such algorithmic approaches. Non-traditional approaches using neuro-inspired processing techniques without implicit kinematics have also shown potential applications. Use of EEG to resolve temporal information may, indeed, help understand movement coordination in robotic arm.
Third International Conference on Innovative Computing Technology (INTECH 2013), 2013
ABSTRACT Target-oriented approaches have been commonly used in robotics. In 3D space, movement of... more ABSTRACT Target-oriented approaches have been commonly used in robotics. In 3D space, movement of a robotic arm depends on the target position which can either follow a forward or inverse kinematics approach to reach the target. Predicting the movement of a robotic arm requires prior learning through methods such as transformation matrices or other machine learning techniques. In this paper, we built an online robotic arm to extract movement datasets and have used machine learning algorithms to predict robotic arm articulation. For efficient training, small training datasets were used for learning purpose. Classification is used as a scheme to replace prediction-correction approach and to test whether the method can function as a replacement of usual forward kinematics schemes or predictor-corrector methods in directing a remotely controlled robotic articulator. This study reports significant classification accuracy and efficiency on real and synthetic datasets generated by the device. The study also suggests linear SVM and Naïve Bayes algorithms as alternatives for computational intensive learning schemes while predicting articulator movement in laboratory environments.
Proceedings of the 2014 International Conference on Interdisciplinary Advances in Applied Computing - ICONIAAC '14, 2014
Simple spiking models have been known to replicate detailed mathematical models firing properties... more Simple spiking models have been known to replicate detailed mathematical models firing properties with reliable accuracy in spike timing. We modified the adaptive exponential integrate and fire mathematical model to reconstruct different cerebellar neuronal firing patterns. We were able to reconstruct the firing dynamics of various types of cerebellar neurons and validated with previously published experimental studies. To model the neurons, we exploited particle swarm optimization to fit the parameters. The study showcases the match of electro-responsiveness of the neuronal models to data from biological neurons. Results suggest that models are close reconstructions of the biological data since frequency and spike-timing closely matched known values and were similar to those in previously published detailed computationally intensive biophysical models. Such spiking models have a number of applications including design of largescale circuit models in order to understand physiological dysfunction and for various computational advantages.
IEEE International Conference on Bioinformatics and Biomedicine Workshops, 2009
Notice of Violation of IEEE Publication Principles"PathMapper-An Integrative Approach for On... more Notice of Violation of IEEE Publication Principles"PathMapper-An Integrative Approach for Oncogene Pathway Identification"by Asha Vijayan, Bessey Elen Skariah, Bipin G. Nair, Gerald H. Lushington, Sabarinath Subramanian, Mahesh Visvanathanin the Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine Workshop, 2009. BIBMW 2009, November 2009, pp. 267-271After careful and considered review of the content and authorship of this paper by
Classifying Movement Articulation for Robotic Arms via Machine Learning, Sep 1, 2013