G. Radhakrishnan currently serves as Vice-Chairperson at Department of Computer Science, Amrita School of Engineering.He is currently pursuing his Ph.D.


Publication Type: Conference Paper
Year of Publication Publication Type Title
2015 Conference Paper P. Vivek, G. Radhakrishnan, Deepa Gupta, and T.S.B. Sudarshan, “Clustering of robotic environment using image data stream”, in International Conference Communication, Control and Intelligent Systems, CCIS 2015, 2015, pp. 208-213.[Abstract]

Mobile robots are being used in various applications like space shuttles, intelligent home security, military applications or other service oriented applications where human intervention is limited. A robot has to understand its environment by analyzing the data to take the appropriate actions in the given environment. Mostly the data collected from the sensors on the robots are huge and continuous, making it impossible to store the entire data in main memory and hence allowing only single scan of data. Traditional clustering algorithms like k-means cannot be used in such environment as they require multiple scan of data. This paper presents an experimental study on the implementation of Stream KM++, a data stream clustering algorithm that effectively cluster these time series robotic image data within the memory restrictions under various conditions. Promising results are obtained from the various experiments carried out. More »»
2015 Conference Paper G. Radhakrishnan, Deepa Gupta, Sindhuula, S., Khokhawat, S., and T.S.B. Sudarshan, “Experimentation and analysis of time series data from multi-path robotic environment”, in 2015 IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2015, 2015.[Abstract]

Autonomous mobile robots are increasingly used in many application areas. In most applications, they have to explore and gather knowledge about the environment they are deployed in. These robots transfer real time data about the environment continuously. This paper discusses a set of experiments that have been carried out to simulate various robotic environments. A robot attached with four sensors is used to collect information about the environment as the robot moves in multiple straight line paths. Time series data collected from these experiments are clustered using data mining techniques. Experimental results show clustering accuracies vary depending on the number of clusters formed. © 2015 IEEE.

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2012 Conference Paper G. Radhakrishnan, Deepa Gupta, Abhishek, R., Ajith, A., and T.S.B. Sudarshan, “Analysis of multimodal time series data of robotic environment”, in International Conference on Intelligent Systems Design and Applications, ISDA, Kochi, 2012, pp. 734-739.[Abstract]

<p>Autonomous mobile robots equipped with an array of sensors are being increasingly deployed in disaster environments to assist rescue teams. The sensors attached to the robots send multimodal time series data about the disaster environments which can be analyzed to extract useful information about the environment in which the robots are deployed. A set of data mining tasks that effectively cluster various robotic environments have been investigated. The effectiveness of these data mining techniques have been demonstrated using an available robotic dataset. The accuracy of the proposed technique has been measured using a manual reference cluster set. © 2012 IEEE.</p>

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