Radha D. currently serves as Assistant Professor at Department of Computer Science, Amrita School of Engineering, Bengaluru. She is currently pursuing Ph.D in the area of High Performance computing in Image Processing.
Education
| Year of Passing | Degree | Name of the Board/University |
|---|---|---|
| 2007 | M. Tech. in CSE | Dr. MGR University |
| 1997 | B. E. in CSE | Madurai Kamaraj Univeristy |
Publications
| Year of Publication | Title |
|---|---|
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2019 |
S. Bajpai and Radha D., “Smart Phone as a Controlling Device for Smart Home using Speech Recognition”, in International Conference on Communication and Signal Processing – ICCSP’ 19 , Melmaruvathur, Tamilnadu, India, 2019. |
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2019 |
S. Raguvir and Radha D., “Analysis of Explicit Parallelism of Image Preprocessing Algorithms–-A Case Study”, in Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB), Cham, 2019.[Abstract] The need for the image processing algorithm is inevitable in the present era as every field involves the use of images and videos. The performance of such algorithms can be improved using parallelizing the tasks in the algorithm. There are different ways of parallelizing the algorithm like explicit parallelism, implicit parallelism, and distributed parallelism. The proposed work shows the analysis of the performance of the explicit parallelism of an image enhancement algorithm named median filtering in a multicore system. The implementation of explicit parallelism is done using MATLAB. The performance analysis is based on primary measures like speedup time and efficiency. More »» |
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2017 |
, Radha D., and Amudha, J., “Effectual Training For Object Detection Using Eye Tracking Data Set”, in 2nd International conference on Inventive Computation Technologies(ICICT-2017), Coimbatore, 2017. |
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2017 |
Radha D. and Nithia, K. P. T., “A Case Study on Social Network Analysis: Thesaurus Book ”, in 2nd International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS-2017), R.V. College of Engineering, Bengaluru, 2017. |
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2017 |
Radha D. and Kulkarni, S., “A Social Network Analysis of World Cities Network ”, in the 2nd International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS-2017), R.V. College of Engineering, Bengaluru, 2017. |
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2017 |
Radha D. and Kavikuil, K., “Centrality Measures to Analyze Transport Network for Congestion Free Shipment ”, in 2nd International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS-2017), R.V. College of Engineering, Bengaluru, 2017. |
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2017 |
J. Amudha and Radha D., “Optimization of Rules in Neuro-Fuzzy Inference Systems”, in International Conference on Computational Vision and Bio-inspired Computing (ICCVBIC 2017), Inventive Research Organization and RVS Technical Campus, Coimbatore, 2017. |
| Year of Publication | Title |
|---|---|
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2018 |
P. Saleena, Swetha, P. K., and Radha D., “Analysis and visualization of airport network to strengthen the economy”, International Journal of Engineering and Technology(UAE), vol. 7, pp. 708-713, 2018.[Abstract] The world's eminent airports are directly or indirectly connected to many other airports. Every airport is considered as a node and the route can be considered as edge connecting them. The work analyzes the USA airport network using different centrality measures of social network analysis. The centrality measures calculated on airport network help in identification of certain characteristics of the airports. Some of the characteristics are like the busiest airport and the airports which influence trade, alternate path, fastest route, nearest airports, etc. The characteristics helps to find the designated airports meant for improving the economy. The results of this paper say about the prominent communication and connections among the airports in the U.S.A. The tools used for the analysis are UCINET 6 and NetDraw. © 2018 Saleena. P et. al. More »» |
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2015 |
A. J., Radha D., and S., S., “Analysis of fuzzy rule optimization models”, International Journal of Engineering and Technology, vol. 7, pp. 1564-1570, 2015.[Abstract] Optimization without losing the accuracy and interpretability of rules is a major concern in rule based system. Fuzzy Inference system characterized by uncertainty tolerance is the best way to represent a knowledge based system. Optimization of rule based systems starts by incorporating selflearning ability to a fuzzy inference system. This can be achieved by neural networks, there by developing a neuro fuzzy inference system. This paper analyses different neuro fuzzy inference systems.The analysis has been performed in different types of datasets in terms of dimensionality and noises. Analysis results concludes that the neuro fuzzy model DENFIS (Dynamically Evolving Neuro Fuzzy Inference System) shows an improved performance when handling with high dimensional data. Simulation results on low dimensional data exhibits similar performance in ANFIS (Adaptive Neuro Fuzzy Inference System) and Denfis. More »» |
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2014 |
Radha D., Amudha, J., and Jyotsna C, “Study of Measuring Dissimilarity between Nodes to Optimize the Saliency Map”, Int.J.Computer Technology & Applications, vol. 5, no. 3, pp. 993-1000, 2014.[Abstract] An analytical conclusion based on eye tracking data sets has shown that Graph Based Visual Saliency (GBVS) measures saliency in a better way. GBVS promotes higher saliency at the center of the image plane and strongly highlights salient regions even for the locations that are far-away from object borders. It predicts human fixations more consistently than the standard algorithms. Every pixel in an image is mapped as an individual graph node in the activation map. This in turn increases the computational time. Hence the objective of this paper is to analyze the performance of saliency measure in GBVS by modeling different grouping strategies to represent a node. Here, we concentrate on finding the dissimilarity between the nodes by grouping pixels as a node with overlapping or non-overlapping pixels in the surrounding nodes which optimize the saliency closer to the Eye-Tracker’s saliency. The different grouping strategies of GBVS are analyzed across several performance measures like Normalized Scanpath Saliency the Linear Correlation Coefficient, Area Under Curve, , Similarity, Kullback – Leibler Divergence to prove its efficiency. Key terms – Visual Attention Models, Saliency maps, Eye-Tracking, Grouping pixels. More »» |
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2013 |
Radha D. and Amudha, J., “Detection of Unauthor- ized Human Entity in Surveillance Video”, International Journal of Engineering and Technology (IJET), vol. 5, no. 3, pp. 3101-3108, 2013.[Abstract] With the ever growing need for video surveillance in various fields, it has become very important to automate the entire process in order to save time, cost and achieve accuracy. In this paper we propose a novel and rapid approach to detect unauthorized human entity for the video surveillance system. The approach is based on bottom-up visual attention model using extended Itti Koch saliency model. Our approach includes three modules- Key frame extraction module, Visual attention model module, Human detection module. This approach permits detection and separation of the unauthorized human entity with higher accuracy than the existing Itti Koch saliency model. |
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