Dr. B. Rajathilagam is an Associate Professor, Department of Computer Science and Engineering, Amrita Vishwa Vidyapeetham, Coimbatore campus. She completed her Ph.D in 2013 under Centre for Excellence in Computational Engineering, Amrita Vishwa Vidyapeetham, Coimbatore campus. She has been at Amrita School of Engineering since 2000. Her research interests include Signal Processing, representation of signals, filter design, feature operators, pattern recognition, medical signal processing, thermal imaging and computer vision. She has been a research scholar at University of California, Riverside, USA under Prof. Sathish Tripathi  and  Prof. Michalis in 2004. She has also visited Birmingham University, UK in 2007. She completed a funded project for ISRO in 2009 and worked as a full time research scholar in Centre for Excellence in Computational Engineering during 2007-2009. She received the ‘Senior Woman Educator and Scholar’ award from the organization ‘National Foundation for Entrepreneurship Development (NFED), 2014, on the occasion of Women’s day. 

Currently she is working on applications of signal processing and is a reviewer for Pattern Recognition Elsevier Journal. She has been part of organising international conferences including ADCOM 2006, ICONNIAC 2014 and ACM Women in Computing conducted by Department of Computer Science and Engineering. She has conducted two national level workshops MADCA 2k14 and MAGS3D 2k14 in 2014 in collaboration with industry. She is working on parallelizing algorithms that develop filters, feature descriptors, segmentation and detection of objects and human and compression for Multimedia signals. The feature descriptors are being tested for Deep learning in identifying human aging.  She has also attended the Professors Meet of International Conference on High Performance Computing (HiPC), 2016 as part of a collaboration with NVIDIA.


Publication Type: Journal Article
Year of Conference Publication Type Title
2015 Journal Article R. Shwetha and Rajathilagam, B., “Super resolution of mammograms for breast cancer detection”, International Journal of Applied Engineering Research, vol. 10, pp. 21453-21465, 2015.[Abstract]

Mammography has been the most popular method for the early detection of the breast cancer. Due to low contrast of mammograms typical diagnostic signs such as masses and micro calcification are difficult to detect. So to create a high resolution mammogram super resolution (SR) technique can be used. This technique will make a high resolution image from a series of low resolution images of the same scene. A novel algorithm with interpolation for super resolution reconstruction has been proposed here. It has taken a interpolation technique that preserves edges without introducing any artifact. This also avoids pixilation, over smoothing and blurring of images. In our method we have used denoising, deblurring and registration technique to improve the quality of low resolution images and fused them to produce a higher resolution image. The proposed algorithm is a hybrid of bilinear interpolation and FCBI method with edge detecting criteria. © Research India Publications.

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2015 Journal Article S. C, Sandeep, R., and Rajathilagam, B., “Comparison of Segmentation Algorithms”, IJAER, 2015.
2012 Journal Article B. Rajathilagam, Rangarajan, M., and Soman, K. P., “Frequency analysis of signals and images using G-Lets”, International Journal of Imaging and Robotics, vol. 8, pp. 30-48, 2012.[Abstract]

This paper presents a method of frequency analysis for discrete signals using G-lets. From a group of transformations and representation theory, a finite basis of the signal space is obtained. The projections of the signal onto this basis are called G-lets. G-lets, due to the nature of transformations used, contain oscillations in such a manner that the difference between consecutive G-let coefficients is proportional to the local frequency. The signal frequency, in turn, is proportional to the difference in amplitude of the signal at any point. A dilation operation is defined to capture the frequencies without use of a windowing function, by highlighting the highest frequency of the existing signal in a G-let. Considering features of a signal as a combination of frequencies, feature extraction of 1-D signals and images are examined. The beginning and end of each feature are identified by the spread of low frequencies in the neighborhood of a high frequency. Results are demonstrated using dihedral groups, for simple 1-D signals, an ECG signal, and 'Lena' image. A qualitative comparison is provided with wavelets and Fourier analysis. © 2012 by IJIR (CESER Publications).

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2012 Journal Article B. Rajathilagam, Rangarajan, M., and Soman, K. P., “G-Lets: Signal Processing Using Transformation Groups”, vol. arXiv:1201.2995v1, 2012.
2012 Journal Article B. Rajathilagam, Rangarajan, M., and P, S. K., “G-Lets: A New Signal Processing Algorithm”, International Journal of Computer Applications, vol. 37 , no. 6, pp. 1-7, 2012.[Abstract]

Different signal processing transforms provide us with unique decomposition capabilities. Instead of using specific transformation for every type of signal, we propose in this paper a novel way of signal processing using a group of transformations within the limits of Group theory. For different types of signal different transformation combinations can be chosen. It is found that it is possible to process a signal at multiresolution and extend it to perform edge detection, denoising, face recognition, etc by filtering the local features. For a finite signal there should be a natural existence of basis in it’s vector space. Without any approximation using Group theory it is seen that one can get close to this finite basis from different viewpoints. Dihedral groups have been demonstrated for this purpose.

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Publication Type: Conference Proceedings
Year of Conference Publication Type Title
2015 Conference Proceedings B. Rajathilagam, Murali, R., and Balaji, B., “Dynamic Context-specific User Profiles by Regression Modelling”, Proceedings of GSTF CGAT. 2015.
2005 Conference Proceedings B. Rajathilagam, Jayaraj, B., and P. Rangan, V., “Naming, location tracking, synchronizing and aggregating wireless sensor networks”, In the proceedings of SPIE, vol. 5993. Optics East, Boston, USA, 2005.[Abstract]

The growth in utilization of sensor networks in every field of science has compelled the research community to device a common addressing technique for sensor networks. To achieve this, we propose an addressing and naming scheme for sensors based on a hierarchical model of the sensor network in this paper. We show that, the complete scheme allows accessing and tracking sensor devices by names and inherently facilitates data aggregation. We study the performance of and optimize the hierarchical architecture and use algorithms for bounding timing delays, width and height. Finally, we suggest a gateway-based architecture to establish a secure layer above the routing protocol to perform safe communication and location tracking. From our analysis it is seen that the methodology can be adopted for various scenarios like disaster areas, habitat monitoring, target tracking, medical monitoring, battlefield, etc. More »»
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