M
M. Pallikonda Rajasekaran
Researcher at Kalasalingam University
Publications - 83
Citations - 1149
M. Pallikonda Rajasekaran is an academic researcher from Kalasalingam University. The author has contributed to research in topics: Computer science & Image segmentation. The author has an hindex of 14, co-authored 67 publications receiving 692 citations.
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Proceedings ArticleDOI
An IoT based patient monitoring system using raspberry Pi
TL;DR: Monitoring patient's body temperature, respiration rate, heart beat and body movement using Raspberry Pi board brings out the solution for effective patient monitoring at reduced cost and also reduces the trade-off between patient outcome and disease management.
Journal ArticleDOI
An unsupervised learning method with a clustering approach for tumor identification and tissue segmentation in magnetic resonance brain images
TL;DR: The proposed hybrid SOM-FKM algorithm assists the radio surgeon by providing an automated tissue segmentation and tumor identification, thus enhancing radio therapeutic procedures.
Proceedings ArticleDOI
Indigenous Health Tracking Analyzer Using IoT
V. Muneeswaran,Mr.P. Nagaraj,M. Pallikonda Rajasekaran,N.Sai Chaithanya,S. Babajan,S.Udaykumar Reddy +5 more
TL;DR: In this article, a health tracking analyzer for mobile health applications is presented, which can be used to check and store the healthcare-related data in server by using a Wi-Fi Module.
Journal ArticleDOI
An automated hybrid approach using clustering and nature inspired optimization technique for improved tumor and tissue segmentation in magnetic resonance brain images
Anitha Vishnuvarthanan,M. Pallikonda Rajasekaran,Vishnuvarthanan Govindaraj,Yudong Zhang,Arunprasath Thiyagarajan +4 more
TL;DR: The proposed combinational algorithm offers a better support to a radiologist in the process of diagnosing the pathologies, since; it utilizes both optimization and clustering techniques.
Proceedings ArticleDOI
VLSI Implementation of Image Compression using TSA Optimized Discrete Wavelet Transform Techniques
TL;DR: In this paper, the VLSI implementation of HAAR wavelet-based image compression is proposed and designed and provides a hardware-free architecture with low cost.