Work place: Nehru Memorial College (Affiliated to Bharathidasan University), Puthanampatti, Tiruchirappalli-Dt, Tamil Nadu, India - 621 007

E-mail: murugans1976@gmail.com


Research Interests: Antenna Technology, Communications, Microwave Measurements, Microwave Technology


Dr.S.Murugan received his M.Sc degree in Applied Mathematics from Anna University in 1984 and M.Phil degree in Computer Science from Regional Engineering College, Tiruchirappalli in 1994. He is an Associate Professor in the Department of Computer Science, Nehru Memorial College (Autonomous), affiliated to Bharathidasan University since 1986. He has 32 years of teaching experience in the field of Computer Science. He has completed his Ph.D. degree in Computer Science with a specialization in Data Mining from Bharathiyar University in 2015.
His research interest includes Data and Web Mining. He has published many research articles in reputed National and International journals.

Author Articles
GNVDF: A GPU-accelerated Novel Algorithm for Finding Frequent Patterns Using Vertical Data Format Approach and Jagged Array

By P. Sumathi S.Murugan

DOI: https://doi.org/10.5815/ijmecs.2021.04.03, Pub. Date: 8 Aug. 2021

In the modern digital world, online shopping becomes essential in human lives. Online shopping stores like Amazon show up the "Frequently Bought Together" for their customers in their portal to increase sales. Discovering frequent patterns is a fundamental task in Data Mining that find the frequently bought items together. Many transactional data were collected every day, and finding frequent itemsets from the massive datasets using the classical algorithms requires more processing time and I/O cost. A GPU accelerated Novel algorithm for finding the frequent patterns using Vertical Data Format (GNVDF) has been introduced in this research article. It uses a novel pattern formation. In this, the candidate i-itemsets is divided into two buckets viz., Bucket-1 and Bucket-2. Bucket-1 contain all the possible items to form candidate-(i+1) itemsets. Bucket-2 has the items that cannot include in the candidate-(i+1) itemsets. It compactly employs a jagged array to minimize the memory requirement and remove common transactions among the frequent 1-itemsets. It also utilizes a vertical representation of data for efficiently extracting the frequent itemsets by scanning the database only once. Further, it is GPU-accelerated for speeding up the execution of the algorithm. The proposed algorithm was implemented with and without GPU usage and compared. The comparison result revealed that GNVDF with GPU acceleration is faster by 90 to 135 times than the method without GPU.

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Compact MIMO Shorted Microstrip Antenna for 5G Applications

By S.Murugan

DOI: https://doi.org/10.5815/ijwmt.2021.01.03, Pub. Date: 8 Feb. 2021

The objective of the work is to design a compact MIMO antenna at 3.5 GHz suitable for 5G applications. MIMO antenna is suitable choice for increasing the signal to noise ratio of mobile communication systems. The channel capacity can be increased by improving signal to noise ratio. At the same time, high isolation between the elements should be maintained.  Planar inverted F antenna (PIFA) is used as a unit element for MIMO antenna in this work. The unit element dimensions are 9.5 x 7 mm2. Two shorting pins are used for getting better impedance matching. Four elements are arranged in the FR4 substrate with dielectric constant 4.4 and thickness of 1.6 mm. The performance of 4 element Multiple Input Multiple Output (MIMO) antenna is optimized using HFSS software. The results show that the   better impedance matching at desired frequency band and a gain of 4.2 dB in the bore-sight axis is obtained for the four elements. The gain of four element antenna is improved from - 0.52 dB to 4.2 dB than two element antenna. The isolation between the elements is obtained below -15 dB . The overall volume of the antenna is 25.3 x 26.8 x 1.6 mm3, which ensures compactness suitable for mobiles.

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