Prof. Dr. Arun Balodi
Electronics and Communication Engineering, Dayananda Sagar University, Bengaluru, India
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Academic Career Length
11Years (2016-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications2 January 2021 in Clean Technologies and Environmental Policy
Journal Article
RKRoshan Kumar
Roshan KumarMIMohamed Ismail
Mohamed IsmailWZWei Zhao
Wei Zhao
Mohammad Noori
Arvind R YadavDr. Arun Balodi +6
1 January 2019 in Neural Computing and Applications
Journal Article
Dr. Arun BalodiRAR. S. Anand
R. S. Anand
MDM. L. Dewal
M. L. Dewal
ARAnurag Rawat
Anurag Rawat
1 June 2016 in Computers in Biology and Medicine
Journal Article
Dr. Arun BalodiMDM.L. Dewal
M.L. DewalRAR.S. Anand
R.S. AnandARAnurag Rawat
Anurag Rawat18 April 2023 in Wireless Personal Communications
Journal Article
Manoj TolaniABAmbar Bajpai
Ambar BajpaiDr. Arun Balodi
Sunny -RSRajat Kumar Singh
Rajat Kumar Singh22 September 2025 in IEEE Access
Journal Article
SHShams Ul Haq
Shams Ul HaqEAErfan Abbasian
Erfan AbbasianIKImran Ahmed Khan
Imran Ahmed KhanOSOwais Ahmad Shah
Owais Ahmad ShahDr. Arun BalodiVSVakkalakula Bharath Sreenivasulu
Vakkalakula Bharath Sreenivasulu +1
This paper introduces a novel 10T SRAM cell optimized for low-power, high-reliability biomedical wearable systems. The proposed design features a read-assist mechanism to suppress read disturbs, enhancing read static noise margin (RSNM), while a decoupled pull-down path for the ‘0’ storage node improves write SNM (WSNM). Leveraging Stanford CNTFET models, HSPICE simulations reveal a 1.12× and 14.02× enhancement in RSNM and WSNM, respectively, at 0.3V over the conventional 6T cell. Additionally, the design achieves substantial power savings—3.02× (read), 1.97× (write), and 1.89× (leakage)—compared to state-of-the-art 9T/10T CNTFET SRAMs. Monte Carlo analysis further demonstrates superior robustness, with 2.38× and 6.62× higher yield in RSNM and WSNM at 0.3V, validating its reliability for energy-constrained biomedical applications.
8 September 2020 in IETE Journal of Research
Journal Article
Dr. Arun BalodiRAR. S. Anand
R. S. AnandMDM. L. Dewal
M. L. DewalARAnurag Rawat
Anurag RawatThis paper exhibits a computer-aided diagnosis system for the severity analysis of mitral regurgitation (MR) and assesses the discriminatory capability of Daubechies wavelet-based texture modeling. The Daubechies wavelet family has been utilized for the image decomposition because of its approximate shift invariance property. Seven statistical texture features have been utilized after the decomposition of the image up to four levels and after that concatenated. A supervised classifier, support vector machine (SVM) has been utilized with 10-fold cross-validation approach. The highest classification accuracies are 99.12 ± 0.44 utilizing db2, 99.70 ± 0.29 utilizing db4, and 97.68 ± 1.04 utilizing db4 wavelet in A2C, A4C, and PLAX respectively. The exploratory outcomes show that the proposed algorithm is effective and db4 beat among the Daubechies wavelet family considered amid this audit for precise severity investigation of the MR images.
About Me
Dr Arun Balodi, Senior Member IEEE, Fellow IETE, Life Member ISTE is currently working as Professor and chairman at the Department of Electronics and Communication Engineering, Dayananda Sagar University in Bangalore, India. Dr Arun completed his PhD from the Department of Electrical Engineering, Indian Institute of Technology, Roorkee, India, 2018, M. Tech., Digital Signal Processing, Govind Ballabh Pant Engineering College, Pauri, India, 2010, and B.Tech. in Electronics and Communication Engineering, Uttar Pradesh Technical University, Lucknow, India, 2005. He was awarded Gold Medal in M. Tech., and the Academic Excellence Award in 2010 and 2011. His research area is Biomedical Signal and Image Processing, Artificial Intelligence. Dr. Arun has more than 15 years of teaching and research experience in various institutions in India. His research area is Digital Signal and Image Processing, Medical Image Analysis, Machine Learning and Pattern Recognition. He has published over 65 papers in peer-reviewed journals, international conferences, and 07 Patents. He is an active researcher and reviewer for various indexed Journals and Conferences and has given various technical talks. He has actively participated in more than 50 FDPs and more than 100 webinars and conducted workshops in the domain of Signal and Image Processing. He has delivered expert talks in more than 21 STPs, conferences, and webinars.