Dr. T. K. Battula
Jawaharlal Nehru Technological University Kakinada, GITAM (Deemed to be University) School of Technology, Andhra University College of Engineering
0000-0002-8316-7310
About
Dr. T. K. Battula completed his B.E(ECE), M.E(ECE) and Ph.D.(ECE) from Andhra University, Visakhapatnam, Andhra Pradesh India. He worked in GIT, GITAM University for a period of 12 years at various levels as Assistant Professor, Sr.Assistant Professor and Associate Professor. He is currently working as Professor in Department of ECE, University College of Engineering Kakinada, Jawaharlal Nehru Technological University, Kakinada since 2016. His Research Areas of Interest are Fractional Order Systems, Nano Electronics, Fractional Order Signal Processing, and Analog VLSI.
Employment
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Jawaharlal Nehru Technological University Kakinada Professor2016 - Present
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GITAM (Deemed to be University) School of Technology Associate Professor2000 - 2013
Education
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Andhra University College of Engineering PhD2006 - 2009
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Andhra University College of Engineering M.E1998 - 2000
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Andhra University College of Engineering B.E1993 - 1997
Projects & Funding
Projects & funding information is unavailable.
Publications (136)
- Brain Tumor Classification Leveraging SWIN Transformer with Hybrid Whale Remora Optimization Based Hierarchical VGGNet19 Save
- Interference-resilient design of aperiodic phased arrays for 5G applications using modified Laplacian invasive weed optimization Save
- A novel hybrid machine and deep learning approach for brain tumor classification based patient survival time prediction Save
- Exploring the role of behavioral analytics and anomaly detection in securing mobile networks for critical infrastructure Save
- FPGA-Based Handwritten Digit Recognition Using 3-D Hop Net and Equilibrium Optimization Save
- ECG beat classification with fractional order differentiator and machine learning techniques Save
- A hybrid approach for machine learning based beat classification of ECG using different digital differentiators and DTCWT Save
- A novel attention based RNN-Kalman filter for accurate position forecasting framework Save
- An efficient noise reduction technique in underwater acoustic signals using enhanced optimization-based residual recurrent neural network with novel loss function Save
- CARNet: An Efficient Cascaded and Attention-Based RNN Architecture for Modulation Classification in Cognitive Radio Network Using Improved Kookaburra Optimization Strategy Save