Faculty Profile

Dr. Ganeswara Padhy

Dr. Ganeswara Padhy

Professor, JKU

  • JK Institute Of Technology, JKU
  • Ph.D Electronics Science
  • Area of Expertise: AI, Technology
Dr. Ganeswara Padhy worked as a Lecturer at CV Raman Polytechnic (2007–2011) in the Department of Electronics & Telecommunication, where he taught subjects like Basic Electronics, Instrumentation, Microprocessor and Microcontroller, DSP, and Communication Techniques. He served as a Senior Lecturer at Khalikote University (2011–2015) in the Department of Electronics & Telecommunication, teaching courses related to Image Processing, Signal Processing, and Communication Systems while supervising undergraduate and postgraduate projects. He worked as a Guest Faculty at SBR Government Women’s College (2016–2022) in the Departments of Physics and Computer Science, teaching programming and computer science subjects. Currently, he is working as a Senior Project Associate at CSIR–IMMT (from September 2022), contributing to research on machine learning–based electrode design for energy storage devices.

• Published research paper titled “Super Capacitor Properties of Partially Oxidised MXene Quantum Dots/Graphene Hybrids: Fabrication of Flexible/Wearable Micro-Supercapacitor Devices” in Chemical Engineering Journal (2024) focusing on machine learning–based prediction of electrochemical properties.

• Research work on “Emerging Vanadium-Doped Cobalt Chloride Carbonate Hydroxide for Flexible Electrochromic Micro-Supercapacitor”, where machine learning models were used to predict charge-discharge characteristics using RGB inputs.

• Developed Least Mean Fourth Based Constrained Adaptive Order Statistic Filters for Image Restoration using MATLAB to improve restoration of noisy images.

• Conducted research on Image Restoration using Adaptive Non-linear Order Statistic Filters, improving image quality and noise reduction techniques.

• Developed a technique for Automatic Localization of Pupil using Histogram Thresholding and Region-Based Mask Filter, contributing to computer vision applications.

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