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Subhransu Padhee

Researcher at National Institute of Technology, Rourkela

Publications -  35
Citations -  387

Subhransu Padhee is an academic researcher from National Institute of Technology, Rourkela. The author has contributed to research in topics: Buck converter & PID controller. The author has an hindex of 11, co-authored 29 publications receiving 278 citations. Previous affiliations of Subhransu Padhee include Sambalpur University & Thapar University.

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Journal ArticleDOI

Recent Advances in Multifunctional Sensing Technology on a Perspective of Multi-Sensor System: A Review

TL;DR: Different aspects of design and implementation of multifunctional sensor technology have been addressed in this paper and can serve as a basic reading material for students and researchers pursuing research on a multi-sensor system.
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Overview of High-Step-Up DC–DC Converters for Renewable Energy Sources

TL;DR: A wide variety of DC–DC conversion topologies (both transformerless and isolated) with wide voltage conversion ratio have been reviewed and various soft-switching techniques are implemented to improve the converter efficiency.

Automatic Face Detection Using Color Based Segmentation

TL;DR: A skin based segmentation algorithm for face detection in color images with detection of multiple faces and skin regions is proposed because skin color has proven to be a useful and robust cue for face Detection, localization and tracking.
Proceedings ArticleDOI

Data logging and supervisory control of process using LabVIEW

TL;DR: This paper takes a case study of plant consisting of multiple boilers where multiple process variables of the boilers need to be acquired from the field and monitored and logged in a database for further analysis and supervisory control.
Journal ArticleDOI

Parametric System Identification and Robust Controller Design for Liquid-Liquid Heat Exchanger System

TL;DR: In this paper, a single-input single-output, linear time-invariant time-delay model of a liquid-liquid heat exchanger system is presented, where the prediction error method is used to estimate the model parameters.