Dr. Praveen Kumar Yadaw
@kalingauniversity.ac.in
Assistant Professor, Department of Electrical Engineering
Kalinga University
Biography
Dr. Praveen Kumar Yadaw is an accomplished Assistant Professor at Kalinga University, Raipur, Chhattisgarh, India. With a rich and diverse academic and administrative background, Dr. Praveen Kumar Yadaw brings over 14 years of experience to the field, demonstrating a profound expertise in both research and educational administration.
Academic and Administrative Experience:
Dr. Praveen Kumar Yadaw has held several prestigious positions throughout their career. Most recently, they served as the Chief Controller of Examinations at Anjaneya University since July 2023. Prior to this role, they were the Controller of Examinations at Shree DAVARA University. Their previous roles also include serving as Assistant Controller of Examinations and Head of the Department in the School of Engineering & Technology at ISBM University. At ISBM University, Dr. Praveen Kumar Yadaw was instrumental in various administrative functions including curriculum design, research and development.
EDUCATION
Ph.D. (Luminescent)
M. tech
B. Tech.
M. Sc.
RESEARCH, TEACHING, or OTHER INTERESTS
Spectroscopy, Electrical and Electronic Engineering, Multidisciplinary, Signal Processing
FUTURE PROJECTS
Spectroscopy Rare Earth
Herein the synthesis, structural characterization and photoluminescence behavior of Er/Yb activated Gd(2)O(3 )phosphor is presented. Yb and Er-doped Gd2O3 phosphor samples were synthesized through the conventional solid-state-reaction (SSR) method and their structural aspects were examined by the X-ray diffraction (XRD) analysis. Morphology of the prepared phosphor samples was examined by Scanning Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM). The emission and excitation spectra of the prepared phosphor samples were systematically recorded. They displayed intense emission in the visible region. The excitation spectra recorded at 613 nm emission showed two prominent excitation peaks at 254 and 278 nm. Similar to that excitation with various emission peaks were also observed correspondingly at 467, 545 and 613 nm. The present study gives basic information about the transition of (Yb, Er) activated rare earth as well as the spectroscopic parameters of the prepared p
Applications Invited
Scholar Research
ICSSR 2024-09 to 2025-10 | Grant Indian Council of Social Science Research (New Delhi, IN) https://icssr.org/declaration-results-special-call-collaborative-research-proj
Applications Invited
Scopus Publications
Scholar Citations
Scholar h-index
Scopus Publications
Praveen Kumar Yadaw, J. Mitrić, N. Romčević, Vikas Dubey, N. Kumar Swamy, M. C. Rao, and Ravindranadh Koutavarapu
Springer Science and Business Media LLC
Santosh Kumar, Akash Ingle, Sandeep Roy, Manju, Rajendra Kumar, and Praveen Kumar Yadaw
IEEE
Advancements in DC-DC converters have significantly enhanced the performance, functionality, and applicability of electronic systems across various sectors. This paper aims to provide an in-depth examination of recent innovations in DC-DC converters, encompassing their foundational principles, state-of-the-art advancements, and emerging trends. By exploring the pivotal role of DC-DC converters in diverse applications, the study seeks to elucidate the primary catalysts driving innovation in this domain and map out its evolutionary path. The comprehensive review delves into the evolution, fundamental concepts, categorizations, technological advancements, and emerging trends of DC-DC converters. Key areas of emphasis include the integration of wide bandgap semiconductors, enhanced circuit topologies, digital control techniques, high-frequency operations, and miniaturization. Furthermore, the paper evaluates prospective directions, comparative studies, and performance metrics within the DC-DC converter arena. Researchers, engineers, and industry professionals in power electronics stand to gain valuable insights from this synthesis of recent research endeavors. The scope of the review encompasses advanced circuit configurations, digital control strategies, wide bandgap semiconductor applications, performance assessment criteria, and potential avenues for future exploration. Ultimately, this comprehensive analysis aims to deepen our comprehension of DC-DC converters and their prospective impact on the future landscape of power electronics.
Ravindra Changala, Praveen Kumar Yadaw, Mansoor Farooq, Mohammed Saleh Al Ansari, Veera Ankalu Vuyyuru, and S Muthuperumal
IEEE
In the realm of surveillance systems, ensuring robust anomaly detection capabilities is crucial for safeguarding against potential security breaches or hazardous incidents. This work uses the Sparse Autoencoder architecture to provide a unique method for improving anomaly identification in surveillance imagine information. The methodology begins with the collection of a comprehensive dataset, termed DCSASS, comprising videos from security cameras capturing a diverse range of abnormal and typical actions across various categories. Each video is meticulously labeled as normal or abnormal based on its content, facilitating the differentiation between routine operations and potential security threats. Individual frames extracted from the videos serve as image samples for subsequent model training and evaluation in anomaly detection tasks. In deep learning-driven anomaly identification networks, the preprocessing stage uses Min-Max Normalization to normalize pixel values, improving model stability and efficacy. Subsequently, the Sparse Autoencoder architecture, consisting of encoder and decoder components, is utilized for anomaly detection. The encoder, designed using convolutional neural network (CNN) architecture, extracts meaningful features from input images while inducing sparsity in learned representations through techniques like L1 regularization. Experimentation with various hyperparameters and architectural choices optimizes performance, with the decoder symmetrically mirroring the encoder's architecture to ensure accurate reconstruction of input images. The proposed approach outperforms existing methods such as CNN, RF and SVM, achieving 99% accuracy, making it 2.3% superior to other methods. Implemented in Python, our methodology demonstrates its efficacy in effectively capturing and reconstructing meaningful representations of input images, thereby enhancing anomaly detection capabilities in surveillance image data for improved security and safety measures.
Praveen Kumar Yadaw, R.K. Padhi, Vikas Dubey, M.C. Rao, and N. Kumar Swamy
Elsevier BV
Abhijeet R. Kadam and Sanjay J. Dhoble
Elsevier
RECENT SCHOLAR PUBLICATIONS
MOST CITED SCHOLAR PUBLICATIONS
Publications
Up-Conversion Behavior of Er3+/Yb3+-Activated Gd2O3 Phosphor for Magnetic Resonance Application