Dibyalekha Nayak | Computer vision | Women Researcher Award

Dr . Dibyalekha Nayak | Computer vision | Women Researcher Award

Assistant professor at Shah and Anchor Kutchhi Engineering College, India

Dr. Dibyalekha Nayak is a dedicated academician and emerging researcher with deep expertise in image processing, adaptive compression, and VLSI design. Her professional journey is marked by a strong commitment to teaching, scholarly research, and technological advancement. With over a decade of teaching experience and a recently completed Ph.D. from KIIT University, Bhubaneswar, her research has produced several publications in SCI-indexed journals and international conferences. Dr. Nayak’s contributions reflect an interdisciplinary approach, combining deep learning techniques with low-power hardware design to address complex challenges in wireless sensor networks and multimedia systems. She has actively participated in faculty development programs and technical workshops, continuously upgrading her knowledge. Her professional philosophy emphasizes ethics, hard work, and continuous learning. Currently serving as an Assistant Professor at Shah and Anchor Kutchi Engineering College in Mumbai, she aspires to make impactful contributions to the field of electronics and communication through research, innovation, and collaboration.

Professional Profile 

Education🎓

Dr. Dibyalekha Nayak holds a Ph.D. in Image Processing from the School of Electronics at KIIT University, Bhubaneswar, where she completed her research between September 2018 and May 2024. Her doctoral work focused on advanced techniques in image compression and saliency detection using deep learning and compressive sensing. She completed her Master of Technology (M.Tech) in VLSI Design from Satyabhama University, Chennai, in 2011, graduating with a commendable CGPA of 8.33. Prior to that, she earned her Bachelor of Engineering (B.E.) in Electronics and Telecommunication from Biju Patnaik University of Technology (BPUT), Odisha, in 2008, with a CGPA of 6.5. Her academic background provides a strong foundation in both theoretical electronics and practical applications in image processing and circuit design. The combination of image processing and VLSI design throughout her academic journey has enabled her to engage in cross-disciplinary research and foster innovation in both hardware and software domains.

Professional Experience📝

Dr. Dibyalekha Nayak has accumulated over 12 years of rich academic experience in various reputed engineering institutions across India. Currently, she serves as an Assistant Professor at Shah and Anchor Kutchi Engineering College, Mumbai, affiliated with Mumbai University, where she joined in July 2024. Prior to this, she worked as a Research Scholar at KIIT University (2018–2024), contributing significantly to image processing research. Her earlier roles include Assistant Professor positions at institutions such as College of Engineering Bhubaneswar (2016–2018), SIES Graduate School of Technology, Mumbai (2014), St. Francis Institute of Technology, Mumbai (2013), and Madha Engineering College, Chennai (2011–2012). Across these roles, she has taught a variety of undergraduate and postgraduate courses, supervised student projects, and contributed to departmental development. Her teaching areas span digital electronics, VLSI design, image processing, and communication systems, demonstrating a strong alignment between her teaching and research activities.

Research Interest🔎

Dr. Dibyalekha Nayak’s research interests lie at the intersection of image processing, deep learning, and VLSI design, with a special focus on adaptive compression, saliency detection, and compressive sensing. Her doctoral research addressed the development of innovative, low-complexity algorithms for image compression using techniques like block truncation coding and DCT, tailored for wireless sensor network applications. She is also deeply interested in integrating deep learning frameworks into image enhancement and compression tasks to improve performance in real-world environments. Additionally, her background in VLSI design supports her interest in low-power hardware architectures for efficient implementation of image processing algorithms. Dr. Nayak is particularly motivated by research problems that bridge the gap between theoretical innovation and practical implementation, especially in the fields of embedded systems and multimedia communication. Her interdisciplinary research aims to create scalable, energy-efficient, and intelligent solutions for future communication and sensing technologies.

Award and Honor🏆

While Dr. Dibyalekha Nayak’s profile does not explicitly mention formal awards or honors, her scholarly achievements speak volumes about her academic excellence and dedication. She has published multiple research articles in prestigious SCI and Web of Science indexed journals such as Multimedia Tools and Applications, Mathematics, and Computers, reflecting the quality and impact of her research. She has been actively involved in reputed international conferences including IEEE and Springer Lecture Notes, where she has presented and published her research findings. Her work on saliency-based image compression and fuzzy rule-based adaptive block compressive sensing has received commendation for its innovation and applicability. Furthermore, her selection and sustained work as a Research Scholar at KIIT University for over five years highlights the recognition she has earned within academic circles. Her consistent participation in technical workshops, faculty development programs, and collaborations also demonstrate her growing reputation and standing in the field of electronics and image processing.

Research Skill🔬

Dr. Dibyalekha Nayak possesses a versatile and robust set of research skills aligned with modern-day challenges in image processing and electronics. She is proficient in developing image compression algorithms, saliency detection models, and adaptive techniques using block truncation coding, fuzzy logic, and DCT-based quantization. Her technical expertise extends to deep learning architectures tailored for image enhancement and compressive sensing in wireless sensor networks. Additionally, she has a strong command of VLSI design methodologies, enabling her to work on low-power circuit design and hardware implementation strategies. Dr. Nayak is also skilled in scientific programming, using tools such as MATLAB and Python, along with LaTeX for research documentation. She has a clear understanding of research methodologies, simulation frameworks, and performance analysis metrics. Her experience in preparing manuscripts for SCI-indexed journals and conference presentations showcases her technical writing abilities. Overall, her analytical mindset and hands-on skills make her a competent and impactful researcher.

Conclusion💡

Dr. Dibyalekha Nayak is a highly dedicated and emerging researcher in the fields of Image Processing, Deep Learning, and VLSI. Her academic journey reflects perseverance, scholarly depth, and a clear focus on impactful research. Her SCI-indexed publications, teaching experience, and cross-domain knowledge make her a deserving candidate for the Best Researcher Award.

Publications Top Noted✍

  • Title: Fuzzy Rule Based Adaptive Block Compressive Sensing for WSN Application
    Authors: D. Nayak, K. Ray, T. Kar, S.N. Mohanty
    Journal: Mathematics, Volume 11, Issue 7, Article 1660
    Year: 2023
    Citations: 6

  • Title: A novel saliency based image compression algorithm using low complexity block truncation coding
    Authors: D. Nayak, K.B. Ray, T. Kar, C. Kwan
    Journal: Multimedia Tools and Applications, Volume 82, Issue 30, Pages 47367–47385
    Year: 2023
    Citations: 4

  • Title: Walsh–Hadamard Kernel Feature-Based Image Compression Using DCT with Bi-Level Quantization
    Authors: D. Nayak, K. Ray, T. Kar, C. Kwan
    Journal: Computers, Volume 11, Issue 7, Article 110
    Year: 2022
    Citations: 3

  • Title: Sparsity based Adaptive BCS color image compression for IoT and WSN Application
    Authors: D. Nayak, T. Kar, K. Ray
    Journal: Signal, Image and Video Processing, Volume 19, Issue 8, Pages 1–7
    Year: 2025

  • Title: Hybrid Image Compression Using DCT and Autoencoder
    Authors: D. Nayak, T. Kar, K. Ray, J.V.R. Ravindra, S.N. Mohanty
    Conference: 2024 IEEE Pune Section International Conference (PuneCon), Pages 1–6
    Year: 2024

  • Title: Performance Comparison of Different CS based Reconstruction Methods for WSN Application
    Authors: D. Nayak, K.B. Ray, T. Kar
    Conference: 2021 IEEE 2nd International Conference on Applied Electromagnetics, Signal Processing, & Communication (AESPC)
    Year: 2021

  • Title: A Comparative Analysis of BTC Variants
    Authors: D. Nayak, K.B. Ray, T. Kar
    Conference: Proceedings of International Conference on Communication, Circuits, and Systems (LNEE, Springer)
    Year: 2021

  • Title: Low Power Error Detector Design by using Low Power Flip Flops Logic
    Authors: D. Chaini, P. Malgi, S. Lopes
    Journal: International Journal of Computer Applications, ISSN 0975-8887
    Year: 2014

Assoc Prof Dr. Qi Jia | Object Detection and Recognition | Best Researcher Award

Publications

Temporal refinement and multi-grained matching for moment retrieval and highlight detection

  • Authors: Zhu, C., Zhang, Y., Jia, Q., Wang, W., Liu, Y.
  • Journal: Multimedia Systems
  • Year: 2025

Bilevel progressive homography estimation via correlative region-focused transformer

  • Authors: Jia, Q., Feng, X., Zhang, W., Pu, N., Sebe, N.
  • Journal: Computer Vision and Image Understanding
  • Year: 2025

PMGNet: Disentanglement and entanglement benefit mutually for compositional zero-shot learning

  • Authors: Liu, Y., Li, J., Zhang, Y., Pu, N., Sebe, N.
  • Journal: Computer Vision and Image Understanding
  • Year: 2024

WBNet: Weakly-supervised salient object detection via scribble and pseudo-background priors

  • Authors: Wang, Y., Wang, R., He, X., Jia, Q., Fan, X.
  • Journal: Pattern Recognition
  • Year: 2024

A rotation robust shape transformer for cartoon character recognition

  • Authors: Jia, Q., Chen, X., Wang, Y., Ling, H., Latecki, L.J.
  • Journal: Visual Computer
  • Year: 2024

Prof. Larbi Guezouli | Object Detection and Recognition | Best Researcher Award

Prof. Larbi Guezouli | Object Detection and Recognition | Best Researcher Award

Professor at Higher National School of Renewable Energies, Environment, Algeria

👨‍🎓 Profiles

Scopus

Orcid

Publications

SES-ReNet: Lightweight deep learning model for human detection in hazy weather conditions

  • Author: Bouafia, Y., Allili, M.S., Hebbache, L., Guezouli, L.
  • Journal: Signal Processing: Image Communication
  • Year: 2025

Human Detection in Clear and Hazy Weather Based on Transfer Learning With Improved INRIA Dataset Annotation

  • Author: Bouafia, Y., Guezouli, L., Lakhlef, H.
  • Journal: International Journal of Computing and Digital Systems
  • Year: 2024

Two-step text detection framework in natural scenes based on Pseudo-Zernike moments and CNN

  • Author: Larbi, G.
  • Journal: Multimedia Tools and Applications
  • Year: 2023

Human Detection in Surveillance Videos Based on Fine-Tuned MobileNetV2 for Effective Human Classification

  • Author: Bouafia, Y., Guezouli, L., Lakhlef, H.
  • Journal: Iranian Journal of Science and Technology – Transactions of Electrical Engineering
  • Year: 2022

Reading signboards for the visually impaired using Pseudo-Zernike Moments

  • Author: Guezouli, L.
  • Journal: Advances in Engineering Software
  • Year: 2022

Mrs. Yasmine Zambou Tsopgni | Object Detection and Recognition | Best Researcher Award

Publications

Tectonic reevaluation of West Cameroon domain: Insights from high-resolution gravity models and advanced edge detection methods

  • Authors: Yasmine, Z.T.; Ghomsi, F.E.K.; Nouayou, R.; Tenzer, R.; Eldosouky, A.M.
  • Journal: Journal of Geodynamics
  • Year: 2024

Contribution of advanced edge-detection methods of potential field data in the tectono-structural study of the southwestern part of Cameroon

  • Authors: Nzeuga, A.R.; Ghomsi, F.E.; Pham, L.T.; Fnais, M.S.; Andráš, P.
  • Journal: Frontiers in Earth Science
  • Year: 2022

Prof. Hoonsoo Lee | Applications of Computer Vision | Best Researcher Award

Hoonsoo Lee | Applications of Computer Vision | Best Researcher Award

Hoonsoo Lee at Chungbuk National University, South Korea

Profiles

Scopus

Orcid

 Academic Background:

He is an Associate Professor in the Dept. of Biosystems Engineering at Chungbuk National University, located in Cheongju, Korea. The university is situated at 1 Chungdae-ro, BLDG# S21-24, RM# 202, Seowon-gu, Cheongju-si, Chungcheongbuk-do, 28644, Republic of Korea.

Education:

Prof. Lee earned his Ph.D. in Agricultural Machinery Engineering from Chungnam National University in August 2015, with a dissertation on the rapid detection of pathogenic infections in watermelon seeds using spectral image analysis. He completed his M.S. in the same field in August 2009, focusing on the development of an electronic nose system for evaluating meat freshness. He holds a B.S. in Bioindustrial Machinery Engineering, which he completed in August 2007.

 Employment History:

Prof. Lee has been an Associate Professor at Chungbuk National University since September 2018. Prior to this, he worked as a PostDoc Researcher at the United States Department of Agriculture (USDA) Agricultural Research Service (ARS) in Beltsville, MD, USA, from August 2015 to August 2018. His experience also includes serving as a Research Assistant at Chungnam National University from June 2008 to August 2015 and an internship at USDA, ARS from July 2010 to June 2011.

 Research Interests:

Prof. Lee’s research focuses on developing nondestructive sensing technology for agricultural and food products. He is also interested in data analysis using hyperspectral imaging in conjunction with machine learning and artificial intelligence techniques.

 Research Experience:

Prof. Lee specializes in non-destructive quality measurement of food and agricultural products using vibrational spectroscopic techniques. His work includes developing and commercializing a high-throughput online detection system utilizing optical techniques. He has created hyperspectral and multispectral imaging systems for pathogen-infected seeds and fecal contamination on leafy greens. Additionally, he has developed hyperspectral imaging systems to evaluate food quality, focusing on applications such as detecting physical damages in pears, identifying cracks in tomatoes, assessing color levels in pepper powder, and measuring moisture distribution in cooked meats, rice, and soybeans. Furthermore, he has created a multipurpose floating platform for hyperspectral imaging and monitoring E. coli concentrations in irrigation ponds in Maryland. His research also includes developing Vis/NIR hyperspectral models for assessing the effects of water and fertilizer on crops like cabbage, garlic, and soybeans, as well as laser speckle technology for diagnosing crop stress to enhance precision agriculture practices.

 Publications:

Current trends in the use of thermal imagery in assessing plant stresses: A review
  • Authors: Adhitama Putra Hernanda, R., Lee, H., Cho, J.-I., Cho, B.-K., Kim, M.S.
  • Journal: Computers and Electronics in Agriculture
  • Year: 2024
Chlorophyll Fluorescence Imaging for Environmental Stress Diagnosis in Crops
  • Authors: Park, B., Wi, S., Chung, H., Lee, H.
  • Journal: Sensors
  • Year: 2024
Construction of a sustainable model to predict the moisture content of porang powder (Amorphophallus oncophyllus) based on pointed-scan visible near-infrared spectroscopy
  • Authors: Amanah, H.Z., Rahayoe, S., Harmayani, E., Lee, H.
  • Journal: Open Agriculture
  • Year: 2024
Spectroscopy Imaging Techniques as In Vivo Analytical Tools to Detect Plant Traits
  • Authors: Hernanda, R.A.P., Lee, J., Lee, H.
  • Journal: Applied Sciences (Switzerland)
  • Year: 2023
Snapshot-Based Multispectral Imaging for Heat Stress Detection in Southern-Type Garlic
  • Authors: Ryu, J., Wi, S., Lee, H.
  • Journal: Applied Sciences (Switzerland)
  • Year: 2023