Mohamed Hebaishy | Computer Vision | Excellence in Computer Vision Award

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Assoc. Prof. Dr. Mohamed Hebaishy | Computer Vision | Excellence in Computer Vision Award

Associate Prof. in ERI at Electronics Research Institute, Egypt

Dr. Mohamed Ahmed Hebaishy is a distinguished researcher with extensive expertise in biometrics, iris recognition, image processing, computer vision, and satellite imaging. He has made remarkable contributions through his work in human identification systems, advanced image representation, and security technologies. His career spans academia, research institutions, and international collaborations, combining theoretical innovation with real-world applications in areas such as space research and remote sensing. He has published in reputed journals and conferences, including IEEE and Springer platforms, and actively engages in research that bridges science and technology. Beyond his research output, he has held significant leadership roles, mentored graduate students, and reviewed research projects for universities and conferences. His diverse professional experiences, strong academic foundation, and continuous pursuit of impactful research highlight his commitment to advancing scientific knowledge and addressing global challenges, making him a valuable contributor to the academic and research community.

Professional Profile 

Google Scholar | Scopus Profile | ORCID Profile 

Education

Dr. Mohamed Ahmed Hebaishy completed his Bachelor of Science in Electronic Engineering with a focus on automatic control and measurements at Menoufia University, where he built a strong foundation in control systems and electronics. He later pursued a Master of Science degree in Electronics and Communication at Cairo University, with his thesis centered on developing a fuzzy controller for flexible joint manipulators, reflecting his early focus on control and automation. His academic journey culminated in earning a Doctor of Philosophy in Information Technology from Vladimir State University in the Russian Federation, specializing in control system analysis and data processing. His doctoral thesis focused on using iris image processing in human identification systems, marking the beginning of his long-term contributions to the field of biometrics. Through these academic achievements, he has combined expertise in engineering, computing, and data-driven technologies, equipping him with the knowledge and skills to contribute meaningfully to interdisciplinary research.

Professional Experience

Dr. Mohamed Ahmed Hebaishy has built a rich professional career across academia and research institutions, holding positions that span lecturer, assistant professor, and department head roles. He has served as a researcher at the Electronics Research Institute, contributing to significant projects in informatics and computer science. His work extended to leadership in national space programs, where he played a key role in satellite image processing and payload command systems for EgyptSat missions. He also gained international academic experience as an assistant professor at Shaqra University in Saudi Arabia, where he later became head of the computer science department. His contributions include guiding research projects, supervising theses, and leading academic initiatives. Additionally, he has been a reviewer for major universities and scientific conferences, reflecting his involvement in shaping the academic community. His experience demonstrates a balance of teaching, research, and leadership, making him a well-rounded academic and professional.

Research Interest

Dr. Mohamed Ahmed Hebaishy’s research interests lie at the intersection of biometrics, image processing, computer vision, and artificial intelligence, with a strong emphasis on human identification systems and security technologies. He has worked extensively on iris recognition, exploring innovative approaches to enhance accuracy and efficiency in biometric applications. His interests also extend to satellite imaging and remote sensing, where he has contributed to projects in national space programs, including the development of image processing systems for EgyptSat satellites. In recent years, his focus has broadened to include advanced methods in pattern recognition, machine learning, and computer-aided automation systems. He is also engaged in applied research addressing real-world challenges such as waste sorting, wireless communication, and medical applications of imaging. His diverse interests reflect a commitment to advancing cutting-edge technologies that improve security, automation, and sustainability, while also fostering new interdisciplinary pathways in computer science and engineering.

Award and Honor

Throughout his career, Dr. Mohamed Ahmed Hebaishy has received recognition for his contributions to research, teaching, and leadership within the fields of biometrics, image processing, and space technology. His involvement in the EgyptSat satellite programs and ITIDA-funded security projects demonstrated his ability to translate research into impactful applications, earning him acknowledgment within the scientific community. He has also been invited as a reviewer for universities, research conferences, and scientific committees, reflecting trust in his expertise and judgment. His leadership as head of the computer science department at Shaqra University further highlights his role in shaping academic excellence and guiding student development. While his curriculum vitae does not list specific awards, his record of sustained contributions, successful project leadership, and active engagement in international research platforms stands as a form of recognition in itself. His ongoing publications in reputed journals further strengthen his professional standing as a dedicated and accomplished researcher.

Research Skill

Dr. Mohamed Ahmed Hebaishy possesses a broad set of research skills that reflect his deep expertise in both theoretical and applied aspects of computer science and engineering. He is skilled in biometric system design, with specialization in iris recognition, image processing algorithms, and human identification technologies. His technical capabilities extend to satellite image analysis, data processing, and control systems, where he has led projects involving payload command systems for national space programs. He is proficient in developing and applying advanced algorithms, including fuzzy logic, wavelet transforms, and optimization techniques, to solve complex research problems. His experience also covers interdisciplinary areas such as wireless communication systems, security applications, and automated testing tools. Beyond technical expertise, he has strong skills in project leadership, academic supervision, and research collaboration, enabling him to contribute effectively to both academic and applied research communities. His skill set demonstrates adaptability, innovation, and problem-solving ability.

Publications Top Notes

Title: A comparative study of QTP and load runner automated testing tools and their contributions to software project scenario
Authors: M Imran, M Hebaishy, AS Alotaibi
Year: 2016
Citation: 12

Title: Road extraction from high resolution satellite images by morphological direction filtering and length filtering
Authors: TM Talal, MI Dessouky, A El-Sayed, M Hebaishy, FA El-Samie
Year: 2008
Citation: 12

Title: Increasing the Efficiency of Iris Recognition Systems by Using Multi-Channel Frequencies of Gabor Filter
Authors: AS Alotaibi, MA Hebaishy
Year: 2014
Citation: 7

Title: Extraction of roads from high-resolution satellite images with the discrete wavelet transform
Authors: TM Talal, A El-Sayed, M Hebaishy, MI Dessouky, SA Alshebeili
Year: 2013
Citation: 4

Title: Optimized Daugman’s algorithm for iris localization
Authors: MA Hebaishy
Year: 2008
Citation: 4

Title: Sibs: A sparse encoder utilizing self-inspired bases for efficient image representation
Authors: AN Omara, MA Hebaishy, MS Abdallah, YI Cho
Year: 2024
Citation: 3

Title: Poster: Optimized Daugman’s algorithm for iris localization
Authors: M Hebaishy
Year: 2008
Citation: 3

Title: Fast Fingerprint Identification based on the DoG Filter
Authors: MA Hebaishy, FA Syam
Year: 2025

Title: S-shaped patch antenna array for automotive applications in X-band for wireless communications
Authors: MA Hebaishy
Year: 2024

Title: Building an automatic waste sorting system with controller based wireless sensor smart segregation system
Authors: MA Hebaishy
Year: 2024

Title: Security system based on human iris
Authors: HS Ahmed, MA Hebaishy
Year: 2014

Title: Attitude determination for geostationary satellite using optimized real time image registration algorithm
Authors: AE OA Elsayed, A Farrag, M Hebaishy
Year: 2009

Title: Texture analysis of the human iris for high authentication
Authors: MA Hebaishy, BV Gerkov
Year: 2002

Title: Using phase demodulator for encoding iris
Authors: AS Alotaibi, MA Hebaishy

Conclusion

Dr. Mohamed Ahmed Hebaishy is highly deserving of the Best Researcher Award for his significant contributions to biometrics, image processing, and satellite imaging, which have advanced both scientific understanding and practical applications in security and space research. His extensive academic career, impactful publications, leadership roles, and dedication to mentoring students highlight his commitment to advancing knowledge and fostering innovation. With his proven expertise and strong foundation in applied research, he is well positioned to continue driving advancements in computer vision, human identification systems, and international collaborations, further strengthening his role as a leader in research and society.

Sivanagireddy Kalli | Deep Learning for Computer Vision | Academic Excellence Distinction Award

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Dr. Sivanagireddy Kalli | Deep Learning for Computer Vision | Academic Excellence Distinction Award

Professor at Sridevi Women’s Engineering College, India

Dr. K. Sivanagireddy is a seasoned academician and researcher with over 20 years of experience in teaching, research, and administration. He has served in key academic leadership roles including Dean Academics, Head of Department, and Principal across reputed engineering institutions in Telangana and Andhra Pradesh. His extensive contributions include the publication of more than 60 research papers in SCI, Scopus, and UGC CARE-listed journals, along with participation in over 20 international conferences. He has been a driving force in innovation, holding eight patents—both national and international—and authoring nine technical books. He recently completed a Postdoctoral Fellowship at the University of South Florida (2024) and earned a Ph.D. in Electronics and Communication Engineering from JNTU Hyderabad (2019). His expertise spans areas like VLSI Design, IoT, AI, Embedded Systems, and Medical Image Processing. Recognized nationally and internationally, Dr. Sivanagireddy is also an active member of professional bodies such as IEEE, IAENG, and IAOE.

Professional Profile 

Education🎓

Dr. K. Sivanagireddy has a strong academic foundation rooted in electronics, communication, and embedded systems. He earned his Ph.D. in Electronics and Communication Engineering from Jawaharlal Nehru Technological University, Hyderabad, in 2019. Recently, in 2024, he completed his Postdoctoral Fellowship at the University of South Florida, USA, further enriching his research exposure and global academic outlook. His earlier postgraduate education includes an M.Tech in Embedded Systems from JNTUK, Kakinada (2014), and an M.E in VLSI Design from Vinayaka Missions University, Tamil Nadu (2006). He began his academic journey with a B.Tech in Electronics and Communication Engineering from Bharathidasan University, Tiruchirappalli, in 2002. His education reflects a clear emphasis on digital design, embedded computing, and system optimization, which laid the groundwork for his multifaceted contributions in academia and research. He has also pursued various NPTEL and FDP certifications from top IITs, demonstrating his commitment to lifelong learning and skill enhancement.

Professional Experience📝

Dr. K. Sivanagireddy brings over two decades of professional academic experience, with an emphasis on leadership, research, and teaching. Currently serving as Dean Academics and Professor at Sridevi Women’s Engineering College, Hyderabad since 2019, he previously held the positions of Head of Department and Associate Professor at the same institute. Earlier in his career, he worked at Arjun College of Technology and Science and LITAM, Guntur, where he mentored undergraduate and postgraduate students and handled administrative responsibilities. His contributions extend to coordinating academic accreditations like NAAC and NBA, overseeing student projects, counseling, and organizing technical paper contests. His strategic leadership has helped align institutional goals with academic excellence and research development. With a deep understanding of educational systems, faculty management, and curriculum design, Dr. Sivanagireddy has played a pivotal role in shaping the academic structure of the institutions he served. His professionalism and experience continue to influence engineering education in India.

Research Interest🔎

Dr. Sivanagireddy’s research interests are broad, multidisciplinary, and highly application-oriented. His primary focus lies in Medical Image Processing, Artificial Intelligence, Deep Learning, and IoT-enabled systems, especially for healthcare diagnostics and smart surveillance. He has conducted advanced research in brain tumor detection, cancer classification, heart disease prediction, and autonomous medical devices, often leveraging CNN, LSTM, and hybrid deep learning models. Additionally, his work spans VLSI Design, Embedded Systems, Cybersecurity, Video Surveillance, and Signal Processing, reflecting his versatility. His contributions also extend to developing IoT-integrated intelligent systems, machine learning-based prediction models, and hardware optimization techniques. Many of his projects are focused on societal needs, such as fall detection for the elderly, counterfeit currency detection, and remote health monitoring. His research is rooted in real-world impact, bridging engineering with life sciences and computing. This interdisciplinary approach allows him to explore innovative solutions across both theoretical and applied research domains.

Award and Honor🏆

Dr. K. Sivanagireddy’s scholarly achievements have been widely recognized through multiple national and international honors. He received the International Academic Excellence Award from I2OR in 2022, acknowledging his impactful global research footprint. In 2021, he was conferred with the National Faculty Excellency Award by the International Journal of MC Square Scientific Research, reflecting his outstanding contributions to teaching and innovation. He also earned the National Certificate of Excellence from the Telangana Engineering Colleges Faculty Association in 2020, further emphasizing his role in academic leadership. In addition to these awards, his editorial engagement with the Asian Council of Science Editors and professional memberships with IEEE, IAENG, and IAOE signify his active participation in international scholarly communities. His commitment to excellence, innovation, and quality research has made him a role model in engineering academia, and these accolades underscore his dedication to elevating academic standards at both institutional and national levels.

Research Skill🔬

Dr. Sivanagireddy possesses a diverse and robust set of research skills that span both theoretical modeling and practical application. He is adept in machine learning algorithms, deep learning frameworks, IoT development, and VLSI simulation tools. His proficiency in tools like MATLAB, Python, Verilog, and FPGA platforms has enabled him to develop and deploy intelligent systems for healthcare, security, and automation. He has expertise in image processing techniques, including segmentation, classification, and feature extraction using CNNs, Bi-LSTM, and hybrid models. Additionally, he demonstrates advanced knowledge in medical diagnostics, pattern recognition, and cloud computing integration. His research skillset is not only confined to software but extends to hardware optimization, including CMOS and ASIC design. Through his participation in over 20 conferences and completion of NPTEL certifications from IITs, he maintains up-to-date technical competence. These diverse skills allow him to drive interdisciplinary research, publish impactful papers, and mentor future innovators effectively.

Conclusion💡

Dr. K. Sivanagireddy is highly deserving and well-qualified for the Best Researcher Award. With a prolific publication record, leadership roles, multiple patents, academic books, and contributions to multiple domains in engineering and technology, he stands out as a multidisciplinary scholar and innovator. A stronger emphasis on research impact, international projects, and focused thematic expertise would further elevate his candidacy.

Publications Top Noted✍

  • Title: An effective motion object detection using adaptive background modeling mechanism in video surveillance system
    Authors: SNR Kalli
    Year: 2021
    Citations: 54

  • Title: Early lung cancer prediction using correlation and regression
    Authors: K Sivanagireddy, S Yerram, SSN Kowsalya, SS Sivasankari, J Surendiran, RG Vidhya
    Year: 2022
    Citations: 24

  • Title: Image Compression and reconstruction using a new approach by artificial neural network
    Authors: KSN Reddy, BR Vikram, LK Rao, BS Reddy
    Year: 2012
    Citations: 21

  • Title: A Fast Curvelet Transform Image Compression Algorithm using with Modified SPIHT
    Authors: KSN Reddy, BRS Reddy, G Rajasekhar, KC Rao
    Year: 2012
    Citations: 14

  • Title: A nanoplasmonic branchline coupler for subwavelength wireless networks
    Authors: K Thirupathaiah, KS Reddy, GRS Reddy
    Year: 2021
    Citations: 11

  • Title: Generative Adversarial Networks based Approach for Intrusion Detection System
    Authors: S Kalli, BN Kumar, S Jagadeesh
    Year: 2022
    Citations: 8

  • Title: IMPLEMENTATION OF OBJECT TRACKING AND VELOCITY DETERMINATION
    Authors: SNR Kalli
    Year: 2012
    Citations: 5

  • Title: Image compression by discrete curvelet wrapping technique with simplified SPIHT
    Authors: KSN Reddy, L Rao, P Ravikanth
    Year: 2012
    Citations: 4

  • Title: Identification of criminal & non-criminal faces using deep learning and optimization of image processing
    Authors: K Sivanagireddy, S Jagadeesh, A Narmada
    Year: 2024
    Citations: 3

  • Title: Low memory low complexity image compression using DWT and HS-SPIHT encoder
    Authors: K Sivanagireddy, M Saipravallika, PKC Tejaswini
    Year: 2012
    Citations: 3

  • Title: Reconstruction Using a New Approach By Artificial Neural Network
    Authors: SNRKI Compression
    Year: 2012
    Citations: 3

  • Title: Early Lung Cancer Prediction using Correlation and Regression
    Authors: K Sivanagireddy
    Year: 2022
    Citations: 2

  • Title: Smart Door Lock to Avoid Robberies in ATM
    Authors: VS Reddy, S Kalli, H Gebregziabher, BR Babu
    Year: 2021
    Citations: 2

  • Title: Image Segmentation by Using Modified Spatially Constrained Gaussian Mixture Model
    Authors: S Kalli, BM Bhaskara
    Year: 2016
    Citations: 2

  • Title: Efficient Memory and Low Complexity Image Compression Using DWT with Modified SPIHT Encoder
    Authors: KSN Reddy, VS Reddy, DBR Vikram
    Year: 2012
    Citations: 2

  • Title: Brain Tumor Detection through Image Fusion Using Cross Guided Filter and Convolutional Neural Network
    Authors: MV Srikanth, S Kethavath, S Yerram, SNR Kalli, JB Naik
    Year: 2024
    Citations: 1

  • Title: Autoencoder-based Deep Learning Approach for Intrusion Detection System using Firefly Optimization Algorithms
    Authors: N Kumar Bukka, S Jagadeesh, KS Reddy
    Year: 2024
    Citations: 1

Zhe Zhang | Deep Learning for Computer Vision | Best Researcher Award

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Dr. Zhe Zhang | Deep Learning for Computer Vision | Best Researcher Award

Lecturer at Henan University of Engineering, China

Zhe Zhang is a dedicated researcher specializing in deep learning and spatio-temporal forecasting, with a strong focus on meteorological applications such as tropical cyclone intensity prediction and typhoon cloud image analysis. His academic contributions demonstrate a solid grasp of advanced neural networks and remote sensing technologies, backed by an impressive publication record in high-impact SCI Q1 journals like Knowledge-Based Systems and IEEE Transactions on Geoscience and Remote Sensing. Zhang’s work integrates artificial intelligence with environmental monitoring, making significant strides in predictive modeling from satellite imagery. With a collaborative and interdisciplinary approach, his research contributes to both academic advancement and real-world disaster management. His innovative frameworks, such as spatiotemporal encoding modules and generative adversarial networks, exemplify technical excellence and societal relevance. Zhe Zhang stands out as a rising expert in AI-driven environmental systems and continues to push the frontiers of climate informatics through data-driven methodologies and scalable forecasting frameworks.

Professional Profile 

Education🎓 

Zhe Zhang holds a robust academic background in computer science and artificial intelligence, which has laid a strong foundation for his research in deep learning and remote sensing. He pursued his undergraduate studies in a computer science-related discipline, where he developed an early interest in data analytics and neural networks. Building on this foundation, he advanced to postgraduate education with a focus on machine learning, remote sensing applications, and environmental informatics. His graduate-level research emphasized deep learning-based forecasting models using satellite imagery, leading to early exposure to impactful interdisciplinary research. Throughout his academic journey, he has combined coursework in AI, image processing, and spatio-temporal modeling with practical lab experience and collaborative research projects. His educational trajectory has equipped him with both theoretical knowledge and technical skills, enabling him to develop innovative solutions to complex problems in climate and disaster prediction. Zhang’s educational background reflects a clear trajectory toward research leadership.

Professional Experience📝

Zhe Zhang has accumulated valuable professional experience through academic research positions, collaborative projects, and contributions to high-impact scientific publications. As a core member of multiple research groups focused on environmental AI and satellite image analysis, he has played a pivotal role in designing and developing deep learning frameworks for spatio-temporal prediction tasks. His collaborations span across disciplines, working with experts in meteorology, computer vision, and geospatial analysis. Zhang has contributed significantly to projects involving tropical cyclone intensity estimation, remote sensing super-resolution, and post-disaster damage assessment. In each role, he has demonstrated leadership in designing model architectures, implementing advanced training pipelines, and validating results with real-world data. His experience also includes CUDA-based optimization for remote sensing image processing, showcasing his computational and engineering proficiency. This combination of domain-specific and technical expertise has positioned him as a valuable contributor to AI-driven environmental applications in both academic and applied research environments.

Research Interest🔎

Zhe Zhang’s research interests center on deep learning, spatio-temporal forecasting, and remote sensing. He is particularly focused on developing neural network frameworks to predict and assess tropical cyclone intensity using satellite imagery, addressing critical challenges in climate-related disaster prediction. Zhang is passionate about enhancing model accuracy and generalizability in extreme weather forecasting through spatiotemporal encoding and generative adversarial networks. His work also extends to super-resolution of remote sensing images and object detection for damage assessment, demonstrating a strong interest in post-disaster management applications. He explores innovative ways to integrate multi-source data, such as infrared and visible satellite images, into unified prediction pipelines. Additionally, he is interested in scalable deep learning architectures optimized for high-performance computing environments like CUDA. Zhang’s overarching goal is to bridge the gap between artificial intelligence and environmental science, enabling more accurate, real-time, and actionable insights from complex geospatial datasets. His research continues to evolve toward intelligent Earth observation systems.

Award and Honor🏆

Zhe Zhang has earned academic recognition through his contributions to high-impact publications and collaborative research in deep learning and remote sensing. While specific awards and honors are not listed, his publication record in top-tier SCI Q1 journals such as Knowledge-Based Systems and IEEE Transactions on Geoscience and Remote Sensing attests to his research excellence and scholarly recognition. His first-author and co-authored papers have received commendations within the academic community for their novelty and real-world relevance, especially in the domains of environmental forecasting and image analysis. Additionally, Zhang’s involvement in multidisciplinary research projects indicates that he has likely contributed to grant-funded initiatives and may have been recognized through institutional acknowledgments or research excellence programs. With increasing citation counts and growing visibility in the AI for environmental science space, Zhang is well-positioned to earn future distinctions at national and international levels. His scholarly contributions lay a strong foundation for future honors.

Research Skill🔬

Zhe Zhang possesses a robust set of research skills that span deep learning, remote sensing, image processing, and high-performance computing. He is proficient in designing and implementing convolutional neural networks, spatiotemporal encoding architectures, and generative adversarial networks for geospatial data analysis. His ability to handle satellite imagery and extract meaningful patterns from complex datasets underlines his strengths in data preprocessing, feature engineering, and model optimization. Zhang is skilled in programming languages such as Python and frameworks like TensorFlow and PyTorch, and he is adept at deploying models on CUDA-based environments for accelerated processing. He has demonstrated expertise in both supervised and unsupervised learning, as well as in evaluating model performance using real-world datasets. His publication record reveals a deep understanding of domain-specific applications, including tropical cyclone intensity forecasting and damage detection. These skills enable him to bridge theory and application, making him a versatile and capable researcher in AI and environmental modeling.

Conclusion💡

Zhe Zhang presents a strong and competitive profile for the Best Researcher Award, especially in the fields of Deep Learning and Spatio-temporal Forecasting. The research is:

  • Technically sound (deep learning architectures),

  • Application-driven (cyclone prediction, disaster response),

  • And academically visible (SCI Q1 journal publications).

With slight enhancements in independent project leadership and wider domain application, Zhe Zhang would not only be a worthy recipient but could emerge as a leader in AI-driven environmental modeling.

Publications Top Noted✍

  • Title: Single Remote Sensing Image Super-Resolution via a Generative Adversarial Network With Stratified Dense Sampling and Chain Training
    Authors: Fanen Meng, Sensen Wu, Yadong Li, Zhe Zhang, Tian Feng, Renyi Liu, Zhenhong Du
    Year: 2024
    Citation: DOI: 10.1109/TGRS.2023.3344112
    (Published in IEEE Transactions on Geoscience and Remote Sensing)

  • Title: A Neural Network with Spatiotemporal Encoding Module for Tropical Cyclone Intensity Estimation from Infrared Satellite Image
    Authors: Zhe Zhang, Xuying Yang, Xin Wang, Bingbing Wang, Chao Wang, Zhenhong Du
    Year: 2022
    Citation: DOI: 10.1016/j.knosys.2022.110005
    (Published in Knowledge-Based Systems)

  • Title: A Neural Network Framework for Fine-grained Tropical Cyclone Intensity Prediction
    Authors: Zhe Zhang, Xuying Yang, Lingfei Shi, Bingbing Wang, Zhenhong Du, Feng Zhang, Renyi Liu
    Year: 2022
    Citation: DOI: 10.1016/j.knosys.2022.108195
    (Published in Knowledge-Based Systems)

Dibyalekha Nayak | Computer vision | Women Researcher Award

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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

Prof. Vaclav Skala | Computer Vision | Best Researcher Award

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Prof. Vaclav Skala | Computer Vision | Best Researcher Award

Professor at University of West Bohemia, Czech Republic

👨‍🎓 Publication Profiles

Scopus

Orcid

Publications

A new fully projective O(lg N) line convex polygon intersection algorithm

  • Authors: Václav V. Skala
    Journal: Visual Computer
    Year: 2025

A new fully projective O(log N) point-in-convex polygon algorithm: a new strategy

  • Authors: Václav V. Skala
    Journal: Visual Computer
    Year: 2024

Meshfree Interpolation of Multidimensional Time-Varying Scattered Data

  • Authors: Václav V. Skala, Eliska E. Mourycova
    Journal: Computers
    Year: 2023

Multispectral Image Generation from RGB Based on WSL Color Representation: Wavelength, Saturation, and Lightness

  • Authors: Václav V. Skala
    Journal: Computers
    Year: 2023

Robust Line-Convex Polygon Intersection Computation in E2 using Projective Space Representation

  • Author: Václav V. Skala
    Journal: Machine Graphics and Vision
    Year: 2023