Dr. Malaya Nath | Signal Processing | Best Researcher Award

Dr. Malaya Nath | Signal Processing | Best Researcher Award

Assistant Professor | National Institute of Technology Puducherry | India

Dr. Malaya Kumar Nath is an accomplished researcher and academician in the field of Electronics and Communication Engineering, specializing in Biomedical Signal and Image Processing, Pattern Recognition, Deep Learning, and Computational Neuroscience. His research primarily focuses on developing advanced computational models for medical image analysis, disease diagnosis, and intelligent healthcare systems using signal and image processing techniques integrated with artificial intelligence. Dr. Nath has significantly contributed to diagnostic automation through the application of deep learning architectures such as CNNs and EfficientNet for skin cancer, glaucoma, and retinal image analysis. His scholarly contributions have earned him recognition among the Top two percentage most influential scientists worldwide, as reported by Stanford University and Elsevier in 2025. He has an extensive publication record, with 69 Scopus-indexed documents and over 1,291 citations by 902 documents, achieving an h-index of 21 on Scopus. On Google Scholar, he has accumulated 2,185 citations with an h-index of 24 and an i10-index of 47, reflecting his impactful research influence. His interdisciplinary research integrates biomedical data analytics with machine learning and deep neural frameworks, addressing challenges in medical imaging and healthcare informatics.

Profile

Scopus | ORCID | Google Scholar

Featured Publications

Keerthana, D., Venugopal, V., Nath, M. K., & Mishra, M. (2023). Hybrid convolutional neural networks with SVM classifier for classification of skin cancer. Biomedical Engineering Advances, 5, 100069.

Anbalagan, T., Nath, M. K., Vijayalakshmi, D., & Anbalagan, A. (2023). Analysis of various techniques for ECG signal in healthcare, past, present, and future. Biomedical Engineering Advances, 6, 100089.

Elangovan, P., & Nath, M. K. (2021). Glaucoma assessment from color fundus images using convolutional neural network. International Journal of Imaging Systems and Technology, 31(2), 955–971.

Vijayalakshmi, D., & Nath, M. K. (2020). A comprehensive survey on image contrast enhancement techniques in spatial domain. Sensing and Imaging, 21(1), 40.

Venugopal, V., Raj, N. I., Nath, M. K., & Stephen, N. (2023). A deep neural network using modified EfficientNet for skin cancer detection in dermoscopic images. Decision Analytics Journal, 8, 100278.

Prof. Liying Sun | Control Theory Application | Best Researcher Award

Prof. Liying Sun | Control Theory Application | Best Researcher Award

Prof. Liying Sun | Professor | Shanghai Dianji University | China

Dr. Liying Sun is a distinguished professor at Shanghai Dianji University with extensive expertise in control theory, particularly in nonlinear descriptor systems and Hamiltonian systems. Her research explores advanced mathematical and engineering control methodologies, including finite-time control, adaptive control, and stability analysis of nonlinear and singular Hamiltonian systems. She has made significant contributions to the theoretical development and practical implementation of robust control mechanisms that enhance system performance under uncertainty and external disturbances. Dr. Sun’s work is characterized by the integration of mathematical rigor with engineering applications, contributing to both theoretical advancements and real-world system optimization. Her research has been widely recognized and published in reputable international journals, reflecting her strong academic influence in the fields of automation, control science, and applied mathematics. According to Scopus, she has authored 66 documents, received 578 citations from 432 documents, and holds an h-index of 15, underscoring her impactful contributions to scientific research. Her publications are also well-cited on Google Scholar, confirming her broad recognition in the global academic community.

Publication Profile

Scopus

Featured Publications

  • He, S., Sun, L., & Yang, R. (2025). Finite-time H∞ control of doubly fed induction generator. Asian Journal of Control.

  • He, S., Sun, L., & Yang, R. (2025). Passive control of a set of nonlinear singular Hamiltonian systems. Journal of the Franklin Institute.

  • He, S., Sun, L., & Yang, R. (2025). Adaptive H∞ finite-time boundedness control for a set of nonlinear singular Hamiltonian systems. Control Theory and Technology.

  • Zhang, Z., Sun, L., & Yang, R. (2025). Input-output finite-time stabilization for a class of nonlinear descriptor Hamiltonian systems with actuator saturation. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering.

  • Zhang, Z., & Sun, L. (2025). Input-output finite-time adaptive control of nonlinear descriptor Hamiltonian systems. Fourth International Conference on Advanced Manufacturing.

 

Sarah Popenhagen | Signal Processing | Best Researcher Award

Dr. Sarah Popenhagen | Signal Processing | Best Researcher Award

Junior Researcher | University of Hawai’i at Manoa | United States

Sarah K. Popenhagen is a dedicated Earth and planetary scientist whose interdisciplinary expertise spans infrasound acoustics, machine learning, and airborne data collection. Earning her PhD in Earth and Planetary Sciences at the University of Hawaiʻi at Mānoa under the supervision of Milton Garcés, she leverages advanced audio processing and classification techniques. With robust engineering foundations, she applies these methods to detect and characterize acoustic signals from rocket launches and explosions using devices as ubiquitous as smartphones. Her research bridges cutting-edge computational methods with practical, scalable acoustic sensing, advancing both scientific understanding and real-world monitoring capabilities.

Publication Profile

Scopus

ORCID

Education Background

Sarah K. Popenhagen earned her PhD in Earth and Planetary Sciences from the University of Hawaiʻi at Mānoa, focusing on infrasound acoustics, rocket ignition and trajectory signatures, and machine learning for audio classification. Prior to that, she completed a BSc in Engineering Physics at the University of Illinois Urbana-Champaign, where she received the Laura B. Eisenstein Award. She also broadened her academic perspective as an exchange student in Physics and Astrophysics at the University of Birmingham in the UK, enriching her foundation in interdisciplinary physical sciences.

Professional Experience

During her doctoral studies at the University of Hawaiʻi at Mānoa, Sarah contributed as a Junior Researcher in the Infrasound Laboratory, authoring publications and developing Python tools for audio dataset analysis and visualization. As a Research Assistant in the Earth Sciences Department, she curated and annotated rocket acoustic signatures, designed and evaluated machine learning detection models, and analyzed multimodal explosion data from airborne platforms. Her prior roles include an engineering physics undergraduate researcher at Illinois, where she developed methane-monitoring prototypes, and multiple positions at Idaho National Laboratory and USGS, applying acoustic and seismic analysis to nonproliferation and hydrology challenges.

Awards and Honors

Sarah’s academic distinction is marked by the Laura B. Eisenstein Award, recognizing her outstanding achievement during her undergraduate studies at the University of Illinois Urbana-Champaign. Her selection as an exchange student in Physics and Astrophysics at the University of Birmingham highlights her academic adaptability and merit. Additionally, her impactful contributions to geophysical research, particularly with accessible sensor networks and machine learning methodologies, have garnered recognition in peer-reviewed publications and funded projects, demonstrating both scholarly and practical accolades throughout her burgeoning career.

Research Focus

Sarah’s research centers on detecting and interpreting acoustic signatures of rockets and explosions using machine learning and infrasound analysis. She develops and maintains Python-based tools and repositories for processing open-access audio datasets, enabling training and evaluation of classification models. Her work includes leveraging smartphone audio to study rocket ignition, launch, and trajectory features, designing ensemble learning models for explosion detection with high accuracy, and deploying airborne collection platforms. Her focus combines acoustic physics with AI, aiming to democratize sensor networks for environmental and security monitoring.

Top  Publications

Rocket Launch Detection with Smartphone Audio and Transfer Learning
Published Year: 2025
Citation: 1

Acoustic Rocket Signatures Collected by Smartphones
Published Year: 2025
Citation: 1

Explosion Detection using Smartphones: Ensemble Learning with the Smartphone High-explosive Audio Recordings Dataset and the ESC-50 Dataset
Published Year: 2024
Citation: 4

Acoustic Waves from a Distant Explosion Recorded on a Continuously Ascending Balloon in the Middle Stratosphere
Published Year: 2023
Citation: 9

Skyfall: Signal Fusion from a Smartphone Falling from the Stratosphere
Published Year: 2022
Citation: 12

Conclusion

Sarah K. Popenhagen’s career blends engineering physics, geoscience, and machine learning to tackle complex challenges in acoustic monitoring. Her work harnesses everyday technology—like smartphones—alongside advanced modeling to detect rocket and explosion events with precision and scalability. Through her publications, software development, and interdisciplinary research projects, she contributes to more accessible, effective environmental and geophysical sensing. Her trajectory signals a growing influence in leveraging AI-enhanced acoustics for real-world monitoring, scientific innovation, and societal benefit.