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.

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.

 

 

Yashi Zhou | Signal Processing | Best Researcher Award

Prof. Dr. Yashi Zhou | Signal Processing | Best Researcher Award

Prof. Dr. Yashi Zhou | Senior Engineer | China Academy of Space Technology | China

Yashi Zhou is a Chinese researcher and senior engineer specializing in synthetic aperture radar (SAR) signal processing. He received his B.S. degree in electronic information science and technology from Central South University in 2016 and earned his Ph.D. in space microwave remote sensing systems from the Institute of Electronics, Chinese Academy of Sciences in 2021. He currently works at the Institute of Remote Sensing Satellite, China Academy of Space Technology. With notable contributions in high-resolution, wide-swath SAR technologies, Dr. Zhou has published widely in top conferences and journals. His research integrates technical innovation and practical applications in remote sensing systems.

Publication Profile

ORCID

Education Background

Yashi Zhou completed his undergraduate studies at Central South University, earning a B.S. in electronic information science and technology in 2016. He then pursued his doctoral education at the University of Chinese Academy of Sciences, affiliated with the Institute of Electronics, where he focused on space microwave remote sensing systems and SAR imaging. He received his Ph.D. degree in 2021. His academic background provided a solid foundation in advanced electronic systems, signal processing, and synthetic aperture radar technologies, which now serve as the basis of his engineering and research work in satellite-based remote sensing.

Professional Experience

After receiving his Ph.D., Yashi Zhou joined the Institute of Remote Sensing Satellite at the China Academy of Space Technology in Beijing as a senior engineer. In this role, he has been actively engaged in developing and deploying cutting-edge SAR systems. His work focuses on the design and implementation of wide-swath and high-resolution imaging modes, as well as digital beamforming technologies. Dr. Zhou has collaborated with multidisciplinary teams in both research and application-driven projects, playing a key role in airborne and satellite-based SAR missions and contributing to the advancement of China’s remote sensing capabilities.

Awards and Honors

In recognition of his outstanding contributions to SAR signal processing and remote sensing technology, Yashi Zhou was awarded the Youth Talent Program by the China Association for Science and Technology in 2023. This prestigious national honor acknowledges promising early-career researchers who demonstrate leadership in science and technology innovation. The award underscores Dr. Zhou’s impact and potential in the field, highlighting his role in pushing the boundaries of synthetic aperture radar technologies, particularly in the areas of wide-swath imaging, multichannel systems, and frequency diverse arrays.

Research Focus

Dr. Yashi Zhou’s research focuses primarily on high-resolution and wide-swath synthetic aperture radar (SAR) signal processing and its application in real-world systems. His expertise includes digital beamforming (DBF), multichannel SAR, frequency diverse arrays, and airborne X-band SAR technologies. His work aims to resolve long-standing challenges in SAR imaging, such as phase bias, channel calibration, and Doppler centroid estimation. By integrating theoretical models with experimental data from platforms like the GF-3 satellite and airborne systems, he advances the performance and accuracy of modern SAR imaging systems in remote sensing.

Publication Top Notes

  1. A Novel Approach to Doppler Centroid and Channel Errors Estimation in Azimuth Multi-Channel SAR
    Year: 2019
    Cited by: 28 articles

  2. Digital Beamforming Synthetic Aperture Radar (DBSAR): Experiments and Performance Analysis in Support of 16-Channel Airborne X-Band SAR Data
    Year: 2021
    Cited by: 50 articles

  3. High-Resolution and Wide-Swath SAR Imaging Mode Using Frequency Diverse Planar Array
    Year: 2021
    Cited by: 49 articles

  4. Very High Resolution SAR Imaging With DGPS-Supported Airborne X-Band Data
    Year: 2020
    Cited by: 10 articles

  5. Phase Bias Estimation and Imaging for High-Squint Multichannel SAR in Azimuth
    Year: 2023
    Cited by: 1 articles

Conclusion

Yashi Zhou is an accomplished researcher whose academic training and engineering expertise make significant contributions to the field of remote sensing and SAR technologies. His work bridges the gap between fundamental signal processing and practical implementation in satellite systems. With recognized achievements, numerous high-impact publications, and a national talent award, Dr. Zhou continues to be a key figure in advancing China’s capabilities in spaceborne Earth observation. His future research is expected to further enhance imaging precision and efficiency in SAR systems across national and international platforms.

Ms. Hina Magsi | Signal Processing | Best Researcher Award

Ms. Hina Magsi | Signal Processing | Best Researcher Award

PhD Scholar, Sukkur IBA University, Pakistan

Engr. Hina Magsi is a dedicated lecturer at Mehran University of Engineering & Technology, Khairpur, with over four years of experience in research and teaching in the fields of telecommunication, electronics, embedded systems, AI, and machine learning 📚. She is passionate about advancing the education of her students and has co-supervised several impactful projects, including an IoT-based flood monitoring system 🌊, wireless electric vehicle charging 🚗, and a real-time drowsiness detection system 🛣️. Hina has a strong academic background and actively contributes to research in satellite communication and space weather, with a focus on improving navigation systems using adaptive algorithms 📡.

Publication Profile

ORCID

Education

Hina Magsi holds a PhD in Electrical Engineering (Signal Processing) from Sukkur IBA University, where she earned a CGPA of 3.57 🎓. She completed her M.E. in Electronics and Communication from the same university with a CGPA of 3.47, and her B.E. in Telecommunication Engineering from Mehran University of Engineering & Technology with an impressive CGPA of 3.72. Her thesis topics include MIMO CO-OFDM in Free Space Optical Communication and Received Signal Quality Monitoring (RSQM) algorithms for improved navigation 🧠.

Experience

With diverse experience, Engr. Magsi has worked as a lecturer at Mehran University of Engineering & Technology, delivering courses across various electronic engineering subjects. She previously served as a research associate at Sukkur IBA University, focusing on satellite communication and space weather. Engr. Magsi also worked as a research assistant in bio-sensing, developing adaptive battery-aware methods for smart healthcare systems 💡. Early in her career, she gained practical experience at PTCL and SAKI Institute Sukkur.

Research Interests

Her primary research interests include satellite communication, signal processing, IoT applications, real-time monitoring systems, AI and machine learning in engineering, and adaptive algorithms for improved navigation accuracy 📡🔍. She is particularly interested in applying these technologies to healthcare and transportation systems 🚑🚗.

Awards

Engr. Magsi has received several prestigious awards, including merit-based scholarships at Mehran University and Sukkur IBA University 🎓, as well as the International Merit Scholarship for her Ph.D. at Sukkur IBA University. She has also received funding for research projects from HEC NRPU and has been recognized for her contributions to education and research 🌟.

Publications

  • Accurate Monitoring and Timely Prediction of Ionospheric Scintillation Using Support Vector Machine
  • Improved Navigation Based on Received Signal Quality Monitoring (RSQM)
  • Adaptive Data Length Method for GPS Signal Acquisition in Weak to Strong Fading Conditions
  • Decentralized Energy Efficient Model for Data Transmission in IoT-based Healthcare System
  • A Novel Adaptive Battery-Aware Algorithm for Data Transmission in IoT-based Healthcare Applications