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.

 

 

Yueming Ma | Signal Processing | Best Researcher Award

Mr. Yueming Ma | Signal Processing | Best Researcher Award

Mr. Yueming Ma , PhD Student , Shenyang Institute of Automation Chinese Academy of Sciences , China.

Yueming Ma is a Ph.D. candidate at the Shenyang Institute of Automation, Chinese Academy of Sciences, specializing in marine robotics and underwater signal processing. His academic journey is marked by continuous excellence, from earning his bachelor’s degree in Vehicle Engineering to securing direct admission into the doctoral program through a master-to-Ph.D. transfer. He has co-authored impactful research on passive synthetic aperture methods for underwater vehicles and has presented his work at notable conferences like IEEE OES. His contributions reflect a commitment to advancing underwater acoustic technologies for scientific and industrial applications.

Publication Profile

Google Scholar

🎓 Education Background

Yueming Ma completed his undergraduate studies in Vehicle Engineering from Hefei University of Technology, where he earned academic scholarships for three consecutive years. Following his undergraduate success, he was admitted to the master’s program in Pattern Recognition and Intelligent Systems at the Shenyang Institute of Automation, Chinese Academy of Sciences, based on academic merit. During his postgraduate studies, he transitioned into the Ph.D. program through an internal academic transfer, highlighting his research potential and dedication to scientific exploration in underwater robotics and signal processing.

🧑‍💼 Professional Experience

Yueming Ma has built his professional foundation within the Shenyang Institute of Automation, CAS, engaging in advanced research under the guidance of distinguished scientists like Dr. Jie Sun and Dr. Shuo Li. He has collaborated on underwater signal processing projects, including joint initiatives with the School of Navigation at Northwestern Polytechnical University. His work emphasizes hands-on experimental validation through pool-based and sea trials, including the deployment of the Haiyi 1000 underwater glider. Though early in his career, his research is already contributing to the evolving landscape of underwater sensing technologies.

🏆 Awards and Honors

While formal awards are pending, Yueming Ma’s academic and research excellence is demonstrated through competitive scholarships during his undergraduate years and admission into graduate and doctoral programs based on merit. His selection for conference presentations and publication in high-impact journals like the Journal of Marine Science and Engineering reflect peer recognition and scientific merit. His progression through academic ranks and collaborative research efforts position him as a promising scholar worthy of future accolades and honors in the fields of robotics and signal processing.

🔬 Research Focus

Yueming Ma’s research centers on underwater target detection and signal processing for marine robotic systems. His work emphasizes passive synthetic aperture techniques for direction-of-arrival (DOA) estimation using single-hydrophone underwater vehicles. He addresses challenges such as motion-induced distortions and limited aperture size by developing trajectory correction and motion-compensated models. Validated through real-world trials, his research pushes the boundaries of autonomous underwater sensing. His current interests include integrating data-driven methodologies to enhance DOA estimation robustness under complex underwater motion conditions, combining theory with practical experimentation for marine applications.

📝 Conclusion

Yueming Ma represents a new generation of dedicated marine robotic researchers with an emphasis on signal processing innovation. Through rigorous academic training, hands-on fieldwork, and multidisciplinary collaborations, he contributes meaningful advancements to underwater acoustic sensing. His research achievements, combined with growing publication activity and conference participation, position him as a strong candidate for the Best Researcher Award. With continued dedication, he is set to make significant impacts in underwater robotics, signal intelligence, and autonomous navigation technologies.

📄 Top Publications

  1. Passive Synthetic Aperture for Direction-of-Arrival Estimation Using an Underwater Glider with a Single Hydrophone
    🗓 Year: 2025
    📚 Journal: Journal of Marine Science and Engineering (JMSE)

  2. A Passive Synthetic Aperture Direction-of-Arrival Estimation Method Under the Autonomous Underwater Vehicle Yaw Motion
    🗓 Year: 2024
    📚 Conference: 2024 IEEE OES China Ocean Acoustics (COA)

 

Mr. Javier Blanco-Romero | Information Theory | Best Researcher Award

Mr. Javier Blanco-Romero | Information Theory | Best Researcher Award

PhD Student, Carlos III University of Madrid, Spain

Francisco Javier Blanco Romero is a versatile physicist and an active researcher specializing in cryptography, machine learning, robotics, IoT/IIoT, and networks. Currently based in Madrid, he is pursuing a PhD in Telematic Engineering at Universidad Carlos III de Madrid, where he focuses on integrating post-quantum cryptography into secure communication protocols for IoT and IIoT. With a background in physics and robotics, Francisco is dedicated to advancing security in next-generation communication technologies. 🌍🔐

Publication Profile

Education

Francisco holds a Bachelor’s Degree in Physics from Universidad Complutense de Madrid, specializing in fundamental and theoretical physics. He also completed a Master’s Degree in Robotics at Universidad Miguel Hernández de Elche, where his thesis focused on enhancing communication security in ROS 2. He is currently working towards his PhD in Telematic Engineering at Universidad Carlos III de Madrid, researching post-quantum cryptography integration for IoT and IIoT communication protocols. 🎓📘

Experience

Francisco’s professional career includes roles as a Research Support Technician in the QURSA Project at Carlos III University of Madrid, where he focuses on quantum random number generators and post-quantum cryptography for IoT communication. He has also contributed to various EU projects in innovation and technical management, such as the LIFE and Horizon Europe programs. His prior work includes developing a real-time tracking system and GIS for sustainable urban mobility. Francisco also taught programming courses in multimedia and web development. 💻🔍

Awards and Honors

Francisco has been recognized for his contributions to research and development, including his involvement in various prestigious academic events like the RECSI and QSNS conferences. He has authored several publications and has received scholarships for his academic work, such as those from ValgrAI and Carlos III University. 🏆📜

Research Focus

His research is centered on post-quantum cryptography, particularly its application to secure communication protocols for IoT, IIoT, and robotics. Francisco explores quantum-resistant architectures and their integration into modern cryptographic systems to ensure robust security in the face of quantum computing advancements. His ongoing work on machine learning methods for entropy estimation and quantum random number generators contributes to secure data communication in the post-quantum era. 🔒📡

Conclusion

Francisco Javier Blanco Romero is at the forefront of a rapidly evolving field, combining deep knowledge of cryptography, machine learning, and network security. His work promises significant advancements in securing communication systems for IoT, IIoT, and robotics, ensuring their resilience against emerging quantum technologies. He continues to push the boundaries of cryptography and communication security. 🚀🔑

Publications

Machine Learning Predictors for Min-Entropy Estimation
Published: Entropy, 2025-02-02
Link to Article
Cited by: 1

Evaluating Integration Methods of a Quantum Random Number Generator in OpenSSL for TLS
Published: Computer Networks, 2024-12
Link to Article
Cited by: Not yet cited

Integrating Post-Quantum Cryptography into CoAP and MQTT-SN Protocols
Published: IEEE Symposium on Computers and Communications (ISCC), 2024-06-26
Link to Article
Cited by: Not yet cited

PQSec-DDS: Integrating Post-Quantum Cryptography into DDS Security for Robotic Applications
Published: IX Jornadas Nacionales de Investigación en Ciberseguridad, 2024-05
Link to Article
Cited by: Not yet cited

Guided Waves in Static Curved Spacetimes
Published: arXiv preprint, 2024
Link to Article
Cited by: 1

Onion Routing Key Distribution for QKDN
Published: arXiv preprint, 2024-02
Link to Article
Cited by: Not yet cited