Mr. HaoFei Chen | Sensor Hardware | Young Innovator Award

Mr. HaoFei Chen | Sensor Hardware | Young Innovator Award

Mr. HaoFei Chen | Nanjing University of Science and Technology | China

Chen Haofei is a dynamic researcher specializing in wearable intelligent systems, sensor technology, and machine learning-based recognition models. His research primarily focuses on developing advanced wearable sign language recognition systems for the hearing-impaired, integrating multidisciplinary expertise in kinematic modeling, sensor layout optimization, adaptive algorithm development, and system validation. His work aims to enhance communication accessibility through innovative sensor fusion and data-driven algorithms, achieving recognition accuracies above Ninety Percent. Chen’s technical expertise spans instrumentation science, signal acquisition, data optimization, embedded system design, and intelligent data processing using neural networks and particle swarm optimization. His studies demonstrate a strong commitment to bridging theoretical modeling with real-world application, particularly in high-impact environments such as overpressure explosion testing and multi-dimensional data acquisition. Chen has published influential research articles that contribute significantly to the fields of intelligent measurement and human-computer interaction. According to Scopus, he has 3 citations across 2 indexed documents with an h-index of 1, reflecting the emerging academic influence of his early-stage work. His research outputs are also indexed on Google Scholar, indicating growing international visibility and recognition.

Profile

Scopus

Featured Publication 

Chen, H. (2025). Biomechanical feature extraction for robust sign language recognition with applications. Molecular & Cellular Biomechanics, 22(3), 1322.

Chen, H., & Di, C. (2025). Lightweight sign language intelligent recognition model based on improved R-C3D. Egyptian Informatics Journal, Elsevier.

saeedeh shahbazi | Detection | Best Researcher Award

Ms. saeedeh shahbazi | Detection | Best Researcher Award

researcher, cttc, Spain

👩‍🔬 Saeedeh Shahbazi is a dedicated researcher at the Centre Tecnològic de Telecomunicacions de Catalunya (CTTC), where she specializes in land deformation monitoring. With a strong academic foundation in geophysics and physics, she is also pursuing her PhD at UPC. Saeedeh’s work is driven by innovative approaches to urban area studies using cutting-edge techniques in remote sensing and data analysis.

Publication Profile

ORCID

Education

🎓 Saeedeh holds a Master of Science degree in Geophysics and a Bachelor’s in Physics. Currently, she is furthering her expertise as a PhD candidate at UPC, focusing on geospatial technologies and urban land deformation.

Experience

💼 As a researcher in the Geomatics unit at CTTC, Saeedeh has made significant strides in using Persistent Scatterer Interferometry (PSI) to assess land deformation. She has developed a Python software tool that enables fast, user-friendly post-processing for differential deformation mapping, contributing to urban planning and infrastructure safety.

Research Focus

🔍 Saeedeh’s research revolves around remote sensing and land deformation, particularly focusing on urban areas. Her work with Sentinel-1 data and EGMS products is geared towards detecting building damage risks through detailed deformation analysis. She is committed to improving land monitoring techniques with innovative tools and methodologies.

Awards and Honors

🏆 Saeedeh has not highlighted specific awards, but her ongoing contributions in the RASTOOL project and her automated software tool for deformation analysis stand out as major professional accomplishments.

Publication Top Notes

📄 Constraints on the hydrogeological properties and land subsidence through GNSS and InSAR measurements and well data in Salmas plain, northwest of Urmia Lake, Iran (2021) was published in Hydrogeology Journal and has contributed to subsidence studies. DOI
📄 From EGMS Data to a Differential Deformation Map For Buildings at Continent Level (2024) explores deformation mapping and was published in Procedia Computer Science. DOI
📄 Detection of buildings with potential damage using differential deformation maps (2024), published in ISPRS Journal of Photogrammetry and Remote Sensing, provides insights into building damage detection techniques. DOI
📄 Computing and Sharing the Differential Deformation of the Ground at a Continental Level Using Public EGMS Data (2023) was presented at Environmental Science Proceedings, contributing to large-scale land deformation assessments. DOI
📄 From European Ground Motion Service to Differential Deformation Map for Buildings (2024), published in IGARSS, advances automated building deformation detection techniques. DOI