Hind Hallabia | Data Science | Excellence in Research Award

Dr. Hind Hallabia | Data Science | Excellence in Research Award

Teaching and Researcher Assistant | University Institute of Technology of Saint Etienne Jean Monnet University | France

Dr. Hind Hallabia, affiliated with the University Institute of Technology of Saint-Étienne at Jean Monnet University, France, specializes in remote sensing, pansharpening, and advanced image processing techniques for satellite data analysis. Her research focuses on developing graph-based segmentation methods, superpixel modeling, and data fusion frameworks to enhance multispectral and panchromatic imagery. Dr. Hallabia investigates latent low-rank decomposition, detail-injection mechanisms, and texture-based segmentation models to improve image quality, spatial–spectral fidelity, and analytic accuracy in Earth observation applications. Her work contributes to advances in hazardous-area monitoring, environmental assessment, and optical remote sensing technologies through methodological innovation, algorithm design, and computational enhancements.

Profiles: Scopus | Orcid

Featured Publications

  • Hallabia, H. (2025). A graph-based superpixel segmentation approach applied to pansharpening. Sensors, 25(16), Article 4992. https://doi.org/10.3390/s25164992
    Year: 2025

  • Hallabia, H. (2025). Land and aquatic spectral signatures analysis over a spatio-temporal hazardous area acquired by Worldview satellite. Annual International Congress on Electrical Engineering 2025.
    Year: 2025

  • Hallabia, H. (2025). Advanced trends in optical remotely sensed data fusion: Pansharpening case study. Iris Journal of Astronomy and Satellite Communications.
    Year: 2025

  • Hallabia, H., Hamam, H., & Ben Hamida, A. (2023). A novel detail injection framework using latent low-rank decomposition for multispectral pan-sharpening. Multimedia Tools and Applications, 82, 11971–11995. https://doi.org/10.1007/s11042-022-12770-x
    Year: 2023

  • Hallabia, H., & Hamam, H. (2021). A graph-based textural superpixel segmentation method for pansharpening application. Proceedings of IGARSS 2021. https://doi.org/10.1109/igarss47720.2021.9553304
    Year: 2021

Milad Samady Shadlu | Signal Processing | Best Researcher Award

Dr. Milad Samady Shadlu | Signal Processing | Best Researcher Award

Senior Researcher | Islamic Azad University | Iran

Dr. Milad Samady Shadlu is a distinguished lecturer at the University of Applied Science and Technology of Iran, specializing in Power Electronics, Renewable Energy Systems, and Control Engineering. He holds a Ph.D. in Electrical Engineering with a focus on Power Systems and Converters. Dr. Shadlu’s expertise spans the design, modeling, and control of high-power converters and inverters, multilevel converter systems, renewable energy integration, and maximum power point tracking (MPPT) methods for photovoltaic applications. With a strong academic foundation in control strategies and optimization methods, he has led and collaborated on numerous research projects focused on enhancing power system stability, harmonic mitigation, and fault diagnosis in renewable and grid-connected systems. Dr. Shadlu’s research portfolio comprises over twenty-four publications in reputed journals and international conferences, including notable works on MPPT algorithms, model predictive control, and fault analysis in modular multilevel converters and HVDC systems. He has authored and translated several books in the fields of energy storage, converter technology, and solar power generation, reflecting his commitment to academic knowledge dissemination. As an educator, he has taught a wide range of courses in electrical and control engineering and was honored as the Best Lecturer by the University of Applied Science and Technology of North Khorasan. His recognitions include the Best Researcher Award from the Young Researchers and Elite Club of North Khorasan Province. Dr. Shadlu’s current research focuses on fault detection in modular multilevel converters, hybrid control strategies combining extremum seeking and model predictive control methods for PV systems, and advanced control mechanisms for microgrids and active filters. With a robust record of scholarly output, including highly cited works on power converter control and renewable energy applications, Dr. Milad Samady Shadlu stands as an influential figure in modern power electronics research. 133 Citations, 24 Documents, 6 h-index.

Profiles: Scopus | ORCID | Google Scholar

Featured Publications

1. Fatemi, S. M., Shadlu, M. S.*, & Talebkhah, A. (2019). Comparison of three-point P&O and hill climbing methods for maximum power point tracking in PV systems. Proceedings of the 10th International Power Electronics, Drive Systems and Technologies Conference (PEDSTC), IEEE. Citations: 33.

2. Shadlu, M. S.* (2019). Comparison of maximum power point tracking (MPPT) algorithms to control DC-DC converters in photovoltaic systems. Recent Advances in Electrical & Electronic Engineering, 12(4), 355–368. Citations: 19.

3. Shadlu, M. S.* (2018). A comparative study between two MPPT algorithms for photovoltaic energy conversion system based on modular multilevel converter. Proceedings of the Iranian Conference on Electrical Engineering (ICEE), IEEE. Citations: 16.

4. Talebkhah, A., Shadlu, M. S.*, & Fatemi, S. M. (2019). Control strategy of a single-phase active power filter with adjustable DC link capacitor voltage for THD reduction in non-linear loads. Proceedings of the 10th International Power Electronics, Drive Systems and Technologies Conference (PEDSTC), IEEE. Citations: 14.

5. Shadlu, M. S.* (2017). Fault detection and diagnosis in voltage source inverters using principal component analysis. Proceedings of the 4th IEEE International Conference on Knowledge-Based Engineering and Innovation (KBEI). Citations: 14.