Pascal Lorenz | Information Theory | Research Excellence Award

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Prof. Dr. Pascal Lorenz | Information Theory | Research Excellence Award

Professor | University of Haute Alsace | France

Prof. Dr. Pascal Lorenz is an internationally recognized researcher in advanced communication networks, network security, and intelligent systems, serving at the University of Haute Alsace, France. His work spans next-generation wireless systems, deterministic networking, 5G/6G network slicing, vehicular networks, IoT communication models, and federated learning security.

He has contributed significantly to the design of secure, efficient, and scalable network architectures, with a focus on deterministic resource allocation, encrypted communication tunnels, and multi-gateway behavior in large-scale IoT deployments. His interdisciplinary work integrates network optimization, machine learning-based security detection, and privacy-preserving communication frameworks.

Prof. Lorenz collaborates widely with global research teams and continues to shape modern communication technologies through impactful publications, editorial roles, and supervision of advanced research in intelligent systems and next-generation networking.

Profiles: Scopus | Orcid | Google Scholar

Featured Publications

  1. Krishna, M. B., & Lorenz, P. (2024). Deterministic network slice instance policy for intra and inter slice resource management in 5G. IEEE Transactions on Vehicular Technology. Citation count: 5. Year: 2024.

  2. Atia, O. B., Al Samara, M., Bennis, I., Gaber, J., Abouaissa, A., & Lorenz, P. (2024). EMDG-FL: Enhanced malicious model detection based on genetic algorithm for federated learning. In 2024 IEEE Wireless Communications and Networking Conference (WCNC) (pp. 1–6). Citation count: 5. Year: 2024.

  3. Suma, V., Baig, Z., Shanmugam, S. K., & Lorenz, P. (2023). Inventive systems and control. Ph.D. dissertation, Department of Computer Science, City University of Hong Kong. Citation count: 5. Year: 2023.

  4. Diab, T., Gilg, M., Lorenz, P., & Drouhin, F. (2022). Using I2P (Invisible Internet Protocol) encrypted virtual tunnels for a secure and anonymous communication in VANETs: I2P vehicular protocol (IVP). Wireless Personal Communications, 127(3), 2625–2644. Citation count: 5. Year: 2022.

  5. Abakar, K. S., Bennis, I., Abouaissa, A., & Lorenz, P. (2022). A multi-gateway behaviour study for traffic-oriented LoRaWAN deployment. Future Internet, 14(11), 312. Citation count: 5. Year: 2022.

Milad Samady Shadlu | Signal Processing | Best Researcher Award

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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.