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.

Seyed Abolfazl Hosseini | Communication Systems | Best Researcher Award

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Dr. Seyed Abolfazl Hosseini | Communication Systems | Best Researcher Award

Faculty member | Islamic Azad University | Iran

Dr. Seyed Abolfazl Hosseini is a distinguished faculty member in the Department of Electrical Engineering at Islamic Azad University, Tehran, Iran, specializing in Communication Systems Engineering and Computer Science. He holds a Ph.D. and M.Sc. in Electrical Engineering (Communications Systems Engineering) from Tarbiat Modares University and K.N. Toosi University of Technology, respectively, and a B.Sc. in Control Engineering from Sharif University of Technology. Over his academic career, Dr. Hosseini has served in several leadership capacities, including Dean of the Electrical and Electronics Research Centre, Dean of the Engineering Faculty, and Director of the Department of Communications Engineering. His professional experience extends beyond academia, having served as CEO of Pars Kelend Co., where he managed projects in electronic surveillance, IoT, AI, and network services, and as an expert engineer at the Tehran Traffic Control Company, where he developed remote toll systems and air quality detection methods using image processing. His research primarily focuses on hyperspectral image analysis, digital signal and image processing, machine learning, cryptography, and MIMO communication systems. Dr. Hosseini has authored numerous high-impact publications in leading journals such as IEEE Access, Earth Science Informatics, and Remote Sensing Letters, contributing novel methods in feature reduction, watermarking, and energy-efficient communications. His technical expertise spans rational function approximation, fractal theory, and advanced machine learning algorithms applied to remote sensing and data compression. A dedicated educator and mentor, he has supervised many M.Sc. and Ph.D. candidates and actively participated in developing technical standards and research-driven industrial collaborations. Through his integrated contributions to academia, research, and technology innovation, Dr. Hosseini continues to advance modern communication and computational systems.11 Citations, 3 Documents, 2 h-index.

Profiles: Scopus | ORCID | Google Scholar

Featured Publications

1. Hosseini, S. A.*, & Ghassemian, H. (2016). Rational function approximation for feature reduction in hyperspectral data. Remote Sensing Letters, 7(2), 101–110. Citations: 42.

2. Hosseini, S. A.*, & Ghassemian, H. (2015). Hyperspectral data feature extraction using rational function curve fitting. International Journal of Pattern Recognition and Artificial Intelligence. Citations: 23.

3. Hosseini, S. A.*, & Ghassemian, H. (2013). A new hyperspectral image classification approach using fractal dimension of spectral response curve. Proceedings of the Iranian Conference on Electrical Engineering (ICEE). Citations: 17.

4. Hosseini, A., & Ghassemian, H. (2012). Classification of hyperspectral and multispectral images by using fractal dimension of spectral response curve. 20th Iranian Conference on Electrical Engineering (ICEE), 1452–1457. Citations: 19.

5. Beitollahi, M., & Hosseini, S. A.* (2018). Using Savitsky–Golay smoothing filter in hyperspectral data compression by curve fitting. Proceedings of the Iranian Conference on Electrical Engineering (ICEE). Citations: 15.