Reza Faraji | Electrical Engineering | Best Researcher Award

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Dr. Reza Faraji | Electrical Engineering | Best Researcher Award

PhD Candidate | University of Science and Culture | Iran

Dr. Reza Faraji is a dedicated PhD candidate in Electrical and Computer Engineering at Islamic Azad University (IAU), with a collaborative affiliation with the University of Science and Culture (USC). His primary area of research is nanoelectronics, with a specialization in Quantum-dot Cellular Automata (QCA) and reversible computing. He earned his Master’s degree in QCA design from USC, where his thesis focused on designing low-power, high-performance digital circuits. Reza’s academic foundation is reinforced by research assistant roles and engagement in advanced nano-circuit projects. His ongoing research includes the design of a Multilayer Reversible ALU (RALU) using Fredkin and HN gates optimized for 6G-enabled IoT systems, and device modeling of AlN/β- and ε-Ga₂O₃ Tri-Gate MOSHEMTs using DFT and TCAD simulations for mm-Wave applications on diamond substrates. These projects aim to advance low-power and high-efficiency architectures for next-generation communication and computation systems. He actively collaborates with Dr. Abdalhossein Rezai (USC) on QCA-based circuit design and with Dr. Amir Amini (IAU, West Tehran Branch) on nanoscale device modeling. Although he has no patents or books published yet, his work shows promise for industrial applications in IoT and semiconductor sectors. Currently, Reza has 5 published documents, 18 citations and an h-index of 3, reflecting the growing impact of his research in the scientific community. His key areas of expertise include QCA, reversible logic, ALU design, nanoscale HEMTs, and energy-efficient digital architectures for 6G and future nanoelectronic systems.

Profiles: Scopus | Google Scholar | ORCID

Featured Publications

1. Faraji-Dana, R., & Chow, Y. L. (2002). The current distribution and AC resistance of a microstrip structure. IEEE Transactions on Microwave Theory and Techniques, 38(9), 1268–1277. 
Cited by: 149

2. Mehdipour, A., Mohammadpour-Aghdam, K., & Faraji-Dana, R. (2007). Complete dispersion analysis of Vivaldi antenna for ultra wideband applications. Progress In Electromagnetics Research, 77, 85–96. 
Cited by: 134

3. Hosseininejad, S. E., Rouhi, K., Neshat, M., Faraji-Dana, R., & Abdolali, A. (2019). Reprogrammable graphene-based metasurface mirror with adaptive focal point for THz imaging. Scientific Reports, 9(1), 2868. 
Cited by: 107

4. Abbas-Azimi, M., Arazm, F., Rashed-Mohassel, J., & Faraji-Dana, R. (2007). Design and optimization of a new 1–18 GHz double ridged guide horn antenna. Journal of Electromagnetic Waves and Applications, 21(4), 501–516. 
Cited by: 76

5. Faraji-Dana, R., & Chow, Y. (1990). Edge condition of the field and AC resistance of a rectangular strip conductor. IEE Proceedings H – Microwaves, Antennas and Propagation, 137(2), 133–140. 
Cited by: 71

Ali Yahia Cherif | Electrical Engineering | Best Researcher Award

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Mr. Ali Yahia Cherif | Electrical Engineering | Best Researcher Award

Phd Student | Oum Elbouaghi University | Algeria

Ali Yahia-Cherif is a seasoned Algerian electrical and automation engineer born in 1991, with extensive expertise in predictive control systems, renewable energy, and electronic system diagnostics. He holds a Master’s in Industrial Automation and a Bachelor’s in Automatic Control from the University of Mentouri Constantine, and has been pursuing a Ph.D. in Electrical and Automatic Engineering since 2014 at the University of Larbi Ben M’hidi, focusing on the development of experimental platforms for shunt active filter systems. His professional experience spans over a decade, including roles as a principal engineer and project manager in high and low-current installations—covering solar energy systems, surveillance, access control, and fire safety networks. He also worked as an engineer in security systems and has teaching experience in programming and electrical engineering at the university level. His leadership in the F.E.A.T scientific electronics club demonstrates his early commitment to innovation. Ali has authored over 12 scientific publications in reputable journals such as IET Renewable Power Generation, International Journal of Circuit Theory and Applications, and European Journal of Electrical Engineering, addressing advanced topics in predictive control, hybrid energy storage, and photovoltaic systems. He has presented his work at key international conferences including GPECOM, IREC, and ICEEAC.

Profile: Google Scholar 

Featured Publications

1. Meddour, S., Rahem, D., Yahia Cherif, A., Hachelfi, W., & Hichem, L. (2019). A novel approach for PV system based on metaheuristic algorithm connected to the grid using FS-MPC controller. Energy Procedia, 162, 57–66. 
Cited by: 32

2. Remache, S. E. I., Yahia Cherif, A., & Barra, K. (2019). Optimal cascaded predictive control for photovoltaic systems: Application based on predictive emulator. IET Renewable Power Generation, 13(15), 2740–2751. 
Cited by: 29

3. Yahia Cherif, A., Remache, S. E. I., Barra, K., & Wira, P. (2019). Adaptive model predictive control for three phase voltage source inverter using ADALINE estimator. In 2019 1st Global Power, Energy and Communication Conference (GPECOM) (pp. 164–169). IEEE. 
Cited by: 7

4. Yahia Cherif, A., Hicham, L., & Kamel, B. (2018). Implementation of finite set model predictive current control for shunt active filter. In 2018 9th International Renewable Energy Congress (IREC) (pp. 1–6). IEEE. 
Cited by: 6

5. Meddour, S., Rahem, D., Wira, P., Laib, H., Yahia Cherif, A., & Chtouki, I. (2022). Design and implementation of an improved metaheuristic algorithm for maximum power point tracking algorithm based on a PV emulator and a double-stage grid-connected system. European Journal of Electrical Engineering, 24(3), 423–430.
Cited by: 5

Hao-Ting Lin | Feature-Driven Optimization | Best Researcher Award

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Assoc Prof. Dr. Hao-Ting Lin | Feature-Driven Optimization | Best Researcher Award

Associate Professor | National Chung Hsing University | Taiwan

Dr. Hao-Ting Lin is an Associate Professor at National Chung Hsing University specializing in robotics, control systems, and bio-industrial mechatronics engineering, with a strong focus on automation, intelligent systems, and sustainable agricultural technologies. He holds advanced degrees in mechanical and control engineering and has developed expertise in the design and integration of robotic mechanisms, sensor-based control, and pneumatic power systems for poultry and agricultural applications. His professional experience includes leading over 30 completed and several ongoing research and consultancy projects, authoring 20 peer-reviewed journal papers, and publishing a book chapter on smart agricultural technologies. Dr. Lin’s research focuses on IoT-enabled humane poultry slaughtering systems, deep learning-based chicken image recognition, precision pneumatic seeders, energy-efficient control strategies for wastewater and agricultural machinery, and robotic sorting systems. He has been granted 12 patents covering innovations in pneumatic automation, exoskeleton systems, aquaculture water quality management, and intelligent sewing and seeding devices. Dr. Lin’s contributions integrate control theory, mechatronics, artificial intelligence, and real-time systems to advance productivity, animal welfare, and sustainability in agriculture and industry. His scholarly impact is reflected in 170 citations by 161 documents, 20 published documents, and an h-index of 6.

Profile: Scopus | ORCID

Featured Publications

1. Lin H.T.*, Suhendra, Development and Implementation of an IoT-Enabled Smart Poultry Slaughtering System Using Dynamic Object Tracking and Recognition. Sensors, 2025, 25(16), 5028.

2. Chen H.C., Rohman Y.F., Ashlah M.B., Lin H.T., Sean W.Y.*, Electrification of Agricultural Machinery: One Design Case of a 4 kW Air Compressor. Energies, 2024, 17(15), 3647.

3. Lin H.T.*, Lee Y.H., Implementing a Precision Pneumatic Plug Tray Seeder with High Seeding Rates for Brassicaceae Seeds via Real-Time Trajectory Tracking Control. Actuators, 2023, 12(9), 340.

4. Liu H.W., Chen C.H., Tsai Y.C., Hsieh K.W., Lin H.T.*, Identifying Images of Dead Chickens with a Chicken Removal System Integrated with a Deep Learning Algorithm. Sensors, 2021, 21(11), 3579.

5. Lin H.T.*, A Novel Real-Time Path Servo Control of a Hardware-in-the-Loop for a Large-Stroke Asymmetric Rod-Less Pneumatic System under Variable Loads. Sensors, 2017, 17(6), 1283.