Saeid Seyedi | quantum | Best Researcher Award

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Dr. Saeid Seyedi | quantum | Best Researcher Award

Member, BASU, Iran

Saeid Seyedi is a prominent Iranian researcher in the field of Computer Engineering, specializing in Quantum-dot Cellular Automata (QCA) and nanotechnology. He holds a Ph.D. from Bu-Ali Sina University and is a member of the Young Researchers and Elite Club at Islamic Azad University (IAU). Recognized among the world’s top 2% scientists by Stanford University, Saeid has made significant contributions in fault-tolerant hardware, low-power nanoelectronics, wireless sensor networks, and QCA-based technology. With an extensive publication record and active roles as a reviewer for prestigious journals, his research is disseminated globally. He is also involved in various research projects, aiming to advance nanotechnology and computational hardware. Saeid’s work has earned him recognition in both academic and professional circles, and he is committed to advancing knowledge in quantum computing, electronics, and nanoscience.

Profile

Education

Saeid Seyedi pursued his academic journey in Computer Engineering, earning his Master’s degree from the Islamic Azad University Science and Research Branch in Tabriz, Iran (2015-2017). His studies focused on Quantum-dot Cellular Automata (QCA) technology and its applications in nanoelectronics. After completing his Master’s degree, he continued his studies at Bu-Ali Sina University, where he is currently working towards his Ph.D. in Computer Engineering. His academic interests are deeply rooted in advanced computational systems, nanotechnology, and quantum computing, and he has shown a strong commitment to exploring the intersection of these fields. Saeid has been an active member of the Young Researchers and Elite Club at IAU since 2018, which further demonstrates his dedication to both learning and contributing to the academic community. His work continues to push the boundaries of nanoscale technologies and fault-tolerant hardware designs.

Research Focus

Saeid Seyedi’s research primarily revolves around Quantum-dot Cellular Automata (QCA) and nanotechnology, focusing on designing and optimizing nanoscale circuits for computing and electronics. His work aims to push the limits of conventional semiconductor technology by exploring QCA as a promising alternative for future electronics. He also delves into fault-tolerant hardware, ensuring the reliability of nanoscale systems in real-world applications. His interests extend to the application of nanotechnology in wireless sensor networks, IoT, and cloud computing. Another key area of his research involves low-power design techniques for nanoelectronics, ensuring energy efficiency in future computing systems. Additionally, Saeid has contributed to the development of image processing techniques in nanoscience, particularly in the context of nanoelectronics. Through his work, he seeks to contribute to the advancement of quantum computing, digital design, and the broader field of nanoelectronics, addressing critical challenges in the next generation of computational systems.

Publications 

  1. An Approximate XOR-based Full-Adder in Quantum Cellular Automata ⚡🔬
  2. An Efficient New Design of Nano-Scale Comparator Circuits Using Quantum-Dot Technology 🧠⚙️
  3. Efficient Design and Implementation of Approximate FA, FS, and FA/S Circuits for Nanocomputing in QCA 🖥️🔧
  4. Quantum-Based Serial-Parallel Multiplier Circuit Using an Efficient Nano-Scale Serial Adder 🔢💡
  5. A Space-Efficient Universal and Multi-Operative Reversible Gate Design Based on Quantum-Dots 🌀🔗
  6. A Fault-Tolerant Image Processor for Executing the Morphology Operations Based on a Nanoscale Technology 🖼️🔒
  7. A New Nano-Scale and Energy-Optimized Reversible Digital Circuit Based on Quantum Technology ⚡💡
  8. An Efficient Structure for Designing a Nano-Scale Fault-Tolerant 2:1 Multiplexer Based on Quantum-Dot Cellular Automata 🔄⚙️
  9. Designing a Multi-Layer Full-Adder Using a New Three-Input Majority Gate Based on Quantum Computing 🧮💻
  10. Designing a Three-Level Full-Adder Based on Nano-Scale Quantum Dot Cellular Automata 🧑‍💻🔢
  11. Design and Analysis of Fault-Tolerant 1:2 Demultiplexer Using Quantum-Dot Cellular Automata Nano-Technology 🔄🔋
  12. A New Cost-Efficient Design of a Reversible Gate Based on Nano-Scale Quantum-Dot Cellular Automata Technology 🏷️🧠
  13. A New Coplanar Design of a 4-Bit Ripple Carry Adder Based on Quantum-Dot Cellular Automata Technology ⬆️🔢
  14. A Fault-Tolerance Nanoscale Design for Binary-to-Gray Converter Based on QCA 🔄💡
  15. Designing a New 4:2 Compressor Using an Efficient Multi-Layer Full-Adder Based on Nanoscale Quantum-Dot Cellular Automata 🔧💡

Changqing Xia | Computer Science | Best Researcher Award

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Prof. Changqing Xia | Computer Science | Best Researcher Award

Researcher, Shen Zi Institute, Chinese Academy of Sciences, China

Dr. Changqing Xia is a leading researcher in the fields of cyber–physical systems, artificial intelligence (AI), and network computation. He has focused his career on advancing the integration of computing, communication, and control in smart manufacturing systems. Dr. Xia’s expertise lies in developing AI-driven solutions that optimize resource allocation, network scheduling, and real-time data management in industrial environments. With numerous publications in prestigious journals, Dr. Xia is at the forefront of intelligent system design and advanced production technologies.

Profile

Orcid

Strengths for the Award

Dr. Changqing Xia demonstrates outstanding contributions to the fields of cyber–physical systems (CPS), artificial intelligence, and network scheduling, particularly with a focus on industrial applications. His recent works such as Deterministic Network–Computation–Manufacturing Interaction Mechanism for AI-Driven Cyber–Physical Production Systems and Co-Design of Control, Computation, and Network Scheduling Based on Reinforcement Learning illustrate his innovative approach to merging computation with physical manufacturing environments. His expertise in using AI, reinforcement learning, and computational intelligence to improve production systems and real-time scheduling significantly advances the field. Moreover, his research on 5G-based positioning and data scheduling under mixed-criticality scenarios provides solutions to current industrial challenges, making him a forward-looking researcher whose work is at the cutting edge of smart manufacturing and industrial automation. His ability to integrate multiple domains such as control, communication, and computing positions him as a highly versatile and impactful researcher.

Areas for Improvement

While Dr. Xia’s research portfolio is robust, focusing on a broader application of his methodologies across different industries, outside of cyber-physical production systems, could further expand the impact of his work. His publications heavily concentrate on industrial environments, but applying his AI-driven methods to fields like healthcare, smart cities, or autonomous systems could diversify his research impact. Additionally, greater collaboration with other interdisciplinary fields could bring fresh perspectives and opportunities for expanding his work into more novel, groundbreaking areas. Another area of improvement could be increasing public engagement or educational outreach, which would help communicate his research more broadly to a non-specialist audience.

Publications Top Notes:

  1. Deterministic Network–Computation–Manufacturing Interaction Mechanism for AI-Driven Cyber–Physical Production Systems
    IEEE Internet of Things Journal (2024-05-15)
    DOI: 10.1109/JIOT.2024.3367350
  2. Co-Design of Control, Computation, and Network Scheduling Based on Reinforcement Learning
    IEEE Internet of Things Journal (2024-02-01)
    DOI: 10.1109/JIOT.2023.3305708
  3. A Self-Triggered Approach for Co-Design of MPC and Computing Resource Allocation
    IEEE Internet of Things Journal (2024)
    DOI: 10.1109/JIOT.2024.3392563
  4. Computational-Intelligence-Based Scheduling with Edge Computing in Cyber–Physical Production Systems
    Entropy (2023-12)
    DOI: 10.3390/e25121640
  5. Control–Communication–Computing Co-Design in Cyber–Physical Production System
    IEEE Internet of Things Journal (2023-03-15)
    DOI: 10.1109/JIOT.2022.3221932
  6. Indoor Fingerprint Positioning Method Based on Real 5G Signals
    Conference Paper (2023-01-05)
    DOI: 10.1145/3583788.3583819
  7. Mixed-Criticality Industrial Data Scheduling on 5G NR
    IEEE Internet of Things Journal (2022-06-15)
    DOI: 10.1109/JIOT.2021.3121251
  8. Real-Time Scheduling of Massive Data in Time Sensitive Networks With a Limited Number of Schedule Entries
    IEEE Access (2020)
    DOI: 10.1109/ACCESS.2020.2964690

Conclusion

Dr. Changqing Xia is a strong candidate for the “Best Researcher Award” due to his significant contributions to the fields of AI, network computation, and industrial CPS. His research innovations in optimizing industrial systems through cutting-edge computational and network scheduling methods provide solutions to contemporary challenges in smart manufacturing and data-intensive environments. With minor refinements in expanding his interdisciplinary reach and public engagement, Dr. Xia’s already impactful work could lead to even broader recognition in both the academic and industrial spheres. His achievements reflect not only technical depth but also practical applicability, making him highly deserving of this prestigious award.