Reyad EL KHAZALI | AI-Based Robot Perception | Research Excellence Award

Research Excellence Award

Reyad EL KHAZALI — Khalifa University, United Arab Emirates

Reyad EL KHAZALI
Affiliation Khalifa University
Country United Arab Emirates
Scopus ID 6603299146
Documents 81
Citations 1,645
h-index 20
Subject Area AI-Based Robot Perception
Event International Robotics and Automation Awards
ORCID 0000-0003-3205-7477

Reyad El-Khazali is an Associate Professor at Khalifa University whose academic work encompasses control engineering, fractional-order systems, signal processing, mechatronics, power systems and related engineering applications. Khalifa University’s institutional profile confirms his academic appointment and doctoral training in control engineering. [1]

Abstract

The Research Excellence Award profile recognizes Reyad El-Khazali for an established academic record in electrical and control engineering. His documented research includes fractional-order control, signal processing, mechatronics, power systems and related engineering applications. His publication activity and academic appointment at Khalifa University provide a basis for evaluating his research record within a robotics and automation recognition framework.[2]

Keywords

Research Excellence Award; Reyad El-Khazali; Khalifa University; control engineering; fractional-order systems; signal processing; mechatronics; power systems; robotics and automation; artificial intelligence; robot perception.

Introduction

Reyad El-Khazali is an Associate Professor at Khalifa University with a research background in electrical engineering and control engineering. His documented interests include nonlinear and fractional control, mechatronics, communication and digital signal processing, and fractional chaotic systems. His work provides an engineering foundation relevant to intelligent and automated systems. [1]

Research Profile

El-Khazali’s research profile spans control systems, fractional-order dynamics, signal processing, mechatronics, power systems and drone-related applications. Independent scholarly records also identify his work in fractional-order controllers and related mathematical models. These areas intersect with robotics through system modelling, feedback control, sensing, signal processing and autonomous-system engineering. [3]

Research Contributions

A notable contribution of El-Khazali’s research is the application of fractional-order methods to engineering control and signal-processing problems. His published work includes a fractional-order digital phase-locked loop and studies of fractional-order operators. Such research contributes theoretical and computational approaches that can support stable, responsive and mathematically informed automated systems. [2]

Publications

El-Khazali has published research across control engineering and related electrical-engineering fields. A documented publication, “Fractional-order digital phase-locked loop,” appeared in the proceedings of ICECS 2007 and is indexed with DOI 10.1109/ICECS.2007.4511152. More recent records also document his participation in research on AI-enabled drones for date-palm pollination. [1]

Research Impact

The supplied research metrics list 81 documents, 1,645 citations and an h-index of 20. These figures indicate a substantial scholarly publication and citation record, although bibliometric values can change as databases update. His institutional affiliation and continuing research activity provide additional context for assessing the academic reach of his work. [2]

Award Suitability

El-Khazali’s documented academic career, research output and work in control, signal processing, mechatronics and intelligent aerial-system applications provide relevant evidence for consideration under a research-excellence framework. Suitability for an AI-Based Robot Perception award should additionally be evaluated against the award’s specific criteria and verified evidence directly connecting his publications to robot perception.[3]

Conclusion

Reyad El-Khazali presents an established academic profile in electrical and control engineering, supported by research publications, institutional experience and documented scholarly impact. His work intersects with automation through control, signal processing, mechatronics and aerial-system applications. These characteristics provide a reasonable academic basis for consideration for research-excellence recognition. [1]

References

  1. Fractional-order dynamical models of love
    https://www.researchgate.net/publication/229331672_Fractional-order_dynamical_models_of_love
  2. The General Solution of Singular Fractional-Order Linear Time-Invariant Continuous Systems with Regular Pencils
    https://www.mdpi.com/1099-4300/20/6/400
  3. AI-enabled drones for date palm pollination
    https://www.nature.com/articles/s41598-026-39739-2

Assist. Prof. Dr. Danyun Xu | AI-Based Robot Perception | Excellence in Research Award

Assist. Prof. Dr. Danyun Xu | AI-Based Robot Perception | Excellence in Research Award

Jianghan University | China

Assist. Prof. Dr. Danyun Xu is an Associate Professor at the College of Life Science, Jianghan University, specializing in mushroom resource development and bioactive peptide research. Her work integrates AI-assisted molecular simulation and multi-omics approaches to elucidate functional mechanisms and support the development of innovative functional foods. She has led projects funded by the National Natural Science Foundation of China, published multiple high-impact papers in journals such as Food Chemistry and npj Science of Food, AI-Based Robot Perception and contributed to advancing high-value utilization of edible mushroom resources, promoting industrial innovation in healthy food products.

Citation Metrics (Scopus)

60
45
30
15
0

Citations
55

Publications
8

h-index
4

Citations

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Mr. Zhaohui Chen | AI-Based Robot Perception | Research Excellence Award

Mr. Zhaohui Chen | AI-Based Robot Perception | Research Excellence Award

The University of Sydney | Australia 

Mr. Zhaohui Chen is a PhD candidate in Civil Engineering at the University of Sydney, focusing on AI-driven infrastructure assessment, disaster response, and digital-twin systems. His research combines computer vision, multimodal learning, and agentic AI to develop scalable, interpretable frameworks for large-scale damage assessment, AI-Based Robot Perception and decision support. He has published multiple first-author papers in leading journals, including Nature Communications and Automation in Construction. His work aims to enable real-world engineering decision-making under uncertainty by integrating robotics-enabled inspection, intelligent automation, and digital-twin technologies.

Citation Metrics (Scopus)

24
18
12
6
0

Citations
23

Documents
2

h-index
2

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View Scopus Profile

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