Iyad Almadani, Ph.D.

Post Doctoral Fellow
mlmadani@memphis.edu

Bio

Iyad Mohd Eid H Almadani is an expert in computer vision, deep learning, and intelligent sensing systems. At the forefront of innovation, Iyad’s work bridges advanced artificial intelligence with practical engineering solutions. He has developed cutting-edge depth-aware vision systems for intelligent livestock monitoring, enabling accurate measurement of anatomical features and estrus detection in sows using a single camera setup. His expertise spans depth estimation, object detection, and image segmentation, driving forward impactful applications in both research and industry.

Iyad’s work also extends to image restoration in degraded visual environments. By simulating challenging conditions such as fog and rain, he creates realistic datasets to train AI models to recover clear, high-quality images. These advancements enhance the safety and reliability of drones, surveillance systems, and automated transportation technologies.

In addition to his research, Iyad brings extensive hands-on experience in electrical and industrial automation. He has led projects involving control panel wiring, PLC programming, and the integration of sensors, actuators, and variable frequency drives (VFDs) for both AC and DC systems. Iyad is passionate about merging practical engineering with AI-driven innovation to develop systems that address real-world challenges.

Education

Electrical and Computer Engineering, Ph.D., 2025, University of Memphis

Electrical and Computer Engineering, M.S., 2023, University of Memphis

Mechatronics Engineering, B.S., Al-Balqa’ Applied University – Faculty of Engineering Technology, Amman, Jordan

Selected Publications

  • Almadani, Iyad. (2025). Vision-Based Estrus Detection: Integrating YOLO Segmentation, Thermal Imaging, and Depth Estimation for Precision Livestock Monitoring. Ph.D. Dissertation. The University of Memphis ProQuest Dissertations & Theses.  2025. 32119248.
  • Almadani, Iyad & Robinson, Aaron & Abuhussein, Mohammed. (2025). Integration of YOLOv9 Segmentation and Monocular Depth Estimation in Thermal Imaging for Prediction of Estrus in Sows Based on Pixel Intensity Analysis. Digital. 5. 22. 10.3390/digital5020022.
  • Almadani, Iyad & Abuhussein, Mohammed & Robinson, Aaron. (2024). YOLOv8-Based Estimation of Estrus in Sows Through Reproductive Organ Swelling Analysis Using a Single Camera. Digital. 4. 898-913. 10.3390/digital4040044.
  • Abuhussein, Mohammed & Almadani, Iyad & Robinson, Aaron & Younis, Mohammed. (2024). Enhancing Obscured Regions in Thermal Imaging: A Novel GAN-Based Approach for Efficient Occlusion Inpainting. J. 7. 218-235. 10.3390/j7030013.
  • Almadani, Iyad & Ramos, Brandon & Abuhussein, Mohammed & Robinson, Aaron. (2024). Advanced Swine Management: Infrared Imaging for Precise Localization of Reproductive Organs in Livestock Monitoring. Digital. 4. 446-460. 10.3390/digital4020022.
  • Abuhussein, Mohammed & Robinson, Aaron & Almadani, Iyad. (2023). Review of Unsupervised Segmentation Techniques on Long Wave Infrared Images. International Journal of Advanced Computer Science and Applications. 14. 10.14569/IJACSA.2023.01406138.
  • Almadani, Iyad. (2023). Organ Localization and Detection in Sows Using Machine Learning and Deep Learning in Computer Vision. Electronic Theses and Dissertations. 3104.
    https://digitalcommons.memphis.edu/etd/3104
  • Almadani, I., Abuhussein, M., Robinson, A.L. (2022). Sow Localization in Thermal Images Using Gabor Filters. In: Arai, K. (eds) Advances in Information and Communication. FICC 2022. Lecture Notes in Networks and Systems, vol 438. Springer, Cham. https://doi.org/10.1007/978-3-030-98012-2_44
 
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