Manipulation

Houbara Wildlife AI

Artificial Intelligence for Understanding Houbara Wildlife in UAE

AI-enabled models to analyze sensory data for bird pose, behavior, vegetation cover, and trap picture analysis.

IFHC scientists collect large amounts of sensory data including pictures and videos from wild and captive houbara populations. This project investigates AI-enabled computational models to analyze and understand this data for interpretative tasks including bird pose, behavior, vegetation cover, and trap picture analysis.

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Houbara Wildlife AI

Related Publications

B. Hasanen, B. Suthar, Y. Zweiri, L. Seneviratne, I. Hussain, "Design of Twisted String Actuated Flexure Joint for Supernumerary Robotic Arm for Bi-manual Tasks," IEEE Sensors, 2024.

M. Belal, M. Alsheikh, A. Aljarah, I. Hussain, "Deep Learning Approaches for Enhanced Lower-Limb Exoskeleton Control: A Review," IEEE Access, 2024.

B. Suthar, M. Awad, I. Hussain, L. Seneviratne, Y. Zweiri, "Design of Robotic Finger Using Twisted String Actuator with Modular Passive Return Rotational Joints," Mechatronics, 2024.

DOI

A. Khan et al., "Tomato Maturity Recognition with Convolutional Transformers," Scientific Reports, 2024.

DOI

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