IECE - Institute of Emerging and Computer Engineers

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    • mustafa.abro@ieee.org
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    Author 2
    ORCID
    0000-0003-1874-1889
    Ghulam E Mustafa Abro
    Interdisciplinary Research Centre for Aviation and Space Exploration King Fahd University of Petroleum and Minerals (KFUPM) Saudi Arabia
    Summary
    Edited Journals
    IECE Contributions
    Articles
    2
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    Free Access | Review Article | 12 October 2024
    Innovations in 3D Object Detection: A Comprehensive Review of Methods, Sensor Fusion, and Future Directions
    by
    Ghulam E Mustafa Abro
    ,
    Zain Anwar Ali
    ,
    Summaiya Rajput
    IECE Transactions on Sensing, Communication, and Control | Volume 1, Issue 1: 3-29, 2024 | DOI:10.62762/TSCC.2024.989358
    Abstract
    This review paper offers a thorough assessment of three-dimensional object recognition methods, an essential element in the perception frameworks of autonomous systems. This analysis emphasises the integration of LiDAR and camera sensors, providing a distinctive contrast with more economical alternatives like camera-only or camera-Radar combinations. This study objectively evaluates performance and practical implementation issues, such as cost and operational efficiency, thereby elucidating the limitations of existing systems and proposing avenues for further research. The insights provided render it a significant asset for enhancing 3D object recognition and autonomy in intelligent systems. More >
    Graphical Abstract
    Innovations in 3D Object Detection: A Comprehensive Review of Methods, Sensor Fusion, and Future Directions
    Free Access | Research Article | 23 September 2024
    Signal Strength-Based Alien Drone Detection and Containment in Indoor UAV Swarm Simulations
    by
    Ghulam E Mustafa Abro
    ,
    Zain Anwar Ali
    ,
    Ayman M Abdallah
    IECE Transactions on Intelligent Systematics | Volume 1, Issue 2: 58-67, 2024 | DOI:10.62762/TIS.2024.807714
    Abstract
    A Novel simulation framework using self-governing drones is used to locate and reduce unauthorized drones in interior environments. The recommended method uses Received Signal Strength Indicator (RSSI) to identify an alien agent drone, which has different signal characteristics than the approved swarm of UAVs. Real-time threat detection is possible with this technology. After detecting the drone, the swarm organizes itself to encircle and besiege it for 10 seconds, making it inert before returning to their original positions. This unique solution uses RSSI to quickly identify and mitigate enclosed area concerns. It provides a reliable and effective indoor drone security solution. The simulat... More >
    Graphical Abstract
    Signal Strength-Based Alien Drone Detection and Containment in Indoor UAV Swarm Simulations