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Eng. Maha Yaghi is a MSc in Electrical and Computer Engineering student and a Teaching Assistant of Electrical and Computer Engineering at Abu Dhabi University. She is interested in Mobile applications, Embedded systems, Artificial Intelligence, Robotics, Machine Learning in Medicine, and Hardware and Software programming. She received her B.Sc. in Computer Engineering from Abu Dhabi University in 2019 and was a consistent dean’s lister. She has led her capstone team towards developing a Computer-Aided Diagnosis and Visualization system of Cancerous Nodules in 2019 and has also worked on various projects, including a Quarantine tracking app and a Wearable IoT-based Joint Flexion Sensing device for Computer-Aided Diagnosis of Motion Ailments and received multiple awards.
Recent advancements in cloud computing, artificial intelligence, and the internet of things (IoT) create new opportunities for autonomous industrial environments monitoring. Nevertheless, detecting anomalies in harsh industrial settings remains challenging. This paper proposes an edge-fog-cloud architecture with mobile IoT edge nodes carried on autonomous robots for thermal anomalies detection in aluminum factories. We use companion drones as fog nodes to deliver first response services and a cloud back-end for thermal anomalies analysis. We also propose a self-driving deep learning architecture and a thermal anomalies detection and visualization algorithm. Our results show our robot surveyors are low-cost, deliver reduced response time, and more accurately detect anomalies compared to human surveyors or fixed IoT nodes monitoring the same industrial area. Our self-driving architecture has a root mean square error of 0.19 comparable to VGG-19 with a significantly reduced complexity and three times the frame rate at 60 frames per second. Our thermal to visual registration algorithm maximizes mutual information in the image-gradient domain while adapting to different resolutions and camera frame rates.
Mohammed Ghazal; Tasnim Basmaji; Maha Yaghi; Mohammad Alkhedher; Mohamed Mahmoud; Ayman El-Baz. Cloud-Based Monitoring of Thermal Anomalies in Industrial Environments Using AI and the Internet of Robotic Things. Sensors 2020, 20, 6348 .
AMA StyleMohammed Ghazal, Tasnim Basmaji, Maha Yaghi, Mohammad Alkhedher, Mohamed Mahmoud, Ayman El-Baz. Cloud-Based Monitoring of Thermal Anomalies in Industrial Environments Using AI and the Internet of Robotic Things. Sensors. 2020; 20 (21):6348.
Chicago/Turabian StyleMohammed Ghazal; Tasnim Basmaji; Maha Yaghi; Mohammad Alkhedher; Mohamed Mahmoud; Ayman El-Baz. 2020. "Cloud-Based Monitoring of Thermal Anomalies in Industrial Environments Using AI and the Internet of Robotic Things." Sensors 20, no. 21: 6348.
Low-cost and real-time measurement of limbs motion and joints flexion is an important step in the assessment of body motion quality with wide ranging applications in physical therapy, sports, and choreographed motions. In this paper, we propose a mobile system for sensing and classifying limbs motion and joints flexion. The system consists of sensing units wearable on all joints of interest. The sensing units use 9 degrees of freedom sensors to capture and log the joints angles over time at a high sampling rate during a capture session. When the session is complete, the units upload the data to a server for analysis. An accompanying mobile app downloads and uses the data to visualize the motion quality against a prerecorded golden standard pattern with added tolerance. We quantize and visualize the portion of the signal over time where deviation from the golden standard is observed. Part of the visualization includes using time to align the deviations with a visual recording of the motions to allow the user to use this feedback to improve their motions, much like how a personal trainer does. Our system is also useful in quantifying improvements in physical therapy when the captured signal is compared over the length of the treatment plan. Our results show the effectiveness of the proposed system in motion quality assessment.
Mohammed Ghazal; Marah Alhalabi; Luay Fraiwan; Maha Yaghi; Lina Alkhatib. Assessment of Motion Quality using an IoT-Based Wearable and Mobile Joint Flexion Sensors. 2019 7th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW) 2019, 44 -48.
AMA StyleMohammed Ghazal, Marah Alhalabi, Luay Fraiwan, Maha Yaghi, Lina Alkhatib. Assessment of Motion Quality using an IoT-Based Wearable and Mobile Joint Flexion Sensors. 2019 7th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW). 2019; ():44-48.
Chicago/Turabian StyleMohammed Ghazal; Marah Alhalabi; Luay Fraiwan; Maha Yaghi; Lina Alkhatib. 2019. "Assessment of Motion Quality using an IoT-Based Wearable and Mobile Joint Flexion Sensors." 2019 7th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW) , no. : 44-48.