publications

2025

1. Diaphragmatic Breathing Guidance Using a Humanoid Robot with a Soft Robotic Haptic Interface for Anxiety Reduction

   Adnan Saood, Yang Liu, and Adriana Tapus

   In Social Robotics, 2025

2024

1. Patent

   FOCUSED ULTRASOUND SONICATION WITH MECHANICAL AND ELECTRONIC STEERING ALONG A COMPUTED TRAJECTORY

   Adnan SAOOD, Jonathan VAPPOU, Florent NAGEOTTE, and 1 more author

   2024

   EP Patent EP4445859A1
2. Designing a Haptic Interface for Enhanced Non-Verbal Human-Robot Interaction: Integrating Heart and Lung Emotional Feedback

   Adnan Saood, Yang Liu, Heng Zhang, and 1 more author

   In 2024 IEEE-RAS 23rd International Conference on Humanoid Robots (Humanoids), 2024

2023

1. Design of Robotic Trajectories for Volumetric Ultrasound-induced Blood-Brain Barrier Permeabilization

   Adnan Saood, Florent Nageotte, Benoit Larrat, and 1 more author

   In International Symposium on Therapeutic Ultrasounds, Lyon, France, mai 2023, 2023
2. Implementation of a Volumetric Blood-Brain Barrier Permeabilization Robotic Platform

   Adnan Saood, Maciej Bednarczyk, Benoit Larrat, and 2 more authors

   In Computer and Robot-Assisted Surgery (CRAS), Paris, France, septembre 2023, 2023

2021

1. COVID-19 lung CT image segmentation using deep learning methods: U-Net versus SegNet

   Adnan Saood and Iyad Hatem

   BMC Medical Imaging, 2021

   DOI

   

2018

1. Conference

   Two Proposed Indoor Multi-Cameras Positioning Systems Compared to Classical Geometry System

   Adnan Saood, Nada Salman, Ali Alreyahi, and 1 more author

   In 2018 International Conference on Computational Approach in Smart Systems Design and Applications (ICASSDA), 2018

   Abs DOI Bib

   Positioning systems in indoor environments are of a great concern in automation and robotics domains where performing critical tasks requires precision. However, to make these systems widely applicable they must be cost-effective. The objective of this paper is to develop two different 3D positioning systems based on neural networks and adaptive neuro-fuzzy techniques. Sample images of a recognizable object were taken using three low-cost cameras as training and testing data for these systems. Positioning results of the proposed systems are compared with results of the classical geometrical method. The results show positioning errors on the scale of millimeters and the neural network system produces the smallest error.

   @inproceedings{saood2018two,
     keywords = {Indoor positioning, Multi-camera systems, Classical geometry system, Accuracy, Robustness},
     url = {https://ieeexplore.ieee.org/abstract/document/8477636},
     doi = {https://doi.org/10.1109/ICASSDA.2018.8477636},
     title = {Two Proposed Indoor Multi-Cameras Positioning Systems Compared to Classical Geometry System},
     author = {Saood, Adnan and Salman, Nada and Alreyahi, Ali and Hatem, Iyad},
     booktitle = {2018 International Conference on Computational Approach in Smart Systems Design and Applications (ICASSDA)},
     pages = {1--6},
     year = {2018},
     organization = {IEEE}
   }