ORCID Profile
0000-0002-1593-6296
Current Organisation
University of New South Wales
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Publisher: IEEE
Date: 10-2016
Publisher: IEEE
Date: 09-2015
Publisher: IEEE
Date: 07-2016
Publisher: IEEE
Date: 03-2012
Publisher: IEEE
Date: 10-2016
Publisher: Elsevier BV
Date: 04-2018
DOI: 10.1016/J.JBIOMECH.2018.02.006
Abstract: Simulating and analysing eye movement is useful for assessing visual system contribution to discomfort with respect to body movements, especially in virtual environments where simulation sickness might occur. It can also be used in the design of eye prosthesis or humanoid robot eye. In this paper, we present two biomechanic ocular models that are easily integrated into the available musculoskeletal models. The model was previously used to simulate eye-head coordination. The models are used to simulate and analyse eye movements. The proposed models are based on physiological and kinematic properties of the human eye. They incorporate an eye-globe, orbital suspension tissues and six muscles with their connective tissues (pulleys). Pulleys were incorporated in rectus and inferior oblique muscles. The two proposed models are the passive pulleys and the active pulleys models. Dynamic simulations of different eye movements, including fixation, saccade and smooth pursuit, are performed to validate both models. The resultant force-length curves of the models were similar to the experimental data. The simulation results show that the proposed models are suitable to generate eye movement simulations with results comparable to other musculoskeletal models. The maximum kinematic root mean square error (RMSE) is 5.68° and 4.35° for the passive and active pulley models, respectively. The analysis of the muscle forces showed realistic muscle activation with increased muscle synergy in the active pulley model.
Publisher: MDPI AG
Date: 29-09-2021
DOI: 10.3390/AI2040029
Abstract: Continuous action spaces impose a serious challenge for reinforcement learning agents. While several off-policy reinforcement learning algorithms provide a universal solution to continuous control problems, the real challenge lies in the fact that different actuators feature different response functions due to wear and tear (in mechanical systems) and fatigue (in biomechanical systems). In this paper, we propose enhancing the actor-critic reinforcement learning agents by parameterising the final layer in the actor network. This layer produces the actions to accommodate the behaviour discrepancy of different actuators under different load conditions during interaction with the environment. To achieve this, the actor is trained to learn the tuning parameter controlling the activation layer (e.g., Tanh and Sigmoid). The learned parameters are then used to create tailored activation functions for each actuator. We ran experiments on three OpenAI Gym environments, i.e., Pendulum-v0, LunarLanderContinuous-v2, and BipedalWalker-v2. Results showed an average of 23.15% and 33.80% increase in total episode reward of the LunarLanderContinuous-v2 and BipedalWalker-v2 environments, respectively. There was no apparent improvement in Pendulum-v0 environment but the proposed method produces a more stable actuation signal compared to the state-of-the-art method. The proposed method allows the reinforcement learning actor to produce more robust actions that accommodate the discrepancy in the actuators’ response functions. This is particularly useful for real life scenarios where actuators exhibit different response functions depending on the load and the interaction with the environment. This also simplifies the transfer learning problem by fine-tuning the parameterised activation layers instead of retraining the entire policy every time an actuator is replaced. Finally, the proposed method would allow better accommodation to biological actuators (e.g., muscles) in biomechanical systems.
Publisher: IEEE
Date: 10-2016
Publisher: Institution of Engineering and Technology (IET)
Date: 2010
DOI: 10.1049/EL.2010.1778
Publisher: IEEE
Date: 04-2017
Publisher: IEEE
Date: 2008
Publisher: IEEE
Date: 10-2017
Publisher: IEEE
Date: 07-2008
Publisher: IEEE
Date: 04-2013
Publisher: IEEE
Date: 10-2017
Publisher: IEEE
Date: 03-2012
Publisher: IEEE
Date: 04-2013
Publisher: IEEE
Date: 04-2013
Publisher: IEEE
Date: 06-2014
Publisher: MDPI AG
Date: 11-2018
DOI: 10.20944/PREPRINTS201810.0756.V1
Abstract: Automated skin lesion analysis is one of the trending fields that has gained attention among the dermatologists and healthcare practitioners. Skin lesion restoration is an essential preprocessing step for lesion enhancements for accurate automated analysis and diagnosis. Digital hair removal is a non-invasive method for image enhancement by solving the hair-occlusion artefact in previously captured images. Several hair removal methods were proposed for skin delineation and removal. However, manual annotation is one of the main challenges that hinder the validation of these proposed methods on a large number of images or using benchmarking datasets for comparison purposes. In the presented work, we propose a realistic hair simulator based on context-aware image synthesis using image-to-image translation techniques via conditional adversarial generative networks for generation of different hair occlusions in skin images, along with the ground-truth mask for hair location. Besides, we explored using three loss functions including L1-norm, L2-norm and structural similarity index (SSIM) to maximise the synthesis quality. For quantitatively evaluate the realism of image synthesis, the t-SNE feature mapping and Bland-Altman test are employed as objective metrics. Experimental results show the superior performance of our proposed method compared to previous methods for hair synthesis with plausible colours and preserving the integrity of the lesion texture.
Publisher: IEEE
Date: 11-2009
Publisher: IEEE
Date: 10-2017
Publisher: IEEE
Date: 10-2016
Publisher: IEEE
Date: 10-2017
Publisher: IEEE
Date: 10-2013
Publisher: IEEE
Date: 10-2017
Publisher: Elsevier BV
Date: 04-2019
Publisher: IEEE
Date: 12-2010
Publisher: IEEE
Date: 04-2017
Publisher: Elsevier BV
Date: 08-2020
Publisher: IEEE
Date: 06-2014
Publisher: IEEE
Date: 10-2015
DOI: 10.1109/SMC.2015.85
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2014
Publisher: Institution of Engineering and Technology (IET)
Date: 07-2017
DOI: 10.1049/EL.2016.4709
Publisher: IEEE
Date: 11-2009
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: Elsevier BV
Date: 11-2019
DOI: 10.1016/J.APERGO.2019.102883
Abstract: Vergence-accommodation conflict (VAC) is the main contributor to visual fatigue during immersion in virtual environments. Many studies have investigated the effects of VAC using 3D displays and expensive complex apparatus and setup to create natural and conflicting viewing conditions. However, a limited number of studies targeted virtual environments simulated using modern consumer-grade VR headsets. Our main objective, in this work, is to test how the modern VR headsets (VR simulated depth) could affect our vergence system, in addition to investigating the effect of the simulated depth on the eye-gaze performance. The virtual scenario used included a common virtual object (a cube) in a simple virtual environment with no constraints placed on the head and neck movement of the subjects. We used ocular biomechanics and eye tracking to compare between vergence angles in matching (ideal) and conflicting (real) viewing conditions. Real vergence angle during immersion was significantly higher than ideal vergence angle and exhibited higher variability which leads to incorrect depth cues that affects depth perception and also leads to visual fatigue for prolonged virtual experiences. Additionally, we found that as the simulated depth increases, the ability of users to manipulate virtual objects with their eyes decreases, thus, decreasing the possibilities of interaction through eye gaze. The biomechanics model used here can be further extended to study muscular activity of eye muscles during immersion. It presents an efficient and flexible assessment tool for virtual environments.
Publisher: IEEE
Date: 04-2013
Publisher: IEEE
Date: 12-2010
Publisher: IEEE
Date: 10-2016
Publisher: IEEE
Date: 10-2014
Publisher: Institution of Engineering and Technology (IET)
Date: 2008
DOI: 10.1049/EL:20081754
Publisher: IEEE
Date: 11-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: Springer International Publishing
Date: 2022
Publisher: IEEE
Date: 10-2016
DOI: 10.1109/SMC.2015.89
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2018
Publisher: IEEE
Date: 07-2007
Publisher: IEEE
Date: 10-2015
DOI: 10.1109/SMC.2015.91
Publisher: IEEE
Date: 10-2017
Publisher: IEEE
Date: 12-2014
Publisher: IEEE
Date: 2003
Publisher: IEEE
Date: 12-2013
Publisher: IEEE
Date: 11-2016
Publisher: IEEE
Date: 10-2012
Publisher: IEEE
Date: 10-2017
Publisher: IEEE
Date: 10-2017
Publisher: IEEE
Date: 10-2017
No related grants have been discovered for Mohammed Hossny.