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Publications

2023
Hossein Mehnatkesh, Seyed Mohammad Jafar Jalali, Abbas Khosravi, and Saeid Nahavandi. 2023. “An intelligent driven deep residual learning framework for brain tumor classification using MRI images.” Expert Systems with Applications, 213. Publisher's Version
Hadi Mahamivanan, Navid Ghassemi, Mohammad Tayarani Darbandi, Afshin Shoeibi, Sadiq Hussain, Farnad Nasirzadeh, Roohallah Alizadehsani, Darius Nahavandi, Abbas Khosravi, and Saeid Nahavandi. 2023. “Material recognition for construction quality monitoring using deep learning methods.” Construction Innovation. Publisher's Version
Lars Kooijman, Stefan Berti, Houshyar Asadi, Saeid Nahavandi, and Behrang Keshavarz. 2023. “Measuring vection: a review and critical evaluation of different methods for quantifying illusory self-motion.” Behavior Research Methods. Publisher's Version
Archit Krishna Kamath, Tanmoy Dam, Heera Lal Maurya, Padmini Singh, Ranjith Ravindranathan Nair, and Saeid Nahavandi. 2023. “Modelling and Sliding Mode Control of a Stereo Vision Augmented 6 DoF Quadrotor System.” In 2023 21st International Conference on Advanced Robotics, ICAR 2023, Pp. 425 – 430. Publisher's Version
Mohammadreza Chalak Qazani, Behrooz Shirani Bidabadi, Houshyar Asadi, Saeid Nahavandi, and Farnoosh Shirani Bidabadi. 2023. “Multiobjective Optimization of Roll-Forming Procedure Using NSGA-II and Type-2 Fuzzy Neural Network.” IEEE Transactions on Automation Science and Engineering, Pp. 1–0. Publisher's Version
Mohammadreza Chalak Qazani, Houshyar Asadi, Muhammad Zakarya, Chee Peng Lim, Alan Wee-Chung Liew, Mansour Karkoub, and Saeid Nahavandi. 2023. “A Neural Network-Based Motion Cueing Algorithm Using the Classical Washout Filter for Comprehensive Driving Scenarios.” IEEE Transactions on Intelligent Transportation Systems, Pp. 1–0. Publisher's Version
Alireza Hosseinnajad, Navid Mohajer, and Saeid Nahavandi. 2023. “Novel barrier Lyapunov function-based backstepping fault tolerant control system for an ROV with thruster constraints.” Ocean Engineering, 285. Publisher's Version
Houshyar Asadi, Tobias Bellmann, Mohammadreza Chalak Qazani, Shady Mohamed, Chee Peng Lim, and Saeid Nahavandi. 2023. “A Novel Decoupled Model Predictive Control-Based Motion Cueing Algorithm for Driving Simulators.” IEEE Transactions on Vehicular Technology, 72, 6, Pp. 7024 – 7034. Publisher's Version
Mohammadreza Chalak Qazani, Houshyar Asadi, Yutao Chen, Moloud Abdar, Mansour Karkoub, Shady Mohamed, Chee Peng Lim, and Saeid Nahavandi. 2023. “An Optimal Nonlinear Model Predictive Control- Based Motion Cueing Algorithm Using Cascade Optimization and Human Interaction.” IEEE Transactions on Intelligent Transportation Systems, 24, 9, Pp. 9191 – 9202. Publisher's Version
Li Zhang, Sam Slade, Chee Peng Lim, Houshyar Asadi, Saeid Nahavandi, Haoqian Huang, and Hang Ruan. 2023. “Semantic segmentation using Firefly Algorithm-based evolving ensemble deep neural networks.” Knowledge-Based Systems, 277. Publisher's Version
H.M. Dipu Kabir, Moloud Abdar, Abbas Khosravi, Seyed Mohammad Jafar Jalali, Amir F. Atiya, Saeid Nahavandi, and Dipti Srinivasan. 2023. “SpinalNet: Deep Neural Network With Gradual Input.” IEEE Transactions on Artificial Intelligence, 4, 5, Pp. 1165 – 1177. Publisher's Version
Roohallah Alizadehsani, Mohamad Roshanzamir, Navid Hoseini Izadi, Raffaele Gravina, H.M. Dipu Kabir, Darius Nahavandi, Hamid Alinejad-Rokny, Abbas Khosravi, U. Rajendra Acharya, Saeid Nahavandi, and Giancarlo Fortino. 2023. “Swarm Intelligence in Internet of Medical Things: A Review.” Sensors, 23, 3. Publisher's Version
Ngoc Duy Nguyen, Thanh Thi Nguyen, Nhat Truong Pham, Hai Nguyen, Dang Tu Nguyen, Thanh Dang Nguyen, Chee Peng Lim, Michael Johnstone, Asim Bhatti, Douglas Creighton, and Saeid Nahavandi. 2023. “Towards designing a generic and comprehensive deep reinforcement learning framework.” Applied Intelligence, 53, 3, Pp. 2967 – 2988. Publisher's Version
H.M. Dipu Kabir, Subrota Kumar Mondal, Sadia Khanam, Abbas Khosravi, Shafin Rahman, Mohammadreza Chalak Qazani, Roohallah Alizadehsani, Houshyar Asadi, Shady Mohamed, Saeid Nahavandi, and U. Rajendra Acharya. 2023. “Uncertainty aware neural network from similarity and sensitivity[Formula presented].” Applied Soft Computing, 149. Publisher's Version
Maryam Habibpour, Hassan Gharoun, Mohammadreza Mehdipour, AmirReza Tajally, Hamzeh Asgharnezhad, Afshar Shamsi, Abbas Khosravi, and Saeid Nahavandi. 2023. “Uncertainty-aware credit card fraud detection using deep learning.” Engineering Applications of Artificial Intelligence, 123. Publisher's Version
Moloud Abdar, Soorena Salari, Sina Qahremani, Hak-Keung Lam, Fakhri Karray, Sadiq Hussain, Abbas Khosravi, U. Rajendra Acharya, Vladimir Makarenkov, and Saeid Nahavandi. 2023. “UncertaintyFuseNet: Robust uncertainty-aware hierarchical feature fusion model with Ensemble Monte Carlo Dropout for COVID-19 detection.” Information Fusion, 90, Pp. 364 – 381. Publisher's Version
Lars Kooijman, Houshyar Asadi, Shady Mohamed, and Saeid Nahavandi. 2023. “A virtual reality study investigating the train illusion.” Royal Society Open Science, 10, 4. Publisher's Version
2022
Mohammad Rokonuzzaman, Navid Mohajer, and Saeid Nahavandi. 7/7/2022. “Effective adoption of vehicle models for autonomous vehicle path tracking: a switched MPC approach.” International Journal of Vehicle Mechanics and Mobility, 61, 5. Publisher's VersionAbstract
Efficient path tracking plays a key role in the overall ride experience of Autonomous Vehicles (AVs). Model Predictive Control (MPC) is one of the most competent techniques which is capable of handling multiple variables and constraints. The performance of this controller heavily relies on the proper choice of the vehicle model used to predict future states. This work proposes a novel MPC framework for the effective adoption of vehicle models to achieve a compromise between MPC’s performance and computational cost. To this aim, a Switched MPC (SMPC) using vehicle models with different levels of complexity and fidelity is developed. The SMPC uses a novel supervision scheme to adopt the appropriate vehicle model based on the models’ prediction error and MPC’s solution time. Two different configurations of SMPC are implemented: (1) A Fuzzy Logic System (FLS), and (2) An adaptive switching supervisor …
Mohsen Dalvand, Saeid Nahavandi, and Robert D. Howe. 6/6/2022. “General Forward Kinematics for Tendon-Driven Continuum Robots,” 10, Pp. 60330 - 60340. Publisher's VersionAbstract
Unlike the inverse kinematics problem of  n -tendon continuum robots, the forward kinematics problem lacks a closed-form analytical solution. In this paper, a novel forward kinematics algorithm for  n -tendon single-segment flexible continuum robots is developed that can determine the resulting beam configuration for any given set of actuator displacements. The algorithm determines key parameters of all possible n - to 1-tendon combinations and examines them against evaluation criteria to find the final beam configuration as well as the active/slack status of the tendons at this configuration. The algorithm employs a previously developed analytical loading model for  n -tendon continuum robots with general tendon positioning to evaluate the tension loads in tendons for each combination. Potential energy of the beam is also calculated for all combinations and utilized to choose among multiple potential solutions. The model is derived to account for the bending and axial compliance of the manipulator as well as tendon compliance. A multi-tendon continuum robot system is employed to experimentally evaluate the proposed forward kinematics solution. Multiple experiments are carried out for the exhaustive list of all possible combinations of different sets of tendon displacements and the results are reported. The proposed forward kinematics algorithm may be used to understand the implication of control errors and their nonlinear effects for optimal selection of hardware and control algorithm for safety and reliability purposes.
Ruth Cooper and Saeid Nahavandi. 6/2022. “Feeling the full force of haptic technology https://createdigital.org.au/feeling-the-full-force-of-haptic-technology/”. Publisher's VersionAbstract

Through his pioneering work integrating haptic technology into robots and virtual reality systems, Engineers Australia’s Professional Engineer of the Year Saeid Nahavandi is transforming how we engage with simulated experiences.....


 

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