Dr Vaibhav Gandhi
| Name | Dr Vaibhav Gandhi |
|---|---|
| Job title | Director of Programmes - Product Design and Engineering |
| Research institute | |
| Primary appointment | Design Engineering & Mathematics |
| Email address | v.gandhi@mdx.ac.uk |
| ORCID | https://orcid.org/0000-0003-1121-7419 |
| Contact category | Academic staff |
Biography
Biography Vaibhav joined the department of Design Engineering & Mathematics, Faculty of Science & Technology, Middlesex University London in 2013 where he is currently the Director of Programmes for Product Design and Engineering programmes. Vaibhav has previously served as Programme Leader for BEng MEng Design Engineering suite of programmes and BEng/MEng Electronic Engineering (2016-21). His research interests include brain-computer interfaces (BCIs), biomedical signal processing, computational intelligence, computational neuroscience, user-centric graphical user interfaces, and assistive robotics. Vaibhav served as a Deputy Chair appointed by QAA (The UK's Quality body for Higher Education) to lead the review of Subject Benchmark Statements for Engineering in 2021-22. He is a Senior Fellow of The Higher Education Academy, a Life Member of the Indian Society for Technical Education (ISTE), Member of the Chartered Management Institute (CMI), and a Consultant for Doctorate in Professional Studies (DProf). Vaibhav received a First Class (Dist.) degree in Instrumentation & Control engineering in 2000, a First Class (Dist.) Masters degree in Electrical engineering in 2002 and a PhD degree in Computing & Engineering in 2012. From
2001 to 2002, Vaibhav gained experience working at the Space Application Center,
Indian Space Research Organization, India. During this time, Vaibhav applied neural network
techniques to address a satellite image classification problem. Vaibhav was a recipient of the UK-India Education & Research Initiative (UKIERI) scholarship for his PhD research in the area of Brain-Computer Interface for assistive robotics carried out at the Intelligent Systems Research Center, University of Ulster, UK, and partly at IIT Kanpur, India. His PhD research was focused on quantum mechanics motivated EEG signal processing and an intelligent adaptive user-centric human-computer interface design for real-time control of a mobile robot for the BCI users. His post-doctoral research involved work on shadow-hand multi-fingered mobile robot control using the EMG/muscle signals, with contributions also in the 3D printing aspects of a robotic hand. More information (and videos) about his research on Brain-Computer Interfaces for assistive robotics can be found on his book website https://booksite.elsevier.com/9780128015438/index.php. His current research and publications can be found on the tab Research Outputs and Interests.
Teaching As Director of Programmes for Product Design and Engineering, Vaibhav has the responsibility for the design, development, assessment, management, and quality enhancement processes for the below programmes. BEng Product Design Engineering (last cohort Sept./Oct. 2022) BSc Honours Architectural Technology BEng Mechatronics and Robotics (October 2022 onwards) BEng Mechatronics (last cohort Sept./Oct. 2021) BEng Robotics (last cohort Sept./Oct. 2021) BEng Computer Systems Engineering MSc Mechatronic Systems Engineering MSc Telecommunications Engineering MSc/PGDip/PGCert Building Information Modelling Management and Integrated Digital Delivery MSc Automation and Digital Manufacturing Vaibhav also contributes to teaching the following UG and PG modules within the Design Engineering department: Control Systems (2013 - ) - Year 2/Level 5 Robotics & Mechatronics (2013-2019/20) - Year 2/Level 5 Design Engineering Major Project (2013 - ) - Year 3/Level 6 Electronics/Computer Systems Engineering Major Project (2021 - ) - Year 3/Level 6 Industrial Automation & Control (2013-2019/20) - Year 3/Level 6 Design Engineering Dissertation (2018-2019) - Year 3/Level 6 Team Project (2017 - 2021/22) - Year 4/Level 7 MEng Robotic Systems and Control (2017 - 2020/21) - MSc Robotics Group Project (2017 - 2020/21) - MSc Robotics Thesis - MSc Robotics
Education and qualifications
Grants
Prizes and Awards
Research outputs
Analysis of machine learning methods for speech disfluency classification
Sharma. N., Gandhi, V. and Mahapatra, P. 2024. Analysis of machine learning methods for speech disfluency classification. Yang, X.S., Sherratt, S., Dey, N. and Joshi, A. (ed.) 9th International Congress on Information and Communication Technology. London, UK 19 - 22 Feb 2024 Singapore Springer. pp. 13-22 https://doi.org/10.1007/978-981-97-3556-3_2Bridging neuroscience and robotics: spiking neural networks in action
Jones, A., Gandhi, V., Mahiddine, A. and Huyck, C. 2023. Bridging neuroscience and robotics: spiking neural networks in action. Sensors. 23 (21), pp. 1-14. https://doi.org/10.3390/s23218880Exploration of functional connectivity of brain to assess cognitive and physical health parameters using brain-computer interface
Murugavalli, K., Ramalakshmi, R., Pallikonda Rajasekaran, M. and Gandhi, V. 2023. Exploration of functional connectivity of brain to assess cognitive and physical health parameters using brain-computer interface. International Journal of Biomedical Engineering and Technology. 43 (2), pp. 101-130. https://doi.org/10.1504/IJBET.2022.10052922Classification of EEG signals on SEED dataset using improved CNN
Ramar, B., Ramalakshmi, R., Gandhi, V. and Pandiselvam, P. 2023. Classification of EEG signals on SEED dataset using improved CNN. 2nd International Conference on Edge Computing and Applications. Namakkal, India 19 - 21 Jul 2023 IEEE. pp. 1095-1102 https://doi.org/10.1109/ICECAA58104.2023.10212279Comparative analysis of various machine learning techniques for classification of speech disfluencies
Sharma, N., Kumar, V., Mahapatra, P. and Gandhi, V. 2023. Comparative analysis of various machine learning techniques for classification of speech disfluencies. Speech Communication. 150, pp. 23-31. https://doi.org/10.1016/j.specom.2023.04.003Classification of EEG signals on standing, walking and running dataset using LSTM-RNN
Murugavalli, K., Ramalakshmi, R., Pallikonda Rajasekaran, M. and Gandhi, V. 2022. Classification of EEG signals on standing, walking and running dataset using LSTM-RNN. Sharma, V., Singh, M. and Sinha, J. (ed.) International Conference on Advances in Computing, Communication Control and Networking (ICAC3N). Greater Noida, India 16 - 17 Dec 2022 IEEE. pp. 1624-1630 https://doi.org/10.1109/ICAC3N56670.2022.10074500Neuromorphic building blocks for locomotion pattern generation
Yang, Z. and Gandhi, V. 2022. Neuromorphic building blocks for locomotion pattern generation. 2022 International Conference on Machine Learning, Control, and Robotics (MLCR). Suzhou, China 29 - 31 Oct 2022 IEEE. https://doi.org/10.1109/MLCR57210.2022.00010Robot Operating System (ROS) controlled anthropomorphic robot hand
Krawczyk, M., Gandhi, V. and Yang, Z. 2022. Robot Operating System (ROS) controlled anthropomorphic robot hand. Journal of Scientific and Industrial Research. 81 (9), pp. 901-910. https://doi.org/10.56042/jsir.v81i09.45313Exploring new traffic prediction models to build an intelligent transport system for Smart Cities
Mehta, V., Mapp, G. and Gandhi, V. 2022. Exploring new traffic prediction models to build an intelligent transport system for Smart Cities. IEEE/IFIP Network Operations and Management Symposium. Hungary 25 - 29 Apr 2022 pp. 1-6Developing traffic predictions from source to destination using probabilistic modelling
Mehta, V., Gandhi, V. and Mapp, G. 2021. Developing traffic predictions from source to destination using probabilistic modelling. Third UK Mobile, Wearable and Ubiquitous Systems Research Symposium. Online via Zoom 05 - 06 Jul 2021Design and development of the sEMG-based exoskeleton strength enhancer for the legs
Cenit, M. and Gandhi, V. 2020. Design and development of the sEMG-based exoskeleton strength enhancer for the legs. Journal of Mechatronics, Electrical Power, and Vehicular Technology. 11 (2), pp. 64-74. https://doi.org/10.14203/j.mev.2020.v11.64-74What makes a social robot good at interacting with humans?
Onyeulo, E. and Gandhi, V. 2020. What makes a social robot good at interacting with humans? Information. 11 (1), pp. 1-13. https://doi.org/10.3390/info11010043A survey of modern exogenous fault detection and diagnosis methods for swarm robotics
Graham Miller, O. and Gandhi, V. 2020. A survey of modern exogenous fault detection and diagnosis methods for swarm robotics. Journal of King Saud University – Engineering Science. 33 (1), pp. 43-53. https://doi.org/10.1016/j.jksues.2019.12.005Developing traffic prediction and congestion algorithms for a C-ITS network
Mehta, V., Gandhi, V. and Mapp, G. 2019. Developing traffic prediction and congestion algorithms for a C-ITS network. Second UK Mobile, Wearable and Ubiquitous Systems Research Symposium. Dept of Computer Science, University of Oxford, UK 01 Jul 2019Exploring real time traffic signalling using probabilistic approach in intelligent transport system
Mehta, V., Gandhi, V. and Mapp, G. 2018. Exploring real time traffic signalling using probabilistic approach in intelligent transport system. 3rd CommNet2 PhD Autumn School. University of Sheffield, Sheffield, UK 17 - 19 Sep 2018Exploring real time traffic signalling using probabilistic approach in intelligent transport system
Mehta, V., Gandhi, V. and Mapp, G. 2018. Exploring real time traffic signalling using probabilistic approach in intelligent transport system. Mobi-UK 2018. University of Cambridge, Cambridge, UK 12 - 13 Sep 2018Wrist movement detector for ROS based control of the robotic hand
Krawczyk, M., Yang, Z., Gandhi, V., Karamanoglu, M., Franca, F., Priscila, L., Xiaochen, W. and Geng, T. 2018. Wrist movement detector for ROS based control of the robotic hand. Advances in Robotics & Automation. 7 (1). https://doi.org/10.4172/2168-9695.1000182Development of an EMG-controlled mobile robot
Bisi, S., De Luca, L., Shrestha, B., Yang, Z. and Gandhi, V. 2018. Development of an EMG-controlled mobile robot. Robotics. 7 (3), pp. 1-13. https://doi.org/10.3390/robotics7030036Using robot operating system (ROS) and single board computer to control bioloid robot motion
Kalyani, G., Yang, Z., Gandhi, V. and Geng, T. 2017. Using robot operating system (ROS) and single board computer to control bioloid robot motion. 18th Towards Autonomous Robotic Systems (TAROS) Conference. Guildford, Surrey, UK 19 - 21 Jul 2017 Springer. pp. 41-50 https://doi.org/10.1007/978-3-319-64107-2_4Project-based cooperative learning to enhance competence while teaching engineering modules
Gandhi, V., Yang, Z. and Aiash, M. 2017. Project-based cooperative learning to enhance competence while teaching engineering modules. International Journal of Continuing Engineering Education and Life-Long Learning. 27 (3), pp. 198-208. https://doi.org/10.1504/IJCEELL.2017.10003462Neuron-based control mechanisms for a robotic arm and hand
Singh, N., Huyck, C., Gandhi, V. and Jones, A. 2017. Neuron-based control mechanisms for a robotic arm and hand. International Journal of Computer, Electrical, Automation, Control and Information Engineering. 11 (2), pp. 221-229. https://doi.org/10.5281/zenodo.1128871ROS based autonomous control of a humanoid robot
Kalyani, G., Gandhi, V., Yang, Z. and Geng, T. 2016. ROS based autonomous control of a humanoid robot. 25th International Conference on Artificial Neural Networks (ICANN). Barcelona, Spain 06 - 09 Sep 2016 Springer. pp. 550-551 https://doi.org/10.1007/978-3-319-44778-0Brain computer interface: a review
Parmar, P., Joshi, A. and Gandhi, V. 2015. Brain computer interface: a review. NUiCONE 2015: 5th Nirma University International Conference on Engineering. Nirma University, Ahmedabad, India 26 - 28 Nov 2015 Institute of Electrical and Electronics Engineers (IEEE). pp. 1-6 https://doi.org/10.1109/NUICONE.2015.7449615Evaluating quantum neural network filtered motor imagery brain-computer interface using multiple classification techniques
Gandhi, V., Prasad, G., Coyle, D., Behera, L. and McGinnity, T. 2015. Evaluating quantum neural network filtered motor imagery brain-computer interface using multiple classification techniques. Neurocomputing. 170, pp. 161-167. https://doi.org/10.1016/j.neucom.2014.12.114EMG based elbow joint powered exoskeleton for biceps brachii strength augmentation
Krasin, V., Gandhi, V., Yang, Z. and Karamanoglu, M. 2015. EMG based elbow joint powered exoskeleton for biceps brachii strength augmentation. International Joint Conference on Neural Networks (IJCNN 2015). Killarney, Republic of Ireland 12 - 17 Jul 2015 Institute of Electrical and Electronics Engineers (IEEE). pp. 1-6 https://doi.org/10.1109/IJCNN.2015.7280643Characterising information correlation in a stochastic Izhikevich neuron
Yang, Z., Gandhi, V., Karamanoglu, M. and Graham, B. 2015. Characterising information correlation in a stochastic Izhikevich neuron. International Joint Conference on Neural Networks (IJCNN 2015). Killarney, Republic of Ireland 12 - 17 Jul 2015 Institute of Electrical and Electronics Engineers (IEEE). pp. 1-5Brain-computer interfacing for assistive robotics: electroencephalograms, recurrent quantum neural networks and user-centric graphical user interfaces
Gandhi, V. 2014. Brain-computer interfacing for assistive robotics: electroencephalograms, recurrent quantum neural networks and user-centric graphical user interfaces. Elsevier.EEG-based mobile robot control through an adaptive brain–robot interface
Gandhi, V., Prasad, G., Coyle, D., Behera, L. and McGinnity, T. 2014. EEG-based mobile robot control through an adaptive brain–robot interface. IEEE Transactions on Systems Man and Cybernetics: Systems. 44 (9), pp. 1278-1285. https://doi.org/10.1109/TSMC.2014.2313317Image classification based on textural features using unsupervised neural network
Gandhi, V. 2006. Image classification based on textural features using unsupervised neural network. 1st International Indian Geographical Congress. Hyderabad, India 05 - 07 Oct 2006EEG filtering with quantum neural networks for a Brain-Computer Interface (BCI)
Gandhi, V., Prasad, G., Coyle, D., Behera, L. and McGinnity, T. 2012. EEG filtering with quantum neural networks for a Brain-Computer Interface (BCI). Young researchers futures meeting: Neural engineering. University of Warwick 19 - 21 Sep 2012 pp. 21A novel EEG signal enhancement approach using a recurrent quantum neural network for a Brain Computer Interface
Gandhi, V., Prasad, G., Coyle, D., Behera, L. and McGinnity, M. 2011. A novel EEG signal enhancement approach using a recurrent quantum neural network for a Brain Computer Interface. Technically Assisted Rehabilitation. Berlin, Germany 17 - 18 Mar 2011An intelligent Adaptive User Interface (iAUI) for enhancing the communication in a Brain-Computer Interface (BCI)
Gandhi, V., Prasad, G., Coyle, D., Behera, L. and McGinnity, M. 2011. An intelligent Adaptive User Interface (iAUI) for enhancing the communication in a Brain-Computer Interface (BCI). UKIERI workshop on the Fusion of Brain-Computer Interface and Assistive Robotics. University of Ulster 07 - 08 Jul 2011Interfacing a dynamic interface paradigm for multiple target selection using a two class brain-computer interface
Gandhi, V., Coyle, D., Prasad, G., Bharti, C., Behera, L. and McGinnity, M. 2009. Interfacing a dynamic interface paradigm for multiple target selection using a two class brain-computer interface. Indo-US Workshop on System of Systems Engineering. IIT Kanpur, India 26 - 28 Oct 2009 https://doi.org/10.1049/cp.2009.1690Quantum neural network based surface EMG signal filtering for control of robotic hand
Gandhi, V. and McGinnity, M. 2013. Quantum neural network based surface EMG signal filtering for control of robotic hand. IJCNN 2013: The International Joint Conference on Neural Networks. Dallas, TX, USA 04 - 09 Aug 2013Quantum neural network-based EEG filtering for a brain-computer interface
Gandhi, V., Prasad, G., Coyle, D., Behera, L. and McGinnity, T. 2013. Quantum neural network-based EEG filtering for a brain-computer interface. IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/TNNLS.2013.2274436Intelligent adaptive user interfaces for BCI based robotic control
Gandhi, V., Prasad, G., McGinnity, M., Coyle, D. and Behera, L. 2013. Intelligent adaptive user interfaces for BCI based robotic control. BCI meeting. USA Graz University of Technology Publishing House. https://doi.org/10.3217/978-3-85125-260-6-130EEG denoising with a recurrent quantum neural network for a brain-computer interface
Gandhi, V., Arora, V., Behera, L., Prasad, G., Coyle, D. and McGinnity, T. 2011. EEG denoising with a recurrent quantum neural network for a brain-computer interface. 2011 International Joint Conference on Neural Networks (IJCNN). San Jose, CA, USA 31 Jul - 05 Aug 2011 IEEE. pp. 1583-1590 https://doi.org/10.1109/IJCNN.2011.6033413A recurrent quantum neural network model enhances the EEG signal for an improved brain-computer interface
Gandhi, V., Arora, V., Behera, L., Prasad, G., Coyle, D. and McGinnity, T. 2011. A recurrent quantum neural network model enhances the EEG signal for an improved brain-computer interface. in: IET Seminar on Assisted Living 2011 London Institution of Engineering and Technology. pp. 42-47A novel paradigm for multiple target selection using a two class brain computer interface
Gandhi, V., Prasad, G., Coyle, D., Behera, L. and McGinnity, M. 2009. A novel paradigm for multiple target selection using a two class brain computer interface. Irish Signal & Systems Conference. Dublin, Ireland 10 - 11 Jun 2009 Dublin IET. https://doi.org/10.1049/cp.2009.1690Image classification based on textural features using Artificial Neural Network (ANN)
Shah, S. and Gandhi, V. 2004. Image classification based on textural features using Artificial Neural Network (ANN). Journal of The Institution of Engineers (India): Series A. 84, pp. 72-77.3780
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