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Arfan Ghani is working as an Associate Professor in Computer Engineering at the ´ó·¢¿ìÈý¹ÙÍø University of Ras al Khaimah, UAE. He gained academic qualifications and experience working in UK institutions including Ulster, Coventry, and Newcastle. His industrial research and development experience includes working at Intel Research, University of Cambridge and Vitesse Semiconductors Denmark. Arfan has over 18 years of applied research experience and has supervised PhDs to successful completion. He has published in leading journals and conferences and secured substantial collaborative funding from EPSRC, EU, Innovate UK, Royal Academy of Engineering, and German Aerospace Centre. He serves as an Associate Editor of Elsevier Neurocomputing, Guest Editor, Technical Programme Committee member for several IEEE/IET conferences and a keynote speaker. He has received several awards including the best paper and winner from the European Neural Network Society in 2007.

Arfan is a member of IET, Chartered Engineer (CEng) and Fellow of the Higher Education Academy in the UK. His research expertise and aim is to develop technologies and direct research in the multi-disciplinary area of neuromorphic computing, reconfigurable hardware accelerators (FPGAs), smart IoT devices, and applied machine learning.

Dr Ghani got his Master of Science by research degree in Computer Systems Engineering from the Technical University of Denmark (DTU) in Copenhagen in 2003 and PhD by research from the University of Ulster in the United Kingdom in 2008. He was the recipient of a fully funded PhD award (03 years) from the higher education council in the United Kingdom.

During his academic tenure in the UK, he completed Post Graduate Certificate in Learning and Teaching in Higher Education (PgCert) and Post Graduate Diploma (PgDip) in Academic Leadership and Management from the University of Bolton in Greater Manchester, UK.

Awards:

• European Neural Network Society Award, 2008 (Winner and the best paper award), UK
• Part of the chip design team (Breakthrough in Gigabit Technology), 2003, Exbit Technology, Denmark

Postgraduate Supervisions:

• PhD successful completions (UK)
• MSc. thesis supervisions (UK)
• PhD thesis examination committee member (UK, Germany and Pakistan)

Selected Publications:

• Rawad Hodeify, Arfan Ghani, et al, Extracellular Adenosine Triphosphate Protects Against Cytotoxicity Induced by Elevated Extracellular Calcium in Human Proximal Kidney Cells: Using Deep Learning to Predict Cytotoxicity, Cellular Physiology and Biochemistry, Sep 2022, pp. 1-21, IF: 5.14 – (Q2), in press
• Arfan Ghani, T Dowrick and LJ McDaid, OSPEN: An Open-Source Platform for Emulating Neuromorphic Hardware, International Journal of Reconfigurable and Embedded Systems (IJRES), Vol. 99, No. 1, Sep 2022, pp. 1-8, Scopus indexed (in press).
• Arfan Ghani, Engineering education at the age of Industry 5.0 – higher education at the crossroads, World Transactions on Engineering and Technology Education, WIETE Vol.20, No.2, 2022. Pp 1-6 (Q2)
• Suarez-Mash, D., Ghani, A., See, C. H., Keates, S., & Yu, H. (2022). Using Deep Neural Networks to Classify Symbolic Road Markings for Autonomous Vehicles. EAI Endorsed Transactions on Industrial Networks and Intelligent Systems, 9(31), (Scopus indexed)
• Ghani, A.; Aina, A.; See, C.H.; Yu, H.; Keates, S. Accelerated Diagnosis of Novel Coronavirus (COVID-19)—Computer Vision with Convolutional Neural Networks (CNNs). Electronics 2022, 11, 1148. (IF: 2.397) – Q2
• An, S.; Xia, S.; Ma, Y.; Ghani, A.; See, C.H.; Abd-Alhameed, R.A.; Niu, C.; Yang, R. A Low Power Sigma-Delta Modulator with Hybrid Architecture. Sensors2020, 20, 5309. ; (IF: 3.275)– Q1
• Khan, S.Q., Ghani, A. and Khurram, M. (2020) ‘Frequency-dependent synaptic plasticity model for neurocomputing applications’, Int. J. Bio-Inspired Computation, Vol. 16, No. 1, pp.56–66 (IF: 3.977) – Q1
• Ghani, A.; See, C.H.; Sudhakaran, V.; Ahmad, J.; Abd-Alhameed, R. Accelerating Retinal Fundus Image Classification Using Artificial Neural Networks (ANNs) and Reconfigurable Hardware (FPGA). Electronics 2019, 8, 1522. (IF: 2.397) – Q2
• A Ghani et al (2019), New level doubling architecture of cascaded Multilevel inverter, IET Power Electronics, Pp: 1-12, 2019. 10.1049/iet-pel.2018.5512 (IF: 2.611) – Q1
• Arfan Ghani, Healthcare electronics—A step closer to future smart cities, ICT Express, Volume 5, Issue 4, 2019, Pages 256-260, ISSN 2405-9595, . (IF: 4.317)- Q1
• E. Atamuratov, Z.A. Atamuratova, A. Yusupov, A. Ghani, Characterising lateral capacitance of MNOSFET with localised trapped charge in nitride layer, Results in Physics, Volume 11, 2018, Pages 656-658, ISSN 2211-3797, . (IF: 3.042) – Q2
• Khan, S. Q., Ghani, A., & Khurram, M. (2017). Population coding for neuromorphic hardware. Neurocomputing, 239, 153–164. doi:10.1016/j.neucom.2017.02.013 (IF: 4.072) – Q1
• Muller, T, Thiemann, P, See, CH, Ghani, A & Bati, A , 2017, ‘Direct Flux Control – A Sensorless Control Method of PMSM for all Speeds – Basics and Constraints’ Electronics Letters, Vol 53, no. 16, pp. 1110-1111 (IF: 1.389) – Q2
• A Ghani et al (2017), Calibration Model for Detection of Potential Demodulating Behaviour in Biological Media Exposed to RF Energy, IET Science, Measurement and Technology, SMT-2017-0105, pp: 1-18. (IF: 1.389) – Q2
• A Ghani et al (2014), Step forward to map fully parallel energy-efficient cortical columns on field-programmable gate arrays, IET Science, Measurement & Technology, 8(6), p: 432 – 440. DOI: 10.1049/iet-smt.2014.0004 (IF: 1.389) – Q2
• A Ghani (2015), Neuro-inspired building blocks for parallel Implementation of Spiking Neural Networks on Reconfigurable Hardware (FPGAs), International Journal of Electronics and Electrical Engineering, Vol (3)3, pp: 1-13 (Scopus)
• A Ghani et al (2012), Evaluating the Generalisation Capability of a CMOS based Synapse, Elsevier Neurocomputing 1(83), pp. 188 – 197. ( IF: 3.241)- Q1
• A Ghani et al (2010), Computing with Biologically Inspired Neural Oscillators: Application to Colour Image Segmentation, Advances in Artificial Intelligence, pp. 1-12 () (Scopus)
• A Ghani et al (2007), Challenges for large-scale implementations of Spiking Neural Networks on FPGAs, Neurocomputing 71 (1-3), pp. 13-29. (IF: 3.241)- Q1
• A Ghani et al (2009), Spiking Neural Network Performs Discrete Cosine Transform for Visual Images, LNCS, Springer-Verlag, pp. 1-6.
• A Ghani et al (2009), Neuro-inspired Speech Recognition with Recurrent Spiking Neurons, LNCS 5163, Springer-Verlag, pp. 513-522.
• A Ghani et al (2010), Neuro-Inspired Speech Recognition Based on Reservoir Computing, Advances in Speech Recognition, pp. 1 -34
• VK Viswambaran, KV Anjana, E Zhou, A Ghani, Performance analysis of FPGA based maximum power point tracking algorithms for photovoltaic applications, Electrical and Computing Technologies and Applications (ICECTA), 2017
• T Mueller, C See, A Ghani, P Thiemann, Simulation of PMSM in maxwell 3D/simplorer to optimize direct flux control, Industrial Electronics (ISIE), 2017 IEEE 26th International Symposium on Modelling and simulation of maximum power point tracking algorithms & review of MPPT techniques for PV applications, UAE.
• A Ghani, CH See, H Migdadi, R Asif, RAA Abd-Alhameed, JM Noras, Reconfigurable neurons-making the most of configurable logic blocks (CLBs), Internet Technologies and Applications (ITA), 2015, 475-478
• M Al Yaman, A Ghani, A Bystrov, P Degenaar, P Maaskant FPGA design of a pulse encoder for optoelectronic neural stimulation and recording arrays, Biomedical Circuits and Systems Conference (BioCAS), 2013 IEEE, 190-193
• A Ghani, LJ McDaid, A Belatreche, P Kelly, S Hall, T Dowrick, S Huang, Evaluating the training dynamics of a CMOS based synapse, Neural Networks (IJCNN), The 2011 International Joint Conference on, 1162-1168
• A Belatreche, LP Maguire, TM McGinnity, A Ghani, LJ McDaid, Application of biologically inspired neural oscillators to colour image segmentation, Neural Networks (IJCNN), The 2010 International Joint Conference on neural networks, pp. 1-8
• A Ghani, LJ McDaid, A Belatreche, W Ahmed, Neuro inspired reconfigurable architecture for hardware/software co-design, SOC Conference, 2009. SOCC 2009. IEEE International, 287-290
• A Ghani, TM McGinnity, LP Maguire, J Harkin, Neuro-inspired speech recognition with recurrent spiking neurons, International Conference on Artificial Neural Networks, 513-522
• A Ghani, M McGinnity, L Maguire, J Harkin, Hardware/Software Co-Design for Spike Based Recognition, arXiv preprint arXiv:0807.2282
• A Ghani, TM McGinnity, LP Maguire, J Harkin, Area efficient architecture for large scale implementation of biologically plausible spiking neural networks on reconfigurable hardware, Field Programmable Logic and Applications, 2006. FPL’06.
• A Ghani, Multiplier-less reconfigurable architectures for spiking neural networks, Institute of Neuromorphic Engineering, 10.2417/1200806.0055. pp. 1-3
• A Ghani, TM McGinnity, LP Maguire, JG Harkin, Analyzing the framework of ‘Reservoir Computing for hardware implementation, NIPS workshop on Echo State Networks, 1-2, Canada.
• A Ghani, SH Usmani, MA Chaudhairy, M Asif, Design and modeling of indigenous supercomputing facility for complex solutions, Actual Problems of Electron Devices Engineering, 2004. APEDE 2004
• Arfan Ghani, Sabir Hussain Usmani, High-Speed Low Power Design in CMOS, Symposium on Topics in Semiconductors and Workshop on Nanotechnologies, 2004.

Technical Reports:

• University of Ulster, UK: 05 research reports (EPSRC)
• Newcastle University, UK: 03 research reports (EU-FP7)
• Bolton University, UK: 02 research reports (German Aerospace Centre)
• Intel Research, Cambridge, UK: 02 research reports

Consultancy Reports:

• Knowledge Transfer Partnership, UK (03)
• EU-FP7 (05)
• EPSRC, UK (02)

• Microprocessors
• Embedded System Design
• Computer Organization
• Special Topics in Computing