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Kofi Appiah

Dr Kofi Appiah BSc, MSc (Oxon), MSc (SoC),PhD, PGCHE, FHEA, MIEEE

Lecturer in Computer Science


Experienced in the use of computational intelligence techniques for the analysis and modelling of large video datasets for finding patterns and extracting knowledge. My current research focuses on the interface between embedded computer vision, machine learning and neuroscience; mainly to understand and model the biological vision system on parallel architectures like Field Programmable Gate Arrays (FPGA). I have developed several neural network and image processing algorithms for FPGA and ARM processors. I have also published over 40 peer-reviewed academic papers including journals on various real-time image processing algorithms for security surveillance and biologically plausible neural network models.

  • About

    Dr Appiah holds a BSc in Computer Science from KNUST, completed an MSc in Computer Science at University of Oxford and an MSc in Electronic Engineering at Royal Institute of Technology (KTH) in Sweden. He joined the Computer Vision Group at Durham University under the supervision of Professor Andrew Hunter and moved with him to University of Lincoln, where he completed his PhD in 2010. In 2002, he worked briefly with Handel-C and FPGA at the Photonics Research Group at Aston University.

    Before joining Sheffield Hallam University in 2017, he worked at Nottingham Trent University as a Lecturer/Senior Lecturer from 2013, lead and contributed to various undergraduate modules (including Systems Technology, Internet Technology, Python Programming and Artificial Intelligence) and postgraduate modules (including Embedded Systems and Group Design Project). He also supervised a number of research degree students as the director of studies/second supervisor.

    Kofi spent a year working as a Lecturer at the University of Science and Technology in Ghana, before joining the Embedded and Intelligent Systems (ESI) Research Group at University of Essex in Colchester as Senior Research Officer in December 2012. He also worked on part-time basis as a Development Engineer with Metrarc Ltd, Cambridge, before joining NTU in November 2013.

    Dr Appiah is active in the embedded computer vision research area and acts as a reviewer for the following journals

    • IEEE Transactions on Neural Networks and Learning Systems
    • IEEE Transactions on Circuits and Systems for Video Technology
    • IEEE Transaction on VLSI
    • IEEE Transaction on Computers
    • Journal of Computer Vision and Image Understanding – Elsevier
    • Pattern Recognition Letters
    • IET Science, Measurement and Technology
    • PLOS ONE

    Specialist areas of interest

    Computer Vision
    Image Processing
    Embedded Systems
    Internet of Things
    Pattern Recognition
    Neural Networks
    Neuromorphic Hardware

  • Teaching

    Subject area

    Computer Systems and Networks




    Network Server Management and Configuration
    Network and Systems Security
    Research Methods

  • Research

    2012 - Worked as part of the Embedded and Intelligence Systems research group (University of Essex) on the SYSIASS project, aimed at the development of an Autonomous and Intelligent Healthcare System, funded by European Regional Development Funds. 

    2009 - Worked with Imperial College, Wifore, Tactical Systems Design and Phrisk on TOTALCARE, a TSB-funded project to deliver an end-to-end demonstrator of a novel digital health-care service and monitoring framework. 

    2007 – Worked with e2v technologies on Basic Robust Architecture for Integrated Neural Sensors (BRAINS), a TSB-funded project to deliver new, high speed method of integrated data processing by the correct combination of parallel and serial techniques. 

    2005 - Worked with SecuraCorp, Eastern Kentucky University’s Department of Justice and Safety Centre, on Algorithm-Based Object Recognition and Tracking (ABORAT), a US Department of Homeland Security-funded project for developing video analytics which detect video anomalies using an embedded Video Processing Unit.

    Collaborators and sponsors

    Sundance Multiprocessor Technology Limited
    Qioptiq Limited
    School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
    Computational Neuroscience and Cognitive Robotics Laboratory, Nottingham Trent University, Nottingham
    Embedded and Intelligent Systems (EIS) Research Laboratory, University of Essex, Colchester
    Laboratory of Vision Engineering, University of Lincoln, Lincoln

  • Publications

    Journal articles

    Munro, J., Appiah, K., & Dickinson, P. (2014). Investigating informative performance metrics for a multicore game world server. Entertainment Computing, 5 (1), 1-17.

    Conference papers

    Bouchut, Q., Appiah, K., Lotfi, A., & Dickinson, P. (2018). Ensemble One-vs-One SVM Classifier for Smartphone Accelerometer Activity Recognition. In 20th IEEE International Conferences on High Performance Computing and Communications (HPCC), Exeter, 28 June 2018. IEEE

    Anderez, D.O., Appiah, K., Lotfi, A., & Langesiepen, C. (2017). A hierarchical approach towards activity recognition. In ACM International Conference Proceeding Series, Part F128530, 269-274.

    Zhai, X., Appiah, K., Ehsan, S., Hu, H., Gu, D., McDonald-Maier, K., ... Howells, G. (2013). Application of ICmetrics for embedded system security. In Proceedings - 2013 4th International Conference on Emerging Security Technologies, EST 2013, 89-92.

    Appiah, K., Hunter, A., Dickinson, P., & Meng, H. (2010). Binary object recognition system on FPGA with bSOM. In Proceedings - IEEE International SOC Conference, SOCC 2010, 254-259.

    Appiah, K., Hunter, A., Dickinson, P., & Meng, H. (2010). Accelerated hardware video object segmentation: From foreground detection to connected components labelling. In Computer Vision and Image Understanding, 114 (11), 1282-1291. Elsevier:

  • Other activities

    External Examiner – University of Derby (BEng Computer Network Engineering)

  • Postgraduate supervision





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