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Dr S. Ali Ghorashi

Senior Lecturer in Computer Science

Intelligent Systems, School of Architecture, Computing and Engineering (ACE).

With more than 20 years of experience in industry and academia on optimization, resource allocation and applications of Artificial Intelligence and Machine Learning algorithms in Internet of Things (IoT), Positioning, Cellular and Ad-hoc Networks, I am a senior member of IEEE and have published more than 100 journal and conference papers and have experience in National and European projects in 3G, 4G, and self-drive cars. Prior to UEL, I have worked at some UK and Middle East HEIs such as King’s College London, Middlesex University and Goldsmiths, University of London. Ph.D. and MSc students who are interested in working with me, please contact: s.a.ghorashi@uel.ac.uk

  • EB.1.94
    University of East London
    Docklands Campus
    University Way
    London, E16 2RD
    United Kingdom
    E16 2RD
  • s.a.ghorashi@uel.ac.uk

    With more than 20 years of experience in industry and academia on optimization, resource allocation and applications of Artificial Intelligence and Machine Learning algorithms in Internet of Things (IoT), Positioning, Cellular and Ad-hoc Networks, I am a senior member of IEEE and have published more than 100 journal and conference papers and have experience in National and European projects in 3G, 4G and self-drive cars. Prior to UEL, I have worked at some UK and Middle East HEIs such as King’s College London, Middlesex University and Goldsmiths, University of London.



    BSc, MSc, PhD.

    Overview

    My field of interest includes applications of Artificial Intelligence and Machine Learning in 5G and beyond Telecommunication Networks, IoT, Positioning, Smart Cities and Healthcare. 

    PhD and MSc students who are interested in working with me, please contact: s.a.ghorashi@uel.ac.uk




    Collaborators

    • test

    Research

    Selected Publications:

    2020:

    •Throughput improvement by mode selection in hybrid duplex wireless networks,
    Wireless Networks, pp. 1–13, 2020.
    •Using synthetic data to enhance the accuracy of fingerprint-based localization: a deep learning approach,
    IEEE Sensors Letters, vol. 4, no. 4, pp. 1-4, 2020.
    •Low-cost localisation considering LOS/NLOS impacts in challenging indoor environments,
    International Journal of Sensor Networks, vol. 32, no. 1, pp. 15–24, 2020.

    2019:

    •A novel smartphone application for indoor positioning of users based on machine learning,
    Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers, pp. 430–437, 2019.
    •Maximum entropy-based semi-definite programming for wireless sensor network localization,
    IEEE Internet of Things Journal, vol. 6, no. 2, 2019.
    •Delay analysis in full-duplex heterogeneous cellular networks,
    IEEE Transactions on Vehicular Technology, vol. 68, no. 10, pp. 9713-9721, 2019.
    •No-reference video quality estimation based on machine learning for passive gaming video streaming applications,
    IEEE Access, vol. 7, pp. 74511–74527, 2019.
    •New reconstructed database for cost reduction in indoor fingerprinting localization,
    IEEE Access, vol. 7, pp. 104462–104477, 2019.
    •A survey on implementation and applications of full duplex wireless communications,
    Physical Communication, vol. 34, pp. 121–134, 21019.
    •Cooperative ranging-based detection and localization: centralized and distributed optimization methods,
    Cooperative Localization and Navigation: Theory, Research, and Practice (book chapter), pp. 301, 2019.

    2018:

    •Maximum likelihood estimation for multiple camera target tracking on Grassmann tangent subspace,
    IEEE Transactions on Cybernetics, vol. 48, no. 1, pp. 77–89, 2018.
    •A privacy preserving method for crowdsourcing in indoor fingerprinting localization,
    IEEE 8th International Conference on Computer and Knowledge Engineering (ICCKE), 2018.
    •Maximum entropy-based semi-definite programming for wireless sensor network localization,
    IEEE Internet of Things Journal, vol. 6, no. 2, pp. 3480–3491, 2018.
    •Distributed diffusion-based spectrum sensing for cognitive radio sensor networks considering link failure,
    IEEE Sensors Journal, vol. 18, no. 20, pp. 8617–8625, 2018.
    •A fingerprint method for indoor localization using autoencoder based deep extreme learning machine,
    IEEE Sensors Letters, vol. 2, no. 1, 2018.
    •Impact of connecting to the nth nearest node in dedicated device-to-device communications,
    Electronics Letters, vol. 54, no. 8, pp. 535–537, 2018.

    Before 2018:

    •Distributed spectrum sensing for cognitive radio sensor networks using diffusion adaptation,
    IEEE Sensors Letters, vol. 1, no. 5, 2017.
    •Generalised Kalman-consensus filter,
    IET Signal Processing, vol. 11, no. 5, pp. 495–502, 2017.
    •FD device-to-device communication for wireless video distribution,
    IET Communications, vol. 11, no. 7, pp. 1074–1081, 2017.
    •Wireless sensor network localization in harsh environments using SDP relaxation,
    IEEE Communications Letters, vol. 20, no. 1, pp. 137–140, 2016.
    •Spectrum decision in cognitive radio networks using multi-armed bandit,
    IEEE 5th International Conference on Computer and Knowledge Engineering (ICCKE), 2015.
    •Context aware and channel-based resource allocation for wireless body area networks,
    IET Wireless Sensor Systems, vol. 3, no. 1, pp. 16–25, 2013.
    •Spectrum leasing for OFDM-Based cognitive radio networks,
    IEEE Transactions on Vehicular Technology, vol. 62, no. 5, pp. 2131–2139, 2013.
    •A novel channel estimation technique for OFDM systems with robustness against timing offset,
    IEEE Transactions on Consumer Electronics, vol. 57, no. 2, 2011.
    •A new wavelet based algorithm for estimating respiratory motion rate using UWB radar,
    International Conference on Biomedical and Pharmaceutical Engineering, pp. 1–3, 2009.
    •Scheduling as an important cross-layer operation for emerging broadband wireless systems,
    IEEE Communications Surveys & Tutorials, vol. 11, no. 2, pp. 74–86, 2009.
    •Challenges of real-time secondary usage of spectrum,
    Computer Networks, vol. 52, no. 4, pp. 816–830, 2008.

    Publications

    My field of interest includes applications of Artificial Intelligence and Machine Learning in 5G and beyond Telecommunication Networks, IoT, Positioning, Smart Cities and Healthcare.  PhD and MSc students who are interested in working with me, please contact: s.a.ghorashi@uel.ac.uk



    Interests

    Coming Soon.



    Teaching