Seyed Ali Ghorashi

Dr S.Ali Ghorashi

Senior Lecturer

Senior Lecturer in Computer Science, Smart Cities & Intelligent Technologies Research Groups

Department of Engineering & Computing , School of Architecture, Computing and Engineering

  • 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, can contact: s.a.ghorashi@uel.ac.uk

Qualifications

  • BSc, MSc, PhD, FHEA
On This Page

OVERVIEW

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 worked at some UK and Middle East HEIs such as King's College London, Middlesex University and Goldsmiths, University of London.

External roles

Technical reviewer of prestigious journals such as:

  • IEEE Transactions on Cybernetics
  • IEEE Transactions on Artificial Intelligence
  • IEEE IoT Journal
  • Expert Systems With Applications (Elsevier)
  • IEEE Transactions on Wireless Communications
  • IEEE Sensors Journal
  • IEEE Transactions on Vehicular Technology
  • IEEE Communications Magazine
  • IEEE Transactions on Signal Processing
  • Signal Processing (Elsevier) and more

Guest editor of journals:

  • Special issue of Sensors journal (MDPI) in Machine Learning (ML) in Internet of Things (IoT) and Indoor Positioning/Localization
  • Special issue on intelligent software services for IoT and edge computing, Journal of Reliable Intelligent
  • I also have been the Communication committee chair of the 22nd Iranian Conference on Electrical Engineering (ICEE 2014)

CURRENT RESEARCH

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

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

PUBLICATIONS

2021

  •  
  • "Generative Adversarial Networks (GANs) in networking: A comprehensive survey & evaluation," Computer Networks, p. 108149, 2021.
  • "A fingerprint technique for indoor localization using autoencoder based semi-supervised deep extreme learning machine," Signal Processing, vol. 181, p. 107915, 2021.
  • "Privacy preserving in indoor fingerprint localization and radio map expansion," Peer-to-Peer Networking and Applications, vol. 14, no. 1, pp. 121-134, 2021.
  •  

2020

  •  
  • "Joint Coordinate Optimization in Fingerprint-Based Indoor Positioning," IEEE Communications Letters, vol. 25, no. 4, pp. 1192-1195, 2020.
  • "Joint Optimization of Power and Location in Full-Duplex UAV Enabled Systems," IEEE Systems Journal (early access), 2020.
  • "Fingerprinting based indoor localization considering the dynamic nature of wi-fi signals," Wireless Personal Communications, vol. 115, no. 2, pp. 1445-1464, 2020.
  • "Resource Allocation in Full-Duplex UAV Enabled Multi Small Cell Networks," IEEE Transactions on Mobile Computing (early access), 2020.
  • "A Low-complexity trajectory privacy preservation approach for indoor fingerprinting positioning systems," Journal of Information Security and Applications, vol. 53, p. 102515, 2020.
  • "Power allocation for D2D communications using max-min message-passing algorithm," IEEE Transactions on Vehicular Technology, vol. 69, no. 8, pp. 8443-8458, 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.
  •  

TEACHING

MSc Big Data Technologies

This programme is ideal if you want to pursue a career as a Big Data scientist or expert, deriving valuable insights and business-relevant knowledge from massive amounts of data.

Read more

MSc Information Security and Digital Forensics

This MSc in Information Security and Digital Forensics addresses system vulnerabilities and explores preventative measures, repair and detection. You will experience high exposure with industry, and external specialist lecturers deliver their knowledge through lectures and seminars.

Read more

MSc Artificial Intelligence

Studying the MSc Artificial Intelligence programme at the University of East London will provide graduates with a solid foundation for entering careers across a broad range of specialism in machine learning, computing and IT careers.

Read more

MODULES

  •  
  • Artificial Intelligence & Machine Vision
  • Data Communications and Networks
  • Computer Systems and Networks
  • Wireless & Mobile Networks
  • Applied Mathematics
  • Statistics & Probability for Engineering Applications
  • Engineering Mathematics
  • Stochastic Processes
  •