Signal Processing and Communications Laboratory

Department of Engineering

Bashar I. Ahmad

Background - Research - Publications

Position: Senior Research Fellow

Office Location: BN3-07, SigProC Laboratory, Baker Building, CUED.

Work Telephone: +44(0)122 333 2768

E-mail: b.i.ahmad [at] eng.cam.ac.uk

Background

Bashar is a senior research fellow at the Engineering Department, and was elected in 2016 as a Research Fellow (JRF) of Wolfson College, Cambridge University. He received his B.Eng. (Hons.) in electronic engineering in 2007 and a Ph.D. degree in statistical inference in 2011; both from the University of Westminster, U.K. He also undertook the Msc Communications and Signal Processing (electives) at Imperial College London, U.K.

Prior to joining SigProC, Bashar was a postdoctoral researcher at Imperial College London. He was a project manager at BT (DSPG) in London between 2011 and 2012. Bashar also worked previously as a R&D engineer with the RF systems and architectures research group at NXP Semiconductors, Redhill, U.K., and as a SoC design engineer at PHILIPS Semiconductor, Southampton, U.K..

Research Interests

- Bayesian inference and meta-level tracking (e.g. intent prediction, anomaly detection, multi-target tracking and sensor data fusion).

- Sub-Nyquist data acquisition and processing (compressed sensing, alias-free sampling and finite rate of innovation).

- Supervised learning (e.g. for automatic object recognition) from noisy multivariate time series data.

- Radar signal processing.

- Cognitive radio: dynamic spectrum access, baseband processing and prototypes.

- Human computer interaction, mainly under the influence of pertubrations, multimodal HCI and VR/AR-HUDs .

Publications

Peer-Reviewed Articles

  1. Q. Li, B. I. Ahmad and S. Godsill, "Sequential Dynamic Leadership Inference Using Bayesian Monte Carlo Methods," IEEE Transactions on Aerospace and Electronic Systems (T-AES), 2021. [IEEE]
  2. R. Gan, B. I. Ahmad and S. Godsill, "Lévy State-space Models for Tracking and Intent Prediction of Highly Manoeuvrable Objects," IEEE Transactions on Aerospace and Electronic Systems (T-AES), 2021. [IEEE]
  3. R. Gan, J. Liang, B. I. Ahmad, and S. Godsill, “Modelling Intent and Destination Prediction Within A Bayesian Framework: Predictive Touch As a Usecase”, Data-Centric Engineering, vol. 2, issue 1, 2020. [DCE]
  4. J. Liang, B. I. Ahmad , R. Gan, P. Langdon, R. Hardy, and S. Godsill, "On Destination Prediction Based on Markov Bridging Distributions", IEEE Signal Processing Letters, vol. 26, issue 11, pp. 1663-1667, 2019. [IEEE]
  5. B. I. Ahmad , C. Hare, H. Singh, A. Shabani, B. Lindsay, L. Skrypchuk, P. M. Langdon and S. J. Godsill, “Touchless Selection Schemes for Intelligent Automotive User Interfaces with Predictive Mid-air Touch”, International Journal of Mobile Human-Computer Interaction (IJMHCI) , vol. 11, issue 3, pp. 18-39, 2019. [IJMHCI]
  6. B. I. Ahmad, P. M. Langdon, J. Liang, S. J. Godsill, M. Delgado and T. Popham, “Driver and Passenger Identification from Smartphone Data”, IEEE Transactions on Intelligent Transportation Systems, issue 4, vol. 20, pp.1278-1288, 2019. [ IEEE]
  7. B. I. Ahmad , J. Murphy, P. M. Langdon and S. J. Godsill, “Bayesian Intent Prediction in Object Tracking Using Bridging Distributions”, IEEE Transactions on Cybernetics, issue 1, vol. 48, pp. 215-227,2018. [IEEE] [arXiv]
  8. B. I. Ahmad , J. K. Murphy, S. J. Godsill, P. M. Langdon and R. Hardy, “Intelligent Interactive Displays in Vehicles with Intent Prediction: A Bayesian Framework”, IEEE Signal Processing Magazine, issue 2, vol. 34, pp. 82-94, 2017. [IEEE]
  9. A. Tarczynski and B. I. Ahmad , “Estimation of Fourier Transform Using Alias-free Hybrid-Stratified Sampling”, IEEE Transactions on Signal Processing , issue 12, vol. 64, pp. 3065-3076, 2016. [IEEE]
  10. B. I. Ahmad , J. Murphy, P. M. Langdon, S. J. Godsill, R. Hardy and L. Skrypchuk, “Intent Inference for Pointing Gesture Based Interactions in Vehicles”, IEEE Transactions on Cybernetics, issue 4, vol. 46, pp. 878 - 889, 2016. [IEEE]
  11. B. I. Ahmad and A. Tarczynski, “Spectral Analysis of Stratified Sampling: A Means to Perform Efficient Multiband Spectrum Sensing”, IEEE Transactions on Wireless Communications , issue 1, vol. 11, pp. 178-187, 2012. [IEEE]
  12. B. I. Ahmad and A. Tarczynski, “A SARS Method for Reliable Spectrum Sensing in Multiband Communication Systems”, IEEE Transactions on Signal Processing , issue 12, vol. 59 , pp. 6008-6020, 2011. [IEEE]
  13. B. I. Ahmad and A. Tarczynski, “Wideband Spectrum Sensing Technique Based on Random Sampling on Grid: Achieving Lower Sampling Rates”, Digital Signal Processing, Elsevier, vol. 90, pp. 466-476, 2011. [Elsevier]
  14. B. I. Ahmad and A. Tarczynski, “Reliable Wideband Multichannel Spectrum Sensing Using Randomized Sampling Schemes”, Signal Processing, Elsevier, vol. 90, pp. 2232-2242, 2010. [Elsevier]

Book and Book Chapters

  • H. Sun, C. Wang and B. I. Ahmad. From Internet of Things to Smart Cities: Enabling Technologies, Taylor and Francis Group: CRC Press, 2017. [CRC Press]
  • B. I. Ahmad , P. M. Langdon and S. J. Godsill. Applying Bayesian Modelling for Inclusive Design under Health and Situational Induced Impairments: An Overview. Book chapter in Virtual Reality , Nova Science Publishers, 2017. ISBN: 978-1-53612-040-0. [Chapter Manuscript]
  • B. I. Ahmad, H. Sun, C. Ling and A. Nallanathan. Paving A Wider Way for Multimedia Over Cognitive Radios: An Overview of Wideband Spectrum Sensing Algorithms. Book chapter in Multimedia Over Cognitive Radio Networks: Algorithms, Protocols, and Experiments, Taylor & Francis: CRC Press, 2014. [Chapter Manuscript]

Peer-Reviewed Conference Papers

  1. B. I. Ahmad, J. Grey, M. Newman and S. Harman, “Low-latency Convolutional Neural Network for Automatic Classification of Unknown Drone Types”, in Proc. of the European Radar Conference (EuRAD), Milan, 2022
  2. B. I. Ahmad, J. Grey, M. Newman and S. Harman, “Low-Latency Convolution Neural Network for Estimating Drone Physical Parameters with Radar”, in Proc. of the International Radar Conference (RADAR), Edinburgh, 2022.
  3. B. I. Ahmad and S. Harman, “Tracking of Target Body and Micro-Doppler Components in Drone Surveillance Radar”, in Proc. of the International Radar Conference (RADAR), Edinburgh, 2022
  4. H. Dale, M. Jahangir, M. Antoniou, C. Baker, S. Harman and B. I. Ahmad, “Convolutional Neural Networks for Robust Classification of Drones”, in Proc. of IEEE Radar Conference (RadarConf), New York, 2022.
  5. J. Liang, B. I. Ahmad, M. Jahangir and S. Godsill, “Detection of Malicious Intent in Non-cooperative Drone Surveillance”, in Proc. of the 10th International Conference of the Sensor Signal Processing for Defence (SSPD), 2021.
  6. S. Harman and B. I. Ahmad, "Need for Simultaneous Tracking and Recognition in Drone Surveillance Radar," in Proc. of International Radar Symposium (IRS ‘21), 2021.
  7. M. Jahangir, B. I. Ahmad and C. Baker, “Robust Drone Classification Using Two-Stage Decision Trees and Results from SESAR SAFIR Trials”, in Proc. of IEEE International Radar Conference (RADAR 2020), 2020.
  8. M. Jahangir, B. I. Ahmad and C. Baker, “The Application of Performance Metrics to Staring radar for Drone Surveillance”, in Proc. of the 17th European Radar Conference (EuRAD 2020), 2020.
  9. C. Bennett, M. Jahangir, F. Fioranelli, B. I. Ahmad and J. Le Kernec, “Use of Symmetrical Peak Extraction in Drone Micro-Doppler Classification for Staring Radar”, in Proc. of 2020 IEEE Radar Conference (RadarConf), 2020.
  10. J. Liang, B. I. Ahmad, and S. Godsill, “Simultaneous Intent Prediction and State Estimation using an Intent-driven Intrinsic Coordinate Model”, in Proc. of the IEEE International Workshop on Machine Learning for Signal Processing (MLSP ’20), 2020.
  11. Q. Li., J. Liang, B. I. Ahmad and S. Godsill, “Inferring Dynamic Group Leadership Using Sequential Bayesian Methods”, in Proc. of 43rd IEEE International Conf. on Acoustics, Speech and Signal Processing Conference (ICASSP '20), 2020.
  12. M. Al-Ani, M. R. Belmont, J. Christmas, A. Tarczynski and B. I. Ahmad, “On Random sampling and Fourier Transform Estimation in Sea Waves Prediction”, in Proc. of IEEE Event-Based Control, Communication and Signal Processing Conference, 2020.
  13. B. I. Ahmad , P. M. Langdon and S. J. Godsill, “A Bayesian Framework for Intent Prediction in Object Tracking”, Proc. of 42nd IEEE International Conference on Acoustics, Speech and Signal Processing Conference (ICASSP '19) , Brighton, 2019.
  14. R. Gan, J. Liang, B. I. Ahmad , and S. Godsill, “Bayesian Intent Prediction for Fast Maneuvering Objects Using Variable Rate Particle Filters”, Proc. of the IEEE International Workshop on Machine Learning For Signal Processing (MLSP ’19), 2019.
  15. M. Al-Ani, A. Tarczynski and B. I. Ahmad , “High-Order Hybrid Stratified Sampling: Fast Uniform-Convergence Fourier Transform Estimation”, Proc. of the 52nd Annual Asilomar Conference on Signals, Systems, and Computers, pp. 1019-1023. IEEE, 2018.
  16. B. I. Ahmad, P. M. Langdon and S. J. Godsill, “A Meta-tracking Approach to Predicting the Intent of Driver or Passenger,” Proc. of the 21st International Conference on Information Fusion (FUSION ’18), Cambridge, 2018.
  17. B. I. Ahmad, P. M. Langdon and S. J. Godsill, “Evaluation of Pointing Facilitation Schemes for Predictive Touch with Free Hand Pointing Gestures in Automotive,” Proc. of the ACM Int. Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI ‘18), Toronto, 2018.
  18. B. I. Ahmad, P. M. Langdon, L. Skrypchuk and Simon J. Godsill, “Probabilistic Modelling of Demand of Pointing Tasks While Driving”, Proc. of the 9th Int. Conf. on Applied Human Factors and Ergonomics (AHFE '18), FL, 2018.
  19. B. I. Ahmad and P. M. Langdon, “Stabilising Touch Interactions in Aerospace and Vibrating Environments”, Proc. of the 20th Int. Conference on Human Computer Interaction (HCII ’18) , 2018.
  20. B. I. Ahmad, T. Ardeshiri, S. J. Godsill, P. M. Langdon and T. Popham, “Modelling Received Signal Strength from On-Vehicle BLE Beacons Using Skewed Distributions: A Study”, Proc. of the 20th International Conference on Information Fusion (Fusion ’17), Xi’an, 2017.
  21. B. I. Ahmad, P. M. Langdon, Lee Skrypchuk and Simon J. Godsill, “How Does Eye-Gaze Relate to Gesture Movement in an Automotive Pointing Task?”, Proc. of the 8th International Conference on Applied Human Factors and Ergonomics (AHFE 2017), LA, 2017.
  22. H. Buchner, K. Helwani, B. I. Ahmad, and S. J. Godsill, “Low-Complexity Multichannel Adaptive Filtering in Compressive Domains”, Proc. of 42nd IEEE International Conference on Acoustics, Speech and Signal Processing Conference (ICASSP '17), New Orleans, 2017.
  23. T. Ardeshiri, B. I. Ahmad, P. M. Langdon and S. J. Godsill, “Efficient Bridging-based Destination Inference in Object Tracking ”, Proc. of 42nd IEEE International Conference on Acoustics, Speech and Signal Processing Conference (ICASSP '17), New Orleans, 2017
  24. B. I. Ahmad and A. Tarczynski, "A Novel Sub-Nyquist Fourier Transform Estimator Based on Alias-free Hybrid Stratified Sampling", Proc. of 41st IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '16), Shanghi, 2016.
  25. B. I. Ahmad , P. M. Langdon and S. J. Godsill, “You Do Not Have to Touch to Select: A Study on Predictive In-car Touchscreen with Mid-air Selection”, Proc. of the ACM Int. Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI '16), MI, USA, 2016 .
  26. B. I. Ahmad, P. M. Langdon and S. J. Godsill, “Applying Bayesian Modelling for Inclusive Design under Health and Situational Induced Impairments”, Proc. of the 11th International Conference on Disability, Virtual Reality & Associated Technologies (ICDVRAT), LA, USA, 2016.
  27. B. I. Ahmad , J. K. Murphy, P. M. Langdon and S. J. Godsill, “Predictive Pointing from Automotive to Inclusive Design”, Proc. of the 18th International Conference on Human Computer Interaction (HCII ’16) , 2016.
  28. B. I. Ahmad , J. Murphy, P. M. Langdon and S. J. Godsill, “Destination Inference Using Bridging Distributions”, Proc. of 40th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '15), Brisbane, 2015.
  29. B. I. Ahmad , P. M. Langdon, S. J. Godsill, R. Hardy and L. Skrypchuk, “Intelligent In-Vehicle Touchscreen Aware of the User Intent: A Pilot Study”, Proc. of the ACM Int. Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI '15), 2015.
  30. B. I. Ahmad , P. M. Langdon, S. J. Godsill, R. Hardy, L. Skrypchuk and R. Donkor, “Touchscreen Usability and Input Performance in Vehicles under Different Road Conditions: An Evaluative Study”, Proc. of the ACM Int. Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI '15), 2015 .
  31. B. I. Ahmad , P. M. Langdon and S. J. Godsill, “Intelligent Intent-aware Touchscreen Systems Using Gesture Tracking with Endpoint Prediction”, Proc. of the 17th International Conference on Human Computer Interaction (HCII ’15) , 2015.
  32. B. I. Ahmad , J. Murphy, P. M. Langdon and S. J. Godsill, “Filtering Perturbed in-vehicle pointing gesture trajectories: Improving the reliability of the intent inference”, Proc. of the IEEE Int. Workshop on Machine Learning for Signal Processing (MLSP ’14), 2014.
  33. B. I. Ahmad , J. Murphy, P. M. Langdon and S. J. Godsill, “Bayesian Target Prediction From Partial Finger Tracks: Aiding Interactive Displays in Vehicles”, Proc. of the 17th Int. Conference on Information Fusion (Fusion ‘14), 2014.
  34. B. I. Ahmad , P. M. Langdon, S. J. Godsill, Robert Hardy, E. Dias, L. Skrypchuk, “Interactive Displays in Vehicles: Improving Usability with a Pointing Gesture Tracker and Bayesian Intent Predictors", Proc. of the ACM Int. Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI '14), 2014.
  35. B. I. Ahmad , P. Langdon, P. Bunch and S. Godsill, “Probabilistic Intentionality Prediction for Target Selection Based on Partial Cursor Tracks”, Proc. of the 16th International Conference on HCI (HCII '14) , 2014.
  36. B. I. Ahmad, W. Dai and C. Ling, “Model-based Compressive Harmonics-aware Matching Pursuit: An Evaluation”, Proc. of the 47th Annual Asilomar Conference on Signals, Systems, and Computers, CA, 2013.
  37. M. Al-Ani, B. I. Ahmad and A. Tarczynski, “Non-Compressive Wideband Spectrum Sensing With Sub-Nyquist Sampling Rates”, Proc. of the 47th Annual Asilomar Conference on Signals, Systems, and Computers, CA, 2013.
  38. B. I. Ahmad, M. Al-Ani, A. Tarczynski, Wei Dai and Cong Ling, “Compressive and Non-compressive Wideband Spectrum Sensing at Sub-Nyquist Rates”, Proc. of the European Signal Processing Conference (EUSIPCO ‘13), Marrakech, 2013.
  39. T. Weber, B. I. Ahmad and M. Ihle, “Sub-Nyquist Sampling for TDR Sensors: Finite Rate of Innovation with Dithering”, Proc. of the International Workshop on Compressed Sensing Applied to Radar , Bonn, 2013.
  40. B. I. Ahmad, W. Dai and C. Ling, “Reliable Sub-Nyquist Wideband Spectrum Sensing Based on Randomised Sampling”, Proc. of the International Workshop on Compressed Sensing Applied to Radar , Bonn, 2012.
  41. B. I. Ahmad and A. Tarczynski, “A SARS Multiband Spectrum Sensing Method in Wideband Communication Systems Using RSG”, Proc. of the 19th Europ. Signal Processing Conf. (EUSIPCO‘11 ), Barcelona, pp. 1219-1223, 2011.
  42. M. Al-Ani, B. I. Ahmad and A. Tarczynski, “ A Novel Fourier Transform Estimation Method Using Random Sampling”, Proc. of the 19th European Signal Processing Conf. (EUSIPCO‘11), Barcelona, pp. 859-863, 2011.
  43. B. I. Ahmad and A. Tarczynski, “A Spectrum Sensing Method Based on Stratified Sampling”, Proc. of the IEEE International Symposium on Circuits and Systems ( ISCAS ‘11 ) , Rio De Janeiro, pp. 402-405, 2011.
  44. B. I. Ahmad and A. Tarczynski, “The Effect of Cyclostationarity on A DASP-Based Spectrum Sensing Method”, Proc. of the 9th International Conference on Sampling Theory and Its Applications , Singapore, 2011.
  45. M. Al-Ani, B. I. Ahmad and A. Tarczynski, “The Effect of Missing Samples on the Quality of the Spectral Analysis”, Proc. of the 9th International Conference on Sampling Theory and Its Applications , Singapore, 2011.
  46. B. I. Ahmad , A. Tarczynski and M. Al-Ani, “A DASP Multiband Spectrum Sensing Method Based on Total Random Sampling on Grid Without Replacement”, Proc. of the 9th International Conference on Sampling Theory and Its Applications (SAMPTA ‘11) , Singapore, 2011.
  47. B. I. Ahmad and A. Tarczynski, “A DASP Approach to Wideband Multichannel Spectrum Sensing”, Proc. of the 18th European Signal Processing Conference (EUSIPCO ‘10) , Aalborg, Denmark, pp. 865-869, 2010.
  48. B. I. Ahmad and A. Tarczynski, “Spectrum Sensing in Multichannel Communication Systems Using Randomized Sampling”, Proc. of the 17th Eur. Signal Processing Conf. (EUSIPCO‘09) , Glasgow, pp.1690-1695, 2009.
  49. B. I. Ahmad and A. Tarczynski, “Evaluation of Several Reconstruction Methods of Band-limited Signals”, Proc. of the IEEE International Conference on Signals, Circuits and Systems , pp.1-5, 2008.

Granted Patents

  1. Apparatus and method for infering vehicle entry location, jointly with Jaguar Land Rover, 2017. GB2565908A
  2. Apparatus and method for determining object intent, jointly with Jaguar Land Rover, 2017. GB2565798A
  3. Determining a State of a Tracked Object Part I, jointly with Jaguar Land Rover, 2017. GB2564145A
  4. Determining a State of a Tracked Object Part II, jointly with Jaguar Land Rover, 2017. GB2564146A
  5. Apparatus and Method for Determining an Intended Target, jointly with Jaguar Land Rover, 2014. US10719133B2 EP3164786B1 JP6438579B2 KR20170015454A

.Videos

1- Pedestrian (Driver/Passenger) Intent Prediction

This video depicts, in real-time, the estimated likelihood of a user (e.g. driver or passenger) returning to a given endpoint (e.g. a parked vehicle) and his/her time of arrival (if relevant).

This video shows how incoperating meta-level information (e.g. intended destination) can lead to better and more certain forecasting of the object (e.g. pedestrian) future trajectory:

2- Predictive Touch

The following video shows an early prototype of a predictive display system, which employs a gesture-tracker in conjunction with suitable Bayesian intent predictors.

The next video shows: 1) various components of the predictive system, 2) selected free hand pointing gestures in a moving car and 3) prediction results, in real-time.

3- Maritime Situational Awareness

Prediction of the intended destination-harbour of a vessel and estimated time of arrival uisng bridging-distributions:

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