Publications
2025
Identifying Behaviours Indicative of Illegal Fishing Activities in Automatic Identification System Data
Zhou, Y., Davies, R., Wright, J., Ablett, S., & Maskell, S. (n.d.). Identifying Behaviours Indicative of Illegal Fishing Activities in Automatic Identification System Data. Journal of Marine Science and Engineering, 13(3), 457. doi:10.3390/jmse13030457
A Massively Parallel SMC Sampler for Decision Trees
Drousiotis, E., Varsi, A., Phillips, A. M., Maskell, S., & Spirakis, P. G. (n.d.). A Massively Parallel SMC Sampler for Decision Trees. Algorithms, 18(1), 14. doi:10.3390/a18010014
An SMC Sampler for Decision Trees with Enhanced Initial Proposal for Stochastic Metaheuristic Optimization
Drousiotis, E., Varsi, A., Spirakis, P. G., & Maskell, S. (2025). An SMC Sampler for Decision Trees with Enhanced Initial Proposal for Stochastic Metaheuristic Optimization. In Unknown Conference (pp. 123-137). Springer Nature Switzerland. doi:10.1007/978-3-031-75623-8_10
Conditional Importance Resampling for an Enhanced Sequential Monte Carlo Sampler
Habibi, S., Drousiotis, E., Varsi, A., Maskell, S., Moore, R., & Spirakis, P. G. (2025). Conditional Importance Resampling for an Enhanced Sequential Monte Carlo Sampler. In Unknown Conference (pp. 169-184). Springer Nature Switzerland. doi:10.1007/978-3-031-75623-8_13
2024
Fully Bayesian Wideband Direction-of-Arrival Estimation and Detection via RJMCMC
Parameterizing Hierarchical Particle Filters with Concept Drift for Time-varying Parameter Estimation
Murphy, J., Rosato, C., Millard, A., & Maskell, S. (2024). Parameterizing Hierarchical Particle Filters with Concept Drift for Time-varying Parameter Estimation. In 2024 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) (pp. 1-6). IEEE. doi:10.1109/apsipaasc63619.2025.10848671
Personalised antimicrobial susceptibility testing with clinical prediction modelling informs appropriate antibiotic use.
Howard, A., Hughes, D. M., Green, P. L., Velluva, A., Gerada, A., Maskell, S., . . . Hope, W. (2024). Personalised antimicrobial susceptibility testing with clinical prediction modelling informs appropriate antibiotic use.. Nature communications, 15(1), 9924. doi:10.1038/s41467-024-54192-3
Target tracking in a complex simulated world
Griffith, E. J., Phillips, A. M., Mai, L. P., Maskell, S., & Ralph, J. F. (2024). Target tracking in a complex simulated world. In D. L. Hickman, H. Bürsing, P. J. Soan, & O. Steinvall (Eds.), Electro-Optical and Infrared Systems: Technology and Applications XXI (pp. 20). SPIE. doi:10.1117/12.3031696
Enhanced SMC<sup>2</sup>: Leveraging Gradient Information from Differentiable Particle Filters Within Langevin Proposals
Rosato, C., Murphy, J., Varsi, A., Horridge, P., & Maskell, S. (2024). Enhanced SMC<sup>2</sup>: Leveraging Gradient Information from Differentiable Particle Filters Within Langevin Proposals. In 2024 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) (pp. 1-8). IEEE. doi:10.1109/mfi62651.2024.10705779
Identifying Diagnostic Arguments in Abstract Argumentation1
Robinson, J., Atkinson, K., Maskell, S., & Reed, C. (2024). Identifying Diagnostic Arguments in Abstract Argumentation1. IOS Press. doi:10.3233/faia240339
A qualitative exploration of barriers to efficient and effective structured medication reviews in primary care: Findings from the DynAIRx study.
Abuzour, A. S., Wilson, S. A., Woodall, A. A., Mair, F. S., Clegg, A., Shantsila, E., . . . Walker, L. E. (2024). A qualitative exploration of barriers to efficient and effective structured medication reviews in primary care: Findings from the DynAIRx study.. PloS one, 19(8), e0299770. doi:10.1371/journal.pone.0299770
Enhanced SMC$^2$: Leveraging Gradient Information from Differentiable Particle Filters Within Langevin Proposals
Bayesian Calibration to Address the Challenge of Antimicrobial Resistance: A Review
Rosato, C., Green, P. L., Harris, J., Maskell, S., Hope, W., Gerada, A., & Howard, A. (2024). Bayesian Calibration to Address the Challenge of Antimicrobial Resistance: A Review. IEEE Access, 12, 100772-100791. doi:10.1109/access.2024.3427410
A Poisson Multi-Bernoulli Mixture approach to tracking trains using Distributed Acoustic Sensing
Fontana, M., Hayder, T., Freilinger, W., García-Fernández, Á. F., & Maskell, S. (2024). A Poisson Multi-Bernoulli Mixture approach to tracking trains using Distributed Acoustic Sensing. In 2024 27th International Conference on Information Fusion (FUSION) (pp. 1-8). IEEE. doi:10.23919/fusion59988.2024.10706405
Identifying Drug-Drug Interactions in Spontaneous Reports Utilizing Signal Detection and Biological Plausibility Aspects.
Kontsioti, E., Maskell, S., Anderson, I., & Pirmohamed, M. (2024). Identifying Drug-Drug Interactions in Spontaneous Reports Utilizing Signal Detection and Biological Plausibility Aspects.. Clinical pharmacology and therapeutics. doi:10.1002/cpt.3258
The No-U-Turn Sampler as a Proposal Distribution in a Sequential Monte Carlo Sampler without Accept/Reject
Devlin, L., Carter, M., Horridge, P., Green, P. L., & Maskell, S. (2024). The No-U-Turn Sampler as a Proposal Distribution in a Sequential Monte Carlo Sampler without Accept/Reject. IEEE Signal Processing Letters, 1-5. doi:10.1109/lsp.2024.3386494
A General-Purpose Fixed-Lag No-U-Turn Sampler for Nonlinear Non-Gaussian State Space Models
Varsi, A., Devlin, L., Horridge, P., & Maskell, S. (2024). A General-Purpose Fixed-Lag No-U-Turn Sampler for Nonlinear Non-Gaussian State Space Models. IEEE Transactions on Aerospace and Electronic Systems, 1-16. doi:10.1109/taes.2024.3374720
COVID-19 risk-mitigation in reopening mass cultural events: population-based observational study for the UK Events Research Programme in Liverpool City Region
Burnside, G., Cheyne, C. P., Leeming, G., Humann, M., Darby, A., Green, M. A., . . . Buchan, I. E. (2024). COVID-19 risk-mitigation in reopening mass cultural events: population-based observational study for the UK Events Research Programme in Liverpool City Region. Journal of the Royal Society of Medicine. doi:10.1177/01410768231182389
On Diagnostic Arguments in Abstract Argumentation
Robinson, J., Atkinson, K., Maskell, S., & Reed, C. (2024). On Diagnostic Arguments in Abstract Argumentation. In CEUR Workshop Proceedings Vol. 3757 (pp. 27-40).
Response to the comment on: Exploring the impact of design criteria for reference sets on performance evaluation of signal detection algorithms: The case of drug-drug interactions.
Kontsioti, E., Maskell, S., & Pirmohamed, M. (2024). Response to the comment on: Exploring the impact of design criteria for reference sets on performance evaluation of signal detection algorithms: The case of drug-drug interactions.. Pharmacoepidemiology and drug safety, 33(1), e5731. doi:10.1002/pds.5731
2023
An O(log<sub>2</sub> N) SMC<sup>2</sup> Algorithm on Distributed Memory with an Approx. Optimal L-Kernel
Rosato, C., Varsi, A., Murphy, J., & Maskell, S. (2023). An O(log<sub>2</sub> N) SMC<sup>2</sup> Algorithm on Distributed Memory with an Approx. Optimal L-Kernel. In 2023 IEEE Symposium Sensor Data Fusion and International Conference on Multisensor Fusion and Integration (SDF-MFI) (pp. 1-8). IEEE. doi:10.1109/sdf-mfi59545.2023.10361452
How might dynamic artificial intelligence (DynAIRx) be used to support prescribing to ensure efficient medication reviews?
Abuzour, A., Wilson, S., Woodall, A., Mair, F., Bollegala, D., Cant, H., . . . Walker, L. (2023). How might dynamic artificial intelligence (DynAIRx) be used to support prescribing to ensure efficient medication reviews?. American Academy of Family Physicians. doi:10.1370/afm.22.s1.4823
Bayesian Decision Trees Inspired from Evolutionary Algorithms
Drousiotis, E., Phillips, A. M., Spirakis, P. G., & Maskell, S. (2023). Bayesian Decision Trees Inspired from Evolutionary Algorithms. In Unknown Conference (pp. 318-331). Springer International Publishing. doi:10.1007/978-3-031-44505-7_22
A Shared Memory SMC Sampler for Decision Trees
Drousiotis, E., Varsi, A., Spirakis, P. G., & Maskell, S. (2023). A Shared Memory SMC Sampler for Decision Trees. In 2023 IEEE 35th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD) (pp. 209-218). IEEE. doi:10.1109/sbac-pad59825.2023.00030
Non-Myopic Sensor Control for Target Search and Track Using a Sample-Based GOSPA Implementation
Hernandez, M., García-Fernández, Á. F., & Maskell, S. (2023). Non-Myopic Sensor Control for Target Search and Track Using a Sample-Based GOSPA Implementation. IEEE Transactions on Aerospace and Electronic Systems, 1-17. doi:10.1109/taes.2023.3324908
Data-Driven Clustering and Bernoulli Merging for the Poisson Multi-Bernoulli Mixture Filter
Fontana, M., García-Fernández, Á. F., & Maskell, S. (2023). Data-Driven Clustering and Bernoulli Merging for the Poisson Multi-Bernoulli Mixture Filter. IEEE Transactions on Aerospace and Electronic Systems, 59(5), 5287-5301. doi:10.1109/taes.2023.3253662
Trustworthy Data and AI Environments for Clinical Prediction: Application to Crisis-Risk in People with Depression
Msosa, Y. J., Grauslys, A., Zhou, Y., Wang, T., Buchan, I., Langan, P., . . . Kehoe, D. (2023). Trustworthy Data and AI Environments for Clinical Prediction: Application to Crisis-Risk in People with Depression. IEEE Journal of Biomedical and Health Informatics, 1-12. doi:10.1109/jbhi.2023.3312011
Protecting Children from Online Exploitation: Can a Trained Model Detect Harmful Communication Strategies?
Cook, D., Zilka, M., DeSandre, H., Giles, S., & Maskell, S. (2023). Protecting Children from Online Exploitation: Can a Trained Model Detect Harmful Communication Strategies?. In Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society (pp. 5-14). ACM. doi:10.1145/3600211.3604696
The Design of Relativistic Ultrafast Electron Diffraction and Imaging (RUEDI) Facility for Materials in Extremes.
Murooka, Y., Bryan, W., Clarke, J., Ellis, M., Kirkland, A. I., Maskell, S., . . . Browning, N. D. (2023). The Design of Relativistic Ultrafast Electron Diffraction and Imaging (RUEDI) Facility for Materials in Extremes.. Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada, 29(Supplement_1), 1487-1488. doi:10.1093/micmic/ozad067.764
Bayesian estimation for Bell state rotations
Anastassiou, L., Ralph, J. F., Maskell, S., & Kok, P. (2023). Bayesian estimation for Bell state rotations. AVS Quantum Science, 5(2). doi:10.1116/5.0147878
Probabilistic Decision Trees for Predicting 12-Month University Students Likely to Experience Suicidal Ideation
Drousiotis, E., Joyce, D. W., Dempsey, R. C., Haines, A., Spirakis, P. G., Shi, L., & Maskell, S. (2023). Probabilistic Decision Trees for Predicting 12-Month University Students Likely to Experience Suicidal Ideation. In Unknown Conference (pp. 475-487). Springer Nature Switzerland. doi:10.1007/978-3-031-34111-3_40
Repeated Filtering for Smoothing Particle Filters
Anderson, S. L., Stone, L. D., & Maskell, S. (2023). Repeated Filtering for Smoothing Particle Filters. Journal of Advances in Information Fusion, 18(1), 35-46.
Machine learning assisted calibration of stochastic agent-based models for pandemic outbreak analysis
Exploring the impact of design criteria for reference sets on performance evaluation of signal detection algorithms: the case of drug-drug interactions.
Kontsioti, E., Maskell, S., & Pirmohamed, M. (2023). Exploring the impact of design criteria for reference sets on performance evaluation of signal detection algorithms: the case of drug-drug interactions.. Pharmacoepidemiology and drug safety. doi:10.1002/pds.5609
Extracting Self-Reported COVID-19 Symptom Tweets and Twitter Movement Mobility Origin/Destination Matrices to Inform Disease Models
Rosato, C., Moore, R. E., Carter, M., Heap, J., Harris, J., Storopoli, J., & Maskell, S. (2023). Extracting Self-Reported COVID-19 Symptom Tweets and Twitter Movement Mobility Origin/Destination Matrices to Inform Disease Models. Information, 14(3), 170. doi:10.3390/info14030170
Information fusion and tracking using Bernoulli filters for maritime surveillance
Ransom, M. J., Ralph, J. F., & Maskell, S. (2023). Information fusion and tracking using Bernoulli filters for maritime surveillance. In IET Conference Proceedings Vol. 2022 (pp. 477-482). Institution of Engineering and Technology (IET). doi:10.1049/icp.2022.2364
Using hybrid multiobjective machine learning to optimise sonobuoy placement patterns
Taylor, C. M., Maskell, S., & Ralph, J. F. (2023). Using hybrid multiobjective machine learning to optimise sonobuoy placement patterns. IET RADAR SONAR AND NAVIGATION, 17(3), 374-387. doi:10.1049/rsn2.12347
Coherent Long-Time Integration and Bayesian Detection With Bernoulli Track-Before-Detect
Uney, M., Horridge, P., Mulgrew, B., & Maskell, S. (2023). Coherent Long-Time Integration and Bayesian Detection With Bernoulli Track-Before-Detect. IEEE SIGNAL PROCESSING LETTERS, 30, 239-243. doi:10.1109/LSP.2023.3253039
Defending the unknown: Exploring reinforcement learning agents' deployment in realistic, unseen networks
Acuto, A., Maskell, S., & Jack, D. (2023). Defending the unknown: Exploring reinforcement learning agents' deployment in realistic, unseen networks. In CEUR Workshop Proceedings Vol. 3652 (pp. 22-35).
Joint optimization of sonar waveform selection and sonobuoy placement
Taylor, C. M., Maskell, S., Narykov, A., & Ralph, J. F. (2023). Joint optimization of sonar waveform selection and sonobuoy placement. In 2023 SENSOR SIGNAL PROCESSING FOR DEFENCE CONFERENCE, SSPD (pp. 86-90). doi:10.1109/SSPD57945.2023.10256991
2022
Cold atom inertial sensors for navigation applications
Wright, M. J., Anastassiou, L., Mishra, C., Davies, J. M., Phillips, A. M., Maskell, S., & Ralph, J. F. (n.d.). Cold atom inertial sensors for navigation applications. Frontiers in Physics, 10. doi:10.3389/fphy.2022.994459
Refining epidemiological forecasts with simple scoring rules
Moore, R. E., Rosato, C., & Maskell, S. (2022). Refining epidemiological forecasts with simple scoring rules. PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES, 380(2233). doi:10.1098/rsta.2021.0305
Can We Automate the Analysis of Online Child Sexual Exploitation Discourse?
The Design and Operation of a New Relativistic Ultrafast Electron Diffraction and Imaging (RUEDI) National Facility in the UK
Browning, N. D., Bryan, W., Clarke, J., Ellis, M., Kirkland, A. I., Maskell, S., . . . Welsch, C. (2022). The Design and Operation of a New Relativistic Ultrafast Electron Diffraction and Imaging (RUEDI) National Facility in the UK. Microscopy and Microanalysis, 28(S1), 2764-2765. doi:10.1017/s1431927622010406
A vehicle detector based on notched power for distributed acoustic sensing
Fontana, M., Garcia-Fernandez, A. F., & Maskell, S. (2022). A vehicle detector based on notched power for distributed acoustic sensing. In 2022 25TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION 2022). Retrieved from https://www.webofscience.com/
Gaussian trajectory PMBM filter with nonlinear measurements based on posterior linearisation
Garcia-Fernandez, A. F., Ralph, J., Horridge, P., & Maskell, S. (2022). Gaussian trajectory PMBM filter with nonlinear measurements based on posterior linearisation. In 2022 25TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION 2022). Retrieved from https://www.webofscience.com/
Inference of Stochastic Disease Transmission Models Using Particle-MCMC and a Gradient Based Proposal
Rosato, C., Harris, J., Panovska-Griffiths, J., & Maskell, S. (2022). Inference of Stochastic Disease Transmission Models Using Particle-MCMC and a Gradient Based Proposal. In 2022 25TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION 2022). Retrieved from https://www.webofscience.com/
Poisson multi-Bernoulli mixture filtering with an active sonar using BELLHOP simulation
Narykov, A., Wright, M., Garcia-Fernandez, A. F., Maskell, S., & Ralph, J. F. (2022). Poisson multi-Bernoulli mixture filtering with an active sonar using BELLHOP simulation. In 2022 25TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION 2022). Retrieved from https://www.webofscience.com/
Efficient Learning of the Parameters of Non-Linear Models using Differentiable Resampling in Particle Filters
Maskell, S., Devlin, L., Beraud, V., Horridge, P., & Rosato, C. (2022). Efficient Learning of the Parameters of Non-Linear Models using Differentiable Resampling in Particle Filters. IEEE Transactions on Signal Processing.
Novel Decision Forest Building Techniques by Utilising Correlation Coefficient Methods
Drousiotis, E., Shi, L., Spirakis, P., & Maskell, S. (2022). Novel Decision Forest Building Techniques by Utilising Correlation Coefficient Methods. In Engineering Applications of Neural Networks.
Stone Soup open source framework for tracking and state estimation: enhancements and applications
Barr, J., Harrald, O., Hiscocks, S., Perree, N., Pritchett, H., Vidal, S., . . . Vladimirov, L. (2022). Stone Soup open source framework for tracking and state estimation: enhancements and applications. In SIGNAL PROCESSING, SENSOR/INFORMATION FUSION, AND TARGET RECOGNITION XXXI Vol. 12122. doi:10.1117/12.2618495
Position fixing with cold atom gravity gradiometers
Phillips, A. M., Wright, M. J., Riou, I., Maddox, S., Maskell, S., & Ralph, J. F. (2022). Position fixing with cold atom gravity gradiometers. AVS Quantum Science, 4(2). doi:10.1116/5.0095677
Inference of Stochastic Disease Transmission Models Using Particle-MCMC and a Gradient Based Proposal
Similarity and Consistency Assessment of Three Major Online Drug-Drug Interaction Resources
Kontsioti, E., Maskell, S., Bensalem, A., Dutta, B., & Pirmohamed, M. (2022). Similarity and Consistency Assessment of Three Major Online Drug-Drug Interaction Resources. British Journal of Clinical Pharmacology. doi:10.1111/bcp.15341
A reference set of clinically relevant adverse drug-drug interactions
Kontsioti, E., Maskell, S., Dutta, B., & Pirmohamed, M. (2022). A reference set of clinically relevant adverse drug-drug interactions. SCIENTIFIC DATA, 9(1). doi:10.1038/s41597-022-01159-y
Ensemble Kalman filter based sequential Monte Carlo sampler for sequential Bayesian inference
Wu, J., Wen, L., Green, P. L., Li, J., & Maskell, S. (2022). Ensemble Kalman filter based sequential Monte Carlo sampler for sequential Bayesian inference. STATISTICS AND COMPUTING, 32(1). doi:10.1007/s11222-021-10075-x
Classical Tracking for Quantum Trajectories
Classical Tracking for Quantum Trajectories
Ralph, J. F., Maskell, S., Ransom, M., & Ulbricht, H. (2022). Classical Tracking for Quantum Trajectories. In IEEE 24th International Conference on Information Fusion (FUSION), 2021, pp. 1-8. Retrieved from http://arxiv.org/abs/2202.00276v1
Control Variates for Constrained Variables
Maskell, S., Zhou, Y., & Mira, A. (2022). Control Variates for Constrained Variables. IEEE SIGNAL PROCESSING LETTERS, 29, 2333-2337. doi:10.1109/LSP.2022.3221347
GroundsWell: Community-engaged and data-informed systems transformation of Urban Green and Blue Space for population health - a new initiative.
Hunter, R. F., Rodgers, S. E., Hilton, J., Clarke, M., Garcia, L., Ward Thompson, C., . . . GroundsWell Consortium. (2022). GroundsWell: Community-engaged and data-informed systems transformation of Urban Green and Blue Space for population health - a new initiative.. Wellcome open research, 7, 237. doi:10.12688/wellcomeopenres.18175.1
Optimizing sonobuoy placement using multiobjective machine learning
Taylor, C. M., Maskell, S., & Ralph, J. F. (2022). Optimizing sonobuoy placement using multiobjective machine learning. In 2022 Sensor Signal Processing for Defence Conference (SSPD) (pp. 1-5). IEEE. doi:10.1109/sspd54131.2022.9896216
The DynAIRx Project Protocol: Artificial Intelligence for dynamic prescribing optimisation and care integration in multimorbidity.
Walker, L. E., Abuzour, A. S., Bollegala, D., Clegg, A., Gabbay, M., Griffiths, A., . . . Buchan, I. (2022). The DynAIRx Project Protocol: Artificial Intelligence for dynamic prescribing optimisation and care integration in multimorbidity.. Journal of multimorbidity and comorbidity, 12, 26335565221145493. doi:10.1177/26335565221145493
2021
Fusing Low-Latency Data Feeds with Death Data to Accurately Nowcast COVID-19 Related Deaths
An O(log2N) Fully-Balanced Resampling Algorithm for Particle Filters on Distributed Memory Architectures
Varsi, A., Maskell, S., & Spirakis, P. G. (n.d.). An O(log2N) Fully-Balanced Resampling Algorithm for Particle Filters on Distributed Memory Architectures. Algorithms, 14(12), 342. doi:10.3390/a14120342
Refining Epidemiological Forecasts with Simple Scoring Rules
An analysis on metric-driven multi-target sensor management: GOSPA versus OSPA
Garcia-Fernandez, A. F., Hernandez, M., & Maskell, S. (2021). An analysis on metric-driven multi-target sensor management: GOSPA versus OSPA. In 2021 IEEE 24th International Conference on Information Fusion (FUSION) (pp. 1-8). IEEE. doi:10.23919/fusion49465.2021.9626837
Classical Tracking for Quantum Trajectories
Ralph, J. F., Maskell, S., Ransom, M., & Ulbricht, H. (2021). Classical Tracking for Quantum Trajectories. In 2021 IEEE 24th International Conference on Information Fusion (FUSION) (pp. 1-8). IEEE. doi:10.23919/fusion49465.2021.9626966
Posterior Cramér-Rao Bounds for Tracking Intermittently Visible Targets in Clutter
Hernandez, M. L., Ransom, M. J., & Maskell, S. (2021). Posterior Cramér-Rao Bounds for Tracking Intermittently Visible Targets in Clutter. In 2021 IEEE 24th International Conference on Information Fusion (FUSION) (pp. 1-8). IEEE. doi:10.23919/fusion49465.2021.9626856
SMC samplers for Bayesian Optimisation and Discovery of Additive Kernel Structure
Chatzopoulou, A., Garcia-Fernandez, A. F., Pyzer-Knapp, E., & Maskell, S. (2021). SMC samplers for Bayesian Optimisation and Discovery of Additive Kernel Structure. In 2021 IEEE 24th International Conference on Information Fusion (FUSION) (pp. 1-8). IEEE. doi:10.23919/fusion49465.2021.9626877
Track-before-detect Bernoulli filters for combining passive and active sensors
Ransom, M. J., Hernandez, M. L., Ralph, J. F., & Maskell, S. (2021). Track-before-detect Bernoulli filters for combining passive and active sensors. In 2021 IEEE 24th International Conference on Information Fusion (FUSION) (pp. 1-8). IEEE. doi:10.23919/fusion49465.2021.9626922
A Gaussian Filtering Method for Multitarget Tracking With Nonlinear/Non-Gaussian Measurements
Garcia-Fernandez, A. F., Ralph, J., Horridge, P., & Maskell, S. (2021). A Gaussian Filtering Method for Multitarget Tracking With Nonlinear/Non-Gaussian Measurements. IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, 57(5), 3539-3548. doi:10.1109/TAES.2021.3074200
Modelling bi-static uncertainties in sequential Monte Carlo with the GLMB model
Uney, M., Narykov, A., Ralph, J., & Maskell, S. (2021). Modelling bi-static uncertainties in sequential Monte Carlo with the GLMB model. In 2021 SENSOR SIGNAL PROCESSING FOR DEFENCE CONFERENCE (SSPD) (pp. 85-89). doi:10.1109/SSPD51364.2021.9541502
The No-U-Turn Sampler as a Proposal Distribution in a Sequential Monte Carlo Sampler with a Near-Optimal L-Kernel
Gaussian Tracking With Kent-Distributed Direction-of-Arrival Measurements
Garcia-Fernandez, A., Maskell, S., Horridge, P., & Ralph, J. (2021). Gaussian Tracking With Kent-Distributed Direction-of-Arrival Measurements. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 70(7), 7249-7254. doi:10.1109/TVT.2021.3086558
Increasing the efficiency of Sequential Monte Carlo samplers through the use of approximately optimal L-kernels
Green, P., Devlin, L., Moore, R., Jackson, R., Li, J., & Maskell, S. (2021). Increasing the efficiency of Sequential Monte Carlo samplers through the use of approximately optimal L-kernels. Mechanical Systems and Signal Processing. doi:10.1016/j.ymssp.2021.108028
A Psychology-Driven Computational Analysis of Political Interviews
Cook, D., Zilka, M., Maskell, S., & Alison, L. (2021). A Psychology-Driven Computational Analysis of Political Interviews. In INTERSPEECH 2021 (pp. 1942-1946). doi:10.21437/Interspeech.2021-2249
A Psychology-Driven Computational Analysis of Political Interviews.
Cook, D., Zilka, M., Maskell, S., & Alison, L. (2021). A Psychology-Driven Computational Analysis of Political Interviews.. In H. Hermansky, H. Cernocký, L. Burget, L. Lamel, O. Scharenborg, & P. Motlícek (Eds.), Interspeech (pp. 1942-1946). ISCA. Retrieved from https://doi.org/10.21437/Interspeech.2021
An analysis on metric-driven multi-target sensor management: GOSPA versus OSPA
Garcia-Fernandez, A. F., Hernandez, M., & Maskell, S. (2021). An analysis on metric-driven multi-target sensor management: GOSPA versus OSPA. In 2021 IEEE 24TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION) (pp. 355-362). Retrieved from https://www.webofscience.com/
Classical Tracking for Quantum Trajectories
Ralph, J. F., Maskell, S., Ransom, M., & Ulbricht, H. (2021). Classical Tracking for Quantum Trajectories. In 2021 IEEE 24TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION) (pp. 869-876). Retrieved from https://www.webofscience.com/
Early Predictor for Student Success Based on Behavioural and Demographical Indicators
Drousiotis, E., Shi, L., & Maskell, S. (2021). Early Predictor for Student Success Based on Behavioural and Demographical Indicators. In INTELLIGENT TUTORING SYSTEMS (ITS 2021) Vol. 12677 (pp. 161-172). doi:10.1007/978-3-030-80421-3_19
Efficient Learning of the Parameters of Non-Linear Models using Differentiable Resampling in Particle Filters.
Posterior Cramer-Rao Bounds for Tracking Intermittently Visible Targets in Clutter
Hernandez, M. L., Ransom, M. J., & Maskell, S. (2021). Posterior Cramer-Rao Bounds for Tracking Intermittently Visible Targets in Clutter. In 2021 IEEE 24TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION) (pp. 484-491). Retrieved from https://www.webofscience.com/
SMC samplers for Bayesian Optimisation and Discovery of Additive Kernel Structure
Chatzopoulou, A., Garcia-Fernandez, A. F., Pyzer-Knapp, E., & Maskell, S. (2021). SMC samplers for Bayesian Optimisation and Discovery of Additive Kernel Structure. In 2021 IEEE 24TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION) (pp. 137-144). Retrieved from https://www.webofscience.com/
Track-before-detect Bernoulli filters for combining passive and active sensors
Ransom, M. J., Hernandez, M. L., Ralph, J. F., & Maskell, S. (2021). Track-before-detect Bernoulli filters for combining passive and active sensors. In 2021 IEEE 24TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION) (pp. 885-892). Retrieved from https://www.webofscience.com/
2020
Ensemble Kalman filter based Sequential Monte Carlo Sampler for sequential Bayesian inference
Practical Verification of Neural Network Enabled State Estimation System for Robotics
Huang, W., Zhou, Y., Sun, Y., Sharp, J., Maskell, S., & Huang, X. (2020). Practical Verification of Neural Network Enabled State Estimation System for Robotics. In 2020 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) (pp. 7336-7343). doi:10.1109/IROS45743.2020.9340720
Augmented inertial navigation using cold atom sensing
Phillips, A. M., Wright, M. J., Kiss-Toth, M., Read, I., Riou, I., Maddox, S., . . . Ralph, J. F. (2020). Augmented inertial navigation using cold atom sensing. In Cold Atoms for Quantum Technologies. SPIE. doi:10.1117/12.2583999
Welcome to the first issue of <scp><i>Applied AI Letters</i></scp>
Pyzer‐Knapp, E. O., Cuff, J., Patterson, J., Isayev, O., & Maskell, S. (2020). Welcome to the first issue of <scp><i>Applied AI Letters</i></scp>. Applied AI Letters, 1(1). doi:10.1002/ail2.8
A Fast Parallel Particle Filter for Shared Memory Systems
Varsi, A., Taylor, J., Kekempanos, L., Pyzer Knapp, E., & Maskell, S. (2020). A Fast Parallel Particle Filter for Shared Memory Systems. IEEE Signal Processing Letters, 27, 1570-1574. doi:10.1109/LSP.2020.3014035
A SMC Sampler for Joint Tracking and Destination Estimation from Noisy Data
Vladimirov, L., & Maskell, S. (2020). A SMC Sampler for Joint Tracking and Destination Estimation from Noisy Data. In PROCEEDINGS OF 2020 23RD INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION 2020) (pp. 1108-1115). Retrieved from https://www.webofscience.com/
Bernoulli merging for the Poisson multi-Bernoulli mixture filter
Fontana, M., Garcia-Fenandez, A. F., & Maskell, S. (2020). Bernoulli merging for the Poisson multi-Bernoulli mixture filter. In 2020 IEEE 23rd International Conference on Information Fusion (FUSION) (pp. 1-8). IEEE. doi:10.23919/fusion45008.2020.9190443
Continuous-discrete trajectory PHD and CPHD filters
Garcia-Fernandez, A. F., & Maskell, S. (2020). Continuous-discrete trajectory PHD and CPHD filters. In 2020 IEEE 23rd International Conference on Information Fusion (FUSION) (pp. 1-8). IEEE. doi:10.23919/fusion45008.2020.9190298
Integrated Expected Likelihood Particle Filters
Ransom, M. J., Vladimirov, L., Horridge, P. R., Ralph, J. F., & Maskell, S. (2020). Integrated Expected Likelihood Particle Filters. In 2020 IEEE 23rd International Conference on Information Fusion (FUSION) (pp. 1-8). IEEE. doi:10.23919/fusion45008.2020.9190387
Robust and Efficient Image Alignment Method Using the Student-t Distribution
Zhou, Y., & Maskell, S. (2020). Robust and Efficient Image Alignment Method Using the Student-t Distribution. In PROCEEDINGS OF 2020 23RD INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION 2020) (pp. 1255-1262). Retrieved from https://www.webofscience.com/
Increasing the efficiency of Sequential Monte Carlo samplers through the use of approximately optimal L-kernels
Reliability Validation of Learning Enabled Vehicle Tracking
Reliability Validation of Learning Enabled Vehicle Tracking
Sun, Y., Zhou, Y., Maskell, S., Sharp, J., & Huang, X. (2020). Reliability Validation of Learning Enabled Vehicle Tracking. 2020 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), 9390-9396. doi:10.1109/icra40945.2020.9196932
Continuous-discrete multiple target filtering: PMBM, PHD and CPHD filter implementations
Garcia-Fernandez, A., & Maskell, S. (2020). Continuous-discrete multiple target filtering: PMBM, PHD and CPHD filter implementations. Continuous-discrete multiple target filtering: PMBM, PHD and CPHD filter implementations. doi:10.1109/TSP.2020.2968247
Bernoulli merging for the Poisson multi-Bernoulli mixture filter
Fontana, M., Garcia-Fernandez, A. F., & Maskell, S. (2020). Bernoulli merging for the Poisson multi-Bernoulli mixture filter. In PROCEEDINGS OF 2020 23RD INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION 2020) (pp. 262-269). Retrieved from https://www.webofscience.com/
Continuous-discrete trajectory PHD and CPHD filters
Garcia-Fernandez, A. F., & Maskell, S. (2020). Continuous-discrete trajectory PHD and CPHD filters. In PROCEEDINGS OF 2020 23RD INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION 2020) (pp. 305-312). doi:10.23919/fusion45008.2020.9190298
Integrated Expected Likelihood Particle Filters
Ransom, M. J., Vladimirov, L., Horridge, P. R., Ralph, J. F., & Maskell, S. (2020). Integrated Expected Likelihood Particle Filters. In PROCEEDINGS OF 2020 23RD INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION 2020) (pp. 849-856). Retrieved from https://www.webofscience.com/
Weather Effects on Obstacle Detection for Autonomous Car
Song, R., Wetherall, J., Maskell, S., & Ralph, J. (2020). Weather Effects on Obstacle Detection for Autonomous Car. In Proceedings of the 6th International Conference on Vehicle Technology and Intelligent Transport Systems (pp. 331-341). SCITEPRESS - Science and Technology Publications. doi:10.5220/0009354500002550
Weather Effects on Obstacle Detection for Autonomous Car
Song, R., Wetherall, J., Maskell, S., & Ralph, J. F. (2020). Weather Effects on Obstacle Detection for Autonomous Car. In PROCEEDINGS OF THE 6TH INTERNATIONAL CONFERENCE ON VEHICLE TECHNOLOGY AND INTELLIGENT TRANSPORT SYSTEMS (VEHITS) (pp. 331-341). doi:10.5220/0009354503310341
2019
Efficient Estimation of Probability of Conflict Between Air Traffic Using Subset Simulation
Mishra, C., Maskell, S., Au, S. -K., & Ralph, J. F. (2019). Efficient Estimation of Probability of Conflict Between Air Traffic Using Subset Simulation. IEEE Transactions on Aerospace and Electronic Systems, 55(6), 2719-2742. doi:10.1109/TAES.2019.2899714
Recommendations for the Use of Social Media in Pharmacovigilance: Lessons from IMI WEB-RADR
van Stekelenborg, J., Ellenius, J., Maskell, S., Bergvall, T., Caster, O., Dasgupta, N., . . . Pirmohamed, M. (2019). Recommendations for the Use of Social Media in Pharmacovigilance: Lessons from IMI WEB-RADR. DRUG SAFETY, 42(12), 1393-1407. doi:10.1007/s40264-019-00858-7
Detecting and Tracking Small Moving Objects in Wide Area Motion Imagery (WAMI) Using Convolutional Neural Networks (CNNs)
Robust and Accurate Global Motion Estimation Using the Student-t Distribution
A Generic Anomaly Detection Approach Applied to Mixture-of-unigrams and Maritime Surveillance Data
Zhou, Y., Wright, J., & Maskell, S. (2019). A Generic Anomaly Detection Approach Applied to Mixture-of-unigrams and Maritime Surveillance Data. In 2019 SYMPOSIUM ON SENSOR DATA FUSION: TRENDS, SOLUTIONS, APPLICATIONS (SDF 2019). doi:10.1109/sdf.2019.8916633
On the Bridges: Insight Into the Current and Future Use of Automated Systems as Seen by Royal Navy Personnel
Barrett-Pink, C., Alison, L., Maskell, S., & Shortland, N. (2019). On the Bridges: Insight Into the Current and Future Use of Automated Systems as Seen by Royal Navy Personnel. JOURNAL OF COGNITIVE ENGINEERING AND DECISION MAKING, 13(3), 127-145. doi:10.1177/1555343419855850
A Multi-Sensor Simulation Environment for Autonomous Cars
Song, R., Horridge, P., Pemberton, S., Wetherall, J., Maskell, S., & Ralph, J. (2019). A Multi-Sensor Simulation Environment for Autonomous Cars. In 2019 22ND INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION 2019). doi:10.23919/fusion43075.2019.9011278
Detecting and Tracking Small Moving Objects in Wide Area Motion Imagery (WAMI) Using Convolutional Neural Networks (CNNs)
Zhou, Y., & Maskell, S. (2019). Detecting and Tracking Small Moving Objects in Wide Area Motion Imagery (WAMI) Using Convolutional Neural Networks (CNNs). In 2019 22ND INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION 2019). doi:10.23919/fusion43075.2019.9011271
A Single SMC Sampler on MPI that Outperforms a Single MCMC Sampler
A Single SMC Sampler on MPI that Outperforms a Single MCMC Sampler
Varsi, A., Kekempanos, L., Thiyagalingam, J., & Maskell, S. (2019). A Single SMC Sampler on MPI that Outperforms a Single MCMC Sampler. Retrieved from http://arxiv.org/abs/1905.10252v1
Recommendations on the Use of Mobile Applications for the Collection and Communication of Pharmaceutical Product Safety Information: Lessons from IMI WEB-RADR
Pierce, C. E., de Vries, S. T., Bodin-Parssinen, S., Harmark, L., Tregunno, P., Lewis, D. J., . . . Mol, P. G. M. (2019). Recommendations on the Use of Mobile Applications for the Collection and Communication of Pharmaceutical Product Safety Information: Lessons from IMI WEB-RADR. DRUG SAFETY, 42(4), 477-489. doi:10.1007/s40264-019-00813-6
A CPHD Approximation Based on a Discrete-Gamma Cardinality Model
De Melo, F., & Maskell, S. (n.d.). A CPHD approximation based on a discrete-Gamma cardinality model. IEEE Transactions on Signal Processing.
2018
Estimating the Parameters of Dynamical Systems from Big Data Using Sequential Monte Carlo Samplers
Convolutional Neural Networks for Aerial Vehicle Detection and Recognition
Soleimani, A., Nasrabadi, N. M., Griffith, E., Ralph, J., & Maskell, S. (2018). Convolutional Neural Networks for Aerial Vehicle Detection and Recognition. In NAECON 2018 - IEEE NATIONAL AEROSPACE AND ELECTRONICS CONFERENCE (pp. 186-191). Retrieved from https://www.webofscience.com/
Assessment of the Utility of Social Media for Broad-Ranging Statistical Signal Detection in Pharmacovigilance: Results from the WEB-RADR Project.
Caster, O., Dietrich, J., Kuerzinger, M. -L., Lerch, M., Maskell, S., Noren, G. N., . . . van Stekelenborg, J. (2018). Assessment of the Utility of Social Media for Broad-Ranging Statistical Signal Detection in Pharmacovigilance: Results from the WEB-RADR Project. DRUG SAFETY, 41(12), 1355-1369. doi:10.1007/s40264-018-0699-2
Comparing interrelationships between features and embedding methods for multiple-view fusion
Piroddi, R., Goulermas, Y., Maskell, S., & Ralph, J. (2018). Comparing interrelationships between features and embedding methods for multiple-view fusion. In 2018 21ST INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION) (pp. 1503-1510). Retrieved from https://www.webofscience.com/
Fusing Bearing-only Measurements With and Without Propagation Delays Using Particle Trajectories
Horridge, P., & Maskell, S. (2018). Fusing Bearing-only Measurements With and Without Propagation Delays Using Particle Trajectories. In 2018 21ST INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION) (pp. 989-996). Retrieved from https://www.webofscience.com/
Convolutional Neural Networks for Aerial Vehicle Detection and Recognition
Comparing Interrelationships Between Features and Embedding Methods for Multiple-View Fusion.
Piroddi, R., Goulermas, J. Y., Maskell, S., & Ralph, J. F. (2018). Comparing Interrelationships Between Features and Embedding Methods for Multiple-View Fusion.. In FUSION (pp. 1-5). IEEE. Retrieved from https://ieeexplore.ieee.org/xpl/conhome/8442112/proceeding
Causality Patterns for Detecting Adverse Drug Reactions From Social Media: Text Mining Approach
Bollegala, D., Maskell, S., Sloane, R., Hajne, J., & Pirmohamed, M. (2018). Causality Patterns for Detecting Adverse Drug Reactions From Social Media: Text Mining Approach. JMIR PUBLIC HEALTH AND SURVEILLANCE, 4(2), 292-303. doi:10.2196/publichealth.8214
A System for the Generation of Synthetic Wide Area Aerial Surveillance Imagery
Griffith, E., Mishra, C., Ralph, J. F., & Maskell, S. (2018). A System for the Generation of Synthetic Wide Area Aerial Surveillance Imagery. Simulation Modelling Practice and Theory, 84, 286-308. doi:10.1016/j.simpat.2018.03.003
Langevin Incremental Mixture Importance Sampling
Fasiolo, M., de melo, F., & Maskell, S. (2018). Langevin Incremental Mixture Importance Sampling. Statistics and Computing, 28, 549-561. doi:10.1007/s11222-017-9747-5
A System for the Generation of Synthetic Wide Area Aerial Surveillance Imagery
Uncertainty representation and evaluation for modelling and decision-making in information fusion
De Villiers, J. P., Pavlin, G., Jousselme, A. L., Maskell, S., Waal, A. D. E., Laskey, K., . . . Costa, P. (2018). Uncertainty representation and evaluation for modelling and decision-making in information fusion. Journal of Advances in Information Fusion, 13(2), 198-215.
2017
Moving Object Detection Using Background Subtraction for a Moving Camera with Pronounced Parallax
Zhou, Y., & Maskell, S. (2017). Moving Object Detection Using Background Subtraction for a Moving Camera with Pronounced Parallax. In 2017 SENSOR DATA FUSION: TRENDS, SOLUTIONS, APPLICATIONS (SDF). Retrieved from https://www.webofscience.com/
Dynamical model selection near the quantum-classical boundary
Dynamical model selection near the quantum-classical boundary
Ralph, J. F., Toros, M., Maskell, S., Jacobs, K., Rashid, M., Setter, A. J., & Ulbricht, H. (2018). Dynamical model selection near the quantum-classical boundary. PHYSICAL REVIEW A, 98(1). doi:10.1103/PhysRevA.98.010102
Multiparameter estimation along quantum trajectories with sequential Monte Carlo methods
Ralph, J. F., Maskell, S., & Kacobs, K. (2017). Multiparameter estimation along quantum trajectories with sequential Monte Carlo methods. Physical Review A (Atomic, Molecular and Optical Physics), 96. doi:10.1103/PhysRevA.96.052306
MapReduce particle filtering with exact resampling and deterministic runtime
Thiyagalingam, J., Kekempanos, L., & Maskell, S. (2017). MapReduce particle filtering with exact resampling and deterministic runtime. Eurasip Journal on Advances in Signal Processing, 2017, 23 pages. doi:10.1186/s13634-017-0505-9
Beyond co-occurrence-based ADR detection from Social Media
Bollegala, D., Maskell, S., & Pirmohamed, M. (2017). Beyond co-occurrence-based ADR detection from Social Media. Poster session presented at the meeting of Unknown Conference. Retrieved from https://www.webofscience.com/
When does Social Media add Value to Pharmacovigilance?
Maskell, S. (2017). When does Social Media add Value to Pharmacovigilance?. In DRUG SAFETY Vol. 40 (pp. 1040). Retrieved from https://www.webofscience.com/
Estimating the parameters of dynamical systems from Big Data using Sequential Monte Carlo samplers
Green, P. L., & Maskell, S. (2017). Estimating the parameters of dynamical systems from Big Data using Sequential Monte Carlo samplers. Mechanical Systems and Signal Processing, 93, 379-396. doi:10.1016/j.ymssp.2016.12.023
Nonlinear Kinematics for Improved Helicopter Tracking
Clark, E. J., Griffith, E. J., Maskell, S., & Ralph, J. F. (2017). Nonlinear Kinematics for Improved Helicopter Tracking. In 2017 20TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION) (pp. 1013-1018). Retrieved from https://www.webofscience.com/
RB<sup>2</sup>— PF : A novel filter-based monocular visual odometry algorithm
Zhou, Y., & Maskell, S. (2017). RB<sup>2</sup>— PF : A novel filter-based monocular visual odometry algorithm. In 2017 20th International Conference on Information Fusion (Fusion) (pp. 1-8). IEEE. doi:10.23919/icif.2017.8009745
Multi-parameter estimation along quantum trajectories with Sequential Monte Carlo methods
Causality Patterns for Detecting Adverse Drug Reactions From Social Media: Text Mining Approach (Preprint)
Bollegala, D., Maskell, S., Sloane, R., Hajne, J., & Pirmohamed, M. (2017). Causality Patterns for Detecting Adverse Drug Reactions From Social Media: Text Mining Approach (Preprint). doi:10.2196/preprints.8214
MapReduce Particle Filtering with Exact Resampling and Deterministic Runtime
<i>RB</i><SUP>2</SUP> - <i>PF</i>: A Novel Filter-based Monocular Visual Odometry Algorithm
Zhou, Y., & Maskell, S. (2017). <i>RB</i><SUP>2</SUP> - <i>PF</i>: A Novel Filter-based Monocular Visual Odometry Algorithm. In 2017 20TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION) (pp. 897-904). Retrieved from https://www.webofscience.com/
An intersection-centric auction-based traffic signal control framework
Raphael, J., Sklar, E. I., & Maskell, S. (2017). An Intersection-Centric Auction-Based Traffic Signal Control Framework. In AGENT-BASED MODELING OF SUSTAINABLE BEHAVIORS (pp. 121-142). doi:10.1007/978-3-319-46331-5_6
Estimating the Pertinent Information Present in Social Media, not just what an Algorithm Detects
Maskell, S., Sloane, R., Perkins, S., Heap, J., Hajne, J., Jones, A., & Pirmohamed, M. (2017). Estimating the Pertinent Information Present in Social Media, not just what an Algorithm Detects. Poster session presented at the meeting of Unknown Conference. Retrieved from https://www.webofscience.com/
Looking Longitudinally in Twitter: Reading More than 140 Characters
Maskell, S., Sloane, R., Hajne, J., Heap, J., Perkins, S., Griffith, E., . . . Pirmohamed, M. (2017). Looking Longitudinally in Twitter: Reading More than 140 Characters. Poster session presented at the meeting of Unknown Conference. Retrieved from https://www.webofscience.com/
MapReduce Particle Filtering with Exact Resampling and Deterministic Runtime.
Thiyagalingam, J., Kekempanos, L., & Maskell, S. (2017). MapReduce Particle Filtering with Exact Resampling and Deterministic Runtime. [Artefact].
Parallelising Particle Filters with Deterministic Runtime on Distributed Memory Systems
Varsi, A., Kekempanos, L., Thiyagalingam, J., & Maskell, S. (2017). Parallelising Particle Filters with Deterministic Runtime on Distributed Memory Systems. In IET 3rd International Conference on Intelligent Signal Processing (ISP 2017) (pp. 11 (10 .)). Institution of Engineering and Technology. doi:10.1049/cp.2017.0357
2016
Langevin Incremental Mixture Importance Sampling
Geometric Separation of Superimposed Images
Mehta, M. M., Griffith, E. J., Maskell, S., & Ralph, J. F. (2016). Geometric Separation of Superimposed Images. In 2016 19TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION) (pp. 1244-1251). Retrieved from https://www.webofscience.com/
Using a Bayesian Model for Confidence to Make Decisions that Consider Epistemic Regret
Anderson, R., Hare, N., & Maskell, S. (2016). Using a Bayesian Model for Confidence to Make Decisions that Consider Epistemic Regret. In 2016 19TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION) (pp. 264-269). Retrieved from https://www.webofscience.com/
Efficient estimation of probability of conflict between air traffic using Subset Simulation
Accurate Admission Transcriptomic Signature of the Severity of Acute Pancreatitis
Nunes, Q. M., Lane, B., Huang, W., Altaf, K., Rainbow, L., Armstrong, J., . . . Sutton, R. (2016). Accurate Admission Transcriptomic Signature of the Severity of Acute Pancreatitis. In PANCREAS (Vol. 45, Iss. 10, pp. 1530). Retrieved from https://www.webofscience.com/
An empirical investigation of adaptive traffic control parameters
Raphael, J., Sklar, E. I., & Maskell, S. (2016). An empirical investigation of adaptive traffic control parameters. In CEUR Workshop Proceedings Vol. 1678.
Parameter estimation from big data using a sequential monte carlo sampler
Green, P. L., & Maskell, S. (2016). Parameter estimation from big data using a sequential monte carlo sampler. In PROCEEDINGS OF ISMA2016 INTERNATIONAL CONFERENCE ON NOISE AND VIBRATION ENGINEERING AND USD2016 INTERNATIONAL CONFERENCE ON UNCERTAINTY IN STRUCTURAL DYNAMICS (pp. 4111-4119). Retrieved from https://www.webofscience.com/
2015
Stochastic Particle Flow for Nonlinear High-Dimensional Filtering Problems
Social media and pharmacovigilance: A review of the opportunities and challenges
Sloane, R., Osanlou, O., Lewis, D., Bollegala, D., Maskell, S., & Pirmohamed, M. (2015). Social media and pharmacovigilance: A review of the opportunities and challenges. BRITISH JOURNAL OF CLINICAL PHARMACOLOGY, 80(4), 910-920. doi:10.1111/bcp.12717
Datasets reflecting students' and teachers' views on the use of learning technology in a UK university
Limniou, M., Downes, J. J., & Maskell, S. (2015). Datasets reflecting students' and teachers' views on the use of learning technology in a UK university. BRITISH JOURNAL OF EDUCATIONAL TECHNOLOGY, 46(5), 1081-1091. doi:10.1111/bjet.12332
First Steps Toward an Auction-Based Traffic Signal Controller
Raphael, J., Maskell, S., & Sklar, E. (2015). First Steps Toward an Auction-Based Traffic Signal Controller. In ADVANCES IN PRACTICAL APPLICATIONS OF AGENTS, MULTI-AGENT SYSTEMS, AND SUSTAINABILITY Vol. 9086 (pp. 300-303). doi:10.1007/978-3-319-18944-4_32
From Goods to Traffic: First Steps Toward an Auction-Based Traffic Signal Controller
Raphael, J., Maskell, S., & Sklar, E. (2015). From Goods to Traffic: First Steps Toward an Auction-Based Traffic Signal Controller. In ADVANCES IN PRACTICAL APPLICATIONS OF AGENTS, MULTI-AGENT SYSTEMS, AND SUSTAINABILITY Vol. 9086 (pp. 187-198). doi:10.1007/978-3-319-18944-4_16
2014
Probabilistic graphical detector fusion for localization of faces and facial parts
Liu, C. Y., Zhou, Y., de Melo, F., & Maskell, S. (2014). Probabilistic graphical detector fusion for localization of faces and facial parts. In 2014 Sensor Data Fusion: Trends, Solutions, Applications (SDF) (pp. 1-6). IEEE. doi:10.1109/sdf.2014.6954708
Efficient Data Structures for Large Scale Tracking
Lane, R. O., Briers, M., Cooper, T. M., & Maskell, S. R. (2014). Efficient Data Structures for Large Scale Tracking. In 2014 17TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION). Retrieved from https://www.webofscience.com/
Hybrid Gauss-Hermite Filter
de Melo, F. E., & Maskell, S. (2014). Hybrid Gauss-Hermite Filter. In IET Conference on Data Fusion & Target Tracking 2014: Algorithms and Applications (pp. 4.1). Institution of Engineering and Technology. doi:10.1049/cp.2014.0530
2013
Optimised Proposals for Improved Propagation of Multi-Modal Distributions in Particle Filters
Maskell, S., & Julier, S. (2013). Optimised Proposals for Improved Propagation of Multi-Modal Distributions in Particle Filters. In 2013 16TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION) (pp. 296-303). Retrieved from https://www.webofscience.com/
Articulated human body parts detection based on cluster background subtraction and foreground matching
Bhaskar, H., Mihaylova, L., & Maskell, S. (2013). Articulated human body parts detection based on cluster background subtraction and foreground matching. Neurocomputing, 100, 58-73. doi:10.1016/j.neucom.2011.12.039
2012
Robust background subtraction for automated detection and tracking of targets in wide area motion imagery
Kent, P., Maskell, S., Payne, O., Richardson, S., & Scarff, L. (2012). Robust background subtraction for automated detection and tracking of targets in wide area motion imagery. In C. Lewis, & D. Burgess (Eds.), SPIE Proceedings Vol. 8546 (pp. 85460Q). SPIE. doi:10.1117/12.965300
Maneuvering target tracking using an unbiased nearly constant heading model
Kountouriotis, P. A., & Maskell, S. (2012). Maneuvering target tracking using an unbiased nearly constant heading model. In 15th International Conference on Information Fusion, FUSION 2012 (pp. 2249-2255).
An application of sequential Monte Carlo samplers: an alternative to particle filters for non-linear non-gaussian sequential inference with zero process noise
Maskell, S. (2012). An application of sequential Monte Carlo samplers: an alternative to particle filters for non-linear non-gaussian sequential inference with zero process noise. In 9th IET Data Fusion & Target Tracking Conference (DF&TT 2012): Algorithms & Applications (pp. 13). IET. doi:10.1049/cp.2012.0413
2011
Group Object Structure and State Estimation With Evolving Networks and Monte Carlo Methods
Gning, A., Mihaylova, L., Maskell, S., Pang, S. K., & Godsill, S. (2011). Group Object Structure and State Estimation With Evolving Networks and Monte Carlo Methods. IEEE Transactions on Signal Processing, 59(4), 1383-1396. doi:10.1109/tsp.2010.2103062
2010
Welcome to fusion 2010, the 13th International conference on information fusion and welcome to Edinburgh
Maskell, S. (2010). Welcome to fusion 2010, the 13th International conference on information fusion and welcome to Edinburgh. In 13th Conference on Information Fusion, Fusion 2010 (pp. 4-5).
Smoothing algorithms for state–space models
Briers, M., Doucet, A., & Maskell, S. (2010). Smoothing algorithms for state–space models. Annals of the Institute of Statistical Mathematics, 62(1), 61-89. doi:10.1007/s10463-009-0236-2
A Bayesian approach to joint tracking and identification of geometric shapes in video sequences
Minvielle, P., Doucet, A., Marrs, A., & Maskell, S. (2010). A Bayesian approach to joint tracking and identification of geometric shapes in video sequences. Image and Vision Computing, 28(1), 111-123. doi:10.1016/j.imavis.2009.05.002
Fusion of data from sources with different levels of trust
Nevell, D. A., Maskell, S. R., Horridge, P. R., & Barnett, H. L. (2010). Fusion of data from sources with different levels of trust. In 2010 13th International Conference on Information Fusion (pp. 1-7). IEEE. doi:10.1109/icif.2010.5711842
2009
A scalable method of tracking targets with dependent distributions
Horridge, P., & Maskell, S. (2009). A scalable method of tracking targets with dependent distributions. In 2009 12th International Conference on Information Fusion, FUSION 2009 (pp. 603-610).
Searching for, initiating and tracking multiple targets using existence probabilities
Horridge, P., & Maskell, S. (2009). Searching for, initiating and tracking multiple targets using existence probabilities. In 2009 12th International Conference on Information Fusion, FUSION 2009 (pp. 611-617).
Statistical Methods for Target Tracking
Maskell, S. (n.d.). Statistical Methods for Target Tracking. In Unknown Book (pp. 2820-2829). Wiley. doi:10.1002/9780470050118.ecse645
2008
Evolving networks for group object motion estimation
Gning, A., Mihaylova, L., Maskell, S., Sze Kim Pang., & Godsill, S. (2008). Evolving networks for group object motion estimation. In IET Seminar on Target Tracking and Data Fusion: Algorithms and Applications (pp. 97-106). IEE. doi:10.1049/ic:20080061
Ground target group structure and state estimation with particle filtering
Gning, A., Mihaylova, L., Maskell, S., Sze, K. P., & Godsill, S. (2008). Ground target group structure and state estimation with particle filtering. In Proceedings of the 11th International Conference on Information Fusion, FUSION 2008. doi:10.1109/ICIF.2008.4632343
Human body parts tracking using pictorial structures and a genetic algorithm
Bhaskar, H., Mihaylova, L., & Maskell, S. (2008). Human body parts tracking using pictorial structures and a genetic algorithm. In 2008 4th International IEEE Conference Intelligent Systems. IEEE. doi:10.1109/is.2008.4670489
Population based particle filtering
Bhaskar, H., Mihaylova, L., & Maskell, S. (2008). Population based particle filtering. In IET Seminar on Target Tracking and Data Fusion: Algorithms and Applications (pp. 29-38). IEE. doi:10.1049/ic:20080054
Tracking with inter-visibility variables
Horridge, P., & Maskell, S. (2008). Tracking with inter-visibility variables. In IET Seminar on Target Tracking and Data Fusion: Algorithms and Applications (pp. 59-68). IEE. doi:10.1049/ic:20080057
Using ship tracking methods to assist in quality controlling and bias adjusting meteorological observations in a marine environment
Hill, J. G. T., Maskelf, S. R., & Cole, M. (2008). Using ship tracking methods to assist in quality controlling and bias adjusting meteorological observations in a marine environment. In IET Seminar on Target Tracking and Data Fusion: Algorithms and Applications (pp. 167-174). IEE. doi:10.1049/ic:20080069
A Bayesian approach to fusing uncertain, imprecise and conflicting information
Maskell, S. (2008). A Bayesian approach to fusing uncertain, imprecise and conflicting information. Information Fusion, 9(2), 259-277. doi:10.1016/j.inffus.2007.02.003
2007
Background modeling using adaptive cluster density estimation for automatic human detection
Bhaskar, H., Mihaylova, L., & Maskell, S. (2007). Background modeling using adaptive cluster density estimation for automatic human detection. In INFORMATIK 2007 - Informatik Trifft Logistik, Beitrage der 37. Jahrestagung der Gesellschaft fur Informatik e.V. (GI) Vol. 2 (pp. 130-134).
A Tutorial on Particle Filters for Online Nonlinear/NonGaussian Bayesian Tracking
A Tutorial on Particle Filters for Online Nonlinear/NonGaussian Bayesian Tracking (2009). In Bayesian Bounds for Parameter Estimation and Nonlinear Filtering/Tracking. IEEE. doi:10.1109/9780470544198.ch73
2006
Distributed tracking of stealthy targets using particle filters
Maskell, S. R., Weekes, K. R., & Briers, M. (2006). Distributed tracking of stealthy targets using particle filters. In IEE Seminar on Target Tracking: Algorithms and Applications (pp. 17-26). IEE. doi:10.1049/ic:20060553
Fast particle smoothing
Klaas, M., Briers, M., de Freitas, N., Doucet, A., Maskell, S., & Lang, D. (2006). Fast particle smoothing. In Proceedings of the 23rd international conference on Machine learning - ICML '06 (pp. 481-488). ACM Press. doi:10.1145/1143844.1143905
Joint Tracking and Classification of Airbourne Objects using Particle Filters and the Continuous Transferable Belief Model
Powell, G., Marshall, D., Smets, P., Ristic, B., & Maskell, S. (2006). Joint Tracking and Classification of Airbourne Objects using Particle Filters and the Continuous Transferable Belief Model. In 2006 9th International Conference on Information Fusion (pp. 1-8). IEEE. doi:10.1109/icif.2006.301718
Real-Time Tracking Of Hundreds Of Targets With Efficient Exact JPDAF Implementation
Horridge, P., & Maskell, S. (2006). Real-Time Tracking Of Hundreds Of Targets With Efficient Exact JPDAF Implementation. In 2006 9th International Conference on Information Fusion (pp. 1-8). IEEE. doi:10.1109/icif.2006.301561
Fast particle smoothing: If I had a million particles
Klaas, M., Briers, M., De Freitas, N., Doucet, A., Maskell, S., & Lang, D. (2006). Fast particle smoothing: If I had a million particles. In ICML 2006 - Proceedings of the 23rd International Conference on Machine Learning Vol. 2006 (pp. 481-488).
Fixed-lag sequential Monte Carlo data association
Briers, M., Doucet, A., Maskell, S. R., & Horridge, P. R. (2006). Fixed-lag sequential Monte Carlo data association. In O. E. Drummond (Ed.), SPIE Proceedings Vol. 6236 (pp. 62360S). SPIE. doi:10.1117/12.665684
A Single Instruction Multiple Data Particle Filter
Maskell, S., Alun-Jones, B., & Macleod, M. (2006). A Single Instruction Multiple Data Particle Filter. In 2006 IEEE Nonlinear Statistical Signal Processing Workshop (pp. 51-54). IEEE. doi:10.1109/nsspw.2006.4378818
Multi-target out-of-sequence data association: Tracking using graphical models
Maskell, S. R., Everitt, R. G., Wright, R., & Briers, M. (2006). Multi-target out-of-sequence data association: Tracking using graphical models. Information Fusion, 7(4), 434-447. doi:10.1016/j.inffus.2005.07.001
2005
Bayesian visual tracking with existence process
Vermaak, J., Maskell, S., Briers, M., & Perez, P. (2005). Bayesian visual tracking with existence process. In IEEE International Conference on Image Processing 2005 (pp. I-721). IEEE. doi:10.1109/icip.2005.1529852
How to best track a crowd?
Maskell, S., Briers, M., Everitt, R., & Horridge, P. (2005). How to best track a crowd?. In Proceedings of SPIE - The International Society for Optical Engineering Vol. 5809 (pp. 590-593).
Recursive track-before-detect with target amplitude fluctuations
Rutten, M. G., Gordon, N. J., & Maskell, S. (2005). Recursive track-before-detect with target amplitude fluctuations. In IEE Proceedings - Radar, Sonar and Navigation Vol. 152 (pp. 345). Institution of Engineering and Technology (IET). doi:10.1049/ip-rsn:20045041
Tracking using a radar and a problem specific proposal distribution in a particle filter
Maskell, S., Briers, M., Wright, R., & Horridge, P. (2005). Tracking using a radar and a problem specific proposal distribution in a particle filter. In IEE Proceedings - Radar, Sonar and Navigation Vol. 152 (pp. 315). Institution of Engineering and Technology (IET). doi:10.1049/ip-rsn:20045033
Poisson models for extended target and group tracking
Gilholm, K., Godsill, S., Maskell, S., & Salmond, D. (2005). Poisson models for extended target and group tracking. In O. E. Drummond (Ed.), SPIE Proceedings. SPIE. doi:10.1117/12.618730
A comparison of the particle and shifted Rayleigh filters in their application to a multisensor bearings-only problem
Clark, M., Maskell, S., Vinter, R., & Yaqoob, M. (2005). A comparison of the particle and shifted Rayleigh filters in their application to a multisensor bearings-only problem. In 2005 IEEE Aerospace Conference (pp. 2142-2147). IEEE. doi:10.1109/aero.2005.1559505
A unifying framework for multi-target tracking and existence
Vermaak, J., Maskell, S., & Briers, M. (2005). A unifying framework for multi-target tracking and existence. In 2005 7th International Conference on Information Fusion (pp. 9 pp.). IEEE. doi:10.1109/icif.2005.1591862
Joint target tracking and identification-Part I: sequential Monte Carlo model-based approaches
Minvielle, P., Marrs, A. D., Maskell, S., & Doucet, A. (2005). Joint target tracking and identification-Part I: sequential Monte Carlo model-based approaches. In 2005 7th International Conference on Information Fusion (pp. 8 pp.). IEEE. doi:10.1109/icif.2005.1591863
Joint target tracking and identification. Part II. Shape video computing
Minvielle, P., Marrs, A. D., Maskell, S., & Doucet, A. (2005). Joint target tracking and identification. Part II. Shape video computing. In 2005 7th International Conference on Information Fusion (pp. 8 pp.). IEEE. doi:10.1109/icif.2005.1591864
Online sensor registration
Vermaak, J., Maskell, S., & Briers, M. (2005). Online sensor registration. In 2005 IEEE Aerospace Conference (pp. 2117-2125). IEEE. doi:10.1109/aero.2005.1559503
2004
Efficient particle-based track-before-detect in rayleigh noise
Rutten, M. G., Gordon, N. J., & Maskell, S. (2004). Efficient particle-based track-before-detect in rayleigh noise. In Proceedings of the Seventh International Conference on Information Fusion, FUSION 2004 Vol. 2 (pp. 693-700).
Multi-target out-of-sequence data association
Maskell, S., Everitt, R., Wright, R., & Briers, M. (2004). Multi-target out-of-sequence data association. In Proceedings of the Seventh International Conference on Information Fusion, FUSION 2004 Vol. 2 (pp. 1044-1051).
Joint Tracking of Manoeuvring Targets and Classification of Their Manoeuvrability
Maskell, S. (n.d.). Joint Tracking of Manoeuvring Targets and Classification of Their Manoeuvrability. EURASIP Journal on Advances in Signal Processing, 2004(15). doi:10.1155/s1110865704404223
<title>Fast mutual exclusion</title>
Maskell, S., Briers, M., & Wright, R. (2004). <title>Fast mutual exclusion</title>. In O. E. Drummond (Ed.), SPIE Proceedings Vol. 5428 (pp. 526-536). SPIE. doi:10.1117/12.542068
<title>Multipath track association for over-the-horizon radar using Lagrangian relaxation</title>
Rutten, M. G., Maskell, S., Briers, M., & Gordon, N. J. (2004). <title>Multipath track association for over-the-horizon radar using Lagrangian relaxation</title>. In O. E. Drummond (Ed.), SPIE Proceedings. SPIE. doi:10.1117/12.541276
<title>Particle-based track-before-detect in Rayleigh noise</title>
Rutten, M. G., Gordon, N. J., & Maskell, S. (2004). <title>Particle-based track-before-detect in Rayleigh noise</title>. In O. E. Drummond (Ed.), SPIE Proceedings Vol. 5428 (pp. 509-519). SPIE. doi:10.1117/12.541275
<title>Tracking maneuvering targets using a scale mixture of normals</title>
Maskell, S., Gordon, N. J., Everett, N., & Robinson, M. (2004). <title>Tracking maneuvering targets using a scale mixture of normals</title>. In O. E. Drummond (Ed.), SPIE Proceedings Vol. 5428 (pp. 134-144). SPIE. doi:10.1117/12.542071
An introduction to particle filters
Maskell, S. (2004). An introduction to particle filters. In State Space and Unobserved Component Models (pp. 40-72). Cambridge University Press (CUP). doi:10.1017/cbo9780511617010.004
An introduction to particle filters
Maskell, S. (2004). An introduction to particle filters. In State Space and Unobserved Component Models (pp. 40-72). Cambridge University Press. doi:10.1017/cbo9780511617010.004
Distribution in a particle filter
Maskell, S. (2004). Distribution in a particle filter. In Target Tracking 2004: Algorithms and Applications Vol. 2004 (pp. 23-31). IEE. doi:10.1049/ic:20040048
2003
<title>Two-dimensional assignment with merged measurements using Langrangrian relaxation</title>
Briers, M., Maskell, S., & Philpott, M. (2003). <title>Two-dimensional assignment with merged measurements using Langrangrian relaxation</title>. In O. E. Drummond (Ed.), SPIE Proceedings Vol. 5204 (pp. 283-292). SPIE. doi:10.1117/12.503834
Efficient particle filtering for multiple target tracking with application to tracking in structured images
Maskell, S., Rollason, M., Gordon, N., & Salmond, D. (2003). Efficient particle filtering for multiple target tracking with application to tracking in structured images. In Image and Vision Computing Vol. 21 (pp. 931-939). Elsevier BV. doi:10.1016/s0262-8856(03)00087-8
A rao-blackwellised unscented Kalman filter
Briers, M., Maskell, S. R., & Wright, R. (2003). A rao-blackwellised unscented Kalman filter. In Sixth International Conference of Information Fusion, 2003. Proceedings of the (pp. 55-61). IEEE. doi:10.1109/icif.2003.177426
2002
<title>Comparison of EKF, pseudomeasurement, and particle filters for a bearing-only target tracking problem</title>
Lin, X., Kirubarajan, T., Bar-Shalom, Y., & Maskell, S. (2002). <title>Comparison of EKF, pseudomeasurement, and particle filters for a bearing-only target tracking problem</title>. In O. E. Drummond (Ed.), SPIE Proceedings. SPIE. doi:10.1117/12.478508
<title>Efficient particle filtering for multiple target tracking with application to tracking in structured images</title>
Maskell, S., Rollason, M. P., Gordon, N. J., & Salmond, D. J. (2002). <title>Efficient particle filtering for multiple target tracking with application to tracking in structured images</title>. In O. E. Drummond (Ed.), SPIE Proceedings Vol. 4728 (pp. 251-262). SPIE. doi:10.1117/12.478509
<title>Efficient particle filters for joint tracking and classification</title>
Gordon, N. J., Maskell, S., & Kirubarajan, T. (2002). <title>Efficient particle filters for joint tracking and classification</title>. In O. E. Drummond (Ed.), SPIE Proceedings Vol. 4728 (pp. 439-449). SPIE. doi:10.1117/12.478524
<title>Expected likelihood for tracking in clutter with particle filters</title>
Marrs, A., Maskell, S., & Bar-Shalom, Y. (2002). <title>Expected likelihood for tracking in clutter with particle filters</title>. In O. E. Drummond (Ed.), SPIE Proceedings Vol. 4728 (pp. 230-239). SPIE. doi:10.1117/12.478507
A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking
Arulampalam, M. S., Maskell, S., Gordon, N., & Clapp, T. (2002). A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing, 50(2), 174-188. doi:10.1109/78.978374
2001
A tutorial on particle filters for on-line nonlinear/non-Gaussian Bayesian tracking
Maskell, S. (2001). A tutorial on particle filters for on-line nonlinear/non-Gaussian Bayesian tracking. IEE International Seminar Target Tracking: Algorithms and Applications, 2001. doi:10.1049/ic:20010246