2024
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.3258DOI: 10.1002/cpt.3258
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.2024.3386494DOI: 10.1109/lsp.2024.3386494
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.2024.3374720DOI: 10.1109/taes.2024.3374720
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.5731DOI: 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 (Conference Paper)
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). IEEE. doi:10.1109/sdf-mfi59545.2023.10361452DOI: 10.1109/sdf-mfi59545.2023.10361452
How might dynamic artificial intelligence (DynAIRx) be used to support prescribing to ensure efficient medication reviews? (Conference Paper)
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?. In Healthcare services, delivery, and financing. American Academy of Family Physicians. doi:10.1370/afm.22.s1.4823DOI: 10.1370/afm.22.s1.4823
Trustworthy Data and AI Environments for Clinical Prediction: Application to Crisis-Risk in People With Depression. (Journal article)
Msosa, Y. J., Grauslys, A., Zhou, Y., Wang, T., Buchan, I. E., Langan, P., . . . Kehoe, D. (2023). Trustworthy Data and AI Environments for Clinical Prediction: Application to Crisis-Risk in People With Depression.. IEEE J. Biomed. Health Informatics, 27, 5588-5598.
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_22DOI: 10.1007/978-3-031-44505-7_22
A Shared Memory SMC Sampler for Decision Trees (Conference Paper)
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). IEEE. doi:10.1109/sbac-pad59825.2023.00030DOI: 10.1109/sbac-pad59825.2023.00030
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.2023.3324908DOI: 10.1109/taes.2023.3324908
Data-Driven Clustering and Bernoulli Merging for the Poisson Multi-Bernoulli Mixture Filter. (Journal article)
Fontana, M., García-Fernández, Á. F., & Maskell, S. (2023). Data-Driven Clustering and Bernoulli Merging for the Poisson Multi-Bernoulli Mixture Filter.. IEEE Trans. Aerosp. Electron. Syst., 59, 5287-5301.
Fontana, M., Garcia-Fernandez, A. F., & Maskell, S. (2023). Data-driven clustering and Bernoulli merging for the Poisson multi-Bernoulli mixture filter. IEEE Transactions on Aerospace and Electronic Systems, 1-14. doi:10.1109/taes.2023.3253662DOI: 10.1109/taes.2023.3253662
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.2023.3312011DOI: 10.1109/jbhi.2023.3312011
Protecting Children from Online Exploitation: Can a Trained Model Detect Harmful Communication Strategies? (Conference Paper)
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. ACM. doi:10.1145/3600211.3604696DOI: 10.1145/3600211.3604696
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/ozad067.764DOI: 10.1093/micmic/ozad067.764
Burnside, G., Cheyne, C. P., Leeming, G., Humann, M., Darby, A., Green, M. A., . . . Buchan, I. E. (2023). 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:10.1177/01410768231182389DOI: 10.1177/01410768231182389
Anastassiou, L., Ralph, J. F., Maskell, S., & Kok, P. (2023). Bayesian estimation for Bell state rotations. AVS Quantum Science, 5(2). doi:10.1116/5.0147878DOI: 10.1116/5.0147878
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_40DOI: 10.1007/978-3-031-34111-3_40
Repeated Filtering for Smoothing Particle Filters (Journal article)
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.
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.5609DOI: 10.1002/pds.5609
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/info14030170DOI: 10.3390/info14030170
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/rsn2.12347DOI: 10.1049/rsn2.12347
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.2023.3253039DOI: 10.1109/LSP.2023.3253039
Defending the unknown: Exploring reinforcement learning agents' deployment in realistic, unseen networks (Conference Paper)
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 (Conference Paper)
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/SSPD57945.2023.10256991DOI: 10.1109/SSPD57945.2023.10256991
2022
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.2022.994459DOI: 10.3389/fphy.2022.994459
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.2021.0305DOI: 10.1098/rsta.2021.0305
The Design and Operation of a New Relativistic Ultrafast Electron Diffraction and Imaging (RUEDI) National Facility in the UK (Journal article)
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/s1431927622010406DOI: 10.1017/s1431927622010406
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 (Conference Paper)
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 (Conference Paper)
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/
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/
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.
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 (Conference Paper)
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:10.1117/12.2618495DOI: 10.1117/12.2618495
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:10.1116/5.0095677DOI: 10.1116/5.0095677
Similarity and Consistency Assessment of Three Major Online Drug-Drug Interaction Resources (Journal article)
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.15341DOI: 10.1111/bcp.15341
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-yDOI: 10.1038/s41597-022-01159-y
Ensemble Kalman filter based sequential Monte Carlo sampler for sequential Bayesian inference (Journal article)
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-xDOI: 10.1007/s11222-021-10075-x
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
Maskell, S., Zhou, Y., & Mira, A. (2022). Control Variates for Constrained Variables. IEEE SIGNAL PROCESSING LETTERS, 29, 2333-2337. doi:10.1109/LSP.2022.3221347DOI: 10.1109/LSP.2022.3221347
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.18175.1DOI: 10.12688/wellcomeopenres.18175.1
Information fusion and tracking using Bernoulli filters for maritime surveillance (Conference Paper)
Ransom, M. J., Ralph, J. F., & Maskell, S. (2022). Information fusion and tracking using Bernoulli filters for maritime surveillance. In International Conference on Radar Systems (RADAR 2022). Institution of Engineering and Technology. doi:10.1049/icp.2022.2364DOI: 10.1049/icp.2022.2364
Optimizing sonobuoy placement using multiobjective machine learning (Conference Paper)
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). IEEE. doi:10.1109/sspd54131.2022.9896216DOI: 10.1109/sspd54131.2022.9896216
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:10.1177/26335565221145493DOI: 10.1177/26335565221145493
2021
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/a14120342DOI: 10.3390/a14120342
An analysis on metric-driven multi-target sensor management: GOSPA versus OSPA (Conference Paper)
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). IEEE. doi:10.23919/fusion49465.2021.9626837DOI: 10.23919/fusion49465.2021.9626837
Classical Tracking for Quantum Trajectories (Conference Paper)
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). IEEE. doi:10.23919/fusion49465.2021.9626966DOI: 10.23919/fusion49465.2021.9626966
Posterior Cramér-Rao Bounds for Tracking Intermittently Visible Targets in Clutter (Conference Paper)
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). IEEE. doi:10.23919/fusion49465.2021.9626856DOI: 10.23919/fusion49465.2021.9626856
SMC samplers for Bayesian Optimisation and Discovery of Additive Kernel Structure (Conference Paper)
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). IEEE. doi:10.23919/fusion49465.2021.9626877DOI: 10.23919/fusion49465.2021.9626877
Track-before-detect Bernoulli filters for combining passive and active sensors (Conference Paper)
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). IEEE. doi:10.23919/fusion49465.2021.9626922DOI: 10.23919/fusion49465.2021.9626922
A Gaussian Filtering Method for Multitarget Tracking With Nonlinear/Non-Gaussian Measurements (Journal article)
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.2021.3074200DOI: 10.1109/TAES.2021.3074200
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/SSPD51364.2021.9541502DOI: 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 (Preprint)
DOI: 10.48550/arxiv.2108.02498
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.2021.3086558DOI: 10.1109/TVT.2021.3086558
Increasing the efficiency of Sequential Monte Carlo samplers through the use of approximately optimal L-kernels (Journal article)
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.2021.108028DOI: 10.1016/j.ymssp.2021.108028
A Gaussian Filtering Method for Multitarget Tracking With Nonlinear/Non-Gaussian Measurements. (Journal article)
García-Fernández, Á. F., Ralph, J. F., Horridge, P. R., & Maskell, S. (2021). A Gaussian Filtering Method for Multitarget Tracking With Nonlinear/Non-Gaussian Measurements.. IEEE Trans. Aerosp. Electron. Syst., 57, 3539-3548.
A Psychology-Driven Computational Analysis of Political Interviews (Conference Paper)
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.2021-2249DOI: 10.21437/Interspeech.2021-2249
A Psychology-Driven Computational Analysis of Political Interviews. (Conference Paper)
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 (Conference Paper)
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 (Conference Paper)
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 (Conference Paper)
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_19DOI: 10.1007/978-3-030-80421-3_19
Efficient Learning of the Parameters of Non-Linear Models using Differentiable Resampling in Particle Filters. (Journal article)
Rosato, C., Horridge, P. R., Schön, T. B., & Maskell, S. (2021). Efficient Learning of the Parameters of Non-Linear Models using Differentiable Resampling in Particle Filters.. CoRR, abs/2111.01409.
Posterior Cramer-Rao Bounds for Tracking Intermittently Visible Targets in Clutter (Conference Paper)
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 (Conference Paper)
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 (Conference Paper)
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
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/IROS45743.2020.9340720DOI: 10.1109/IROS45743.2020.9340720
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:10.1117/12.2583999DOI: 10.1117/12.2583999
Welcome to the first issue of <scp><i>Applied AI Letters</i></scp> (Journal article)
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/ail2.8DOI: 10.1002/ail2.8
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.2020.3014035DOI: 10.1109/LSP.2020.3014035
A SMC Sampler for Joint Tracking and Destination Estimation from Noisy Data (Conference Paper)
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/
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). IEEE. doi:10.23919/fusion45008.2020.9190443DOI: 10.23919/fusion45008.2020.9190443
Continuous-discrete trajectory PHD and CPHD filters (Conference Paper)
Garcia-Fernandez, A. F., & Maskell, S. (2020). Continuous-discrete trajectory PHD and CPHD filters. In 2020 IEEE 23rd International Conference on Information Fusion (FUSION). IEEE. doi:10.23919/fusion45008.2020.9190298DOI: 10.23919/fusion45008.2020.9190298
Integrated Expected Likelihood Particle Filters (Conference Paper)
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). IEEE. doi:10.23919/fusion45008.2020.9190387DOI: 10.23919/fusion45008.2020.9190387
Robust and Efficient Image Alignment Method Using the Student-t Distribution (Conference Paper)
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 (Preprint)
DOI: 10.48550/arxiv.2004.12838
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/icra40945.2020.9196932DOI: 10.1109/icra40945.2020.9196932
Continuous-discrete multiple target filtering: PMBM, PHD and CPHD filter implementations (Journal article)
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.2020.2968247DOI: 10.1109/TSP.2020.2968247
Bernoulli merging for the Poisson multi-Bernoulli mixture filter (Conference Paper)
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 (Conference Paper)
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/fusion45008.2020.9190298DOI: 10.23919/fusion45008.2020.9190298
Integrated Expected Likelihood Particle Filters (Conference Paper)
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 (Conference Paper)
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. SCITEPRESS - Science and Technology Publications. doi:10.5220/0009354500002550DOI: 10.5220/0009354500002550
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:10.5220/0009354503310341DOI: 10.5220/0009354503310341
2019
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.2019.2899714DOI: 10.1109/TAES.2019.2899714
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/s40264-019-00858-7DOI: 10.1007/s40264-019-00858-7
Detecting and Tracking Small Moving Objects in Wide Area Motion Imagery (WAMI) Using Convolutional Neural Networks (CNNs) (Preprint)
DOI: 10.48550/arxiv.1911.01727
A Generic Anomaly Detection Approach Applied to Mixture-of-unigrams and Maritime Surveillance Data (Conference Paper)
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.2019.8916633DOI: 10.1109/sdf.2019.8916633
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:10.1177/1555343419855850DOI: 10.1177/1555343419855850
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/fusion43075.2019.9011278DOI: 10.23919/fusion43075.2019.9011278
Detecting and Tracking Small Moving Objects in Wide Area Motion Imagery (WAMI) Using Convolutional Neural Networks (CNNs) (Conference Paper)
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/fusion43075.2019.9011271DOI: 10.23919/fusion43075.2019.9011271
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
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/s40264-019-00813-6DOI: 10.1007/s40264-019-00813-6
De Melo, F., & Maskell, S. (n.d.). A CPHD approximation based on a discrete-Gamma cardinality model. IEEE Transactions on Signal Processing.DOI: 10.1109/TSP.2018.2881659
2018
Convolutional Neural Networks for Aerial Vehicle Detection and Recognition (Conference Paper)
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/
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/s40264-018-0699-2DOI: 10.1007/s40264-018-0699-2
Comparing interrelationships between features and embedding methods for multiple-view fusion (Conference Paper)
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 (Conference Paper)
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/
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
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.8214DOI: 10.2196/publichealth.8214
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.2018.03.003DOI: 10.1016/j.simpat.2018.03.003
Langevin Incremental Mixture Importance Sampling (Journal article)
Fasiolo, M., de melo, F., & Maskell, S. (2018). Langevin Incremental Mixture Importance Sampling. Statistics and Computing, 28, 549-561. doi:10.1007/s11222-017-9747-5DOI: 10.1007/s11222-017-9747-5
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 (Conference Paper)
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/
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.98.010102DOI: 10.1103/PhysRevA.98.010102
Multiparameter estimation along quantum trajectories with sequential Monte Carlo methods (Journal article)
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.96.052306DOI: 10.1103/PhysRevA.96.052306
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/s13634-017-0505-9DOI: 10.1186/s13634-017-0505-9
Beyond co-occurrence-based ADR detection from Social Media (Poster)
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/
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 (Journal article)
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.2016.12.023DOI: 10.1016/j.ymssp.2016.12.023
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 (Conference Paper)
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). IEEE. doi:10.23919/icif.2017.8009745DOI: 10.23919/icif.2017.8009745
Causality Patterns for Detecting Adverse Drug Reactions From Social Media: Text Mining Approach (Preprint) (Journal article)
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.8214DOI: 10.2196/preprints.8214
<i>RB</i><SUP>2</SUP> - <i>PF</i>: A Novel Filter-based Monocular Visual Odometry Algorithm (Conference Paper)
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 (Chapter)
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_6DOI: 10.1007/978-3-319-46331-5_6
Estimating the Pertinent Information Present in Social Media, not just what an Algorithm Detects (Poster)
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 (Poster)
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/
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 (Conference Paper)
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). Institution of Engineering and Technology. doi:10.1049/cp.2017.0357DOI: 10.1049/cp.2017.0357
2016
Geometric Separation of Superimposed Images (Conference Paper)
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 (Conference Paper)
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/
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 (Conference Paper)
Raphael, J., Sklar, E. I., & Maskell, S. (2016). An empirical investigation of adaptive traffic control parameters. In CEUR Workshop Proceedings Vol. 1678.
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
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.12717DOI: 10.1111/bcp.12717
Datasets reflecting students' and teachers' views on the use of learning technology in a UK university (Journal article)
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.12332DOI: 10.1111/bjet.12332
First Steps Toward an Auction-Based Traffic Signal Controller (Conference Paper)
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_32DOI: 10.1007/978-3-319-18944-4_32
From Goods to Traffic: First Steps Toward an Auction-Based Traffic Signal Controller (Conference Paper)
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_16DOI: 10.1007/978-3-319-18944-4_16
2014
Probabilistic graphical detector fusion for localization of faces and facial parts (Conference Paper)
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). IEEE. doi:10.1109/sdf.2014.6954708DOI: 10.1109/sdf.2014.6954708
Efficient Data Structures for Large Scale Tracking (Conference Paper)
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 (Conference Paper)
de Melo, F. E., & Maskell, S. (2014). Hybrid Gauss-Hermite Filter. In IET Conference on Data Fusion & Target Tracking 2014: Algorithms and Applications. Institution of Engineering and Technology. doi:10.1049/cp.2014.0530DOI: 10.1049/cp.2014.0530
2013
Optimised Proposals for Improved Propagation of Multi-Modal Distributions in Particle Filters (Conference Paper)
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 (Journal article)
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.2011.12.039DOI: 10.1016/j.neucom.2011.12.039
2012
Robust background subtraction for automated detection and tracking of targets in wide area motion imagery (Conference Paper)
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. SPIE. doi:10.1117/12.965300DOI: 10.1117/12.965300
Maneuvering target tracking using an unbiased nearly constant heading model (Conference Paper)
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 (Conference Paper)
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. IET. doi:10.1049/cp.2012.0413DOI: 10.1049/cp.2012.0413
2011
Group Object Structure and State Estimation With Evolving Networks and Monte Carlo Methods (Journal article)
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.2010.2103062DOI: 10.1109/tsp.2010.2103062
2010
Welcome to fusion 2010, the 13th International conference on information fusion and welcome to Edinburgh (Conference Paper)
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 (Journal article)
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/s10463-009-0236-2DOI: 10.1007/s10463-009-0236-2
A Bayesian approach to joint tracking and identification of geometric shapes in video sequences (Journal article)
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.2009.05.002DOI: 10.1016/j.imavis.2009.05.002
Fusion of data from sources with different levels of trust (Conference Paper)
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. IEEE. doi:10.1109/icif.2010.5711842DOI: 10.1109/icif.2010.5711842
2009
A scalable method of tracking targets with dependent distributions (Conference Paper)
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 (Conference Paper)
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 (Chapter)
Maskell, S. (n.d.). Statistical Methods for Target Tracking. In Unknown Book (pp. 2820-2829). Wiley. doi:10.1002/9780470050118.ecse645DOI: 10.1002/9780470050118.ecse645
2008
Evolving networks for group object motion estimation (Conference Paper)
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. IEE. doi:10.1049/ic:20080061DOI: 10.1049/ic:20080061
Ground target group structure and state estimation with particle filtering (Conference Paper)
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.2008.4632343DOI: 10.1109/ICIF.2008.4632343
Human body parts tracking using pictorial structures and a genetic algorithm (Conference Paper)
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.2008.4670489DOI: 10.1109/is.2008.4670489
Population based particle filtering (Conference Paper)
Bhaskar, H., Mihaylova, L., & Maskell, S. (2008). Population based particle filtering. In IET Seminar on Target Tracking and Data Fusion: Algorithms and Applications. IEE. doi:10.1049/ic:20080054DOI: 10.1049/ic:20080054
Tracking with inter-visibility variables (Conference Paper)
Horridge, P., & Maskell, S. (2008). Tracking with inter-visibility variables. In IET Seminar on Target Tracking and Data Fusion: Algorithms and Applications. IEE. doi:10.1049/ic:20080057DOI: 10.1049/ic:20080057
Using ship tracking methods to assist in quality controlling and bias adjusting meteorological observations in a marine environment (Conference Paper)
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. IEE. doi:10.1049/ic:20080069DOI: 10.1049/ic:20080069
A Bayesian approach to fusing uncertain, imprecise and conflicting information (Journal article)
Maskell, S. (2008). A Bayesian approach to fusing uncertain, imprecise and conflicting information. Information Fusion, 9(2), 259-277. doi:10.1016/j.inffus.2007.02.003DOI: 10.1016/j.inffus.2007.02.003
2007
Background modeling using adaptive cluster density estimation for automatic human detection (Conference Paper)
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 (Chapter)
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.ch73DOI: 10.1109/9780470544198.ch73
2006
Distributed tracking of stealthy targets using particle filters (Conference Paper)
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. IEE. doi:10.1049/ic:20060553DOI: 10.1049/ic:20060553
Fast particle smoothing (Conference Paper)
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. ACM Press. doi:10.1145/1143844.1143905DOI: 10.1145/1143844.1143905
Joint Tracking and Classification of Airbourne Objects using Particle Filters and the Continuous Transferable Belief Model (Conference Paper)
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. IEEE. doi:10.1109/icif.2006.301718DOI: 10.1109/icif.2006.301718
Real-Time Tracking Of Hundreds Of Targets With Efficient Exact JPDAF Implementation (Conference Paper)
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. IEEE. doi:10.1109/icif.2006.301561DOI: 10.1109/icif.2006.301561
Fast particle smoothing: If I had a million particles (Conference Paper)
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 (Conference Paper)
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. SPIE. doi:10.1117/12.665684DOI: 10.1117/12.665684
A Single Instruction Multiple Data Particle Filter (Conference Paper)
Maskell, S., Alun-Jones, B., & Macleod, M. (2006). A Single Instruction Multiple Data Particle Filter. In 2006 IEEE Nonlinear Statistical Signal Processing Workshop. IEEE. doi:10.1109/nsspw.2006.4378818DOI: 10.1109/nsspw.2006.4378818
Multi-target out-of-sequence data association: Tracking using graphical models (Journal article)
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.2005.07.001DOI: 10.1016/j.inffus.2005.07.001
2005
Bayesian visual tracking with existence process (Conference Paper)
Vermaak, J., Maskell, S., Briers, M., & Perez, P. (2005). Bayesian visual tracking with existence process. In IEEE International Conference on Image Processing 2005. IEEE. doi:10.1109/icip.2005.1529852DOI: 10.1109/icip.2005.1529852
How to best track a crowd? (Conference Paper)
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 (Conference Paper)
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:20045041DOI: 10.1049/ip-rsn:20045041
Tracking using a radar and a problem specific proposal distribution in a particle filter (Conference Paper)
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:20045033DOI: 10.1049/ip-rsn:20045033
Poisson models for extended target and group tracking (Conference Paper)
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:10.1117/12.618730DOI: 10.1117/12.618730
A comparison of the particle and shifted Rayleigh filters in their application to a multisensor bearings-only problem (Conference Paper)
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. IEEE. doi:10.1109/aero.2005.1559505DOI: 10.1109/aero.2005.1559505
A unifying framework for multi-target tracking and existence (Conference Paper)
Vermaak, J., Maskell, S., & Briers, M. (2005). A unifying framework for multi-target tracking and existence. In 2005 7th International Conference on Information Fusion. IEEE. doi:10.1109/icif.2005.1591862DOI: 10.1109/icif.2005.1591862
Joint target tracking and identification-Part I: sequential Monte Carlo model-based approaches (Conference Paper)
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. IEEE. doi:10.1109/icif.2005.1591863DOI: 10.1109/icif.2005.1591863
Joint target tracking and identification. Part II. Shape video computing (Conference Paper)
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. IEEE. doi:10.1109/icif.2005.1591864DOI: 10.1109/icif.2005.1591864
Online sensor registration (Conference Paper)
Vermaak, J., Maskell, S., & Briers, M. (2005). Online sensor registration. In 2005 IEEE Aerospace Conference. IEEE. doi:10.1109/aero.2005.1559503DOI: 10.1109/aero.2005.1559503
2004
Efficient particle-based track-before-detect in rayleigh noise (Conference Paper)
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 (Conference Paper)
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 (Journal article)
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/s1110865704404223DOI: 10.1155/s1110865704404223
<title>Fast mutual exclusion</title> (Conference Paper)
Maskell, S., Briers, M., & Wright, R. (2004). <title>Fast mutual exclusion</title>. In O. E. Drummond (Ed.), SPIE Proceedings. SPIE. doi:10.1117/12.542068DOI: 10.1117/12.542068
<title>Multipath track association for over-the-horizon radar using Lagrangian relaxation</title> (Conference Paper)
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:10.1117/12.541276DOI: 10.1117/12.541276
<title>Particle-based track-before-detect in Rayleigh noise</title> (Conference Paper)
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. SPIE. doi:10.1117/12.541275DOI: 10.1117/12.541275
<title>Tracking maneuvering targets using a scale mixture of normals</title> (Conference Paper)
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. SPIE. doi:10.1117/12.542071DOI: 10.1117/12.542071
An introduction to particle filters (Chapter)
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/cbo9780511617010.004DOI: 10.1017/cbo9780511617010.004
An introduction to particle filters (Chapter)
Maskell, S. (2004). An introduction to particle filters. In State Space and Unobserved Component Models (pp. 40-72). Cambridge University Press. doi:10.1017/cbo9780511617010.004DOI: 10.1017/cbo9780511617010.004
Distribution in a particle filter (Conference Paper)
Maskell, S. (2004). Distribution in a particle filter. In Target Tracking 2004: Algorithms and Applications. IEE. doi:10.1049/ic:20040048DOI: 10.1049/ic:20040048
2003
<title>Two-dimensional assignment with merged measurements using Langrangrian relaxation</title> (Conference Paper)
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. SPIE. doi:10.1117/12.503834DOI: 10.1117/12.503834
Efficient particle filtering for multiple target tracking with application to tracking in structured images (Conference Paper)
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)00087-8DOI: 10.1016/s0262-8856(03)00087-8
A rao-blackwellised unscented Kalman filter (Conference Paper)
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. IEEE. doi:10.1109/icif.2003.177426DOI: 10.1109/icif.2003.177426
2002
<title>Comparison of EKF, pseudomeasurement, and particle filters for a bearing-only target tracking problem</title> (Conference Paper)
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:10.1117/12.478508DOI: 10.1117/12.478508
<title>Efficient particle filtering for multiple target tracking with application to tracking in structured images</title> (Conference Paper)
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. SPIE. doi:10.1117/12.478509DOI: 10.1117/12.478509
<title>Efficient particle filters for joint tracking and classification</title> (Conference Paper)
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. SPIE. doi:10.1117/12.478524DOI: 10.1117/12.478524
<title>Expected likelihood for tracking in clutter with particle filters</title> (Conference Paper)
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. SPIE. doi:10.1117/12.478507DOI: 10.1117/12.478507
A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking (Journal article)
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:10.1109/78.978374DOI: 10.1109/78.978374
2001
A tutorial on particle filters for on-line nonlinear/non-Gaussian Bayesian tracking (Journal article)
Maskell, S. (2001). A tutorial on particle filters for on-line nonlinear/non-Gaussian Bayesian tracking. IEE International Seminar Target Tracking: Algorithms and Applications. doi:10.1049/ic:20010246DOI: 10.1049/ic:20010246