Prof Suiping Zhou
| Name | Prof Suiping Zhou |
|---|---|
| Job title | Professor in Distributed Systems & Networking |
| Research institute | |
| Primary appointment | Computer Science |
| ORCID | https://orcid.org/0000-0002-9920-266X |
| Contact category | Academic staff (past) |
Research outputs
Development of OpenFlow Native Capabilities to optimize QoS
Breiki, M., Zhou, S. and Luo, Y. 2020. Development of OpenFlow Native Capabilities to optimize QoS. 2020 Seventh International Conference on Software Defined Systems (SDS). Paris, France 20 - 23 Apr 2020 IEEE. pp. 67-74 https://doi.org/10.1109/SDS49854.2020.9143890Design and validation of a meter band rate in OpenFlow and OpenDaylight for optimizing QoS
Breiki, M., Zhou, S. and Luo, Y. 2020. Design and validation of a meter band rate in OpenFlow and OpenDaylight for optimizing QoS. Advances in Science, Technology and Engineering Systems Journal. 5 (2), pp. 35-43. https://doi.org/10.25046/aj050205A meter band rate mechanism to improve the native QoS capability of OpenFlow and OpenDaylight
Al Breiki, M., Zhou, S. and Luo, Y. 2019. A meter band rate mechanism to improve the native QoS capability of OpenFlow and OpenDaylight. 2019 International Conference on Advanced Communication Technologies and Networking (CommNet). Rabat, Morocco, Morocco 12 - 14 Apr 2019 IEEE. https://doi.org/10.1109/COMMNET.2019.8742360Guide them through: an automatic crowd control framework using multi-objective genetic programming
Hu, N., Zhong, J., Zhou, J., Zhou, S., Cai, W. and Monterola, C. 2018. Guide them through: an automatic crowd control framework using multi-objective genetic programming. Applied Soft Computing. 66, pp. 90-103. https://doi.org/10.1016/j.asoc.2018.01.037ProactiveCrowd: modeling proactive steering behaviours for agent-based crowd simulation
Luo, L., Chai, C., Ma, J., Zhou, S. and Cai, W. 2018. ProactiveCrowd: modeling proactive steering behaviours for agent-based crowd simulation. Computer Graphics Forum. 37 (1), pp. 375-388. https://doi.org/10.1111/cgf.13303Software systems for data-centric smart city applications
Chen, D., Wang, L. and Zhou, S. 2017. Software systems for data-centric smart city applications. Software: Practice and Experience. 47 (8), pp. 1043-1044. https://doi.org/10.1002/spe.2508Wearable device-based gait recognition using angle embedded gait dynamic images and a convolutional neural network
Zhao, Y. and Zhou, S. 2017. Wearable device-based gait recognition using angle embedded gait dynamic images and a convolutional neural network. Sensors. 17 (3), pp. 1-20. https://doi.org/10.3390/s17030478RA2: predicting simulation execution time for cloud-based design space explorations
Duong, T., Zhong, J., Cai, W., Li, Z. and Zhou, S. 2016. RA2: predicting simulation execution time for cloud-based design space explorations. 2016 IEEE/ACM 20th International Symposium on Distributed Simulation and Real Time Applications. London 21 - 23 Sep 2016 Institute of Electrical and Electronics Engineers (IEEE). pp. 120-127 https://doi.org/10.1109/DS-RT.2016.9Modeling gap seeking behaviors for agent-based crowd simulation
Luo, L., Chai, C., Zhou, S. and Ma, J. 2016. Modeling gap seeking behaviors for agent-based crowd simulation. The 29th International Conference on Computer Animation and Social Agents. Geneva, Switzerland 23 - 25 May 2016 Association for Computing Machinery (ACM). pp. 37-43 https://doi.org/10.1145/2915926.2915944A review of interactive narrative systems and technologies: a training perspective
Luo, L., Cai, W., Zhou, S., Lees, M. and Yin, H. 2015. A review of interactive narrative systems and technologies: a training perspective. Simulation: Transactions of The Society for Modeling and Computer Simulation International. 91 (2), pp. 126-147. https://doi.org/10.1177/0037549714566722Towards a data-driven approach to scenario generation for serious games
Luo, L., Yin, H., Cai, W., Lees, M., Othman, N. and Zhou, S. 2014. Towards a data-driven approach to scenario generation for serious games. Computer Animation and Virtual Worlds. 25 (3-4), pp. 393-402. https://doi.org/10.1002/cav.1588Algorithms for balanced graph bi-partitioning
Wu, J., Jiang, G., Zheng, L. and Zhou, S. 2014. Algorithms for balanced graph bi-partitioning. 2014 IEEEInternational Conference on High Performance Computing and Communications (HPCC). Paris, France 20 - 22 Aug 2014 Institute of Electrical and Electronics Engineers (IEEE). pp. 185-188 https://doi.org/10.1109/HPCC.2014.35Update scheduling for improving consistency in distributed virtual environments
Tang, X. and Zhou, S. 2010. Update scheduling for improving consistency in distributed virtual environments. IEEE Transactions on Parallel and Distributed Systems. 21 (6), pp. 765-777. https://doi.org/10.1109/TPDS.2009.113Modeling and simulation of pedestrian behaviors in crowded places
Koh, W. and Zhou, S. 2011. Modeling and simulation of pedestrian behaviors in crowded places. ACM Transactions on Modeling and Computer Simulation. 21 (3), pp. 1-23. https://doi.org/10.1145/1921598.1921604Interactivity-constrained server provisioning in large-scale distributed virtual environments
Duong, N., Nguyen, T., Zhou, S., Tang, X., Cai, W. and Ayani, R. 2012. Interactivity-constrained server provisioning in large-scale distributed virtual environments. IEEE Transactions on Parallel and Distributed Systems. 23 (2), pp. 304-312. https://doi.org/10.1109/TPDS.2011.107Analysis of an efficient rule-based motion planning system for simulating human crowds
Xiong, M., Lees, M., Cai, W., Zhou, S. and Low, M. 2010. Analysis of an efficient rule-based motion planning system for simulating human crowds. The Visual Computer. 26 (5), pp. 367-383. https://doi.org/10.1007/s00371-010-0421-6Probabilistic classifiers with a generalized Gaussian scale mixture prior
Liu, G., Wu, J. and Zhou, S. 2013. Probabilistic classifiers with a generalized Gaussian scale mixture prior. Pattern Recognition. 46 (1), pp. 332-345. https://doi.org/10.1016/j.patcog.2012.07.016Fuzzy CMAC with incremental Bayesian Ying–Yang learning and dynamic rule construction
Shi, D., Nguyen, M., Zhou, S. and Yin, G. 2010. Fuzzy CMAC with incremental Bayesian Ying–Yang learning and dynamic rule construction. IEEE Transactions on Systems, Man and Cybernetics, Part B. 40 (2), pp. 548-552. https://doi.org/10.1109/TSMCB.2009.203033354
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