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Journal articles

Chen, J., Mao, G., Li, C., Zafar, A. & Zomaya, A. 2017, 'Throughput of Infrastructure-Based Cooperative Vehicular Networks', IEEE Transactions on Intelligent Transportation Systems.
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In this paper, we provide the detailed analysis of the achievable throughput of infrastructure-based vehicular network with a finite traffic density under a cooperative communication strategy, which explores the combined use of vehicle-to-infrastructure (V2I) communications, vehicle-to-vehicle (V2V) communications, the mobility of vehicles, and cooperations among vehicles and infrastructure to facilitate the data transmission. A closed form expression of the achievable throughput is obtained, which reveals the relationship between the achievable throughput and its major performance-impacting parameters, such as distance between adjacent infrastructure points, the radio ranges of infrastructure and vehicles, the transmission rates of V2I and V2V communications, and vehicular density. Numerical and simulation results show that the proposed cooperative communication strategy significantly increases the throughput of vehicular networks, compared with its non-cooperative counterpart, even when the traffic density is low. Our results shed insight on the optimum deployment of vehicular network infrastructure and the optimum design of cooperative communication strategies in vehicular networks to maximize the throughput.

Pourashraf, P., Safaei, F. & Franklin, D.R. 2017, 'A Study of User Perception of the Quality of Video Content Rendered Inside a 3D Virtual Environment', IEEE Journal of Selected Topics in Signal Processing.
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This paper reports on the result of a user study to assess the impact of resolution and frame rate of video on the Quality of Experience of the users, when the video is rendered inside a 3D virtual space, and consequently viewed from arbitrary perspectives. A mathematical model for video rate is presented that expresses the total rate as the product of separate functions of spatial and temporal resolutions. Results from the user study are combined with the model to predict the rate parameters which will result in perceptually acceptable quality using the 3D features of the virtual environment. The results show that by exploiting the insensitivity of users to controlled quality degradation, the downstream network load for the client can be significantly reduced with little or no perceptual impact on the clients.

Qu, X., Yi, W., Wang, T., Wang, S., Xiao, L. & Liu, Z. 2017, 'Mixed-Integer Linear Programming Models for Teaching Assistant Assignment and Extensions', Scientific Programming, vol. 2017.
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© 2017 Xiaobo Qu et al.In this paper, we develop mixed-integer linear programming models for assigning the most appropriate teaching assistants to the tutorials in a department. The objective is to maximize the number of tutorials that are taught by the most suitable teaching assistants, accounting for the fact that different teaching assistants have different capabilities and each teaching assistant's teaching load cannot exceed a maximum value. Moreover, with optimization models, the teaching load allocation, a time-consuming process, does not need to be carried out in a manual manner. We have further presented a number of extensions that capture more practical considerations. Extensive numerical experiments show that the optimization models can be solved by an off-the-shelf solver and used by departments in universities.

Wang, S., Qu, X., Wang, T. & Yi, W. 2017, 'Optimal Container Routing in Liner Shipping Networks Considering Repacking 20 ft Containers into 40 ft Containers', Journal of Advanced Transportation, vol. 2017, pp. 1-9.
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Zhang, T., Jia, W., Yang, B., Yang, J., He, X. & Zheng, Z. 2017, 'MoWLD: a robust motion image descriptor for violence detection', Multimedia Tools and Applications, vol. 76, no. 1, pp. 1419-1438.
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© 2015 Springer Science+Business Media New York Automatic violence detection from video is a hot topic for many video surveillance applications. However, there has been little success in designing an algorithm that can detect violence in surveillance videos with high performance. Existing methods typically apply the Bag-of-Words (BoW) model on local spatiotemporal descriptors. However, traditional spatiotemporal features are not discriminative enough, and also the BoW model roughly assigns each feature vector to only one visual word and therefore ignores the spatial relationships among the features. To tackle these problems, in this paper we propose a novel Motion Weber Local Descriptor (MoWLD) in the spirit of the well-known WLD and make it a powerful and robust descriptor for motion images. We extend the WLD spatial descriptions by adding a temporal component to the appearance descriptor, which implicitly captures local motion information as well as low-level image appear information. To eliminate redundant and irrelevant features, the non-parametric Kernel Density Estimation (KDE) is employed on the MoWLD descriptor. In order to obtain more discriminative features, we adopt the sparse coding and max pooling scheme to further process the selected MoWLDs. Experimental results on three benchmark datasets have demonstrated the superiority of the proposed approach over the state-of-the-arts.

Conferences

Khruahong, S., Kong, X., Sandrasegaran, K. & Liu, L. 2017, 'Multi-Level Indoor Navigation Ontology for High Assurance Location-Based Services', Proceedings of ", The 18th IEEE International Symposium on High Assurance Systems Engineering, The 18th IEEE International Symposium on High Assurance Systems Engineering, Singapore.
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