Frontiers of Computer Science

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Large-scale video compression: recent advances and challenges
Tao TIAN, Hanli WANG
Front. Comput. Sci.    2018, 12 (5): 825-839.   https://doi.org/10.1007/s11704-018-7304-9
Abstract   PDF (785KB)

The evolution of social network and multimedia technologies encourage more and more people to generate and upload visual information, which leads to the generation of large-scale video data. Therefore, preeminent compression technologies are highly desired to facilitate the storage and transmission of these tremendous video data for a wide variety of applications. In this paper, a systematic review of the recent advances for large-scale video compression (LSVC) is presented. Specifically, fast video coding algorithms and effective models to improve video compression efficiency are introduced in detail, since coding complexity and compression efficiency are two important factors to evaluate video coding approaches. Finally, the challenges and future research trends for LSVC are discussed.

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Cyber-physical-social collaborative sensing: from single space to cross-space
Fei YI, Zhiwen YU, Huihui CHEN, He DU, Bin GUO
Front. Comput. Sci.    2018, 12 (4): 609-622.   https://doi.org/10.1007/s11704-017-6612-9
Abstract   PDF (1144KB)

The development of wireless sensor networking, social networking, and wearable sensing techniques has advanced the boundaries of research on understanding social dynamics. Collaborative sensing, which utilizes diversity sensing and computing abilities across different entities, has become a popular sensing and computing paradigm. In this paper, we first review the history of research in collaborative sensing, which mainly refers to single space collaborative sensing that consists of physical, cyber, and social collaborative sensing. Afterward, we extend this concept into cross-space collaborative sensing and propose a general reference framework to demonstrate the distinct mechanism of cross-space collaborative sensing. We also review early works in cross-space collaborative sensing, and study the detail mechanism based on one typical research work. Finally, although cross-space collaborative sensing is a promising research area, it is still in its infancy. Thus, we identify some key research challenges with potential technical details at the end of this paper.

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Cited: WebOfScience(1)
Precise slicing of interprocedural concurrent programs
Xiaofang QI, Zhenliang JIANG
Front. Comput. Sci.    2017, 11 (6): 971-986.   https://doi.org/10.1007/s11704-017-6189-3
Abstract   PDF (654KB)

Program slicing is an effective technique for analyzing concurrent programs. However, when a conventional closure-based slicing algorithmfor sequential programs is applied to a concurrent interprocedural program, the slice is usually imprecise owing to the intransitivity of interference dependence. Interference dependence arises when a statement uses a variable defined in another statement executed concurrently. In this study, we propose a global dependence analysis approach based on a program reachability graph, and construct a novel dependence graph calledmarking-statement dependence graph (MSDG), in which each vertex is a 2-tuple of program state and statement. In contrast to the conventional program dependence graph where the vertex is a statement, the dependence relation in MSDG is transitive. When traversing MSDG, a precise slice will be obtained. To enhance the slicing efficiency without loss of precision, our slicing algorithm adopts a hybrid strategy. The procedures containing interaction statements between threads are inlined and sliced by the slicing algorithm based on program reachability graphs while allowing other procedures to be sliced as sequential programs. We have implemented our algorithm and three other representative slicing algorithms, and conducted an empirical study on concurrent Java programs. The experimental results show that our algorithm computes more precise slices than the other algorithms. Using partial-order reduction techniques, which are effective for reducing the size of a program reachability graph without loss of precision, our algorithm is optimized, thereby improving its performance to some extent.

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A survey on Lyapunov-based methods for stability of linear time-delay systems
Jian SUN, Jie CHEN
Front. Comput. Sci.    2017, 11 (4): 555-567.   https://doi.org/10.1007/s11704-016-6120-3
Abstract   PDF (314KB)

Recently, stability analysis of time-delay systems has received much attention. Rich results have been obtained on this topic using various approaches and techniques. Most of those results are based on Lyapunov stability theories. The purpose of this article is to give a broad overview of stability of linear time-delay systems with emphasis on the more recent progress. Methods and techniques for the choice of an appropriate Lyapunov functional and the estimation of the derivative of the Lyapunov functional are reported in this article, and special attention is paid to reduce the conservatism of stability conditions using as few as possible decision variables. Several future research directions on this topic are also discussed.

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Cited: Crossref(2) WebOfScience(2)
The role of prior in image based 3D modeling: a survey
Hao ZHU,Yongming NIE,Tao YUE,Xun CAO
Front. Comput. Sci.    2017, 11 (2): 175-191.   https://doi.org/10.1007/s11704-016-5520-8
Abstract   PDF (950KB)

The prior knowledge is the significant supplement to image-based 3D modeling algorithms for refining the fragile consistency-based stereo. In this paper, we review the image-based 3D modeling problem according to prior categories, i.e., classical priors and specific priors. The classical priors including smoothness, silhouette and illumination are well studied for improving the accuracy and robustness of the 3D reconstruction. In recent years, various specific priors which take advantage of Manhattan rule, geometry template and trained category features have been proposed to enhance the modeling performance. The advantages and limitations of both kinds of priors are discussed and evaluated in the paper. Finally, we discuss the trend and challenges of the prior studies in the future.

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Urban computing: enabling urban intelligence with big data
Yu ZHENG
Front. Comput. Sci.    2017, 11 (1): 1-3.   https://doi.org/10.1007/s11704-016-6907-2
Abstract   PDF (397KB)
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Cited: WebOfScience(1)
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