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Performance comparisons between cellular-only and cellular/WLAN integrated systems based on analytical models
Guozhi SONG, Liying YANG, Jigang WU, John SCHORMANS
Front Comput Sci. 2013, 7 (4): 486-495.
https://doi.org/10.1007/s11704-013-2198-z
To solve the traffic load imbalance issue in cellular networks, which is often in the form of hot-spots caused by the different user mobility levels, one of the good solutions at present is to construct heterogeneous integrated wireless networks that combine cellular networks and wireless local area networks (WLANs) together. In general, the traffic volume is significantly heavier in the hot-spots of cellular networks and a higher data transferring rate can be provided by introducing aWLAN so as to raise the utilization of the channel and achieve a good balance between user satisfaction and the efficiency of the network. In this paper, we provide a comprehensive performance comparison of the systems both before and after the integration, based on an existing mathematical model, focusing on both the qualitative and the quantitative analysis of changes in the performance of the system to validate the efficiency and superiority of the cellular/WLAN integrated systems over cellular–only systems.
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Part-based methods for handwritten digit recognition
Song WANG, Seiichi UCHIDA, Marcus LIWICKI, Yaokai FENG
Front Comput Sci. 2013, 7 (4): 514-525.
https://doi.org/10.1007/s11704-013-2297-x
In this paper, we intensively study the behavior of three part-based methods for handwritten digit recognition. The principle of the proposed methods is to represent a handwritten digit image as a set of parts and recognize the image by aggregating the recognition results of individual parts. Since part-based methods do not rely on the global structure of a character, they are expected to be more robust against various deformations which may damage the global structure. The proposed three methods are based on the same principle but different in their details, for example, the way of aggregating the individual results.Thus, those methods have different performances. Experimental results show that even the simplest part-based method can achieve recognition rate as high as 98.42% while the improved one achieved 99.15%, which is comparable or even higher than some state-of-the-art method. This result is important because it reveals that characters can be recognized without their global structure. The results also show that the part-based method has robustness against deformations which usually appear in handwriting.
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Online belief propagation algorithm for probabilistic latent semantic analysis
Yun YE, Shengrong GONG, Chunping LIU, Jia ZENG, Ning JIA, Yi ZHANG
Front Comput Sci. 2013, 7 (4): 526-535.
https://doi.org/10.1007/s11704-013-2360-7
Probabilistic latent semantic analysis (PLSA) is a topic model for text documents, which has been widely used in text mining, computer vision, computational biology and so on. For batch PLSA inference algorithms, the required memory size grows linearly with the data size, and handling massive data streams is very diffcult. To process big data streams, we propose an online belief propagation (OBP) algorithm based on the improved factor graph representation for PLSA. The factor graph of PLSA facilitates the classic belief propagation (BP) algorithm. Furthermore, OBP splits the data stream into a set of small segments, and uses the estimated parameters of previous segments to calculate the gradient descent of the current segment. Because OBP removes each segment from memory after processing, it is memory effcient for big data streams. We examine the performance of OBP on four document data sets, and demonstrate that OBP is competitive in both speed and accuracy for online expectation maximization (OEM) in PLSA, and can also give a more accurate topic evolution. Experiments on massive data streams from Baidu further confirm the effectiveness of the OBP algorithm.
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Assessing the quality of metamodels
Zhiyi MA, Xiao HE, Chao LIU
Front. Comput. Sci.. 2013, 7 (4): 558-570.
https://doi.org/10.1007/s11704-013-1151-5
The complexity and diversity of modern software demands a variety of metamodel-based modeling languages for software development. Existing languages change continuously, and new ones are constantly emerging. In this situation, and especially for metamodel-based modeling languages, a quality assurance mechanism for metamodels is needed. This paper presents an approach to assessing the quality of metamodels. A quality model, which systematically characterizes and classifies quality attributes, and an operable measuring mechanism for effectively assessing the quality of metamodels based on the quality model, are presented, using UML as the main example.
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Modelling priority queuing systems with varying service capacity
Mei CHEN, Xiaolong JIN, Yuanzhuo WANG, Xueqi CHENG, Geyong MIN
Front. Comput. Sci.. 2013, 7 (4): 571-582.
https://doi.org/10.1007/s11704-013-2365-2
Many studies have been conducted to investigate the performance of priority queuing (PQ) systems with constant service capacity. However, due to the time-varying nature of wireless channels in wireless communication networks, the service capacity of queuing systemsmay vary over time. Therefore, it is necessary to investigate the performance of PQ systems in the presence of varying service capacity. In addition, self-similar traffic has been discovered to be a ubiquitous phenomenon in various communication networks, which poses great challenges to performance modelling of scheduling systems due to its fractal-like nature. Therefore, this paper develops a flow-decomposition based approach to performance modelling of PQ systems subject to self-similar traffic and varying service capacity. It specifically proposes an analytical model to investigate queue length distributions of individual traffic flows. The validity and accuracy of the model is demonstrated via extensive simulation experiments.
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11 articles
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