Image transmission is one of the biggest challenges in wireless sensor networks because of the limited resource on sensor nodes. We proposed two image transmission schemes driven by reliability and real time considera...
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Image transmission is one of the biggest challenges in wireless sensor networks because of the limited resource on sensor nodes. We proposed two image transmission schemes driven by reliability and real time considerations in order to transfer JPEG images over Zigbee-based sensor networks. By adding two bytes counter in the header of data packet, we can easily solve the repeated data reception problem caused by retransmission mechanism in traditional Zigbees network layer. We proposed an efficient retransmission and acknowledgment mechanism in Zigbees application layer. By classifying different data reception response events, we can provide data packets with differential responses and ensure that image packets can be transferred quickly even with large maximum number of retransmission. Practical results show the effectiveness of our solutions to make image transmission over Zigbee-based sensor networks efficient.
Aiming at the problem of multi-category iris recognition, there proposes a method of iris recognition algorithm based on adaptive Gabor filter. Use DE-PSO to adaptive optimize the Gabor filter parameters. DE-PSO is co...
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The detection of structural changes is an important task in analyzing network evolution, especially for interactions between people, that may be driven by external events. Existing work relies on snapshot data and mis...
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In recent years, with the development of the Internet, it is more and more common for users to buy mobile phones on the Internet. On the one hand, sentiment analysis help customers to fully understand the performance ...
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Topic modeling algorithms such as the latent Dirichlet allocation (LDA) play an important role in machine learning research. Fitting LDA using Gibbs sampler-related algorithms involves a sampling process over K topics...
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Local binary patterns was used to distinguish the Photorealistic Computer Graphics and Photographic Images, however the dimension of the extracted features is too high. Accordingly, the Local Ternary Count based on th...
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An important and widespread topic in cloud computing is text *** often use topic model which is a popular and effective technology to deal with related *** all the topic models,sLDA is acknowledged as a popular superv...
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ISBN:
(纸本)9781509012473
An important and widespread topic in cloud computing is text *** often use topic model which is a popular and effective technology to deal with related *** all the topic models,sLDA is acknowledged as a popular supervised topic model,which adds a response variable or category label with each document,so that the model can uncover the latent structure of a text dataset as well as retains the predictive power for supervised ***,sLDA needs to process all the documents at each iteration in the training *** the size of dataset increases to the volume that one node cannot deal with,sLDA will no longer be *** this paper we propose a novel model named *** which extends sLDA with stochastic variational inference(SVI) and *** can reduce the computational burden of sLDA and MapReduce extends the algorithm with *** makes the training become more efficient and the training method can be easily implemented in a large computer cluster or cloud *** results show that our approach has an efficient training process,and similar accuracy with sLDA.
The problem of community detection in networks has received wide attention and proves to be computationally challenging. In recent years, with the surge of signed networks with positive links and negative links, to fi...
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The problem of community detection in networks has received wide attention and proves to be computationally challenging. In recent years, with the surge of signed networks with positive links and negative links, to find community structure in such signed networks has become a research focus in the area of network science. Although many methods have been proposed to address the problem, their performance seriously depends on the predefined optimization objectives or heuristics which are usually difficult to accurately describe the intrinsic structure of community. In this study, we present a statistical inference method for community detection in signed networks, in which a probabilistic model is proposed to model signed networks and the expectation-maximization–based parameter estimation method is deduced to find communities in signed networks. In addition, to efficiently analyze signed networks without any a priori information, a model selection criterion is also proposed to automatically determine the number of communities. In our experiments, the proposed method is tested in the synthetic and real-word signed networks and compared with current methods. The experimental results show the proposed method can more efficiently and accurately find the communities in signed networks than current methods. Notably, the proposed method is a mathematically principled method.
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