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With the purpose of automatic detection of crowd patterns including abrupt and abnormal changes, a novel approach for extracting motion “textures” from dynamic Spatio-Temporal Volume (STV) blocks formulated by live video streams has been proposed. This paper starts from introducing the common approach for STV construction and corresponding Spatio-Temporal Texture (STT) extraction techniques. Next...
In order to achieve automatic prediction and warning of hazardous crowd behaviors, a Spatio-Temporal Volume (STV) analysis method is proposed in this research to detect crowd abnormality recorded in CCTV streams. The method starts from building STV models using video data. STV slices — called Spatio-Temporal Textures (STT) — can then be analyzed to detect crowded regions. After calculating the Gray...
Software Defined Network (SDN) is able to provide better network management and higher utilization for data center. However the centralized control of entire network may trigger large overhead and limit the scalability of control plane. In this paper, we propose an enhanced mechanism of elephant flow scheduling in SDN-based data center. The mechanism can efficiently reduce the overhead and improve...
English person names are translated into Chinese person names using the combined method-dictionary, entropy alignment model and web mining. Entropy alignment model makes use of the dictionary of person names and surnames, bidirectional conditional probability and transliteration similarity. Web mining makes use of rules, clue words, transliteration similarity and conditional probability. The experimental...
One challenging problem in the study of complex networks is the quantification of relationships between time series recorded across the network. Two information-theoretic measures, i.e., transfer entropy and directed information, have been extensively studied to capture the causality relationship between subsystems of a network. However, the relationship between these two measures have not been fully...
Directed Information (DI) has recently been introduced to quantify the causality between two signals. However, one major remaining issue with DI is the computational complexity which increases dramatically with the length of the signal. Current simplified DI computation methods are either model dependent or focus on short-time intervals losing most of the causal dependencies. In this paper, we introduce...
Chinese medicine is a treasure of Chinese nation as well as a crystallization of Chinese working people's wisdom accumulated over thousands of years, Chinese medicine industry has always been a pivotal position in the pharmaceutical market in China. This paper uses the Gray correlation model based on the theory of information entropy, the method of entropy evaluation is to determine the weight. Using...
Entering-tones are the tones which corresponding Chinese characters are ancient entering-tone characters. The entering tones are recognized using hybrid method which combines rule-based method with statistical method. The syllable which may possibly be an entering-tone or a non-entering tone is called ambiguity syllable. The non-ambiguity syllables can be recognized using rule-based method. The ambiguity...
In this paper, we propose a new method for unsupervised classification of polarimetric synthetic aperture radar interferometry (PolInSAR) images based on Shannon Entropy Characterization. Firstly, we use polarimetric H (entropy) and a parameters to classify the image initially. Then, we reclassify the image according to the span of Shannon Entropy Characterization. Finally, we fuse the results of...
In neurophysiology, it is important to determine the causal relationships between neuronal sites. The major problem with existing methods for quantifying the causality in the brain, e.g. Granger causality, is that they assume an multi-variate autoregressive signal model for the multi-channel EEG signals and do not take the nonlinear dependencies between neuronal oscillations into account. In this...
An algorithm of identifying the strength of random-like property of discrete chaotic sequences is proposed, and then several discrete chaotic maps are analyzed with this method. The pixel position of a color image is permutated by a stronger random-like discrete chaotic sequence, then the image is encrypted by a hyperchaotic sequence. Security analysis of the encrypted image is given detailedly. Simulation...
Directed Information (DI) is used to quantify the causal and dynamic relations between two signals. The main advantage of using DI compared to other measures of causality is that it does not assume an underlying signal model and thus can capture both linear and nonlinear interactions between signals. However, one major problem in computing the DI from data is the high computational cost and the unreliability...
A novel image fusion method based on discrete wavelet packet transform (DWPT) is proposed. First, after the registration of multi-source sensor images, the obtained images are decomposed into several sub-images of different frequency bands by using wavelet packet transform. Second, aiming at the characteristic of the sub-images after decompositions, fusion rules based on local region are chosen to...
Symbolic dynamics is a useful tool in several fields of complexity analysis in nonlinear science. In order to investigate complexities of the human brain electrical activities under different brain functional states, a novel method in terms of symbolic entropy is defined and proposed in this paper. The novel algorithm based on symbolic dynamics is developed for quantitatively measuring the complexity...
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