In this study six different mode switching techniques (i.e. timeout mode switching, non-preferred hand mode switching, barrel button mode switching, pressure mode switching, tilt mode switching and azimuth mode switch...
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In this study six different mode switching techniques (i.e. timeout mode switching, non-preferred hand mode switching, barrel button mode switching, pressure mode switching, tilt mode switching and azimuth mode switching) based on multiple parameters pen input are proposed. The results indicate that the techniques utilizing tilt angle and azimuth offer faster performance than the others.
The community is not only one kind of widely existing organization in networks, but also contains distinct information about topics. The present paper is to define a metric between community members, weigh their seman...
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The community is not only one kind of widely existing organization in networks, but also contains distinct information about topics. The present paper is to define a metric between community members, weigh their semantic similarity, and finally make use of the metric to find more about the Web. In order to achieve this, the usual practice is to establish a "plane" adjacency matrix according to the citation relationship among all community members. However, it is easy to trigger the problem of topic drift. To overcome this weak point, the present paper puts forward firstly the strategy of establishing a three-order adjacency tensor on the 3-dimensional relationship between the seed document, simple document and the author. Secondly, the adjacency tensor is decomposed to obtain the principal component in each dimension. Thirdly, the semantic similarity between authors is defined. The experiment makes it clear that the semantic similarity between the author and people of importance tends to be stable under a particular circumstance.
A stochastic generalization of renormalization-group transformation for continuous-time random walk processes is proposed. The renormalization consists in replacing the jump events from a randomly sized cluster by a s...
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A stochastic generalization of renormalization-group transformation for continuous-time random walk processes is proposed. The renormalization consists in replacing the jump events from a randomly sized cluster by a single renormalized (i.e., overall) jump. The clustering of the jumps, followed by the corresponding transformation of the interjump time intervals, yields a new class of coupled continuous-time random walks which, applied to modeling of relaxation, lead to the general power-law properties usually fitted with the empirical Havriliak-Negami function.
Both expert system and data mining belong to the Artificial Intelligence fields. Association rule is a method of datamining, whose typical application is analyzing the shopping basket in supermarket. The main task of ...
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Both expert system and data mining belong to the Artificial Intelligence fields. Association rule is a method of datamining, whose typical application is analyzing the shopping basket in supermarket. The main task of expert system is ratiocination, while that of association rule is to find out the valuable relationship between each data item. By modifying the apriori arithmetic and the method of the making rules, we mine the decisive rule of database that could be applied in expert system, thereby find out the method of mining decisive rule using association rules.
The purpose of image fusion is to merge complementary information from multi-sensor data for visual perception so as to improve the visual perception of the image. This paper proposed an image fusion method based on B...
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The purpose of image fusion is to merge complementary information from multi-sensor data for visual perception so as to improve the visual perception of the image. This paper proposed an image fusion method based on B-spline gradient. It first divided the source images into the small blocks of size, calculated separately the B-spline gradient of the corresponding small block, added up the gradient module values, calculated the weighted value of each small block gradient module value, each small block multiplied with its respective weighted value, and finally obtained the fusion image small block. This paper finally produced the experiments and results show that, this proposed method is effective and seems to be a better performance than traditional principal component analysis, wavelet transform and Laplacian pyramid.
Firstly equivalence between Pi calculus and Artificial Neural Networks (ANN) is illustrated;furthermore a scheme for using Pi calculus to model ANN is presented. On the basis of them, then a classification based on Pi...
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Firstly equivalence between Pi calculus and Artificial Neural Networks (ANN) is illustrated;furthermore a scheme for using Pi calculus to model ANN is presented. On the basis of them, then a classification based on Pi calculus, which uses the same basic principles of concurrent computation as neural networks, is discussed. Finally its superiority is also discussed.
Measuring post dialysis urea rebound (PDUR) requires a 30- or 60-minute post-dialysis sampling, which is inconvenient. This paper presents a novel technique for predicting equilibrated urea concentration and post dial...
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Measuring post dialysis urea rebound (PDUR) requires a 30- or 60-minute post-dialysis sampling, which is inconvenient. This paper presents a novel technique for predicting equilibrated urea concentration and post dialysis urea rebound in the form of a Takagi-Sugeno-Kang fuzzy inference system. The advantage of this neuro-fuzzy hybrid approach is that it doesn't require 30-60-minute post-dialysis urea sample. Adaptive neuro-fuzzy inference system (ANFIS) was constructed to predict equilibrated urea (C eq ) taken at 60 min after the end of the hemodialysis (HD) session in order to predict PDUR. The accuracy of the ANFIS was prospectively compared with other traditional methods for predicting equilibrated urea (C eq ), PDUR and equilibrated dialysis dose (e q Kt/V). The results are highly promising, and a comparative analysis suggests that the proposed modeling approach outperforms artificial neural networks and other traditional urea kinetic models (UKM).
The inability of incoming students to advance past the traditional first-year calculus sequence is a primary cause of attrition in engineering programs across the country. As a result, this paper will describe an NSF ...
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