Accurate link quality estimation is a fundamental building block in quality aware multi hop routing. In an inherently lossy, unreliable and dynamic medium such as wireless, the task of accurate estimation becomes very...Accurate link quality estimation is a fundamental building block in quality aware multi hop routing. In an inherently lossy, unreliable and dynamic medium such as wireless, the task of accurate estimation becomes very challenging. Over the years ETX has been widely used as a reliable link quality estimation metric. However, more recently it has been established that under heavy traffic loads ETX performance gets significantly worse. We examine the ETX metric＇s behavior in detail with respect to the MAC layer and UDP data; and identify the causes of its unreliability. Motivated by the observations made in our analysis, we present the design and implementation of our link quality measurement metric xDDR - a variation of ETX. This article extends xDDR to support network mobility. Our experiments show that xDDR substantially outperforms minimum hop count, ETX and HETX in terms of end-to-end packet delivery ratio in static as well as mobile scenarios.展开更多
Asymmetric tree-like branched networks are explored by geometric algorithms. Based on the network, an analysis of the thermal conductivity is presented. The relationship between effective thermal conductivity and geom...Asymmetric tree-like branched networks are explored by geometric algorithms. Based on the network, an analysis of the thermal conductivity is presented. The relationship between effective thermal conductivity and geometric structures is obtained by using the thermal-electrical analogy technique. In all studied cases, a clear behaviour is observed, where angle （δ,θ） among parent branching extended lines, branches and parameter of the geometric structures have stronger effects on the effective thermal conductivity. When the angle δ is fixed, the optical diameter ratio β＋ is dependent on angle θ. Moreover, γand m are not related to β＊. The longer the branch is, the smaller the effective thermal conductivity will be. It is also found that when the angle θ〈δ2, the higher the iteration m is, the lower the thermal conductivity will be and it tends to zero, otherwise, it is bigger than zero. When the diameter ratio β1 〈 0.707 and angle δ is bigger, the optimal k of the perfect ratio increases with the increase of the angle δ; when β1 〉 0.707, the optimal k decreases. In addition, the effective thermal conductivity is always less than that of single channel material. The present results also show that the effective thermal conductivity of the asymmetric tree-like branched networks does not obey Murray＇s law.展开更多
Deep learning technology has been widely applied in the finance industry, particularly in the study of stock price prediction. This paper focuses on the prediction accuracy and performance of long-term features and pr...Deep learning technology has been widely applied in the finance industry, particularly in the study of stock price prediction. This paper focuses on the prediction accuracy and performance of long-term features and proposes a Wide & Deep Asymmetrical Bidirectional Legendre Memory Units that captures long-term dependencies in time series through the immediate backpropagation of bidirectional recurrent modules and Legendre polynomial memory units. The proposed model achieves superior stock trend prediction capabilities by combining the memory and generalization capabilities of the Wide & Deep model. Experimental results on the daily trading data set of the constituents of the CSI 300 index demonstrate that the proposed model outperforms several baseline models in medium and long-term trend prediction.展开更多
文摘Accurate link quality estimation is a fundamental building block in quality aware multi hop routing. In an inherently lossy, unreliable and dynamic medium such as wireless, the task of accurate estimation becomes very challenging. Over the years ETX has been widely used as a reliable link quality estimation metric. However, more recently it has been established that under heavy traffic loads ETX performance gets significantly worse. We examine the ETX metric＇s behavior in detail with respect to the MAC layer and UDP data; and identify the causes of its unreliability. Motivated by the observations made in our analysis, we present the design and implementation of our link quality measurement metric xDDR - a variation of ETX. This article extends xDDR to support network mobility. Our experiments show that xDDR substantially outperforms minimum hop count, ETX and HETX in terms of end-to-end packet delivery ratio in static as well as mobile scenarios.
基金Project supported by the State Key Development Program for Basic Research of China (Grant No 2006CB708612)the National Natural Science Foundation of China (Grant No 10572130)the Natural Science Foundation of Zhejiang Province, China (Grant No Y607425)
文摘Asymmetric tree-like branched networks are explored by geometric algorithms. Based on the network, an analysis of the thermal conductivity is presented. The relationship between effective thermal conductivity and geometric structures is obtained by using the thermal-electrical analogy technique. In all studied cases, a clear behaviour is observed, where angle （δ,θ） among parent branching extended lines, branches and parameter of the geometric structures have stronger effects on the effective thermal conductivity. When the angle δ is fixed, the optical diameter ratio β＋ is dependent on angle θ. Moreover, γand m are not related to β＊. The longer the branch is, the smaller the effective thermal conductivity will be. It is also found that when the angle θ〈δ2, the higher the iteration m is, the lower the thermal conductivity will be and it tends to zero, otherwise, it is bigger than zero. When the diameter ratio β1 〈 0.707 and angle δ is bigger, the optimal k of the perfect ratio increases with the increase of the angle δ; when β1 〉 0.707, the optimal k decreases. In addition, the effective thermal conductivity is always less than that of single channel material. The present results also show that the effective thermal conductivity of the asymmetric tree-like branched networks does not obey Murray＇s law.
文摘Deep learning technology has been widely applied in the finance industry, particularly in the study of stock price prediction. This paper focuses on the prediction accuracy and performance of long-term features and proposes a Wide & Deep Asymmetrical Bidirectional Legendre Memory Units that captures long-term dependencies in time series through the immediate backpropagation of bidirectional recurrent modules and Legendre polynomial memory units. The proposed model achieves superior stock trend prediction capabilities by combining the memory and generalization capabilities of the Wide & Deep model. Experimental results on the daily trading data set of the constituents of the CSI 300 index demonstrate that the proposed model outperforms several baseline models in medium and long-term trend prediction.
