The Experts below are selected from a list of 273 Experts worldwide ranked by ideXlab platform
C.f.n. Cowan - One of the best experts on this subject based on the ideXlab platform.
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Equalisation with adaptive Time Lag
IEE Proceedings - Communications, 2005Co-Authors: Y. Gong, C.f.n. CowanAbstract:In an adaptive equaliser, the Time Lag is an important parameter that significantly influences the performance. Only with the optimum Time Lag that corresponds to the best minimum-mean-square-error (MMSE) performance, can there be best use of the available resources. Many designs, however, choose the Time Lag either based on preassumption of the channel or simply based on average experience. The relation between the MMSE performance and the Time Lag is investigated using a new interpretation of the MMSE equaliser, and then a novel adaptive Time Lag algorithm is proposed based on gradient search. The proposed algorithm can converge to the optimum Time Lag in the mean and is verified by the numerical simulations provided.
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EUSIPCO - A variable Time Lag algorithm for the FIR equalizer
2004Co-Authors: Y. Gong, C.f.n. CowanAbstract:In linear equalization, Time Lag is an important parameter that significantly influences the performance. Only with the optimum Time Lag that minimizes the minimum-mean-square-error (MMSE), can we have best use of the available resources. Many designs, however, choose the Time Lag either based on pre-assumption of the channel or simply based on average experience. In this paper, we propose a novel variable Time Lag algorithm with the concept of pseudo fractional Time Lag. The proposed algorithm can converge to the optimum Time Lag in the mean and is verified by the numerical simulations provided in this paper.
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A variable Time Lag algorithm for the FIR equalizer
2004 12th European Signal Processing Conference, 2004Co-Authors: Y. Gong, C.f.n. CowanAbstract:In linear equalization, Time Lag is an important parameter that significantly influences the performance. Only with the optimum Time Lag that minimizes the minimum-mean-square-error (MMSE), can we have best use of the available resources. Many designs, however, choose the Time Lag either based on pre-assumption of the channel or simply based on average experience. In this paper, we propose a novel variable Time Lag algorithm with the concept of pseudo fractional Time Lag. The proposed algorithm can converge to the optimum Time Lag in the mean and is verified by the numerical simulations provided in this paper.
Wenqian Zhao - One of the best experts on this subject based on the ideXlab platform.
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Time Lag effects of global vegetation responses to climate change
Global Change Biology, 2015Co-Authors: Donghai Wu, Xiang Zhao, Shunlin Liang, Tao Zhou, Kaicheng Huang, Bijian Tang, Wenqian ZhaoAbstract:Climate conditions significantly affect vegetation growth in terrestrial ecosystems. Due to the spatial heterogeneity of ecosystems, the vegetation responses to climate vary considerably with the diverse spatial patterns and the Time-Lag effects, which are the most important mechanism of climate–vegetation interactive effects. Extensive studies focused on large-scale vegetation–climate interactions use the simultaneous meteorological and vegetation indicators to develop models; however, the Time-Lag effects are less considered, which tends to increase uncertainty. In this study, we aim to quantitatively determine the Time-Lag effects of global vegetation responses to different climatic factors using the GIMMS3g NDVI Time series and the CRU temperature, precipitation, and solar radiation datasets. First, this study analyzed the Time-Lag effects of global vegetation responses to different climatic factors. Then, a multiple linear regression model and partial correlation model were established to statistically analyze the roles of different climatic factors on vegetation responses, from which the primary climate-driving factors for different vegetation types were determined. The results showed that (i) both the Time-Lag effects of the vegetation responses and the major climate-driving factors that significantly affect vegetation growth varied significantly at the global scale, which was related to the diverse vegetation and climate characteristics; (ii) regarding the Time-Lag effects, the climatic factors explained 64% variation of the global vegetation growth, which was 11% relatively higher than the model ignoring the Time-Lag effects; (iii) for the area with a significant change trend (for the period 1982–2008) in the global GIMMS3g NDVI (P < 0.05), the primary driving factor was temperature; and (iv) at the regional scale, the variation in vegetation growth was also related to human activities and natural disturbances. Considering the Time-Lag effects is quite important for better predicting and evaluating the vegetation dynamics under the background of global climate change.
Shunlin Liang - One of the best experts on this subject based on the ideXlab platform.
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Time Lag effects of global vegetation responses to climate change
Global Change Biology, 2015Co-Authors: Donghai Wu, Xiang Zhao, Shunlin Liang, Tao Zhou, Kaicheng Huang, Bijian Tang, Wenqian ZhaoAbstract:Climate conditions significantly affect vegetation growth in terrestrial ecosystems. Due to the spatial heterogeneity of ecosystems, the vegetation responses to climate vary considerably with the diverse spatial patterns and the Time-Lag effects, which are the most important mechanism of climate–vegetation interactive effects. Extensive studies focused on large-scale vegetation–climate interactions use the simultaneous meteorological and vegetation indicators to develop models; however, the Time-Lag effects are less considered, which tends to increase uncertainty. In this study, we aim to quantitatively determine the Time-Lag effects of global vegetation responses to different climatic factors using the GIMMS3g NDVI Time series and the CRU temperature, precipitation, and solar radiation datasets. First, this study analyzed the Time-Lag effects of global vegetation responses to different climatic factors. Then, a multiple linear regression model and partial correlation model were established to statistically analyze the roles of different climatic factors on vegetation responses, from which the primary climate-driving factors for different vegetation types were determined. The results showed that (i) both the Time-Lag effects of the vegetation responses and the major climate-driving factors that significantly affect vegetation growth varied significantly at the global scale, which was related to the diverse vegetation and climate characteristics; (ii) regarding the Time-Lag effects, the climatic factors explained 64% variation of the global vegetation growth, which was 11% relatively higher than the model ignoring the Time-Lag effects; (iii) for the area with a significant change trend (for the period 1982–2008) in the global GIMMS3g NDVI (P < 0.05), the primary driving factor was temperature; and (iv) at the regional scale, the variation in vegetation growth was also related to human activities and natural disturbances. Considering the Time-Lag effects is quite important for better predicting and evaluating the vegetation dynamics under the background of global climate change.
Y S Sancaktar - One of the best experts on this subject based on the ideXlab platform.
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effects of wall s thermophysical properties on Time Lag and decrement factor
Energy and Buildings, 1998Co-Authors: H Asan, Y S SancaktarAbstract:In this study, the effects of thermophysical properties and thickness of a wall of a building on Time Lag and decrement factor have been investigated. For this purpose, one dimensional transient heat conduction equation was solved using Crank-Nicolson scheme under convection boundary conditions. To the outer surface of the wall, periodic boundary conditions were applied. A very general code which can take care of composite walls under any kind of boundary condition was developed. Single and combined effects of the thickness and thermophysical properties on the Time Lag and decrement factor were investigated. It was found that thermophysical properties have a very profound effect on the Time Lag and decrement factor. The computations were repeated for different building materials and the results are discussed.
H.d. Do - One of the best experts on this subject based on the ideXlab platform.
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Analysis of Dual Diffusion and Non-linear Adsorption Isotherm with a Time Lag Method
Adsorption, 2000Co-Authors: D.d. Do, H.d. DoAbstract:This paper presents an application of the Time Lag method in the analysis of an adsorption system, where dual diffusion mechanism is assumed to exist and the equilibrium relationship between the fluid and adsorbed phases is non-linear. The derived Time Lag is expressed in terms of system parameters and operating conditions in the form of a quadrature. The feature of this solution is that the relative contribution of the pore and surface diffusions is a strong function of upstream pressure when the Time Lag experiment is operated over the non-linear range of the adsorption isotherm. It is this nice feature that we take advantage of to determine the pore and surface diffusivities without resorting to isolation of the pore diffusion by using non-adsorbing gas as a reference, as usually done in many other work. This advantage is not manifested in linear systems where the relative contribution of the pore and surface diffusions is a constant, rendering the delineation of these two processes impossible. Effects of various parameters on the utility of this Time Lag method are discussed in this paper, and application of the method is demonstrated with experimental data of sulfur dioxide adsorption onto Carbolac carbon (Proc. Roy. Soc., A271, 1–18, 1963).
