A new method for plasma boundary reconstruction, based on the toroidal multipolar expansion （TME） scheme, is applied successfully in EAST. TME applies a limited number of toroidal multipolar moments based on toroida...A new method for plasma boundary reconstruction, based on the toroidal multipolar expansion （TME） scheme, is applied successfully in EAST. TME applies a limited number of toroidal multipolar moments based on toroidal coordinates to treat a two-dimensional problem of axisymmetric plasma equilibrium. The plasma boundary reconstructed by TME is consistent with the results by using EFIT. The method is sufficiently reliable and fast for real time shape control.展开更多
<span style="font-family:Verdana;">In a context marked by the proliferation of smartphones and multimedia applications, the processing and transmission of images </span><span style="font-...<span style="font-family:Verdana;">In a context marked by the proliferation of smartphones and multimedia applications, the processing and transmission of images </span><span style="font-family:Verdana;">ha</span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">ve</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> become a real problem. Image compression </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">is</span></span></span><span><span><span style="font-family:;" "=""><span style="font-family:Verdana;"> the first approach to address this problem, it nevertheless suffers from its inability to adapt to the dynamics of limited environments, consisting mainly of mobile equipment and wireless networks. In this work, we propose a stochastic model to gradually estimate an image upon </span><span style="font-family:Verdana;">information</span><span style="font-family:Verdana;"> on its pixels that are transmitted progressively. We consider this transmission as a </span><span style="font-family:Verdana;">dynamical</span><span style="font-family:Verdana;"> process, where the sender </span><span style="font-family:Verdana;">push</span></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">es</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> the data in decreasing significance order. In order to adapt to network conditions and performances, instead of truncating the pixels, we suggest a new method called Fast Reconstruction Method by Kalman Filtering (FRM-KF) consisting </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">of</span></span></span><span><span><span style="font-family:;" "=""><span style="font-family:Verdana;"> recursive inference of the not yet received layers belonging to a sequence of bitplanes. After empirical analysis, we estimate </span><span style="font-family:Verdana;">parameters</span><span style="font-family:Verdana;"> of our model which is a linear discrete Kalman Filter. We assume the initial law of information to be the uniform distribution on the set [0, 255] corresponding to the range of gray levels. The performances of FRM-KF method ha</span></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">ve </span></span></span><span><span><span style="font-family:;" "=""><span style="font-family:Verdana;">been evaluated in terms of the ratios in the quality of data image/size sent and in the quality of image/time required for treatment. </span><span style="font-family:Verdana;">A high</span><span style="font-family:Verdana;"> quality was reached faster with relatively small data (less than 10% of image data is needed to obtain up to the sixth-quality image). The time for treatment also decreases faster with </span><span style="font-family:Verdana;">number</span><span style="font-family:Verdana;"> of received layers. However, we found that the time of image treatment might be large starting from </span><span style="font-family:Verdana;">a image</span><span style="font-family:Verdana;"> resolution of 1024 * 1024. Hence, we recommend </span><span style="font-family:Verdana;">FRM-KF</span><span style="font-family:Verdana;"> method for resolutions less or equal to 512 * 512. A statistical comparative analysis reveals that FRM-KF is competitive and suitable to be implemented </span><span style="font-family:Verdana;">on</span><span style="font-family:Verdana;"> limited </span><span style="font-family:Verdana;">resource</span><span style="font-family:Verdana;"> environments.</span></span></span></span>展开更多
基金supported by the Major State Basic Research Development Program of China （973 program, No. 2009GB103000）, National Natural Science Foundation of China （No. 10835009）, and the Chinese Academy of Sciences with grant ID of KJCX3.SYW.N4
文摘A new method for plasma boundary reconstruction, based on the toroidal multipolar expansion （TME） scheme, is applied successfully in EAST. TME applies a limited number of toroidal multipolar moments based on toroidal coordinates to treat a two-dimensional problem of axisymmetric plasma equilibrium. The plasma boundary reconstructed by TME is consistent with the results by using EFIT. The method is sufficiently reliable and fast for real time shape control.
文摘<span style="font-family:Verdana;">In a context marked by the proliferation of smartphones and multimedia applications, the processing and transmission of images </span><span style="font-family:Verdana;">ha</span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">ve</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> become a real problem. Image compression </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">is</span></span></span><span><span><span style="font-family:;" "=""><span style="font-family:Verdana;"> the first approach to address this problem, it nevertheless suffers from its inability to adapt to the dynamics of limited environments, consisting mainly of mobile equipment and wireless networks. In this work, we propose a stochastic model to gradually estimate an image upon </span><span style="font-family:Verdana;">information</span><span style="font-family:Verdana;"> on its pixels that are transmitted progressively. We consider this transmission as a </span><span style="font-family:Verdana;">dynamical</span><span style="font-family:Verdana;"> process, where the sender </span><span style="font-family:Verdana;">push</span></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">es</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> the data in decreasing significance order. In order to adapt to network conditions and performances, instead of truncating the pixels, we suggest a new method called Fast Reconstruction Method by Kalman Filtering (FRM-KF) consisting </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">of</span></span></span><span><span><span style="font-family:;" "=""><span style="font-family:Verdana;"> recursive inference of the not yet received layers belonging to a sequence of bitplanes. After empirical analysis, we estimate </span><span style="font-family:Verdana;">parameters</span><span style="font-family:Verdana;"> of our model which is a linear discrete Kalman Filter. We assume the initial law of information to be the uniform distribution on the set [0, 255] corresponding to the range of gray levels. The performances of FRM-KF method ha</span></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">ve </span></span></span><span><span><span style="font-family:;" "=""><span style="font-family:Verdana;">been evaluated in terms of the ratios in the quality of data image/size sent and in the quality of image/time required for treatment. </span><span style="font-family:Verdana;">A high</span><span style="font-family:Verdana;"> quality was reached faster with relatively small data (less than 10% of image data is needed to obtain up to the sixth-quality image). The time for treatment also decreases faster with </span><span style="font-family:Verdana;">number</span><span style="font-family:Verdana;"> of received layers. However, we found that the time of image treatment might be large starting from </span><span style="font-family:Verdana;">a image</span><span style="font-family:Verdana;"> resolution of 1024 * 1024. Hence, we recommend </span><span style="font-family:Verdana;">FRM-KF</span><span style="font-family:Verdana;"> method for resolutions less or equal to 512 * 512. A statistical comparative analysis reveals that FRM-KF is competitive and suitable to be implemented </span><span style="font-family:Verdana;">on</span><span style="font-family:Verdana;"> limited </span><span style="font-family:Verdana;">resource</span><span style="font-family:Verdana;"> environments.</span></span></span></span>
