A conformal restriction measure is a probability measure which is used to describe the law of a random connected subset in a simply connected domain that satisfies a certain conformal restriction property. Usually the...A conformal restriction measure is a probability measure which is used to describe the law of a random connected subset in a simply connected domain that satisfies a certain conformal restriction property. Usually there are three kinds of conformal restriction measures: one (called the chordal restriction measure) has two given boundary points of the random set, the second (called the radial restriction measure) has one boundary point and one interior point in the random set, and the third (called the tri-chordal restriction measure) has three boundary points in the random set. In this article, we will define a new probability measure such that the random set associated to it contains one given interior point and does not intersect with the boundary. Furthermore, we will show that this measure can be characterized by one parameter;we will also construct this one-parameter family of measures in two ways and obtain several properties.展开更多
Block multiple measurement vectors (BMMV) is a reconstruction algorithm that can be used to recover the support of block K-joint sparse matrix X from Y = ΨX + V. In this paper, we propose a sufficient condition for a...Block multiple measurement vectors (BMMV) is a reconstruction algorithm that can be used to recover the support of block K-joint sparse matrix X from Y = ΨX + V. In this paper, we propose a sufficient condition for accurate support recovery of the block K-joint sparse matrix via the BMMV algorithm in the noisy case. Furthermore, we show the optimality of the condition we proposed in the absence of noise when the problem reduces to single measurement vector case.展开更多
An Adaptive Measurement Scheme (AMS) is investigated with Compressed Sensing (CS) theory in Cognitive Wireless Sensor Network (C-WSN). Local sensing information is collected via energy detection with Analog-to-Informa...An Adaptive Measurement Scheme (AMS) is investigated with Compressed Sensing (CS) theory in Cognitive Wireless Sensor Network (C-WSN). Local sensing information is collected via energy detection with Analog-to-Information Converter (AIC) at massive cognitive sensors, and sparse representation is considered with the exploration of spatial temporal correlation structure of detected signals. Adaptive measurement matrix is designed in AMS, which is based on maximum energy subset selection. Energy subset is calculated with sparse transformation of sensing information, and maximum energy subset is selected as the row vector of adaptive measurement matrix. In addition, the measurement matrix is constructed by orthogonalization of those selected row vectors, which also satisfies the Restricted Isometry Property (RIP) in CS theory. Orthogonal Matching Pursuit (OMP) reconstruction algorithm is implemented at sink node to recover original information. Simulation results are performed with the comparison of Random Measurement Scheme (RMS). It is revealed that, signal reconstruction effect based on AMS is superior to conventional RMS Gaussian measurement. Moreover, AMS has better detection performance than RMS at lower compression rate region, and it is suitable for large-scale C-WSN wideband spectrum sensing.展开更多
The detection of a particle in electromagnetic plus gravitational fields is investigated. We obtain a set of quantum nondemolition variables. The continuous measurements of these nondemolition parameters are analyzed ...The detection of a particle in electromagnetic plus gravitational fields is investigated. We obtain a set of quantum nondemolition variables. The continuous measurements of these nondemolition parameters are analyzed in the framework of restricted path integral formalism. We manipulate the corresponding propagators, and deduce the probabilities associated with the possible measurement outputs.展开更多
目的分析美国国立卫生研究院卒中量表(National Institutes of Health stroke scale,NIHSS)评分、老年营养风险指数(geriatric nutritional risk index,GNRI)、运动功能独立性评定(motor function independence measure,MFIM)评分与卒...目的分析美国国立卫生研究院卒中量表(National Institutes of Health stroke scale,NIHSS)评分、老年营养风险指数(geriatric nutritional risk index,GNRI)、运动功能独立性评定(motor function independence measure,MFIM)评分与卒中相关性肺炎(stroke-related pneumonia,SAP)风险的关系。方法纳入2021年11月至2022年5月卒中入院的患者,收集入院时NIHSS、GNRI、MFIM评分,根据卒中发病后1周内是否发生肺炎分为SAP组和非SAP组。使用受试者工作特征(receiver operating characteristic,ROC)曲线分析各评分最佳截断点并将评分转换为分类变量,采用多因素logistic回归模型和限制性立方样条分析各评分与SAP之间的关系。结果研究共纳入318例卒中患者,SAP组86例,非SAP组232例。logistic回归结果显示,NIHSS评分(OR=32.783,95%CI:16.366~65.671,P<0.001)、MFIM评分(OR=0.052,95%CI:0.027~0.100,P<0.001)和GNRI评分(OR=0.262,95%CI:0.144~0.476,P<0.001)与SAP存在关联。限制性立方样条分析显示,NIHSS评分(P_(总趋势)<0.001,P_(非线性)=0.002)、GNRI评分(P_(总趋势)<0.001,P_(非线性)<0.001)与SAP风险之间存在非线性剂量-反应关系。结论NIHSS、MFIM、GNRI评分和卒中患者SAP发生风险相关,其中NIHSS和GNRI评分与其存在非线性关联。展开更多
文摘A conformal restriction measure is a probability measure which is used to describe the law of a random connected subset in a simply connected domain that satisfies a certain conformal restriction property. Usually there are three kinds of conformal restriction measures: one (called the chordal restriction measure) has two given boundary points of the random set, the second (called the radial restriction measure) has one boundary point and one interior point in the random set, and the third (called the tri-chordal restriction measure) has three boundary points in the random set. In this article, we will define a new probability measure such that the random set associated to it contains one given interior point and does not intersect with the boundary. Furthermore, we will show that this measure can be characterized by one parameter;we will also construct this one-parameter family of measures in two ways and obtain several properties.
文摘Block multiple measurement vectors (BMMV) is a reconstruction algorithm that can be used to recover the support of block K-joint sparse matrix X from Y = ΨX + V. In this paper, we propose a sufficient condition for accurate support recovery of the block K-joint sparse matrix via the BMMV algorithm in the noisy case. Furthermore, we show the optimality of the condition we proposed in the absence of noise when the problem reduces to single measurement vector case.
基金Supported by the National Natural Science Foundation of China (No. 61102066, 60972058)the China Postdoctoral Science Foundation (No. 2012M511365)the Scientific Research Project of Zhejiang Provincial Education Department (No. Y201119890)
文摘An Adaptive Measurement Scheme (AMS) is investigated with Compressed Sensing (CS) theory in Cognitive Wireless Sensor Network (C-WSN). Local sensing information is collected via energy detection with Analog-to-Information Converter (AIC) at massive cognitive sensors, and sparse representation is considered with the exploration of spatial temporal correlation structure of detected signals. Adaptive measurement matrix is designed in AMS, which is based on maximum energy subset selection. Energy subset is calculated with sparse transformation of sensing information, and maximum energy subset is selected as the row vector of adaptive measurement matrix. In addition, the measurement matrix is constructed by orthogonalization of those selected row vectors, which also satisfies the Restricted Isometry Property (RIP) in CS theory. Orthogonal Matching Pursuit (OMP) reconstruction algorithm is implemented at sink node to recover original information. Simulation results are performed with the comparison of Random Measurement Scheme (RMS). It is revealed that, signal reconstruction effect based on AMS is superior to conventional RMS Gaussian measurement. Moreover, AMS has better detection performance than RMS at lower compression rate region, and it is suitable for large-scale C-WSN wideband spectrum sensing.
文摘The detection of a particle in electromagnetic plus gravitational fields is investigated. We obtain a set of quantum nondemolition variables. The continuous measurements of these nondemolition parameters are analyzed in the framework of restricted path integral formalism. We manipulate the corresponding propagators, and deduce the probabilities associated with the possible measurement outputs.
文摘目的分析美国国立卫生研究院卒中量表(National Institutes of Health stroke scale,NIHSS)评分、老年营养风险指数(geriatric nutritional risk index,GNRI)、运动功能独立性评定(motor function independence measure,MFIM)评分与卒中相关性肺炎(stroke-related pneumonia,SAP)风险的关系。方法纳入2021年11月至2022年5月卒中入院的患者,收集入院时NIHSS、GNRI、MFIM评分,根据卒中发病后1周内是否发生肺炎分为SAP组和非SAP组。使用受试者工作特征(receiver operating characteristic,ROC)曲线分析各评分最佳截断点并将评分转换为分类变量,采用多因素logistic回归模型和限制性立方样条分析各评分与SAP之间的关系。结果研究共纳入318例卒中患者,SAP组86例,非SAP组232例。logistic回归结果显示,NIHSS评分(OR=32.783,95%CI:16.366~65.671,P<0.001)、MFIM评分(OR=0.052,95%CI:0.027~0.100,P<0.001)和GNRI评分(OR=0.262,95%CI:0.144~0.476,P<0.001)与SAP存在关联。限制性立方样条分析显示,NIHSS评分(P_(总趋势)<0.001,P_(非线性)=0.002)、GNRI评分(P_(总趋势)<0.001,P_(非线性)<0.001)与SAP风险之间存在非线性剂量-反应关系。结论NIHSS、MFIM、GNRI评分和卒中患者SAP发生风险相关,其中NIHSS和GNRI评分与其存在非线性关联。
