为进一步提升系统局部性时延,以关系化数据分块作为研究背景,提出系统局部性时延优化算法,采用主机、小型计算机系统接口(Small Computer System Interface, SCSI)通道、单元控制器以及磁盘构建系统,通过关系化数据的邻接关系建立、数...为进一步提升系统局部性时延,以关系化数据分块作为研究背景,提出系统局部性时延优化算法,采用主机、小型计算机系统接口(Small Computer System Interface, SCSI)通道、单元控制器以及磁盘构建系统,通过关系化数据的邻接关系建立、数据法矢预估、数据曲率求解三个预处理过程,在不同比例下取舍关系化数据,完成数据分块存储操作;根据界定的主节点与分块节点间局部性时延,导出时延上界;将关系化数据分块存储网络结构建立在一个二维曼哈顿平面上,经求解结构图的树结构,构建局部性时延优化算法。以节点生命周期、系统生命周期、通信距离以及局部性时延等作为评估指标展开仿真,验证得出所提算法能量均衡性能较好,时延优化效果比较理想。展开更多
Ideally, to achieve optimal production in agriculture, crop stress needs to be measured in real-time, and plant inputs managed in response. However, many important physiological responses like photosynthesis are diffi...Ideally, to achieve optimal production in agriculture, crop stress needs to be measured in real-time, and plant inputs managed in response. However, many important physiological responses like photosynthesis are difficult to measure, and current trade-offs between cost, robustness, and spatial measurement capacity of available plant sensors may prevent practical in-field application of most current sensing techniques. This paper investigates a novel application of laser speckle imaging of a plant leaf as a sensor with an aim, ultimately, to detect indicators of crop stress: changes to the dynamic properties of leaf topography on the scale of the wavelength of laser light. In our previous published work, an initial prototype of the laser speckle acquisition system specific for plant status measurements together with data processing algorithms were developed. In this paper, we report a new area based statistical method that improves robustness of the data processing against disturbances from various sources. Water and light responses of the laser speckle measurements from cabbage leaves taken by the developed apparatus are exhibited via growth chamber experiments. Experimental evidence indicates that the properties of the laser speckle patterns from a leaf are closely related to the physiological status of the leaf. This technology has the potential to be robust, cost effective, and relatively inexpensive to scale.展开更多
文摘为进一步提升系统局部性时延,以关系化数据分块作为研究背景,提出系统局部性时延优化算法,采用主机、小型计算机系统接口(Small Computer System Interface, SCSI)通道、单元控制器以及磁盘构建系统,通过关系化数据的邻接关系建立、数据法矢预估、数据曲率求解三个预处理过程,在不同比例下取舍关系化数据,完成数据分块存储操作;根据界定的主节点与分块节点间局部性时延,导出时延上界;将关系化数据分块存储网络结构建立在一个二维曼哈顿平面上,经求解结构图的树结构,构建局部性时延优化算法。以节点生命周期、系统生命周期、通信距离以及局部性时延等作为评估指标展开仿真,验证得出所提算法能量均衡性能较好,时延优化效果比较理想。
文摘Ideally, to achieve optimal production in agriculture, crop stress needs to be measured in real-time, and plant inputs managed in response. However, many important physiological responses like photosynthesis are difficult to measure, and current trade-offs between cost, robustness, and spatial measurement capacity of available plant sensors may prevent practical in-field application of most current sensing techniques. This paper investigates a novel application of laser speckle imaging of a plant leaf as a sensor with an aim, ultimately, to detect indicators of crop stress: changes to the dynamic properties of leaf topography on the scale of the wavelength of laser light. In our previous published work, an initial prototype of the laser speckle acquisition system specific for plant status measurements together with data processing algorithms were developed. In this paper, we report a new area based statistical method that improves robustness of the data processing against disturbances from various sources. Water and light responses of the laser speckle measurements from cabbage leaves taken by the developed apparatus are exhibited via growth chamber experiments. Experimental evidence indicates that the properties of the laser speckle patterns from a leaf are closely related to the physiological status of the leaf. This technology has the potential to be robust, cost effective, and relatively inexpensive to scale.
