MATLAB模拟粒子散射代码光束传播法（BPM）的大规模全息粒子3D成像 MatLab实现文件。 我们提供了光束传播方法的正向模型和重建算法代码，模拟的样本对象，全息图，重建的粒子以及样本实验捕获的全息图，重建结果。 引文 如果您发现该项目对您的研究有用，请考虑引用我们的论文： 抽象的 我们为3D粒子场的大规模全息重建开发了一种新颖的算法。 我们的方法基于结合稀疏正则化的多散射光束传播方法（BPM），该方法可从单个全息图中恢复高折射率对比度的致密3D粒子。 我们显示，BPM计算的全息图生成的强度统计数据与实验测量值非常匹配，并且比单散射模型提供高达9倍的精度。 为了解决反问题，我们设计了一种计算效率高的算法，与基于最新技术的基于多重散射的技术相比，该算法将计算时间减少了两个数量级。 我们在不同散射强度下的仿真和实验中均展示了卓越的重建精度。 我们表明，对于深成像深度和高粒子密度，BPM重建显着优于单散射方法。 概述图 如何使用程式码 正向模型： 重建： 数据 dz = lambda / 16的模拟对象：object / simulatedData / density_1.6 重建对象的

The dataset is a large-scale detailed 3D face dataset and riggable 3D face prediction. It is included in CVPR2020. 本数据集包含大量细致的3D人脸图像，可用于3D人脸预测，相关论文被收录在CVPR2020。 Yang_FaceScape_A_Large-Scale_High_Quality_3D_Face_Dataset_and_Detailed_CVPR_2020_paper.pdf

Large-scale Incremental Processing Using Distributed Transactions and Notifications.pdf Updating an index of the web as documents are crawled requires continuously transforming a large repository of existing documents as new documents arrive. This task is one example of a class of data processing tasks that transform a large repository of data via small, independent mutations. These tasks lie in a gap between the capabilities of existing infrastructure.

The common wisdom is that distributed transactions do not scale. But what if distributed transactions could be made scalable using the next generation of networks and a redesign of distributed databases? There would be no need for developers anymore to worry about co-partitioning schemes to achieve decent performance. Application development would become easier as data placement would no longer determine how scalable an application is. Hardware provisioning would be simplified as the system ...

F1 Query is a stand-alone, federated query processing platform that executes SQL queries against data stored in different filebased formats as well as different storage systems at Google (e.g., Bigtable, Spanner, Google Spreadsheets, etc.). F1 Query eliminates the need to maintain the traditional distinction between different types of data processing workloads by simultaneously supporting: (i) OLTP-style point queries that affect only a few records; (ii) low-latency OLAP querying of large ...

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大规模贝叶斯层次建模 ，博客文章附带了源代码 。 注意：该代码是GPL-2许可的。 如果出于商业原因，您需要更宽松的许可，请与我联系以获得您的企业的许可。 安装 为了设置必要的环境： 创造一种环境， bhm-at-scale的帮助下， conda env create -f environment.yaml # or `environment.lock.yaml` for exact reproduction 激活新环境 conda activate bhm-at-scale 使用以下bhm-at-scale安装bhm-at-scale ： python setup.py install # or `develop` 然后看一看notebooks文件夹。 依赖性管理和可重复性 如果以后要通过pip和安装软件包，请始终在environment.yaml以及最终在setup.cf

西门子S7-200 smart 模拟量转换库及比例说明（S7-200通用）scale模拟转换.rar scale.zip 对S7-200 的模拟量值进行比例换算.pdf