Visible light positioning has the potential to yield sub-centimeter accuracy in indoor environments, yet conventional received signal strength (RSS)-based localization algorithms cannot achieve this because their performance degrades from optical multipath reflection. However, this part of the optical received signal is deterministic due to the often static and predictable nature of the optical wireless channel. In this paper, the performance of optical channel impulse response (OCIR)-based localization is studied using an artificial neural network (ANN) to map embedded features of the OCIR to the user equipment's location. Numerical results show that OCIR-based localization outperforms conventional RSS techniques by two orders of magnitude using only two photodetectors as anchor points. The ANN technique can take advantage of multipath features in a wide range of scenarios, from using only the DC value to relying on high-resolution time sampling that can result in sub-centimeter accuracy.

DeepAngle is a machine learning-based method to determine the contact angles of different phases in the tomography images of porous materials. Measurement of angles in 3--D needs to be done within the surface perpendicular to the angle planes, and it could become inaccurate when dealing with the discretized space of the image voxels. A computationally intensive solution is to correlate and vectorize all surfaces using an adaptable grid, and then measure the angles within the desired planes. On the contrary, the present study provides a rapid and low-cost technique powered by deep learning to estimate the interfacial angles directly from images. DeepAngle is tested on both synthetic and realistic images against the direct measurement technique and found to improve the r-squared by 5 to 16% while lowering the computational cost 20 times. This rapid method is especially applicable for processing large tomography data and time-resolved images, which is computationally intensive. The developed code and the dataset are available at an open repository on GitHub (https://www.github.com/ArashRabbani/DeepAngle).

在这项研究中，我们为心脏磁共振图像中心室分割掩模的时间外推定了一种像素跟踪方法。像素跟踪过程从心脏周期的末端框架开始，使用可用的手动分割图像来预测终端节感应分割掩码。Superpixels方法用于将原始图像分为较小的单元格，在每个时间范围内，新标签都分配给图像单元，从而导致跟踪心脏壁元件通过不同框架的运动。将收缩期末端的履带掩膜与已经可用的手动分割面罩进行了比较，并且发现骰子得分在0.81至0.84之间。考虑到所提出的方法不一定需要培训数据集这一事实，在培训数据受到限制的情况下，这可能是一种有吸引力的深度学习分割方法的替代方法。

在这项研究中，已经开发了一种方法来改善组织学人胎盘图像的分辨率。为此，已经收集了一系列成对的高分辨率图像，以训练深层神经网络模型，该模型可以预测改善输入图像分辨率所需的图像残差。U-NET神经网络模型的修改版本已量身定制，以找到低分辨率和残留图像之间的关系。在1000张图像的增强数据集上训练了900个时期后，用于预测320张测试图像的相对平均平方误差为0.003。所提出的方法不仅改善了细胞边缘处低分辨率图像的对比度，而且添加了模仿胎盘绒毛空间的高分辨率图像的关键细节和纹理。

这里提出的研究提供了对Ghazal的数字洞察力 - 乌尔都语诗歌中最受赞赏的类型。这项研究探讨了4,754级诗人生产的4,754个诗人，探讨了Urdu Ghazal的主要特征，使其比其他形式更受欢迎和钦佩。提供了详细的解释，以及用于表达爱情，自然，鸟类和鲜花等的单词类型。也认为是诗人在诗歌中对其所属的人讨论的方式。使用多维缩放进行数值分析诗歌风格，以揭示引起批评者注意力的不同诗意作品的词汇分集和相似性/差异，例如iqbal和ghalib，mir taqi mir和mir dard。这里产生的分析对于计算风格学，神经认知诗学和情感分析特别有利。