Automated Silicon-Substrate Ultra-Microtome for Automating the Collection of Brain Sections in Array Tomography

Understanding the structure and working principle of brain neural networks requires three-dimensional reconstruction of brain tissue samples using array tomography method.In order to improve the reconstruction performance,the sequence of brain sections should be collected with silicon wafers for subsequent electron microscopic imaging.However,the current collection of brain sections based on silicon substrate involve mainly manual collection,which requires the involvement of automation techniques to increase collection efficiency.This paper presents the design of an automatic collection device for brain sections.First,a novel mechanism based on circular silicon substrates is proposed for collection of brain sections;second,an automatic collection system based on microscopic object detection and feedback control strategy is proposed.Experimental results verify the function of the proposed collection device.Three objects(brain section,left baffle,right baffle)can be detected from microscopic images by the proposed detection method.Collection efficiency can be further improved with position feedback of brain sections well.It has been experimentally verified that the proposed device can well fulfill the task of automatic collection of brain sections.With the help of the proposed automatic collection device,human operators can be partially liberated from the tedious manual collection process and collection efficiency can be improved.

城市轨道交通动态客流分配仿真方法研究

根据轨道交通网络存在大量换乘路径的特点,改进深度优先搜索算法得出站点间换乘路径的有效出行时间。基于自动票务收集系统(automatic fare collection system,AFC)数据得到的乘客进出闸机时刻,利用仿真方法确定乘客与列车在时间和路径的接续关系,同时考虑始发乘客和换乘乘客路径选择行为的差异,将二者区分配流。动态更新先到乘客利用换乘路径的出行时间,并以更新后的时间作为后续出发乘客的路径选择依据。结果表明,该仿真方法可以有效反映乘客的出行过程,具有较高的配流精度。