Deep learning based classification of aerosol particles from holographic imagery

基于深度学习的全息图像气溶胶颗粒分类

• 批准号: 2596645
• 金额: --
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2596645
• 关键词: Deep; learning; based; classification; aerosol

Year 1 CDT Student, to be updated when details are confirmed (summer 2022) but outline description is: Theme: Measurement techniquesThe impacts aerosol particles have are linked to their origin. Very few experiments are able to record the information to make that distinction in real time [e.g. volcanic ash detection]. However, digital holography combined with deep learning algorithms offer an exciting new potential to make that distinction.

一年级CDT学生，将在确认细节时更新（2022年夏天），但概要描述是：主题：测量技术气溶胶粒子的影响与它们的起源有关。很少有实验能够记录真实的信息来进行区分[例如火山灰检测]。然而，数字全息术与深度学习算法相结合，为实现这一区别提供了令人兴奋的新潜力。