16-19 October 2018
Asia/Taipei timezone


Tutorial Session I: Graphical System Design for Big Physics Applications

(09:00 ~ 10:30 Tuesday Oct. 16)

Speaker: John Wu (National Instruments, Technical Marketing Manager)

From the CERN Large Hadron Collider to the ESO Extremely Large Telescope (ELT), National Instruments LabVIEW has played a critical part in the development, debugging, and deployment of these complex systems. In this tutorial, the concepts of LabVIEW and graphical programming will be introduced. Also, we will explore how to use LabVIEW to simplify common tasks in synchrotron development, such as EPICS protocol communication, as well as graphical programming on low-latency FPGA targets.


Tutorial Session II: Building Predictive Models for Sensor Data Analytics

(10:50 ~ 12:20 Tuesday Oct. 16)

Speaker: Jeffrey Liu (TeraSoft Inc., Senior Application Engineer)

Machine learning and Deep Learning are quickly becoming powerful tools for solving complex modeling problems across a broad range of industries. The benefits of machine learning are being realized in applications everywhere, including predictive maintenance, health monitoring, financial portfolio forecasting, and advanced driver assistance.  However, developing predictive models for signals is not a trivial task. In addition, there is an increasing need for developing smart sensor signal processing algorithms which can be either deployed on edge nodes or on the cloud. 

In this session we will explore how you can use MATLAB for developing predictive models for real world sensor analytics using machine learning and deep learning workflows.


Tutorial Session III: Dive into Python

(13:30 ~ 15:00 Tuesday Oct. 16)

Speaker: Chi-Hung Weng (HongHuTech, Data Scientist)

Speaker will showcase some possible applications written in Python, ranging from web crawling, data cleaning, data visualization, to Machine Learning and Deep Learning. We then make an excursion to Deep Learning, where the following questions are to be answered: what is Deep Learning? what’s the theory behind it? What’s the difference between Deep Learning frameworks such as TensorFlow, Keras, MXNet and Pytorch?


Tutorial Session IV: Deep Learning

(15:20 ~ 17:00 Tuesday Oct. 16)

Speaker: Chi-Hung Weng (HongHuTech, Data Scientist)

Speaker will demonstrate & explain several deep learning applications in Computer Vision, including: image classification, object detection and semantic segmentation. Sample codes and datasets will be provided during the session.