詹亞樹 CHAN, YA-SHU

Interesting in learning new things.
Like to complete the project through teamwork. Like basketball.
Willing to be a listener and share ideas.
Have the courage to try new challenges.

  Taichung City, Taiwan

Job Objective : Software Development Engineer 

Gmail: [email protected]

Telephone: +886 921533758

Education

2017 - 2020

National Taichung University of Education, M.S. degree

Department of Computer Science

Lab : Smart IOT lab 

Advising professor : 張林煌(Lin-huang Chang) 、 李宗翰(Tsung-Han Lee)

Master’s thesis : A Deep Learning Approach of MOS-based Handover Prediction Mechanism for LTE Mobile Networks

Analyze different QoE to satisfy different network service

Use deep learning models(MLP, CNN, RNN, LSTM) for handover prediction.

Use Mininet-WiFi as the simulator test-bed.

Field of research : LTE、Deep learning、QoE、MOS、Handover

Use of Technic : Mininet-WiFi, Iperf, Multilayer Perceptron(MLP),  Convolutional Neural Network(CNN), Recurrent Neural Network(RNN), Long short-term memory(LSTM)

Project Participation 

2018 - 2020 

科技部整合型多年計畫 MOST 107-2221-E-142-002-MY2 : Deep Learning based QoS Design and Implementation Using Software Defined Networks 

The goal of the project is using deep learning models to classify the trac ow in SDN network and setting different QoS rules for the classed results.

Paper published:

Ya-Shu Chan, Tsung-Han Lee, Lin-Huang Chang, “A Deep Learning Prediction Approach for Handover Mechanism in Wireless Network,” 2018 Taiwan Academic Network Conference(Best paper award)

Ya-Shu Chan, Tsung-Han Lee, Lin-Huang Chang, “A Deep Learning Approach of Handover Prediction Mechanism for Software Defined Wireless Networks,” The 18th Conference on Information Technology and Applications in Outlying Islands, 2019

Tsung-Han Lee, Ya-Shu Chan and Lin-Huang Chang, “The MOS-based Handover Mechanism for Video Conference Services in LTE Networks,” in 1st International Workshop on Technology of AI and Wireless Advanced Networking: Dependable Computing and Communication, 2020

2018 - 2020 

教育部行動寬頻課程推廣計畫 

 LoRaWAN, Contiki OS, LinkIt7697, MQTT, MediaTek Cloud Sandbox(MCS)

Using Embedded System LinkIt 7697 to manage IOT sensor. Collect sensor data through LoRaWAN and transport data to MCS through MQTT protocol.

Skill


Programming languages

  • C / C++
  • Java
  • Python

General Skill

  • Linux
  • Network Protocol(TCP/IP, UDP)
  • Software Defined Network
  • Deep Learning(Tensorflow, Keras)
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