CHI-AN, TSAI

EMAIL: [email protected]

Phone: 0978036180 

    

    Hi, I am CHI-AN, TSAI. I am trained and finished my master thesis in National Taiwan University of Science and Technology(NTUST). I am familiar with image processing, computer vision, object detection(YOLO/Mask-RCNN), embedded system and FPGA. I am also good at Python, C/C++ and verilog. I used to be research assistant in NTUST.  During my tenure as a research assistant, the algorithm I developed showed a better performance than other similar algorithms.                                                                                                                                                       I am a adaptable individual who enjoys taking on challenges. Therefore, during my college years, I embarked on a six-month exchange program to Germany, even though I had never been to Europe before. I am also enjoy working collaboratively with others and maintain an open-minded attitude towards learning new things. Currently, I continue to enhance my knowledge through online courses and practice LeetCode to sharpen my skills. 

Work Experience


Research Assistant

National Taiwan University of Science and Technology

Mar 2022 - Jul 2023
Taipei, Taiwan

Execute project from Taiwan Space Agency(TASA)

- Develop an algorithm for satellite remote sensing and achieve better       performance than other similar algorithms.

- Implement feature extraction algorithm on FPGA, increasing 38.5% on FPS.

Lecturer

University Social Responsibility

Mar 2019 - Jul 2019
Nantou, Taiwan

Offer programming course for students of elementary school in remote area.


Education

2021 - 2023

National Chi Nan University

Master's Degree | Electrical Engineering

2016 - 2020

National Chi Nan University

Bachelor's Degree | Electrical Engineering

Skill


  • Python
  • C/C++
  • Verilog

Software


  • OpenCV
  • Linux
  • Vivado
  • KiCAD/Protel 99 SE

Language


  • TOEIC : 775
  • TOEFL : 82

Experience Highlights


Excellence Award  •  Graduation Exhibition

Jun 2023

Won excellence award in graduation exhibition.


Exchange Student  •  Hochschule Osnabrueck, Germany

Sep 2019 - Jan 2020

Participated in a project program consisted of international students. Design and build a PCB broad for bicycle speedometer without any external power.  Eventually, get a 1.7 in Germany Grade System.

Award in project competition.

Jun 2019

Develop a self-propelled cart for removing the egg of apple snail. Responsible for controlling the self-propelled cart to avoid obstacles and adjust position of cart by image.

Intern • Precision Machinery Research and Develop Center(PMC)

Jul 2018 - Aug 2018 

Research and organize agriculture machinery and AIOT techniques.

Publication


Moving Object Detection for Remote Sensing Video with Satellite Jitter

2023 IEEE International Conference on Consumer Electronics (ICCE 2023)

Jan 2023

Master's Thesis


Moving Vector Based Moving Object Detection for Remote Sensing Video with Satellite Jitter and Feature Extraction Hardware Design

        Developing a moving object detection algorithm for  video with satellite jitter. The algorithm apply feature points extraction to located position of objects. After obtaining moving vectors of feature points, distinguish different moving objects by clustering method. Real moving objects can obtaining by removing objects affect by satellite jitter. Implement feature points extractor on FPGA.

        - The proposed algorithm showed a 704% and 257% improvement over previous project and other similar algorithm respectively.

        - Operation time reduce to 3%~5% of its original duration after improvement.

        -  FPS increase 38.5% on FPGA

Projects

Aircraft Recognition 

Establish and trained Mask-RCNN for recognize aircraft in satellite remote sensing image.  

PC Environment: Ubuntu 

A Self-Propelled Cart for Removing the Egg of Apple Snail

Developing a self-propelled vehicle that can automatically detect and remove apple snail eggs consists of three main parts: the vehicle body, camera, and mechanical arm. The vehicle body and the detection are controlled by Raspberry Pi, while the mechanical arm is controlled by Arduino. The self-propelled vehicle is capable of 360-degree pivot rotation and obstacle avoidance during movement. Once the camera detects apple snail eggs, the vehicle can automatically move to the location of the eggs and use the mechanical arm to remove them. Additionally, the vehicle can adjust its position automatically based on the image to achieve the best removal efficiency. This project received an honorable mention in the bachelor's degree thesis competition. 

License Plate Alert System

Running OpenALPR for recognizing driver license plate on Raspberry Pi. The system can read out licence plate and send alert email.

Dog and Cat Recognizer

Build a deep learning model by CNN to recognize dog and cat.

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