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Tianning Li Projects

Option Warning

Link: https://github.com/lit26/Option_Warning

Website: https://lit26.github.io/Option_Warning/

Description: Create a website and a chrome extension that calculates max profit and max loss for option trading.

Multi People Calendar

Link: http://www.litianningl.com.s3-website-us-east-1.amazonaws.com/multi_people_calendar.html

Description: Create calendar for the multi people.

Weekly Schedule Viewer

Link: www.litianningl.com.s3-website-us-east-1.amazonaws.com/weekly_schedule_viewer.html

Description: A platform to display a visual weekly schedule for the frequent weekly events.

Report Finder

Link: www.litianningl.com.s3-website-us-east-1.amazonaws.com/report_finder.html

Description: A website for efficiently getting annual reports and registration statements of public companies in NASDAQ and NYSE.

Tweet Analysis of COVID-19:


Github
Perform sentiment analysis and topic modeling on 553,396 Twitter posts on how people view about the topics across time.

Text Summarization:


Google Colab playground
Using Gensim or Summa package to summarize paragraphs.

Simple Optical Character Recognition:


Google Colab playground
Using pytesseract package to do simple character recognition.

Heart Disease Classification:


Github
Using k-nearest neighbor to do classify heart disease.

Image Classification


Github
Using Deep Learning Model Keras Inception V3 to do image classification like accident detection and car model classification.

Data Jobs Analyzing


Github
Using LDA to cluster the skills needed for different data jobs.
Visualize the distribution of data jobs in the US in Tableau.

Novel Corona Virus 2019 Analyzing


Github
Data analysis the Virus sprend across the global.

Scheduling the Shift Assignment of UW Dining- Local Point


Goal: Create a weekly working shift schedule for student workers according to their availability.

Prerequisite: We get all the schedule from the manager. For testing, we ask students to volunteering fill out questionnaire for their availability and prefer working hours.

Constrain:
  • Each student need to work minimum 10 hours and maximum 19.5 hours per week.
  • Each shift can only taken by one student.
  • The shifts assign to each student cannot conflict.
  • The assigning shift of each student cannot conflict with each student's availability.
Objective:
  • Minimize the difference between scheduled working hours and prefer working hours
  • Maximize the number of stations each student work

Implementation:

Internet of Life Saving Thing (IoLST) for Firefighters


Goal: Development of an Internet of Things environmental sensing and communication system for fire rescue operations.

Sponsor: Zetron Inc.
Team: Tianning Li, Hong Zhang, Shen Yuan Yao
Industry Mentor: Len Cayetano
Faculty Mentor: James K. Peckol 

Project:
  • UW ECE ENGineering INnovation and Entrepreneurship (ENGINE) SHOWCASE winning team.
  • Create a prototype for the first responder (fireman) to help save lives for the firemen and the people inside the fire scene.
  • Explored the concept of the Internet of Thing (IoT)/ the Internet of Life Saving Things (IoLST) which consists of Devices/ Sensors, Connectivity, Data Storage and Analysis..
  • Experience using AWS system.
Device:
  • Arduino: microcontroller.
  • Sensors: collecting temperature and gas reading.
  • Connectivity: Use Hologram SIM card to send data through LTE-M

Cloud:
  • Using AWS EC2 to receive streaming data.
  • Deploy AWS web hosting with user security.
  • Use mixed SQL and NoSQL data to store the information on the AWS EC2 and RDS.
  • Apply simple data analysis and data processing on the data for displaying on the dashboard.
  • Storing past session data on the AWS RDS which can be easily replaced by other data storage system.
  • Building a web dashboard to visualize data and information for the firemen using the D3.js library.

Resources:

Autonomous RC car


Abstract: Drowsiness of driver is one of the most significant cause of road accident. In this work, we present an embedded system that that simulates a real-time drowsiness detection system based on visual information. We propose three different components that communicate via Bluetooth. Specifically, we use a two-wheel car developed on Tiva LaunchPad to simulate a real autopilot car. A Raspberry Pi is used as a detection system to communicate the eye closure with the car and activate the autonomous mode of the car. Moreover, we provide a LCD display of the current car status to mimic a cloud system that monitors the current car status.

Hardware: Tiva-C LaunchPad, Raspberry Pi

Report: Link