Course Overview

Welcome to ECE 445/ME470 Senior Design ZJUI Spring 2022!

Welcome to the class! If you've looked at the course Calendar, you've probably already noticed that this class is quite different from most other classes in the department. The class only meets as a whole for the first four weeks of the semester. During these lectures you will meet the Course Staff, learn about specific requirements, resources, and project choices for the course, and have a chance to meet other students. These are some of the most important weeks for the class since the decisions you make during this time will determine what you'll get out of this class and, in many ways, how much you'll enjoy it.

In this course, you will form teams and propose projects that solve an engineering problem in a unique way. The projects generally involve a device that you will design, build, and demonstrate. We are excited to see what projects you create with this semester! In the midst of an ever changing learning environment, we want to encourage you to think, create, design, and build exemplary projects. We want to ensure that your experience in 445 demonstrates your potential as an engineer graduating from the University of Illinois.

This course is taught hybridly for ME and ECE students, and some projects are mentored by ZJUI faculty. Here are a few items that you will need to consider as we enter into this semester.

ML-based Weather Forecast on Raspberry Pi

Xuanyu Chen, Zheyu Fu, Zhenting Qi, Chenzhi Yuan

Featured Project

#Team Members

Zheyu Fu (zheyufu2@illinois.edu 3190110355)

Xuanyu Chen (xuanyuc2@illinois.edu 3190112156)

Chenzhi Yuan (chenzhi2@illinois.edu 3190110852)

Zhenting Qi (qi11@illinois.edu 3190112155)

#Problem

Weather forecasting is crucial in our daily lives. It allows us to make proper plans and get prepared for extreme conditions in advance. However, meteorologists always get it wrong half of the time and still keep their job :) To overcome the limitations of traditional weather forecasting, machine learning models have become increasingly important in weather forecasting. Building our own weather forecast ML system is a perfect idea for us to analyze vast amounts of area data and generate more accurate and timely weather predictions on the go in our surrounding areas.

#Solution Overview

A weather forecast system can be created by using a few different hardware components and software tools. Our solution mainly consists of two parts. For weather measurement and data collection, temperature, humidity, and barometric pressure sensors are considered the main components. A machine learning-based algorithm is to be applied for data analysis and weather predictions.

#Solution Components

##Hardware Subsystem

Due to the complexity of weather conditions, our system incorporates the following weather indicators and their corresponding collectors:

-a barometric pressure sensor, a temperature sensor, and a humidity sensor

-a digital thermal probe for heat distribution

-an anemometer for wind speed, wind vane for wind direction, and rain gauge for precipitation

The aforementioned equipment would be integrated into a single device, and weatherproof enclosures are needed to protect it. Plus, a Raspberry Pi, either with built-in wireless connectivity or a WiFi dongle, is required for conducting computations.

##Software Subsystem

A practically usable weather forecast system is supposed to make reliable predictions for real-world multi-variable weather conditions. We apply Machine Learning techniques to suffice such generalization to unseen data. To this end, a high-quality dataset for training and evaluating the Machine Learning model is required, and a specially designed Machine Learning model would be developed on such a dataset. Once a well-trained system is obtained, we deploy the such model on portable devices with easy-to-use APIs.

#Criterion for Success

1. The weather measurement prototype with sensors should be able to accurately collect the temperature, humidity, and barometric pressure. etc.

2. A machine learning algorithm should be successfully trained to make predictions on the weather conditions: rainy, sunny, thunderstorm, etc.

3. Our system can forecast the weather in Haining, in real-time, and/or longer-period forecast.

4. The forecasted weather information could be demonstrated elegantly through some UI interface. A display screen would be a baseline, and an application on phones would be extra credit if time permitted.

5. Extra: Make our own weather dataset for Haining. If good, make it open-source.

#Work Distribution

**EE Student Zheyu Fu**:

-Design the sensor module circuit

-Development of visualization interface

**ECE Students Xuanyu Chen & Zhenting Qi**:

-Weather data collection and analysis

-Build and test Machine Learning model on Raspberry Pi

**ME Student Chenzhi Yuan**:

-Physical structure hardware design

-Proper distribution of the sensors to collect accurate data on temperature, humidity, barometric pressure, etc.