Lab Notebook

Video Lecture

Video, Slides

Description

Keeping a professional lab notebook is a requirement of the course. If maintained properly, lab notebooks serve as an official and legal record of the development of the intellectual property related to your project. It also serves as a way to document and track changes to your design, results of all tests performed, and the effort you have put into your project. A well-kept notebook will simplify writing of all required documentation for this course (design review, final paper, etc) as all of the information in those documents should already exist in your notebook. Finally, keeping a notebook is simply good engineering practice and likely will be required by future employers, so it is a good idea to get in the habit of maintaining one now.

The Book: Any notebook with permanent bindings designed for laboratory record keeping is acceptable. Those with pre-numbered pages are required. Ideally, it should have graph rulings on alternate pages, or else quarter-inch square grid on all pages. We will not accept normal spiral-bound notebooks, as these are not permissible in court since pages can be easily replaced. While most of you probably won't be taking your design to court, we want to teach you to get into the habit of keeping legally acceptable records. Some of you may decide you do want to patent your project, so it will be very beneficial to have given yourself the legal advantage from the start.

We will allow you to keep your notebook on a computer, but entries will still need to be printed out and attached to a physical notebook for weekly meetings. Keep in mind also that it may be easier in the long run to scratch out rough graphs and equations on paper, so try to plan ahead. If you know you'll have a lot of graphs, equations, etc., don't make more work for yourself than you need to. Do NOT email your notebook entries to your TA unless he or she specifically requests that you do so.

Notebook entries: Each complete entry should include:

  1. Date
  2. Brief statement of objectives for that session
  3. Record of what was done

The record will include equations, diagrams, and figures. These should be numbered for reference in the narrative portion of the book. Written entries and equations should appear on the right-hand page of each pair. Drawn figures, diagrams, and photocopies extracted from published sources should be placed on the left-hand side, which is graph-ruled. All separate documents should be permanently attached to the notebook. All hand-written entries must be made in pen.

Overall, the book should contain a record that is clear and complete, so that someone else can follow progress, understand problems, and understand decisions that were made in designing and executing the project.

What to include:

There is always something to record:

Suppose you are only "kicking around" design ideas for the project with someone, or scanning library sources. Your objective is what you're hoping to find. The record shows what you found or what you decided and why, even if it isn't final.

One of the most common errors is to fail to record these seemingly "unimportant" activities. Down the road, they may prove crucial in understanding when and where a particular idea came from.

Requirements and Grading

Lab notebooks will be graded according to the lab notebook evaluation sheet at the end of the semester.

Submission and Deadlines

Lab notebooks must be submitted at lab checkout on Reading Day. If you are unable to attend lab checkout, please make arrangements with your TA ahead of time.

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.