Project

# Title Team Members TA Documents Sponsor
22 Fingerprint Recognition Door Lock
 Chengrui Wu
Hanggang Zhu
Haoran Yuan
Lizhuang Zheng
 design_document1.pdf
final_paper1.pdf
final_paper2.pdf
final_paper3.pdf
proposal1.pdf
 Meng Zhang
# Team Members

Chengrui Wu (cw70)
Hanggang Zhu (hz66)
Haoran Yuan (haorany7)
Lizhuang Zheng (lzheng17)

# Project Title

Fingerprint Recognition Door Lock

# Problem

In our Residential College dormitories, each room door requires a student IC card to unlock. However, sometimes students may forget to bring their own card with them when they are out. The current solution is to apply for a temporary card, so a fingerprint recognition door lock can be a better solution. Currently, most fingerprint recognition door locks are integrated units. To install a new one, users must remove the entire old lock, typically requiring professional assistance. But updating all the locks in the Residential College is a huge project, and may affect students’ daily life. Thus, we propose a more user-friendly solution that allows users to integrate advanced fingerprint recognition technology with their current locks, without the needs of extensive installation processes, so that students can install the device by themselves.

# Solution Overview

We aim to create a smart, compact device that can be added to existing locks, enabling fingerprint-based unlocking. So that students can install the device easily by themselves. This device will feature a fingerprint recognition module, a control unit, mechanisms for lock interaction, a mobile app for management and GPS integration, and a wireless communication module. Besides, a security module and a power supply module are needed to support other subsystems.

# Solution Components

## Capacitive Fingerprint Sensor Module

This module will feature a state-of-the-art capacitive fingerprint sensor, known for its high sensitivity and accuracy in capturing detailed fingerprint images. It is designed to efficiently transmit high-resolution fingerprint image data to the Fingerprint Recognition Subsystem. The sensor's advanced technology allows it to quickly and accurately read a fingerprint, even under varying environmental conditions. Its compact size and low power consumption make it an ideal choice for integration into the smart door lock system. The sensor will be interfaced with the STM32 development board, ensuring seamless communication and data transfer between the sensor and the Fingerprint Recognition Subsystem.

## Fingerprint Recognition Subsystem

This will be a high-precision module with algorithms capable of accurately identifying the user's unique fingerprint patterns. It will also be able to store multiple fingerprints the user registered, for shared use among the user and other authorized individuals. The code implementation will be written into STM32 develop board to output True/False signal to the downstream controller subsystem.

## Controller Subsystem

This will be a microcontroller that manages the operations of the device, including processing fingerprint data, controlling lock mechanisms, and coordinating with the mobile app and a wireless communication module designed to retrieve messages from the app. We may choose a STM32 develop board with Wi-Fi module as the platform.

## Software UI

A mobile app for fingerprint recording and remote lock control. It will allow users to manage their fingerprints, remotely control the lock, and adjust settings such as auto-lock and unlock. It will also provide notifications about lock status and usage.

## Wireless Communication Module

An ESP8266 microchip for Wi-Fi connectivity with secure protocols, and a GPS module for location tracking. The microchip will provide the device with Wi-Fi connectivity to communicate with the mobile app, receive updates, and enable remote access and control. The module will also use secure protocols to ensure data privacy and security. Based on the GPS location of the users’ mobile phone, it will allow the lock to unlock automatically when the user's phone is nearby, and lock automatically when it is too far away,

## Security Module

The security module ensures secure wireless communications and app usage, prevents unauthorized access, and verifies user identity. It uses advanced encryption for data transmission and includes mechanisms for detecting and reporting security breaches.

## Mechanical Engine

An actuator to engage/disengage the existing lock mechanism. These will be designed to be compatible with the dorm lock design and will physically engage and disengage the lock mechanism in response to input from the control unit.

## Power Supply Subsystem

This system will include a battery and other components used to power up all the subsystems above, it should be able to last for a significant period.

# Criterion for Success

- Efficient and accurate fingerprint-based unlocking.
- Remote access and control of the lock's status through the app, ensuring exclusive user access.
- Ease of installation and removal from the existing lock, with robust security.
- Lock the door from inside, when the person is left

# Distribution of Work

- Chengrui Wu: Microcontroller and Software App
- Hanggang Zhu: Software App, Fingerprint Recognition and Security Module
- Haoran Yuan: Wireless Communication and Fingerprint Recognition, Chip and sensor selection.
- Lizhuang Zheng: Mechanical Engine, Microcontroller and Power Supply Subsystem

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.