Project

# Title Team Members TA Documents Sponsor
69 Paint Color and Gloss Classification Device
James Lee
Victoria Lee
Chihun Song design_document1.pdf
final_paper1.pdf
photo1.png
photo2.png
presentation1.pdf
proposal1.pdf
video
# Title
Paint / Sheen Analysis Device

# Team Members:
- James Lee (jl212)
- Victoria Lee (vlee33)

# Problem
Homeowners, renters, and especially college students frequently face the challenge of matching existing wall paint and texture for touch up or repairs often without access to the original paint can. While it is possible to peel a physical chip off the wall to scan it, it is an inconvenient process. While mobile apps exist they rely on smartphone cameras which use auto white balance and are heavily infused by ambient lighting. These current solutions do not account for sheen such as matte vs eggshell meaning that even the best color match can look off once applied. This resulted in wasted time and materials and a poor result / color match.

# Solution
We propose a non-destructive "Paint/Surface Analysis Device" that accurately identifies both wall color and sheen without removing a physical paint chip. Our device utilizes a controlled lighting environment and a spectral color sensor to determine the precise color composition (hex code) of the wall. To address the gloss, the device integrates a secondary computer vision subsystem utilizing "raking light" (low-angle side lighting). This illumination technique reveals the paint finish (e.g., gloss vs. semi-gloss) Describe your design at a high-level, how it solves the problem, and introduce the subsystems of your project.

## Subsystem 1: Microcontroller and Processing
Coordinates sensor data acquisition, executes matching algorithms, and manages system timing. It converts spectral data into the standard color space. From there, we match the color to color database stored in memory.
Components: STM32F7 Series Microcontroller (High-performance with DCMI for camera support)
## Subsystem 2: Sheen Analysis
We intend to shine an LED light at a 60 degree angle and measure how much light bounces off. If there is a lot of bounce the surface would be considered glossy if there is little bounce the surface would be considered matte.
Components: Low-angle "Raking Light" LED array, AS7341 11-Channel Spectral Sensor, calibrated neutral-white LED, Photodiode


## Subsystem 3: Spectral Sensing
Measures the absolute color composition of the sample under calibrated internal lighting.
Components: AS7341 11-Channel Spectral Sensor, calibrated neutral-white LED
## Subsystem 4: User Interface
Displays the identified paint brand, color name, and recommended applicator type.
Components: 2.8" TFT LCD Display, Rotary Encoder for menu navigation
## Subsystem 5: Power Management
Regulates external power for sensitive analog sensors and high-current LED subsystems.
Components: 12V DC Wall Adapter, Buck Converters (5V), and Low-Noise LDO Regulators (3.3V)
## Subsystem 6: Enclosure
Blocks outside light and fixes spectral sensor position/angle for reproducible results
Components: Cardboard Box with fixed cutouts for reproducible measurements

# Criterion for Success
Color Accuracy: Achieve a color match with a Delta-E < 3.0 across multiple measurements, which represents a commercially acceptable match for consumer-grade applications.
How Is Color Measured? Calculating Delta E | ALPOLIC®
Sheen Classification: Correctly distinguish between "Gloss," "Semi-Gloss," and “Flat” with 90% accuracy.
Ambient Isolation: Maintain consistent color readings regardless of external room lighting conditions.

CHARM: CHeap Accessible Resilient Mesh for Remote Locations and Disaster Relief

Martin Michalski, Melissa Pai, Trevor Wong

Featured Project

# CHARM: CHeap Accessible Resilient Mesh for Remote Locations and Disaster Relief

Team Members:

- Martin Michalski (martinm6)

- Trevor Wong (txwong2)

- Melissa Pai (mepai2)

# Problem

There are many situations in which it is difficult to access communicative networks. In disaster areas, internet connectivity is critical for communication and organization of rescue efforts. In remote areas, a single internet connection point often does not cover an area large enough to be of practical use for institutions such as schools and large businesses.

# Solution

To solve these problems, we would like to create a set of meshing, cheap, lightweight, and self-contained wireless access points, deployable via drone. After being placed by drone or administrator, these access points form a WiFi network, usable by rescuers, survivors, and civilians. Our network will have QoS features to prioritize network traffic originating from rescuers. Having nodes/access points deployable by drone ensures we are able to establish timely connectivity in areas where search and rescue operations are still unable to reach.

Over the course of the semester, we will produce a couple of prototypes of these network nodes, with built in power management and environmental sensing. We aim to demonstrate our limited network’s mesh capabilities by setting up a mock network on one of the campus quads, and connecting at various locations.

# Solution Components

## Router and Wireless Access Point

Wireless Access for users and traffic routing will be the responsibility of an Omega2 board, with onboard Mediatek MT7688 CPU. For increased signal strength, the board will connect to a RP-SMA antenna via U.FL connector.

The Omega2 will be running OpenWRT, an Linux-based OS for routing devices. We will develop processes for the Omega2 to support our desired QoS features.

## Battery Management System

This module is responsible for charging the lithium-ion battery and ensuring battery health. Specifically, we will ensure the battery management system has the following features:

- Short circuit and overcurrent protection

- Over- and under-voltage protection

- An ADC to provide battery status data to the microcontroller

- 3.3v voltage regulation for the microcontroller and other sensors

In addition to miscellaneous capacitors and resistors, we intend to use the following components to implement the battery management system:

- The MT2492 step-down converter will be used to step down the output voltage of the battery to 3.3 volts. Between the GPS and extra power the microcontroller might consume with an upgraded Wifi antenna, low-dropout regulators would not provide sufficient power in an efficient manner. Instead, we will implement a 2 amp buck converter to improve efficiency and ensure there are no current bottlenecks.

- We will utilize two button-top protected 18650 3400 mAh lithium ion batteries in series to power each node. Placing two of these batteries in series will ensure their combined voltage never falls below the minimum voltage input of the buck converter, and accounting for the buck converter’s inefficiency these batteries should give us about 21 Wh of capacity. The cells we plan on using include a Ricoh R5478N101CD protection IC that provides over-voltage, under-voltage, and over-current protection. Using a standard battery form factor will make them easy to replace in the future as needed.

- A USB-C port with two pulldown resistors will provide 5 volt charging input with up to 3 amps of current, depending on the charger.

- The MT3608 step-up converter will boost the input voltage from the usb-c port and feed it into the charging controller.

- The MCP73844 Charge Management Controller will be used to charge the batteries. This controller supports CC/CV charging and a configurable current limit for safe and effective battery charging.

- The TI ADS1115 ADC will be used for battery voltage monitoring. This chip is used in the official Omega2 expansion board, so it should be easy to integrate in software. We will use a voltage divider to reduce the battery voltage to a range this chip can measure, and this chip will communicate over an I2C bus.

## Sensor Suite

Each node will have a battery voltage sensor and GPS sensor, providing the system with health information for each node. On top of the Wifi-connectivity, each module would have a series of sensors to detect the status of the physical node and helpful environment variables. This sensor suit will have the following features and components to implement it

- Ultimate GPS Module PA1616D will be used for positioning information. This chip utilizes 3.3V which is supplied through our battery management system.

Battery Voltage Monitor

- The TI ADS1115 ADC (mentioned in the BMS section) is for battery voltage monitoring. It interfaces via I2C to the Omega2.

## System Monitor

A system monitor which provides visibility of the overall system status for deployed network nodes. Information that we will show includes: last known location, battery health, and network statistics (e.g. packets per second) from the physical devices.

We plan on using React to provide an intuitive UI, using google-map-react and other React packages to create an interactive map showing the last known location and status of each node.

The backend will be hosted on a server in the cloud. Nodes will continually update the server with their status via POST requests.

# Criterion For Success

We aim to achieve the following performance metrics:

- 1.5 kg maximum mass

- Cover 7500 m^2 (North Quad) with 4 nodes

- Display the last known location, time connected, and battery voltage for all nodes via our system monitor

- 3 hour battery life

- 5 Mb/s WiFi available to laptops and smartphones in the coverage area

[*Link*](https://courses.engr.illinois.edu/ece445/pace/view-topic.asp?id=71252) *to assciated WebBoard discussion*