Project

# Title Team Members TA Documents Sponsor
17 Arduino-Powered Network Flow Visualization Toolbox
 Bolin Zhang
Jiahao Fang
Yiyang Huang
Ziyuan Chen
 design_document2.pdf
design_document3.pdf
final_paper1.pdf
final_paper2.pdf
final_paper3.pdf
proposal1.pdf
proposal2.pdf
video
 Pavel Loskot
## PROJECT DESCRIPTION

Many real-world systems involve flows over networks. Our team aims to build a **modular, reconfigurable hardware emulator** to visualize network flows under capacity constraints on links. Each node can be configured to act as a sink, a source, or a "transfer station" that holds zero flux. This toolset will facilitate the understanding of flow optimization algorithms in a classroom setting.

## SOLUTION OVERVIEW

We use a scalable design where components are easily replaceable to account for network expansion. The emulator should have a central Arduino controller that talks to each node and link to display the capacities and actual flow amounts.

*Tentative: It may be desirable to have a software GUI to display the network alongside the physical model due to space (# LEDs) and protocol (# pins) constraints in each node/link.*

## SOLUTION COMPONENTS

### Subsystem 1: Physical Network Model

- We should build a fully functional physical model where pipes represent network links and the LEDs within show the maximal capacity and real-time flow of "data packets."
- Each node should be configurable as sink, source, or neither ("transfer station") with a user-friendly interface such as buttons or switches.

### Subsystem 2: Software Flow Computer

- We should build an intuitive software interface that allows the user to easily configure nodes (3 modes) and links (capacity) while controlling the LED flow display.
- We should implement a robust and *lightweight* optimization algorithm that efficiently computes network flows on an embedded Arduino board while considering all constraints (node configurations, link capacities).
- Alongside the design process, we should write comprehensive documentation detailing the manuals for software setup, operation, troubleshooting, and our development process.

## CRITERION OF SUCCESS

- The physical model should be modular, i.e., each node has a certain number of "slots" reserved for installing new links (pipes).
- The Arduino software should communicate with all nodes and pipes and update the flows in real-time in response to changes in setup. At the current stage, we aim to serve 4~6 fully connected nodes.
- The algorithm should handle (and report) edge cases such as a network with zero or multiple feasible flows.

## DISTRIBUTION OF WORK

- Ziyuan Chen (ECE) - software developer: maintain the code for flow optimization and Arduino-hardware communication protocol
- Bolin, Jiahao (EE) - hardware developer: handle the physical layout of peripherals (pipes and LEDs), design user interface
- Yiyang Huang (ME) - integration and testing specialist: design the protocol for node configuration and conduct stress tests in edge cases

A Wearable Device Outputting Scene Text For Blind People

Hangtao Jin, Youchuan Liu, Xiaomeng Yang, Changyu Zhu

A Wearable Device Outputting Scene Text For Blind People

Featured Project

# Revised

We discussed it with our mentor Prof. Gaoang Wang, and got a solution to solve the problem

## TEAM MEMBERS (NETID)

Xiaomeng Yang (xy20), Youchuan Liu (yl38), Changyu Zhu (changyu4), Hangtao Jin (hangtao2)

## INSTRUCTOR

Prof. Gaoang Wang

## LINK

This idea was pitched on Web Board by Xiaomeng Yang.

https://courses.grainger.illinois.edu/ece445zjui/pace/view-topic.asp?id=64684

## PROBLEM DESCRIPTION

Nowadays, there are about 12 million visually disabled people in China. However, it is hard for us to see blind people in the street. One reason is that when the blind people are going to the location they are not familiar with, it is difficult for blind people to figure out where they are. When blind people travel, they are usually equipped with navigation equipment, but the accuracy of navigation equipment is not enough, and it is difficult for blind people to find the accurate position of the destination when they arrive near the destination. Therefore, we'd like to make a device that can figure out the scene text information around the destination for blind people to reach the direct place.

## SOLUTION OVERVIEW

We'd like to make a device with a micro camera and an earphone. By clicking a button, the camera will take a picture and send it to a remote server to process through a communication subsystem. After that, text messages will be extracted and recognized from the pictures using neural network, and be transferred to voice messages by Google text-to-speech API. The speech messages will then be sent back through the earphones to the users. The device can be attached to glasses that blind people wear.

The blind use the navigation equipment, which can tell them the location and direction of their destination, but the blind still need the detail direction of the destination. And our wearable device can help solve this problem. The camera is fixed to the head, just like our eyes. So when the blind person turns his head, the camera can capture the text of the scene in different directions. Our scenario is to identify the name of the store on the side of the street. These store signs are generally not tall, about two stories high. Blind people can look up and down to let the camera capture the whole store. Therefore, no matter where the store name is, it can be recognized.

For example, if a blind person aims to go to a book store, the navigation app will tell him that he arrives the store and it is on his right when he are near the destination. However, there are several stores on his right. Then the blind person can face to the right and take a photo of that direction, and figure out whether the store is there. If not, he can turn his head a little bit and take another photo of the new direction.

![figure1](https://courses.grainger.illinois.edu/ece445zjui/pace/getfile/18612)

![figure2](https://courses.grainger.illinois.edu/ece445zjui/pace/getfile/18614)

## SOLUTION COMPONENTS

### Interactive Subsystem

The interactive subsystem interacts with the blind and the environment.

- 3-D printed frame that can be attached to the glasses through a snap-fit structure, which could holds all the accessories in place

- Micro camera that can take pictures

- Earphone that can output the speech

### Communication Subsystem

The communication subsystem is used to connect the interactive subsystem with the software processing subsystem.

- Raspberry Pi(RPI) can get the images taken by the camera and send them to the remote server through WiFi module. After processing in the remote server, RPI can receive the speech information(.mp3 file).

### Software Processing Subsystem

The software processing subsystem processes the images and output speech, which including two subparts, text recognition part and text-to-speech part.

- A OCR recognition neural network which is able to extract and recognize the Chinese text from the environmental images transported by the communication system.

- Google text-to-speech API is used to transfer the text we get to speech.

## CRITERION FOR SUCCESS

- Use neural network to recognize the Chinese scene text successfully.

- Use Google text-to-speech API to transfer the recognized text to speech.

- The device can transport the environment pictures or video to server and receive the speech information correctly.

- Blind people could use the speech information locate their position.