Project

# Title Team Members TA Documents Sponsor
16 Design of a Raspberry Pi–based monitoring system for shared living environments
 Denghan Xiong
Jihao Li
Mujia Li
Shixuan Ma
 proposal1.pdf
 Chao Qian
# Problem

In shared living environments such as dormitories or shared apartments, traditional access control methods such as keys or passwords can be inconvenient and insecure. Keys can be lost or copied, and passwords can be shared or forgotten. These problems make it difficult to ensure security and manage access effectively.

In addition, traditional door locks cannot automatically identify who is entering or keep records of access events. Residents or administrators may want a system that can automatically recognize authorized users and log entry activities for security purposes.

Therefore, there is a need for a lightweight and low-cost smart access control system that can detect people approaching the door, identify authorized users, and manage access automatically.

# Solution Overview

Our solution is to design a smart access control system based on a Raspberry Pi for shared living environments such as dormitories or shared apartments. The system uses a PIR motion sensor to detect human presence near the door. When motion is detected, a USB camera connected to the Raspberry Pi captures images of the person standing at the door.

The captured images are processed using computer vision techniques, and a face recognition algorithm is used to determine whether the person is an authorized user. If the user is recognized, the system activates a relay module to simulate unlocking the door. If the person is not recognized, the system records the event and displays the result.

The system also includes a graphical user interface developed with PyQt5 to display the camera feed, recognition results, and system status. A local database is used to store user information and access records.

# Solution Components

## Subsystem I – Hardware System

### Hardware I.a Motion Detection Sensor
- A PIR motion sensor detects human movement near the door.
- When motion is detected, the sensor sends a signal to the Raspberry Pi.
- This signal triggers the image capture and recognition process.

### Hardware I.b Camera Module
- A USB camera connected to the Raspberry Pi captures images of the person at the door.
- The camera provides real-time video frames for face detection and recognition.

### Hardware I.c Door Control Module
- A relay module controlled by the Raspberry Pi simulates the door unlocking mechanism.
- When an authorized user is detected, the relay activates to unlock the door.

## Subsystem II – Image Processing and Recognition

### Software II.a Image Capture and Processing
- OpenCV is used to capture video frames from the camera.
- Images are preprocessed using techniques such as resizing and color conversion.

### Software II.b Face Recognition Module
- A face recognition algorithm extracts facial features from captured images.
- These features are compared with stored user data to determine the person's identity.

## Subsystem III – User Interface and System Management

### Software III.a Graphical User Interface
- A graphical interface built with PyQt5 displays the camera feed.
- It shows recognition results and system status in real time.

### Software III.b Multithreading Framework
- A multithreaded architecture allows the system to perform multiple tasks simultaneously.
- Tasks such as video capture, face recognition, and interface updates run in parallel.

### Software III.c Local Database Management
- A local database stores authorized user information.
- The database records access logs such as time, identity, and recognition results.

# Criteria of Success

- The PIR sensor can detect human motion and trigger the system automatically.
- The camera can capture images successfully for processing.
- The face recognition module can correctly identify authorized users from stored data.
- The relay module activates to simulate unlocking the door when a valid user is detected.
- The graphical interface displays the camera feed and recognition results in real time.
- The system records access events in the local database.
- The system runs smoothly on the Raspberry Pi with minimal delay.

Wireless Charging Table Supporting Multiple Devices with Arbitrary Placement

Kaiwen Cao, Tianyi Han, Tingkai Liu, Zikai Liu

Featured Project

# Wireless Charging Table Supporting Multiple Devices with Arbitrary Placement

# Problem

While more and more device manufacturers adopt wireless charging into their smartphones and headphones, most currently available wireless charging pads only support a single device and require strict alignment between the device and the coil. Misalignment can negatively influence both user experience and charging efficiency. In certain scenarios such as cafeterias, a table that can simultaneously charge multiple devices with arbitrary placement can be useful and COOL, allowing the users to sit wherever they like and to arbitrarily place their devices.

# Solution Overview

We intend to design and manufacture a table with multiple mobile coils placed in an intermediate layer below. Driven by step motors, a tool grabber attaches the coils using electromagnets and drop them in the right place. Computer vision will be used to recognize devices (phones, AirPods, etc.) and guide the chargers to corresponding locations. Once the coil is in place, it will first communicate with the device (Qi protocol) to verify whether the device can be charged wirelessly. If yes, the charging process will start. Otherwise, the coil will be moved back to its original location. The scheduling algorithm ensures the wires get separated and neat.

# Solution Components

* Mechanical subsystem. The main moving component of the system is a large-scale X-Y moving mechanism under the table. The coils will be placed between two panels above the moving mechanism and will be caught and dropped to the right place by the moving tool head. The tool head will be developed with electromagnets or magnets with Z-axis moving capability.

![sketch](https://courses.grainger.illinois.edu/ece445zjui/pace/getfile/18618)

* Vision detection subsystem. This includes a camera and a processing unit. It detects the locations of phones and other chargeable devices and send their positions to the control unit. In real-world settings, pre-installed surveillance cameras may be used as the video source so that no additional camera is required. Embedded GPU (NVIDIA Jetson Nano as a candidate) or cloud service can be used for image processing.

* Power supply control. It is used to control wireless chargers and supply power to devices if and only if the handshake between charger and device is successful. Status will be reported to the central control unit.

* Central control unit and embedded software. According to the output given by the vision detection system or the feedback from the power supply system, the central control unit should move the chargers with proper scheduling algorithm to pair chargers with devices and keep wires of coil separated and neat.

# Criterion for Success

* The vision detection system can localize chargeable device at an accuracy of over 80% and response within 2s.

* The power system can supply powers when a chargeable device is present, and not supply power when the misdetection happens. Correct feedback can be sent to the central control system.

* The mechanical system moves correctly according to the commands given by the central control system.

* The central control system can send correct commands to the mechanical system given the position information from the vision system and the feedback from the power supply system. It should be able to keep wires of charging coil separated.

# Evaluations on Alternative Solutions

The technology of wireless charging emerged some time ago, but its inclusion in commercial devices doesn’t take off until recent years. Intuitively, wireless charging doesn’t bring much additional convenience compared to the wired charging, but its adoption by major manufacturers has proven its value. Similarly, in certain settings such as the cafeteria, charging without alignment may significantly increase user experience, comparing to having only a few fixed charging locations.

An alternative solution to enable table-scale wireless charging is to deploy multiple coils covering the whole table. But it doesn’t solve the alignment problem unless the coils are heavily overlapped, which has been proven to be difficult by already canceled Apple AirPower.