Project

# Title Team Members TA Documents Sponsor
21 WEARABLE ADAPTIVE RESONANCE MATCHER
Austin Zhuang
Tian Tu
Xinyang Liu
Shiyuan Duan design_document1.pdf
proposal2.pdf
# WEARABLE ADAPTIVE RESONANCE MATCHER

Team Members:

* Yanheng Zhuang (NetID: yzhua2)
* Xinyang Liu (NetID: xl157)
* Tian Tu (NetID: tiantu2)

# Problem

Small implanted devices can receive power wirelessly from an external coil. This works best when the external and implanted circuits are properly matched near the same resonant point.

In practice, coil movement, changes in distance or angle, and nearby tissue-like materials can change the electrical response of the system. This can increase reflected power and reduce transmission to the implanted coil.

A fixed matching circuit may work well for one condition but perform poorly after the environment changes. Our goal is to build a system that can automatically detect this mismatch and retune the external matching circuit without manual adjustment.

# Solution

We will build an adaptive wireless power matching prototype using a custom PCB, two coils, a laboratory vector network analyzer (VNA), and computer control software.

The PCB will contain two electronically adjustable capacitors controlled by an STM32 microcontroller. Changing these capacitances will allow the system to adjust the resonant behavior and input matching of the external coil.

The VNA will measure S11 and S21 and provide feedback to a Python program. The program will read the measurements, command the STM32 to try different capacitor settings, and select the settings that provide the best measured performance.

The VNA is an essential part of our final ECE 445 prototype and demo because it provides the measurement feedback needed for automatic tuning. We will not attempt to integrate the full VNA measurement functionality onto the PCB.

Testing will be performed on a bench using two representative coils, an adjustable nonconductive fixture, and tissue-like loading material. No human or animal testing is included.

# Solution Components

## Main Control PCB Subsystem

This subsystem controls the tuning circuit and communicates with the computer.

An **STM32F072CBT6** microcontroller will generate two independent tuning control signals. A **TLV75533PDBVR** regulator will provide 3.3 V power. A **TPS61040DBVR** boost converter and an **OPA2192IDR** dual op amp will provide the larger adjustable voltages required by the tuning components.

Components:

* STM32F072CBT6 microcontroller
* TLV75533PDBVR 3.3 V regulator
* TPS61040DBVR boost converter
* OPA2192IDR dual op amp
* USB connector and protection circuit
* SWD programming header
* Voltage test points

## Adjustable Matching Subsystem

This subsystem connects to the external coil and changes its electrical response.

It will use one adjustable capacitor in series and one in parallel with the coil. We plan to use **MAVR-000404-0287FT** varactor pairs. Fixed-capacitor and zero-ohm resistor footprints will also be included so the tuning range can be adjusted after measuring the actual coils.

Components:

* 2 × MAVR-000404-0287FT varactor pairs
* Murata GJM C0G/NP0 fixed capacitors
* RF connectors
* Bias resistors
* Decoupling capacitors
* Zero-ohm resistor footprints

## Measurement and Control Software Subsystem

This subsystem measures the system response and performs automatic tuning.

A laboratory two-port VNA will measure S11 and S21. A Python program will communicate with both the VNA and STM32. It will first perform a coarse search over capacitor settings, then search more closely around the best result. The program will automatically save the measurements and selected settings for each test.

Components:

* Laboratory two-port VNA
* Python measurement and tuning program
* USB serial connection between the computer and STM32

## Coil and Test Fixture Subsystem

This subsystem provides a repeatable physical setup for testing the adaptive matching system.

We will use an external transmit coil and a smaller implant-representative receive coil approved by our mentor. A nonconductive fixture will allow repeatable changes in distance, lateral position, and angle. Tissue-like loading material will be used to test whether the system can retune after the electromagnetic environment changes.

Components:

* External transmit coil
* Implant-representative receive coil
* Nonconductive positioning fixture
* Mentor-approved tissue-like test material

# Criterion For Success

The project will be considered successful if it can:

* Independently control both adjustable capacitor channels.
* Automatically collect and save VNA measurements without manual data entry.
* Detect a loss of matching after coil position or nearby loading changes.
* Automatically select new capacitor settings without manual electrical adjustment.
* Improve S11 by at least 6 dB, or reach S11 ≤ -10 dB, in at least three repeatable test conditions.
* Improve VNA-measured S21 by at least 2 dB in at least three repeatable test conditions.
* Complete one automatic tuning cycle within 60 seconds.
* Repeat one selected test three times with final S11 results within 2 dB of each other.

Backpack Buddy - Wearable Proximity/Incident Detection for Nighttime Safety

Jeric Cuasay, Emily Grob, Rahul Kajjam

Backpack Buddy - Wearable Proximity/Incident Detection for Nighttime Safety

Featured Project

# Backpack Buddy

Team Members:

- Student 1 (cuasay2)

- Student 2 (rkajjam2)

- Student 3 (eegrob2)

# Problem

The UIUC campus is relatively a safe place. We have emergency buttons throughout campus and security personnel available regularly. However, crime still occurs and affects students walking alone, especially at night. Staying up late at night working in a classroom or other building can lead to a long scary walk home. Especially when the weather is colder, the streets are generally less populated and walking home at night can feel more dangerous due to the isolation.

# Solution

A wearable system that uses night vision camera sensor and machine learning/intelligence image processing techniques to detect pedestrians approaching the user at an abnormal speed or angle that may be out of sight. The system would vibrate to alert them to look around and check their surroundings.

# Solution Components

## Subsystem 1 - Processing

Processing

Broadcom BCM2711 SoC with a 64-bit quad-core ARM Cortex-A72 processor or potentially an internal microprocessor such as the LPC15xx series for image processing and voltage step-down to various sensors and actuators

## Subsystem 2 - Power

Power

Converts external battery power to required voltage demands of on-system chips

## Subsystem 3 - Sensors

Sensors

Camera - Night Vision Camera Adjustable-Focus Module 5MP OV5647 to detect objects in the dark

Proximity sensor - detects obstacle distance before turning camera on, potentially ultrasonic or passive infrared sensors such as the HC-SR04

Haptic feedback - Vibrating Mini Motor Disc [ADA1201] to alert user something was identified

# Criterion For Success

The Backpack Buddy will provide an image based solution for identifying any imposing figure within the user's blind spots to help ensure the safety of our user. Our solution is unique as there currently no wearable visual monitoring solutions for night-time safety.

potential stuff:

Potentially: GNSS for location tracking, light sensor for outdoors identification, and heartbeat for user stress levels

camera stabilization

heat camera

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