Zhang Jie
Objective: Embedded Development
Education
Northwest University (211 Project)
2022-09 ~ 2026-06
School of Electronic Information | Electronic Information Engineering | B.Sc.
Major courses: FPGA, Microcomputer Principles, C Programming, Computer Networks, Analog/Digital Circuits, Communication Principles, Signals and Systems, etc.
Technical Skills
1. Familiar with embedded Linux driver and application development; experience with U-Boot, Kernel, device tree and filesystem（Linux、Android） construction.
2. Proficient in C/C++/Python; familiar with Shell, Assembly, Verilog, MATLAB, HTML, etc.
3. Skilled with VSCode, Qt, virtual machines, Git, CMake; use AI workflows to assist develop.
4. Experienced with MCUs such as STM32, ESP32,TI; familiar with FreeRTOS scheduling.
5. Knowledgeable about SPI, I2C, UART, CAN and TCP/IP networking.
6. Familiar with PCB layout, component selection and SMD soldering.
7. Proficient with multimeter, oscilloscope, spectrum analyzer, soldering iron, hot plate.
8. Understand computer vision; experience deploying YOLO and LLMs on edge devices.
Projects
Monocular Vision Range-finding System based on RK3566
1. Project description: Developed a full-stack embedded Linux system for learning kernel and application development.
2. Responsibilities (hardware -> drivers -> application):
  • Hardware design & soldering: MIPI DSI, touchscreen, audio I/O, backlight circuits.
  • SDK building: Configure and compile U-Boot, Kernel, device tree and rootfs; port Linux 5.10; use Buildroot; port OpenCV, Qt, ffmpeg.
  • Driver development: Modify device tree; implement drivers for backlight and touchscreen; adjust camera OV5695 driver.
  • Qt development: Design UI with QtCreator for camera view and user interaction.
  • RTSP streaming: Stream video via RTSP for remote monitoring.
  • RKNPU & YOLO: Deploy YOLO (C++) using Rockit/RKNN for real-time detection.
  • Camera calibration: Use MATLAB for Zhang's calibration to obtain intrinsics and distortion.
  • Distance measurement: Use detection and tracking plus camera intrinsics or motion-based monocular methods to estimate distance.
Multi-sensor Home System + Home Assistant + LLM
2023-10 ~ 2024-06
1. Project description: Integrated fall detection, heart-rate/SpO2 monitoring and AI-assisted medical dialogue to explore home healthcare applications.
2. Work performed:
  • Embedded development: Designed and soldered ESP32-S3 main board and multiple peripheral PCBs; implemented NFC + face-recognition lock and sensor data collection with MQTT uplink.
  • Cloud & frontend: Deployed MQTT + Home Assistant; wrote Python backend to store data to MySQL; built PHP API endpoints and frontend JS requests.
  • Assisted team with LLM training and YOLO fall-detection model deployment.
Electro-optic Effect Automated Measurement Device
2024-10
1. Task: Designed an automated measurement system for Pockels effect research on lithium niobate crystals using STM32 and ESP12S.
2. Work performed:
  • Optics & hardware: Optical path with polarizer, EO crystal and analyzer; photodiode measurement. Designed high-voltage DAC (50-800V) with modulation and low-noise amplification for microvolt-level signals.
  • Automation & control: Used ESP12S for Wi-Fi data bridge; implemented automated high-voltage sweep procedure with remote data logging.