Drivers & Communication
From STM32WLE interface drivers, FatFS storage, RTC timestamps, and one-wire acquisition to instrument-based debugging.
View Resume →Embedded Software · Embedded Linux · Engineering Review
I am Xiong Xiao, an Internet of Things undergraduate at Chongqing University of Science and Technology. My job direction is embedded software development and embedded Linux development. My projects cover low-level drivers, communication stability, Linux multithreaded applications, hardware-software debugging, and also extend to AI Agent Tools, data-processing pipelines, and engineering utilities. This site records how I locate problems, verify solutions, and turn ideas into runnable systems.
Capability Path
Real problems: locate, verify, deliver, review.
From STM32WLE interface drivers, FatFS storage, RTC timestamps, and one-wire acquisition to instrument-based debugging.
View Resume →Embedded Linux threading, blocking AI request isolation, Framebuffer display, and network fallback paths for avoiding system freeze.
Browse Projects →Writing projects as reusable technical records: context, diagnosis, verification, results, and methods that can transfer to the next system.
Read Blog →Featured Projects
Problem:When several communication interfaces and acquisition paths were developed in parallel, driver reuse was weak and application symptoms could hide lower-level interface faults.
Technical Action:Separated HAL, BSP, and App layers, wrapped I2C, UART, SPI, RS485, one-wire, and storage modules, then used logic-analyzer and oscilloscope traces to locate level-matching errors.
Outcome:Improved code reuse by more than 50%, and the debugging notes plus compatibility recommendations were absorbed into the team interface standard.
Problem:AI requests, audio playback, sensor sampling, and display refresh shared limited system resources, so one blocking call could become whole-system lag.
Technical Action:Split sampling, interaction, and rendering with pthread, integrated ALSA, OpenSSL, and MPlayer by cross-compilation, and designed a network-failure fallback path.
Outcome:Decoupled AI interaction from environment sampling, keeping local sampling, safety alerts, and basic feedback available during unstable network conditions.
Problem:Vision recognition results could not directly drive the robotic arm because image coordinates, board coordinates, and motion coordinates carried different error sources.
Technical Action:Connected YOLOv5 detection, nine-point board calibration, Alpha-Beta search, and PWM robotic-arm control, then added error compensation across the execution loop.
Outcome:Reached +/-0.5 mm repeat positioning accuracy and inference response within 300 ms; the project received national innovation-program approval and an invention patent.
Problem:Raw CSV files and medical text are hard to query, analyze, and reuse directly as knowledge assets.
Technical Action:Created an end-to-end pipeline for data cleaning, text chunking, entity-relation extraction, knowledge graph output, KGQA, statistical analysis, NL2SQL evaluation, and FastAPI interfaces.
Outcome:Generated cleaned data, medical knowledge graph files in JSON/GraphML/HTML, QA examples, analysis reports, charts, and an API demo.
Latest Notes
A review of concurrency decomposition in an embedded Linux multimodal system: to avoid blocking AI requests and UI lag, I isolated sampling, interaction, and display paths.
A review of multi-interface embedded development: to improve driver reuse and make communication errors traceable, I moved board capabilities into the BSP layer and verified hardware paths with instruments.