I will build wearable device firmware with low power rtos
Helping Companies Build Applications , AI and IoT Products
About this Gig
Wearable Device, high speed data processing, or advanced audio decoding on ESP32 I design Dual Core FreeRTOS architectures that deliver maximum throughput, low latency, and stable multimedia performance.
Core Expertise
- Parallel Processing: Optimized task allocation across both cores for video decode, frame handling, audio pipelines, and data processing.
- Real Time Multimedia: Stable A V sync, efficient MJPEG and AAC MP3 decoding, smooth rendering under load.
- DSP Implementations: FFT, filtering, noise reduction, EQ, and real time signal conditioning.
- Industrial Architecture: FreeRTOS primitives, ring buffers, and task affinity for deterministic, mission critical behavior.
Success Highlights
- Delivered an MJPEG video pipeline with 35 percent faster decode and smooth playback.
- Developed a dual core audio engine achieving sub 10 ms latency with ample CPU margin.
- Built an IoT edge data processor that cut cloud bandwidth by 42% using on-device DSP compression and filtering.
- Improved a clients streaming pipeline achieving 25% lower jitter and stable long duration performance.
If you need peak ESP32 performance for multimedia or IoT workloads, I can deliver a prod ready Solution.
Platform:
ESP32
Sensors:
Sound & vibration
•
Photo diode
•
Camera
My Portfolio
FAQ
Why should I pay for a Dual-Core FreeRTOS solution?
Using the ESP32's Dual-Core architecture ensures complex tasks like MJPEG Video Decoding (Core 0) and AAC/MP3 Audio Playback (Core 1) run in parallel without performance loss, guaranteeing smooth real-time operation.
How do you handle debugging and performance optimization on ESP32 for complex multimedia or IoT projects?
I use a structured optimization workflow combining core affinity tuning, task-level profiling, buffer analysis, and hardware tracing. This includes ESP-IDF performance counters, FreeRTOS runtime stats, logic analyzer validation, and targeted DSP profiling. I identify bottlenecks in decode pipelines,
Which audio/video formats and hardware do you support?
I have experience implementing efficient decoders for formats like MJPEG and AAC/MP3. Supported hardware includes SD Card and I2S DACs.
Is the code efficient for resource-constrained devices?
Yes. The code is written in optimized Embedded C++ and uses techniques like Task Pinning and efficient Digital Signal Processing to maximize the ESP32's limited RAM and processing power.

