The Microchip MCP2518FD is a popular external CAN-FD controller that interfaces with the host MCU over SPI. It performs well under controlled conditions, but its field reliability comes down almost entirely to the quality of the host-side driver. This post documents technical findings, failure signatures, and recovery patterns validated through
Adaptive sampling turns your wearable into a system that feels alive, delivering the right data at the right moment while preserving battery for when it matters. Instead of locking sensor rates in firmware, we set behavior at the app layer with a small, transparent policy engine. The device advertises capabilities,
In modern imaging pipelines, especially ones running on edge devices, every byte matters. When an embedded system captures images, those images have to travel through limited memory, constrained communication buses, and strict power budgets. Large image payloads degrade system performance, slow down recovery pipelines, and create unpredictable behavior. To keep
Real-world lessons from transitioning parallel to serial camera architectures in production embedded systems Imagine an industrial vision system built around a high-performance MCU and a 1 MP camera using the traditional parallel interface, DCMI. It worked, until thermal sensors, proximity modules, and wireless connectivity landed on the same board. Suddenly
Monolithic firmware scales linearly in features but exponentially in complexity. When building IoT devices that integrate multiple sensors, accelerometers, GPS modules, barometers, LoRa communication, and more, a firmware project built as one giant directory quickly becomes unmanageable. It creates compilation bottlenecks (change one sensor driver, recompile everything), dependency hell (unclear
Networks misbehave in the real world. BLE links jitter, Wi-Fi bursts collide, buffers overflow, and low-power sensors occasionally flip a bit. The worst part isn't the packet loss, it's the visible glitch your user sees: a frozen chart, a jumpy graph, or a dropped audio blip.
Picture debugging a critical production issue where the logs show a sensor event was processed before it was captured. Impossible? No, just unsynchronized clocks. That was our reality building distributed embedded systems, since our platforms rely on multiple Systems-on-Module working in concert, each handling different sensors and processing tasks. Without
In every real-time embedded system, interrupts are where the action begins. Whether it's a camera sensor signaling a frame ready, a CAN packet arriving, or a GPIO line pulsing high, the first code to run is almost always the Interrupt Service Routine. But ISRs should be fast, lightweight,
Building a production-ready camera system with 45 MB/s sustained throughput Direct Memory Access promises zero-CPU data transfers, but poor buffer design can turn that promise into corrupted frames, system crashes, and unpredictable behavior. This post shares hard-won lessons from building Hoomanely's camera module, a dual-sensor system capturing
During prototyping of our EverBowl and EverHub hardware, we used the Raspberry Pi Compute Module 4 as our embedded compute platform for early-stage development, a prototyping vehicle ahead of transitioning to Hoomanely's custom, purpose-built System on Module for production. The CM4 requires specific initialization procedures to establish reliable