Firmware
TL;DR: Firmware that passes physical hardware testing still ships with "Heisenberg bugs" that disappear during testing but appear disastrously under chaotic, real-world conditions. The Ghost strategy solves this by introducing an injection point at the seam between the Driver and Logic layers, feeding the Logic layer with
AI
TL;DR: False alerts in production ML systems are not always caused by bad models. A single 12-second real-world event can produce 8 to 10 alerts through sliding-window inference, which runs continuously over overlapping windows, generating alert storms, duplicate alerts, and broken timelines until users stop trusting notifications. Event-triggered inference
Hardware
Most boards are designed under a quiet assumption: everything that should be present will be present. All sensors are populated. All cables are connected. All rails come up cleanly. Firmware discovers what it expects, and the system progresses forward. That assumption holds in the lab. It collapses the moment a
Firmware
Introduction In the world of smart device ecosystems, where continuous sensor monitoring is paramount, communication reliability becomes the cornerstone of system integrity. When developing multi-sensor platforms that demand 24/7 operation, engineers frequently encounter a subtle yet critical challenge: I2C bus lockups caused by peripherals holding the SDA (Serial Data)
AI
Introduction Most edge AI pipelines are accidentally model-first. We optimize inference time, shave milliseconds off convolution layers, and debate INT8 versus INT4—while silently dropping sensor frames upstream. By the time the model runs, the data it sees is already compromised. On constrained hardware like STM32-class processors, this mistake is
Software
Stable BLE streaming can feel like magic when it works: sensors stay responsive, data arrives smoothly, and the app feels “live” without burning the battery or stuttering the UI. The good news is that this isn’t luck—it’s engineering. When you treat BLE as an end-to-end streaming pipeline
AI
Most public audio datasets look clean on paper. Five seconds. Ten seconds. One label per clip. That assumption is exactly where our problems started. The Hidden Problem With Scraped Audio Datasets We started with the usual approach: scrape audio clips from public sources, bucket them by class (bark, howl, cough,
Firmware
In high-performance embedded systems, data integrity and deterministic timing are paramount. Controller Area Network (CAN) has long been the standard for reliable automotive and industrial communication. With the transition to CAN FD (Flexible Data-rate), we gained higher bandwidth and larger payloads. However, standard CAN FD, while robust for short control
Product
Most systems don’t fail when operating at their nominal specifications. They fail when real-world conditions push them into regions that were never fully modeled—transient overloads, off-axis forces, tolerance stack-ups, or repeated micro-stresses over time. Nominal specs are typically defined under controlled laboratory conditions: room temperature (27 °C), centered
AI
Training robust audio classification models requires diverse, high-quality data. But what happens when your dataset is imbalanced, when you have plenty of examples of dogs barking, but far fewer of dogs drinking or sneezing? Traditional augmentation techniques like pitch shifting or time stretching can only stretch your limited samples so