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Scientific CUDA benchmarking framework: 4 implementations x 3 power modes x 5 matrix sizes on Jetson Orin Nano. 1,282 GFLOPS peak, 90% performance @ 88% power (25W mode), 99.5% accuracy validation, edge AI deployment guide.
This system would help a 'running car' to detect the concrete type of an obstacle and then take different approaches according to the 'type' detected by multi-sensor fusion and CV.
This project demonstrates how to control the **brightness of an LED** connected to the **ESP8266** using **Pulse Width Modulation (PWM)** in **MicroPython**. The LED gradually fades in and out, creating a smooth brightness transition.
About Detection of fringe patterns in self-mixing based interferometric signals using EfficientDet and YOLOv5 object-detection model and deploying the model on Jetson Nano