Tech Notes

Motivation: Theoratical: Method: Result: Protocol Check 8byte(64bit) * 260 Samples (ms) 4byte(32bit) * 260 Samples (ms) 2byte(16bit) * 260 Samples (ms) Average (ms) I2C ✔ \ 49 33 41 UART ✔ 329 229 175 244 Parallel_V0.0 ✔ 302 301 301 301 SPI \ \ \ \ #DIV/0!

1. Motivation The reason why we have to develop our own low-level driver (or API) for nano 33 is because the unstable message communication while using official API: Figure 1. Result from receiver.  As Figure 1. shows, We use the official API to send the message 'I hope everything goes well' several times, but the receiver shows an inconsistent result.  When we slow down the transmission speed, the problem solved. However, the lower speed is not acceptable for many use case. That is why we have to build a stable & fast SPI API for  Arduino Nano 33 BLE Sense.  2. Basic Principle of SPI 3. Registers of nRF52840 Figure 2. Registers To-Be-Continue...

OpenPose An open-source library for multi-person pose estimation. It detects key points on the human body, including joints and skeletal connections, using deep learning techniques with convolutional neural networks (CNNs).  DeepPose A method proposed by Microsoft that uses deep learning for estimating key points on the human body. It employs CNNs for end-to-end pose estimation by directly predicting joint positions from images. PoseNet Developed by Google, PoseNet is a lightweight real-time pose estimation system. It uses CNNs to detect key points on the human body and is suitable for applications requiring real-time performance. HRNet (High-Resolution Network) An advanced pose estimation method that focuses on high-resolution feature representations. It maintains and merges multi-scale feature maps to improve the accuracy of key point detection.  AlphaPose An open-source multi-person pose estimation library based on PyTorch. It uses CNNs and confidence maps to detect key po...