Hardware configuration: Dialog Semiconductor DA14683 for master control, with BLE5.0
The schematic diagram is open source, you can see if there is anything worth learning from. This baseboard is designed for their previously launched H7 core board.
Supports various Cortex-M cores, can be used wirelessly in the local area network, and can be used remotely in the external network.
The product design is open source and very cool and beautiful.
The author opens up the schematic diagram and supporting Raspberry Pi code. The simulation bandwidth is already 30MHz, and the frequency response is very close to the theoretical simulation, with a difference of about 0.02dB between 1kHz and 30MHz.
ST has previously launched a board with a static current measurement range of 1nA - 100mA and a dynamic current measurement range of 100nA - 50mA, but it is not open source. This time it’s open source.
The very early model was a bit crude.
The schematic is in the appendix at the end of the document.
The most interesting part is the problem of energy collection without batteries. Currently, it provides energy collection by clicking buttons and solar energy collection. Ambiq Apollo3 for main control.
Hardware design is difficult, but Nordic is all open source, and PCB, BOM, Gerber, schematics, etc. are all provided. The schematic is also annotated.
This project started with STM32F1 and now it uses STM32F3.
Software and hardware are all open source
μSMUs are not meant to replace precision SMUs, but to complement them in cost-sensitive areas where accuracy is not required.
The SMU is a precision power supply device that can measure not only high-resolution voltage sources but also high-resolution current sources. It also integrates bipolar voltage and four-quadrant output functions to facilitate testing of various characteristics. By providing a linear sweep voltage and sweep current, the IV characteristic curve of the instrument can be obtained.
The Stanford Pupper is a four-legged robot designed to help K-12 and undergraduate students engage in exciting robotics research.
Label-free pose estimation from user-defined features via deep learning for all animals