This is a board for a robotic arm controlled by Particle Argon. Settings can be made using a display module that connects to the board. You can teach him to draw or cook. Just use your imagination!

sEMG-DAQ is a wearable 6 channel data acquisition unit for capturing surface electromyographic (sEMG) signals from human arm muscles using SJ2-3593D jack connectors while conditioning, digitizing, processing and transmitting them as sEMG data to an external AI accelerated board through an SM12B-SRSS IDC connector where AI models are run for various applications including robotic control, muscle signals medical assessment and gesture recognition. The board leverages an INA125P instrumentation amplifier together with filter stages utilizing LM324QT op-amps for conditioning and an STM32G4A1VET6 microcontroller for the digitization, processing and data transmission of the signals. Since AI models can only be as good as the data, the design of such a DAQ is necessary to ensure clean, reliable and real-time data for AI applications requiring sEMG data. The board also has USB-FS and JTAG to cater for debugging. The power (5V) is fed through a screw terminal and is regulated by two LDK320AM LDO regulators to offer 5V, 3.3V and 1.8V to meet the requirements of various components on the board.

It is a board based on STM32L4 with a built-in ethernet IC W5500 that allows you to create IoT projects based on the modern ARM Cortex®-M4 core. A connector with SWD interface and USB C for communication are provided for the firmware #IoT #ARM #STM #Ethernet #W5500 #IC #project

This is the project template for the Raspberry Pi Pico 2, the latest addition and update to Pi Pico line up. Raspberry pi pico 2 is equipped with the RP2350, a cutting-edge, high-performance microcontroller designed with enhanced security and versatility in mind. Every element of its design has been upgraded, from the advanced CPU cores to the innovative PIO (Programmable I/O) interfacing subsystem. The Raspberry Pi Foundation has integrated a robust security architecture centered around Arm TrustZone for Cortex-M, ensuring data protection and integrity. Additionally, new low-power states and expanded package options broaden the range of applications, making the Pico 2 an ideal choice for diverse, power-sensitive projects. To learn more about what's the key differences between the original Pi Pico and the new Pi Pico 2, read our blog https://www.flux.ai/p/blog/whats-new-in-the-raspberry-pi-pico-2-a-showdown-with-the-original-raspberry-pi-pico #project-template #template #raspberry #pi #pico2 #newpico

Template for creating an Arduino MKR shield (https://www.arduino.cc/en/hardware#mkr-family) #Arduino #template #project #arduinomkr #zero

Welcome to your new project. Imagine what you can build here.

This is the general design of the R7FA4M1AB3CFM with the minimum configuration for its operation and USB C with LDO and SD card #arm #M4 #IoT #referenceDesign #mcu #renesas #template #reference-design

The semgdaq board is a wearable 6 channel data acquisition unit for capturing surface electromyographic (sEMG) signals from human arm muscles using SJ2-3593D jack connectors while conditioning, digitizing, processing and feature extracting them then transmitting the feature data as vectors to an external AI accelerated board through an SM12B-SRSS IDC connector using 12C and UART communication protocals where AI models are run for various applications including robotic control, muscle signals medical assessment and gesture recognition. The feature vectors are comprised of onset detection, slope sign changes, autoregression coefficients and Short Time Fourier Transform magnitude spectrum data for each segment or window of the signals in real time. This vectors can be used as the basis for further feature extraction on more computationally resourceful hardware where machine learning algorthms can be employed for descision making in the applications mentioned earlier. The board leverages INA125P instrumentation amplifiers together with filter stages utilizing LM324QT op-amps for conditioning and an STM32G4A1VET6 microcontroller for the digitization, processing, feature extraction and data transmission. Since AI models can only be as good as the data, the design of such a DAQ is necessary to ensure clean, reliable and real-time data for AI applications requiring sEMG feature data. The board also has USB-FS and JTAG to cater for debugging and external flash memory to extend its data storage and processing capability. The power (5V) is fed through a screw terminal and is regulated by two LDK320AM LDO regulators to offer 5V, 3.3V and 1.8V to meet the requirements of various components on the board.