An ATtiny13 is more than sufficient to control almost any number of NeoPixels via an IR remote. The NeoController was originally developed as a tester for 800kHz NeoPixel strips. Since there was still so much flash left in the ATtiny13, an IR receiver was integrated so that some parameters can be controlled with an IR remote control. In this way, it is also suitable as a simple and cheap remote-controlled control unit for NeoPixels. Due to its small size (21.6mm x 11.4mm), it can be soldered directly to the LED strip without any problems. The power supply via a USB-C connection enables currents of up to 3A. There is still more than a third of the flash memory left for additional ideas.

This circuit drives 10 LEDs making them flash to the rhythm of the music (using a TIP31C transistor to act as a voltage-controlled switch) Credit: https://www.youtube.com/watch?v=3qZ3uA8hDmE

This bee hotel version is an intermediary version that implements some featuresets. The main feature that we are leaving out is Matter support over Zigbee (for a wider range in the backyard). In this version, we are implmenting: -Legacy WiFi/BT connectivity. -Camera with flash capability -Solar Charging -Energy Storage -Humidity and Temp Sensor

Seeed Studio XIAO ESP32C6 is powered by the highly-integrated ESP32-C6 SoC, built on two 32-bit RISC-V processors, with a high-performance (HP) processor with running up to 160 MHz, and a low-power (LP) 32-bit RISC-V processor, which can be clocked up to 20 MHz. There are 512KB SRAM and 4 MB Flash on the chip, allowing for more programming space, and binging more possibilities to the IoT control scenarios.

NAND programmer based on STM32 processor. It supports parallel NAND and SPI flash programming.

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

AvocAudio is a compact tinyML community board designed for extensive audio data collection for various tinyML applications. It leverages the Raspberry Pi RP2040 and integrates a LoRa-E5 LoRaWAN Transceiver Module for connectivity. Equipped with an SD card slot for local data storage, the board ensures efficient data collection. The board operates on solar power or a lithium-ion battery, ensuring flexible and efficient energy use. #audioDevices #raspberryPi #rp2040 #lorawan #iot #solar

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

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.