A simple walkie-talkie circuit running off an Arduino Nano. Utilizes a BJT to amplify the microphone voltage and transmits the radio signal through a nRF24L01 series Transceiver.

This project involves designing a PCB for the lid assembly of an open-source laptop. The design integrates various sensors, including a microphone, camera, and ambient light sensor, ensuring precise alignment with the display glass. It features touch sensors to control LED lighting, spring-loaded contacts for touch-key interaction, and 3D-printed light diffusers for efficient lighting. Additionally, the PCB includes a power management system with status LEDs and a PFC for connecting to the external laptop PCB. The goal is to create a versatile, upgradeable, and user-friendly component for the laptop's lid. Specific parts of the project include 1. Microphone - Audio input capture 2. Ambient Light Sensor Module - Light intensity measurement 3. Camera - Video capture 4. LDO Regulators (3 TLV74 Series) - Voltage regulation for different components 5. Crystal - Clock generation 6. Touch Sensor Controller - Touch-key interaction 7. Flip-Flop - State keeping in logic circuits 8. LEDs (LTRBR37G Series) - Lighting indication 9. FPC Connector - Interface with main laptop PCB

XIAO MG24 Sense is an ultra-low-power wireless development board based on Silicon Labs' EFR32MG24 SoC, featuring a high-performance 78MHz ARM Cortex®-M33 core. It‘s Matter® native over Thread® and Bluetooth® Low Energy 5.3, all supported by the Arduino® Core. With 4MB Flash onboard, 19 GPIOs, LED, and charging circuit onboard, it boasts extremely low operating current and ultra-low-power modes, making it an ideal choice for IoT applications, especially battery-powered projects utilizing Matter® protocols. Additionally, the onboard analog microphone and six-axis IMU sensors make XIAO MG24 Sense a great fit for TinyML applications such as posture detection

This project is a cough detector. It has a microphone that captures the sound and the ESP32 process it through a machine learning model, classifying it as a cough or not.