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. #raspberryPi #rp2040 #lorawan #iot #solar

Learn how to use AI Auto Layout on this ESP32 Espresso Smart Scale! In one click you’ll see AI Auto Layout perform magic. Pay close attention to how we recommend creating rulesets, zones, and fanouts. By copying the setup in this example on your own project, you’ll have a fully routed board in no time!

Spot the mistake! Learn how to use AI to conduct a design review on an ESP32-based control board. This project is ideal for autonomous or radio-controller robots featuring inputs for sensors, encoders, and a Flysky RC receiver, plus an I2C display for configuration.

Spot the mistake! Learn how to use AI to conduct a design review on an ESP32-based control board. This project is ideal for autonomous or radio-controller robots featuring inputs for sensors, encoders, and a Flysky RC receiver, plus an I2C display for configuration.

Learn how to use AI Auto Layout on this ESP32 Espresso Smart Scale! In one click you’ll see AI Auto Layout perform magic. Pay close attention to how we recommend creating rulesets, zones, and fanouts. By copying the setup in this example on your own project, you’ll have a fully routed board in no time!

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

Use this Copilot's template to design satellite payloads, avionic systems, or space instruments, considering MIL-STD, radiation hardening, and extreme environmental constraints. Modify project requirement properties to adapt to specific needs. #template #project-template

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

Learn how to design PCBs faster with generative AI in this 20 minute hands-on tutorial. You’ll learn how to use Flux Copilot, an AI-powered hardware design assistant, to research parts, review your design, and even connect components. https://youtu.be/FL7e0OXTLic

Spot the mistake! Learn how to use AI to conduct a design review on an ESP32-based control board. This project is ideal for autonomous or radio-controller robots featuring inputs for sensors, encoders, and a Flysky RC receiver, plus an I2C display for configuration.

FLORA has a small but easy to use onboard reset button to reboot the system. The power supply is designed to be flexible and easy to use. There is an onboard polarized 2 JST battery connector with protection schottky diode for use with external battery packs from 3.5v to 16v DC in. Can be used with LiIon/LiPoly, LiFe, alkaline or rechargeable NiMh/NiCad batteries of any size. The FLORA does not have a LiPo charger included by design, this allows safe use with multiple battery types and reduces risk of fire as it is not recommended to charge these batteries on fabric.

The InFiDEL is a cheap (< $5) filament diameter sensor intended for use with FDM 3d printers. The sensor can be calibrated to provide surprisingly precise filament diameter readings in real-time. The main idea is to use the sensor to correct for filament diameter deviations while printing

The Ariel AI Chip, a pioneering component in the realm of artificial intelligence hardware, integrates a suite of electronic elements tailored for high-performance computing applications. At the heart of this assembly lies a CPU with a Radical Transistor architecture, featuring a quad-core setup clocked at 2GHz, identified by the part number CPU-RT-4C-2G. Power management is facilitated through a DC Power Supply, marked DCPS-5V, ensuring a stable 5V supply to the intricate circuitry. The chip's switching capabilities are bolstered by two NPN transistors, NPN-TRANS-001 and NPN-TRANS-002, which play a crucial role in signal modulation. Essential to the chip's operation are the passive components: two 1kΩ resistors (RES-1K and RES-1K-002) and a 10µF capacitor (CAP-10UF), which together with the transistors, form a robust network ensuring reliable performance under varying load conditions. Designed for integration into advanced AI systems, this chip stands out for its innovative use of standard components in a configuration that emphasizes efficiency, reliability, and high-speed data processing capabilities.

Glucose meter is a medical device that determines the approximate concentration of glucose in the blood. A strip containing chemicals that react with glucose in the drop of blood is used for each measurement. Op Amp Circuit description: LMC6484 IC_D is an integrator circuit. LMC6484 IC_A and IC_C are configured as unity gain buffer amplifiers. LMC6484 IC1B is configured as a trans impedance amplifier. Arduino is used for processing of the measured glucose level. The Arduino use the built in ADC module for analog to digital conversion. Further, the Arduino helps to detect the strip also.

OpenSpritzer, an open hardware pressure ejection system for reliably delivering picolitre volumes. Designed for ease of use, robustness and low-cost, the “Openspritzer” is an open hardware “Picospritzer” as routinely used in biological labs around the world. The performance of Openspritzer and commercial alternatives is effectively indistiguishable. It uses Arduino Nano.

A tiny microcontroller board, built around the Atmel ATtiny85, a little chip with a lot of power. We wanted to design a microcontroller board that was small enough to fit into any project, and low cost enough to use without hesitation. Perfect for when you don't want to give up your expensive dev-board and you aren't willing to take apart the project you worked so hard to design. It's our lowest-cost arduino-IDE programmable board!

Use this template if you're planning to get your 1 to 2-layer board manufactured in JLCPCB. It has the via min/max and trace width constraints already baked as global rules. #project-template #template

BLE remote control board. Based on SoC ESP32-C3-MINI-1 that allows you to program it as HID volume multichannel control, you can switch channels with the user button, or use it as arrow keys.

BLE remote control board. Based on SoC ESP32-C3-MINI-1 that allows you to program it as HID volume multichannel control, you can switch channels with the user button, or use it as arrow keys.

Use this Copilot's template to design audio applications like amplifiers, mixers, processors, etc. Modify project requirement properties to adapt to specific needs. #template #project-template

Use this template if you're planning to get your 1 to 2-layer board manufactured in JLCPCB. It has the via min/max and trace width constraints already baked as global rules. #project-template #template

Use this Copilot's template to design audio applications like amplifiers, mixers, processors, etc. Modify project requirement properties to adapt to specific needs. #template #project-template

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Learn how to use Copilot, your AI design assistant, to brainstorm and develop a new idea from concept to custom board design. Discuss requirements, generate architectures, research parts, and draw your schematic.

Learn how to use AI Auto Layout on this ESP32 Espresso Smart Scale! In one click you’ll see AI Auto Layout perform magic. Pay close attention to how we recommend creating rulesets, zones, and fanouts. By copying the setup in this example on your own project, you’ll have a fully routed board in no time!

Use this Copilot's template to design satellite payloads, avionic systems, or space instruments, considering MIL-STD, radiation hardening, and extreme environmental constraints. Modify project requirement properties to adapt to specific needs. #template #project-template

Learn how to use AI Auto Layout on this ESP32 Espresso Smart Scale! In one click you’ll see AI Auto Layout perform magic. Pay close attention to how we recommend creating rulesets, zones, and fanouts. By copying the setup in this example on your own project, you’ll have a fully routed board in no time!

Nice — you can do a clean pulse + latch using a single quad Schmitt-NAND chip: 74HC132 (or 74LVC132 for 3.3 V systems). The HC132 contains four 2-input NAND gates with Schmitt inputs so you can both clean a noisy SYN480R DATA line and build an SR latch (NAND SR is active-LOW) inside one package. Only a few passives and a driver transistor are needed. Below is a ready-to-build recipe (parts, wiring, explanation, tuning tips, and an ASCII schematic) — no extra logic ICs required. Parts (per latch) 1 × 74HC132 (quad 2-input NAND with Schmitt inputs). If your system is 3.3 V use 74LVC132 / 74HC132 rated for 3.3 V. Rin = 47 kΩ (input series) Cfilter = 10 nF (input RC to ground) — tweak for debounce/clean time Rpulldown = 100 kΩ (pull-down at input node, optional) Rpullup = 100 kΩ (pull-up for active-LOW R input so reset is idle HIGH) Rbase = 10 kΩ, Q = 2N2222 (NPN) or small N-MOSFET (2N7002) to drive your load Diode for relay flyback (1N4001) if you drive a coil Optional small cap 0.1 µF decoupling at VCC of IC Concept / how it works (short) Use Gate1 (G1) of 74HC132 as a Schmitt inverter by tying its two inputs together and feeding a small RC filter from SYN480R.DATA. This removes HF noise and provides a clean logic transition. Because it's a NAND with tied inputs its function becomes an inverter with Schmitt behavior. Use G2 & G3 as the cross-coupled NAND pair forming an SR latch (active-LOW inputs S̄ and R̄). A low on S̄ sets Q = HIGH. A low on R̄ resets Q = LOW. Wire the cleaned/inverted output of G1 to S̄. A valid received pulse (DATA high) produces a clean LOW on S̄ (because G1 inverts), setting the latch reliably even if the pulse is brief. R̄ is your reset input (pushbutton, HT12D VT, MCU line, etc.) — idle pulled HIGH. Q drives an NPN/MOSFET to switch your load (relay, LED, etc.). Recommended wiring (pin mapping, assume one chip; use datasheet pin numbers) I’ll refer to the 4 gates as G1, G2, G3, G4. Use G4 optionally for additional conditioning or to build a toggler later. SYN480R.DATA --- Rin (47k) ---+--- Node A ---||--- Cfilter (10nF) --- GND | Rpulldown (100k) --- GND (optional, keeps node low) Node A -> both inputs of G1 (tie inputs A and B of Gate1 together) G1 output -> S̄ (S_bar) (input1 of Gate2) Gate2 (G2): inputs = S̄ and Q̄ -> output = Q Gate3 (G3): inputs = R̄ and Q -> output = Q̄ R̄ --- Rpullup (100k) --- VCC (reset is idle HIGH; pull low to reset) (optional) R̄ can be wired to a reset pushbutton to GND or to an MCU pin Q -> Rbase (10k) -> base of 2N2222 (emitter GND; collector to one side of relay coil) Other side of relay coil -> +V (appropriate coil voltage) Diode across coil If you prefer MOSFET low side switching: Q -> gate resistor 100Ω -> gate of 2N7002 2N7002 source -> GND ; drain -> relay coil low side

Spot the mistake! Learn how to use AI to conduct a design review on an ESP32-based control board. This project is ideal for autonomous or radio-controller robots featuring inputs for sensors, encoders, and a Flysky RC receiver, plus an I2C display for configuration.

Learn how to use AI Auto Layout on this ESP32 Espresso Smart Scale! In one click you’ll see AI Auto Layout perform magic. Pay close attention to how we recommend creating rulesets, zones, and fanouts. By copying the setup in this example on your own project, you’ll have a fully routed board in no time!

make this for me now # Device Summary & Specification Sheet ## 1. Overview A rugged, Arduino-Uno-and-Raspberry-Pi-style single-board micro-PC featuring: - Smartphone-class CPU (Snapdragon 990) - USB-C Power Delivery + 4×AA alkaline backup + ambient-light harvester - On-board Arduino-Uno-compatible ATmega328P - External NVMe SSD via USB3 bridge & optional Thunderbolt 3 eGPU support - 5× USB 3.0 ports, HDMI in/out, Gigabit Ethernet & SFP fiber, Wi-Fi, Bluetooth, LoRa - 0.96″ OLED status display, 3.5 mm audio jack with codec --- ## 2. Key Specifications | Category | Specification | |--------------------|-------------------------------------------------------------------------------| | CPU | Snapdragon 990, octa-core up to 2.84 GHz | | Memory | 6 GB LPDDR4x DRAM | | Storage Interface | PCIe Gen3 ×4 → M.2 NVMe + USB 3.1 Gen1 bridge | | MCU | ATmega328P (Arduino-Uno-compatible) | | Power Input | USB-C PD up to 20 V/5 A; 4×AA alkaline backup; ambient-light photodiode boost | | Power Rails | 12 V, 5 V, 3.3 V, 1.8 V, 1.2 V via buck/buck-boost regulators | | USB Hub | 5× USB 3.0 downstream ports | | Display | 0.96″ 128×64 OLED via I²C/SPI | | Networking | 1 × Gigabit RJ45; 1 × SFP fiber; Wi-Fi 802.11ac + Bluetooth; LoRa SX1276 | | Video I/O | HDMI 2.0 input (RX) & output (TX) | | Audio | 3.5 mm jack + TLV320AIC3101 codec; Bluetooth audio | | Form Factor | Raspberry Pi–style header + Arduino-Uno shield headers; 4× standoff mounts | --- ## 3. Complete Parts List | Part | Function | Qty | |------------------------------------------------------------------------------------------------|-----------------------------------------------|-----| | [Snapdragon 990](https://www.flux.ai/search?type=components&q=Snapdragon%20990) | Main application CPU | 1 | | [LPDDR4x DRAM](https://www.flux.ai/search?type=components&q=LPDDR4x%20DRAM) | System memory | 1 | | [eMMC 64GB](https://www.flux.ai/search?type=components&q=eMMC%2064GB) | On-board storage | 1 | | [M.2 NVMe Connector](https://www.flux.ai/search?type=components&q=M.2%20NVMe%20Connector) | External SSD interface | 1 | | [JMS583](https://www.flux.ai/search?type=components&q=JMS583) | PCIe→USB 3.1 bridge for NVMe | 1 | | [Titan Ridge](https://www.flux.ai/search?type=components&q=Titan%20Ridge) | Thunderbolt 3/eGPU controller | 1 | | [STUSB4500](https://www.flux.ai/search?type=components&q=STUSB4500) | USB-C Power-Delivery controller | 1 | | [LTC4412](https://www.flux.ai/search?type=components&q=LTC4412) | Ideal-diode OR-ing | 1 | | [LTC3108](https://www.flux.ai/search?type=components&q=LTC3108) | Ambient-light (solar) energy harvester | 1 | | [Battery Holder 4×AA](https://www.flux.ai/search?type=components&q=Battery%20Holder%204xAA) | Alkaline backup power | 1 | | [TPS53318](https://www.flux.ai/search?type=components&q=TPS53318) | 6 V→5 V synchronous buck regulator | 1 | | [MCP1700-3302E/TO](https://www.flux.ai/search?type=components&q=MCP1700-3302E/TO) | 6 V→3.3 V LDO | 1 | | [TPS63060](https://www.flux.ai/search?type=components&q=TPS63060) | Buck-boost for 12 V rail (eGPU power) | 1 | | [ATmega328P](https://www.flux.ai/search?type=components&q=ATmega328P) | Arduino-Uno microcontroller | 1 | | [ESP32-WROOM-32](https://www.flux.ai/search?type=components&q=ESP32-WROOM-32) | Wi-Fi + Bluetooth co-processor | 1 | | [SX1276](https://www.flux.ai/search?type=components&q=SX1276) | LoRa transceiver | 1 | | [TUSB8041](https://www.flux.ai/search?type=components&q=TUSB8041) | 5-port USB 3.0 hub IC | 1 | | [Ethernet PHY](https://www.flux.ai/search?type=components&q=Ethernet%20PHY) | Gigabit Ethernet physical transceiver | 1 | | [SFP Cage](https://www.flux.ai/search?type=components&q=SFP%20Cage) | Fiber-optic SFP module connector | 1 | | [TDA19978](https://www.flux.ai/search?type=components&q=TDA19978) | HDMI 2.0 receiver (input) | 1 | | [TFP410](https://www.flux.ai/search?type=components&q=TFP410) | HDMI 2.0 transmitter (output) | 1 | | [TLV320AIC3101](https://www.flux.ai/search?type=components&q=TLV320AIC3101) | Audio codec for 3.5 mm jack & Bluetooth audio | 1 | | [Audio Jack 3.5mm](https://www.flux.ai/search?type=components&q=Audio%20Jack%203.5mm) | Headphone/mic port | 1 | | [SSD1306 OLED](https://www.flux.ai/search?type=components&q=SSD1306%20OLED) | Status display | 1 | --- ## 4. High-Level Design & Implementation Steps 1. **Schematic Creation** - Section off blocks: CPU + memory, power-supply, NVMe/USB bridge, MCU, wireless, USB hub, video, audio, I/O headers. - Place all parts from the list and wire per respective datasheet reference circuits. 2. **Power System** - Implement USB-C PD with STUSB4500 → buck/buck-boost & LDO rails. - Add LTC4412 to OR-ing battery pack into 6 V bus. - Include LTC3108 harvest input to 6 V bus (no recharge to AA). - Add decoupling & bulk caps per regulator datasheets; PTC fuses & ESD diodes on inputs. 3. **SoC & High-Speed Interfaces** - Break out Snapdragon 990 BGA with DDR4 routing (point-to-point). - Route PCIe Gen3 lanes to M.2 slot via JMS583 and to Titan Ridge for TB/eGPU. - Follow high-speed layout guidelines: impedance, length matching, EMI filters. 4. **MCU & Expansion Headers** - Place ATmega328P with UNO-style 2.54 mm headers; add ISP 6-pin, I²C pull-ups, reset circuit. 5. **Connectivity Blocks** - USB hub: TUSB8041 + ESD + chokes → five USB-A 3.0 ports. - Ethernet: RJ45 + magnetics + PHY; SFP cage + PHY/SFP interface. - Wireless: ESP32 module pad + SX1276 + antenna matching; program pads. 6. **Video & Audio** - HDMI RX (TDA19978) & TX (TFP410) blocks with TMDS ESD & termination. - Audio codec + 3.5 mm jack; route differential audio lines; add ESD. 7. **Display & Indicators** - SSD1306 OLED on I²C; add pull-ups & backlight power if needed. - LEDs for power, charge, status. 8. **PCB Layout & Mechanical** - Follow Raspberry Pi 2×20 header footprint & UNO shield hole pattern. - Add reinforcement for rugged use; standoffs at corners. - Define 4-layer stackup: • Top: signal • L2: GND • L3: PWR • Bottom: signal 9. **DRC/ERC Reviews** - Run ERC for decoupling, pull-ups, power nets. - Run DRC for high-speed clearance, differential pairs, via aspect ratios. 10. **Fabrication & Testing** - Export Gerbers, IPC-2581, BOM. - Prototype assembly; verify power sequencing, rails, interfaces. - Functional tests: USB-C PD negotiation, NVMe throughput, eGPU over TB3, wireless comm, audio, OLED. --- This spec sheet and part list should guide your Flux schematic and PCB layout workflow through to fabrication and validation. Let me know if you’d like to deep-dive into any specific block or review datasheet details next!

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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

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

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

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. #raspberryPi #rp2040 #lorawan #iot #solar

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

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

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

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

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

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

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

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

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

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