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

5V Trigger Relay Module For Arduino And Raspberry Pi 5V Trigger Relay Module For Arduino And Raspberry Pi

CheckIt is a daily habit tool. When you complete a habit, flip a switch, then an LED lights up. Future work will include wiring the 24pin FPC port to the Pico which causes an e-paper screen to display a message when a switch is flipped. This PCB runs using a Raspberry Pi Pico and has plans to be battery powered similar to a digital alarm clock.

Basic project template for RP2040 MCU from Raspberry Pi Pico Development board #RaspberryPi #Raspberry #Pi #RPi #Pico #template #project #project-template

The Qwiic PC Fan Controller allows you easily control almost any PC fan over the Qwiic bus using the on board ATtiny microcontroller and control firmware. The control firmware monitors the tachometer output of the fan in order to implement PI Control over the fan speed, allowing you to set your desired speed in real units.

Explore the Raspberry Pi Pico Template Unleash the power of these flexible microcontroller boards, starting at just $4. The Raspberry Pi Pico series features a range of compact, high-performance boards powered by the RP2040 chip. #project-template #template #raspberry #pi #pico

IP65 Marine/Industrial Embedded Computer with Raspberry Pi CM5 and Integrated Power Resilience

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!

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

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

Basic project template for RP2040 MCU from Raspberry Pi Pico Development board #RaspberryPi #Raspberry #Pi #RPi #Pico #template #project #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

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

I designed a Raspberry Pi pico-like board using only AI

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

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

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

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

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

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

como puedo corregir este codigo para que funcione en flux.io import time import random import matplotlib.pyplot as plt import io import base64 from fluxio import Flow flow = Flow() @flow.task def linear_search(arr, target): for i in range(len(arr)): if arr[i] == target: return i return -1 @flow.task def binary_search(arr, target): low = 0 high = len(arr) - 1 while low <= high: mid = (low + high) // 2 if arr[mid] == target: return mid elif arr[mid] < target: low = mid + 1 else: high = mid - 1 return -1 @flow.task def measure_time(func, arr, target): start_time = time.time() func(arr, target) end_time = time.time() return end_time - start_time @flow.task def generate_data_and_measure(): sizes = [100, 1000, 5000, 10000, 50000, 100000] linear_times = [] binary_times = [] for size in sizes: arr = random.sample(range(size * 2), size) target = random.choice(arr) sorted_arr = sorted(arr) linear_time = measure_time(linear_search, arr, target) binary_time = measure_time(binary_search, sorted_arr, target) linear_times.append(linear_time) binary_times.append(binary_time) return sizes, linear_times, binary_times @flow.task def plot_results(sizes, linear_times, binary_times): plt.plot(sizes, linear_times, label='Búsqueda Lineal') plt.plot(sizes, binary_times, label='Búsqueda Binaria') plt.xlabel('Tamaño del Array') plt.ylabel('Tiempo de Ejecución (segundos)') plt.title('Análisis de Complejidad Temporal') plt.legend() plt.grid(True) buf = io.BytesIO() plt.savefig(buf, format='png') buf.seek(0) img_str = base64.b64encode(buf.read()).decode('utf-8') plt.close() return img_str @flow.task def main(): sizes, linear_times, binary_times = generate_data_and_measure() img_str = plot_results(sizes, linear_times, binary_times) return img_str if __name__ == "__main__": flow.run(main)

TelePacks v2.1 - Routed, Part-Assigned, All Checks Passed

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

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

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

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

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

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

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

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

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

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

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

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

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