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The Arduino Nano 33 BLE Sense REV2 Combines A Tiny Form Factor, Different Environment Sensors And The Possibility To Run AI using TINYML and Tensorflow™ Lite [ARD NANO 33 BLE SENSE REV2]
The Arduino Nano 33 BLE Sense REV2 Combines A Tiny Form Factor, Different Environment Sensors And The Possibility To Run AI using TINYML and Tensorflow™ Lite [ARD NANO 33 BLE SENSE REV2] The Arduino Nano 33 BLE Sense REV2 Combines A Tiny Form Factor, Different Environment Sensors And The Possibility To Run AI using TINYML and Tensorflow™ Lite [ARD NANO 33 BLE SENSE REV2] The Arduino Nano 33 BLE Sense REV2 Combines A Tiny Form Factor, Different Environment Sensors And The Possibility To Run AI using TINYML and Tensorflow™ Lite [ARD NANO 33 BLE SENSE REV2] The Arduino Nano 33 BLE Sense REV2 Combines A Tiny Form Factor, Different Environment Sensors And The Possibility To Run AI using TINYML and Tensorflow™ Lite [ARD NANO 33 BLE SENSE REV2]

ARD NANO 33 BLE SENSE REV2

The Arduino Nano 33 BLE Sense REV2 Combines A Tiny Form Factor, Different Environment Sensors And The Possibility To Run AI using TINYML and Tensorflow™ Lite
Part No: ARD NANO 33 BLE SENSE REV2
1 012.00 ZAR
inc
for 1 Each
Specifications, Prices & Availability may change without notice. Images are for illustration purposes only. E&OE

Overview

The Arduino Nano 33 BLE Sense Rev2 is Arduino’s 3.3V AI enabled board in the smallest available form factor with a set of sensors that will allow you without any external hardware to start programming your next project, right away.

Features

With the Arduino Nano 33 BLE Sense Rev2, you can:

  • Build wearable devices that using AI can recognize movements.
  • Build a room temperature monitoring device that can suggest or modify changes in the thermostat.
  • Build a gesture or voice recognition device using the microphone or the gesture sensor together with the AI capabilities of the board.

The main feature of this board, besides the complete selection of sensors, is the possibility of running Edge Computing applications (AI) on it using TinyML. Learn how to use the Tensor Flow Lite library following this instructions or learn how to train your board using Edge Impulse. 

Specifications

Microcontroller

nRF52840 (datasheet)

Operating Voltage

3.3V

Input Voltage (limit)

21V

DC Current per I/O Pin

15 mA

Clock Speed

64MHz

CPU Flash Memory

1MB (nRF52840)

SRAM

256KB (nRF52840)

EEPROM

none

Digital Input / Output Pins

14

PWM Pins

all digital pins

UART

1

SPI

1

I2C

1

Analog Input Pins

8 (ADC 12 bit 200 k samples)

Analog Output Pins

Only through PWM (no DAC)

External Interrupts

all digital pins

LED_BUILTIN

13

USB

Native in the nRF52840 Processor

IMU

BMI270 (datasheet) and BMM150 (datasheet)

Microphone

MP34DT06JTR (datasheet)

Gesture, light, proximity, color

APDS9960 (datasheet)

Barometric pressure

LPS22HB (datasheet)

Temperature, humidity

HS3003 (datasheet)

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