Seeed Studio XIAO ESP32S3 Sense
Seeed Studio XIAO ESP32S3 Sense
The Seeed Studio XIAO ESP32S3 Sense is a compact, feature-rich Internet of Things (IoT) development board designed for a wide range of applications, including embedded machine learning (ML), computer vision, and wireless communication. This module integrates a robust set of peripherals, including a camera, microphone, Wi-Fi, Bluetooth Low Energy (BLE), and rich interface capabilities, making it an ideal choice for IoT prototyping and development.
### Processors and Memory
| ESP32S3 Microcontroller | The heart of the XIAO ESP32S3 Sense is the ESP32S3 system-on-chip (SoC), a powerful, low-power microcontroller with a dual-core 32-bit LX7 processor, operating at up to 240 MHz. |
| 8MB Flash Memory | The board features 8MB of flash memory for storing programs, data, and firmware. |
| 8MB PSRAM | The XIAO ESP32S3 Sense also includes 8MB of pseudo-static random access memory (PSRAM) for additional storage and processing capabilities. |
### Wireless Connectivity
| 2.4GHz Wi-Fi | The module supports 2.4GHz Wi-Fi connectivity, enabling seamless communication with other devices and the internet. |
| BLE 5.0 | The XIAO ESP32S3 Sense features Bluetooth Low Energy (BLE) 5.0, providing a low-power, high-range wireless communication protocol for IoT applications. |
### Multimodal Sensing
| OV2640 Camera Sensor | The board is equipped with an OV2640 camera sensor, capable of capturing high-quality images and video at up to 2 megapixels (1600x1200). |
A digital microphone is integrated into the board, enabling audio input and processing capabilities.
### Interface and Expansion
The XIAO ESP32S3 Sense features a range of interface options, including I2C, I2S, UART, SPI, and more, allowing for expansion and connection to various peripherals and sensors.
The board supports battery charging, making it suitable for battery-powered IoT devices.
### Machine Learning and AI
The ESP32S3 microcontroller is optimized for machine learning (ML) and artificial intelligence (AI) applications, enabling the development of intelligent IoT devices.
### Operating System and Development
| Supports MicroPython, C, and C++ | The XIAO ESP32S3 Sense is compatible with popular programming languages, including MicroPython, C, and C++, making it accessible to a wide range of developers. |
The board is also compatible with the Arduino integrated development environment (IDE), simplifying the development process for Arduino users.
| The Seeed Studio XIAO ESP32S3 Sense is suitable for a variety of IoT applications, including |
Computer vision and machine learning projects
Wireless sensing and monitoring systems
Smart home and building automation
Wearable devices and health monitoring systems
Robotics and drone development
Edge AI and IoT gateways
The Seeed Studio XIAO ESP32S3 Sense is a powerful, feature-rich IoT development board that combines robust processing, wireless connectivity, multimodal sensing, and rich interface capabilities. Its compact size, low power consumption, and extensive software support make it an ideal choice for IoT prototyping and development.
Seeed Studio XIAO ESP32S3 Sense DocumentationOverviewThe Seeed Studio XIAO ESP32S3 Sense is a compact, feature-rich IoT development board that combines the power of ESP32S3 microcontroller with a range of sensors and interfaces. This board is ideal for building IoT projects that require Wi-Fi, Bluetooth Low Energy (BLE), computer vision, audio processing, and machine learning capabilities.Key FeaturesESP32S3 microcontroller with 2.4GHz Wi-Fi and BLE 5.0
OV2640 camera sensor for image capture and processing
Digital microphone for audio capture and processing
8MB FLASH and 8MB PSRAM for storing and running applications
Rich interface options, including UART, I2C, I2S, SPI, and GPIO
Battery charging supported for portable applications
Supports embedded machine learning (ML) capabilitiesCode Examples### Example 1: Wi-Fi Connection and HTTP RequestIn this example, we will demonstrate how to connect to a Wi-Fi network and send an HTTP request using the XIAO ESP32S3 Sense board.```c
#include <WiFi.h>const char ssid = "your_wifi_ssid";
const char password = "your_wifi_password";
const char serverUrl = "http://example.com/";WiFiClient client;void setup() {
Serial.begin(115200);// Initialize Wi-Fi
WiFi.begin(ssid, password);
while (WiFi.status() != WL_CONNECTED) {
delay(1000);
Serial.println("Connecting to WiFi...");
}Serial.println("Connected to WiFi");
Serial.println("Initializing HTTP client...");// Initialize HTTP client
client.setServer(serverUrl, 80);
}void loop() {
// Send HTTP request
int httpRequestCode = client.GET("/");
if (httpRequestCode > 0) {
Serial.println("HTTP request sent successfully!");
client.getString();
} else {
Serial.println("Error sending HTTP request");
}delay(10000);
}
```### Example 2: Image Capture and Upload to Cloud StorageIn this example, we will demonstrate how to capture an image using the OV2640 camera sensor and upload it to a cloud storage service using the XIAO ESP32S3 Sense board.```c
#include <WiFi.h>
#include <HttpClient.h>
#include <OV2640.h>const char ssid = "your_wifi_ssid";
const char password = "your_wifi_password";
const char cloudStorageUrl = "https://your_cloud_storage_url.com/upload";WiFiClient client;
OV2640 camera;void setup() {
Serial.begin(115200);// Initialize Wi-Fi
WiFi.begin(ssid, password);
while (WiFi.status() != WL_CONNECTED) {
delay(1000);
Serial.println("Connecting to WiFi...");
}Serial.println("Connected to WiFi");
Serial.println("Initializing camera...");// Initialize camera
camera.begin();
}void loop() {
// Capture image
camera.capture();
uint8_t imageBuffer = camera.getImageBuffer();
int imageSize = camera.getImageSize();// Upload image to cloud storage
HttpClient httpClient;
httpClient.begin(cloudStorageUrl);
httpClient.addHeader("Content-Type", "image/jpeg");
int httpResponseCode = httpClient.POST(imageBuffer, imageSize);
if (httpResponseCode == 200) {
Serial.println("Image uploaded successfully!");
} else {
Serial.println("Error uploading image");
}delay(10000);
}
```### Example 3: Speech Recognition using Digital Microphone and Machine LearningIn this example, we will demonstrate how to use the digital microphone and machine learning capabilities of the XIAO ESP32S3 Sense board to recognize speech and perform actions based on the recognized commands.```c
#include <WiFi.h>
#include <Audio.h>
#include <TensorFlowLite.h>const char ssid = "your_wifi_ssid";
const char password = "your_wifi_password";WiFiClient client;
Audio audio;
TensorFlowLite tflite;void setup() {
Serial.begin(115200);// Initialize Wi-Fi
WiFi.begin(ssid, password);
while (WiFi.status() != WL_CONNECTED) {
delay(1000);
Serial.println("Connecting to WiFi...");
}Serial.println("Connected to WiFi");
Serial.println("Initializing audio and machine learning...");// Initialize audio and machine learning
audio.begin();
tflite.begin();
}void loop() {
// Record audio
int16_t audioBuffer[1024];
audio.record(audioBuffer, 1024);// Pre-process audio data
int16_t preprocessedAudioBuffer = preprocessAudio(audioBuffer, 1024);// Run machine learning model to recognize speech
int inferenceOutput = tflite.run(preprocessedAudioBuffer, 1024);// Perform action based on recognized command
if (inferenceOutput == 0) {
Serial.println("Recognized command: Turn on light");
// Turn on light
} else if (inferenceOutput == 1) {
Serial.println("Recognized command: Turn off light");
// Turn off light
} else {
Serial.println("Unrecognized command");
}delay(1000);
}
```These code examples demonstrate the capabilities of the Seeed Studio XIAO ESP32S3 Sense board and provide a starting point for building IoT projects that require Wi-Fi, Bluetooth Low Energy (BLE), computer vision, audio processing, and machine learning capabilities.