Ayesha Automation and Equipment Supplier Website: https://ayeshaautomation.com

Features

Brand	FABOTRONIX
Origin	China
Category	Electrical and Electronics, Electronics, Digital Electronics
Price	Call for Price

Short Description

Model: FT-IOT6002

• Main module supporting AI acceleration calculation, multimedia and various IoT sensors should be integrated into the base board.
• Minimum 5-inch TFT LCD with 800x480 or higher resolu- tion and Minimum 8M pixel high resolution camera
• Digital microphones and speakers should support cloud- based speech recognition and audio playback
• Minimum 4 dedicated expansion interfaces support various IoT sensor modules
• Soda OS, the exclusive AIoT operating system, and Pop library
• A dedicated web browser-based learning environment for training Python 3 and C/C++
• Educational contents for IoT sensor control, multimedia and AI

Specifications

Experiment Content:

• Basic IoT Sensor Integration: Interface temperature, humidity, and light sensors to display real-time readings on the LCD.
• Smart Home Simulation: Use PIR sensors for motion detec- tion to automate lights and fans.
• Speech-Activated Devices: Control LEDs or fans using voice commands with Google Assistant integration.
• Environmental Monitoring: Monitor air quality using gas, dust, and temperature sensors and display results.
• AI-Based Object Detection: Detect objects or movements using ultrasonic or PIR sensors and trigger actions like alarms. Gesture Control System: Control RGB LEDs or other devices using hand gestures detected by the gesture sensor.
• Data Logging and Persistence: Store sensor data (tempera- ture, humidity, etc.) in a local or cloud-based database for analysis.
• AI-Powered Smart Farming: Automate irrigation based on soil moisture and temperature data using relays and water pumps.

Main module supporting AI acceleration calculation, multimedia and various IoT sensors should be integrated into the base board.
Minimum 5-inch TFT LCD with 800x480 or higher resolu- tion and Minimum 8M pixel high resolution camera
Digital microphones and speakers should support cloud- based speech recognition and audio playback
Minimum 4 dedicated expansion interfaces support various IoT sensor modules
Soda OS, the exclusive AIoT operating system, and Pop library
A dedicated web browser-based learning environment for training Python 3 and C/C++
Educational contents for IoT sensor control, multimedia and AI

Training Contents:
Introduction to AIoT Home:
Configuration and Practice Environment of AIoT Home 
Python and Linux 101
IoT Application Technology   
Sensor Control:
File and DB-Based Data Persistence 
Audio Recording and Playback
Google Text-to-Speech Converter, Google Assistant and User Device Actions

AI Technology:
Numpy for Fast Multidimensional Matrix Operations 
Pandas for Time Series and Tabular Data 
Analysis Matplotlib for Data Visualization
Supervised and Unsupervised Learning
Theory & Practice for Pop.AI-based Linear and Logistic 
Regression Algorithm, Perceptron, ANN, DNN, and CNN & 
OpenAI DQN-based Reinforcement Learning 
Understanding Tensorflow.

Software Specifications:
Soda OS:
Linux Kernel: 4.19
Desktop: X-Server, Openbox, LightDM, Tint2, blueman, network-manager, conk CLI: Zsh, Tmux, Peco, powerlevel9k thema, Powerline fonts
IDE: Visual Studio Code, NeoVim, Geany
Data Science & AI: Python3, Numpy, Matplotlib, sympy, Pandas, Seaborn, Scipy, Gym Scikit-learn, Tensorflow, Kerast 

Pop Library:
Output Object (C/C++, Python3): Led, Laser, Buzzer, Relay, RGBLed, DCMotor, StepMotor, OLed, PiezoBuzzer, PixelDisplay, TextLCD, FND, Led Bar
Input Object (C/C++, Python3): Switch, Touch, Reed, LimitSwitch, Mercury, Knock, Tilt, Opto, Pir, Flame LineTrace, TempHumi, UltraSonic, Shock, Sound, Potentiometer, CdS, SoilMoisture, Thermistor, Temperature, Gas, Dust, Psd. Gesture
AI (Python3): Linear Regression, Logistic Regression, Perceptron, ANN, DNN, CNN, DQN

Hardware Specifications:
Main Module:
CPU: 6-core NVIDIA Carmel ARM v8.2 64-bit; 6MB L2 + 4MB
L3; Max Freq: 2-core@1900MHz, 4/6-core@1400Mhz 
GPU: 384-core NVIDIA VoltaTM GPU with 48 Tensor Cores 
Max Freq: 1100MHz
Memory: Minimum 8GB 128-bit LPDDR4x@ 1600MHz 
Video Encoder: 2x464MP/sec(HEVC),2x4k@30(HEVC); 6x 1080p@ 60(HEVC), 14x 1080p@ 30(HEVC)
Video Decoder: 2x690MP/sec(HEVC), 2x4k@ 60(HEVC), 4x4k@30(HEVC); 12x1080p@ 60(HEVC), 32x 1080p@
30(HEVC), 16x 1080p@30(H.264)
CSI Camera: Up to 6 cameras (36 via virtual channels); 12 Lanes MIPI CSI-2, D-PHY 1.2 (up to 30 Gbps) Connectivity: Dual Band Wireless Wi-Fi 2GHz/5GHz Band, 867Mbps, 802.11ac; Bluetooth 4.2; 10/100/1000 Base-T Ethernet

Display:
2 multi-mode DP 1.4/eDP 1.4/HDMI 2.0 USB: Min. 4x USB 3.0, Min. USB 2.0 Micro-B

Base Board:
Camera: Image Sensor: Sony IMX219
Resolution: Min. 8M Pixel Native Resolution Sensor (3280 x 2464 or higher Pixel Static Images)
Video: 1080p30, 720p60 and 640x480p90 
Linux Integration: V4L2 Driver Available 
Sound: Sound IC: WM8960
Channel: Input 2ch, Output 2ch 
2CH Microphone
Stereo Speaker

Servo Motor:
Working Speed: 0.12sec/60 (4.8V no Load) 
Stall torque: 1.2kg/cm (4.8V), 1.6kg/cm (6.0V) 

TEXT LCD:
Format Size: 16 X 2 or higher 
LED B/L, Black and White 
Interface: GPIO
LED: (1x3) Group x 3EA: Light Display 
Stand Light Display 1EA
Interface: GPIO RGB LED:
Size: 5pi
Wavelength: RED (630 nm), GREEN (525 nm), BLUE (430nm)
Interface: I2C

PIEZO:
Rated Current: Max 30mA
Sound Output at 10cm(dB): Min 85dB

FAN:
Size: 30 X 30mm 
Speed: 900RPM 
Supply Voltage: 5V LED BAR x 2EA:
Size: 20 X 10mm

Humidity & Temperature Sensor:
Humidity Resolution: 12bit(0.04%RH), 8bit(0.7%RH) 
Humidity Accuracy: +-3%RH or better
Temperature Resolution: 14bit(0.01C), 12bit(0.04C) 
Temperature Accuracy: +-4°C or better

PIR Sensor:
Transmittance: ≥75% 
Detecting Distance: 10~80cm 

DUST Sensor:
Based on Laser Scattering Technology 
Measured Particle Size: 0.3µm~10µm
Measurement Range: PM1.0/PM2.5/PM10: 0~1,000µg/m³   

TFT LCD:
Size: 4 inch
Resolution: 800X480 or higher
IPS technology, High quality and perfect displaying from very wide viewing angle
Interface: HDMI

Illuminance Sensor: 
Sensor: CdS
Power Dissipation (at 25): 100mW 
Temp. Range: -30~+70
Interface: ADC   GAS Sensor:
Measure: LPG, Alcohol, Propane, Hydrogen, CO and Even Methane

Touch Keypad: 
12 Key Input

Sensor Module Block:
Sensor Block1: +5V, +3.3V, GND, I2C, ADC, GPIO 
Sensor Block2: +5V, +3.3V, GND, I2C, ADC, GPIO
Sensor Block3: +5V, +3.3V, GND, SPI, GPIO 
Sensor Block4: +5V, +3.3V, GND, ADC, GPIO
Main Board Size: Minimum 460 X 310 (mm)   

Sensor Pack:
Flame Module:
Sensing Range: 60 Degree   
Eco Sensor Module:
Light Sensor: Illuminance to digital converter; Wide range: 1~ 65535(lx)
Temperature Measure: -40 ~ 85  or higher 
Humidity Measure: 0 ~ 100%r.H.
Pressure Range: 300 ~ 1100hPa or higher
VOC Measure: Ethane, Ethanol, Acetone, Carbon Monoxide, Butadiene, methyl

Carbon Dioxide (CO2) Gas Sensor Module: 
Measuring Range: 0 ~ 10000 ppm or higher Accuracy: ±7%~±50ppm or better

Pixel Display:
Color: Pixel RGB 
Pixel: 8x8

Digital Thermopile Module Laser (DTPML): 
IR Refresh Rate: 50Hz
Digital Resolution: 0.1 Accuracy: ±2% or better

Microwave Motion Sensor Module: 
Frequency Setting: 10.525 GHz (Typ) 
Spurious Emission: -7.3 dBm
Pulse Repetition Frequency: 2KHz 

IR Receiver Module:
Carrier Frequency: 38kHz 

PIR Senor Module:
Sensing Range: 110°
Spectral Response: 5 ~ 14 um

Accessories :
Single-phase power cord 
4mm Banana Socket-1Set
User Manual

Others :
Country of Origin: China
Manufacturing: Assemble in Bangladesh   Inatallation
3 Days Training
Warranty: 01 year

Description

The IoT Training System is a comprehensive platform designed for education and development in AI and IoT. It includes digital microphones and speakers for cloud-based speech recognition and audio playback. The system supports various IoT sensors and operates on Soda OS, featuring the Pop library for AI and sensor applications. It also offers a web-based Python and C/C++ learning environment, providing rich educational content for AI, IoT, and sensor control. With multiple expansion interfaces for IoT modules, the system ensures adaptability and versatility, making it ideal for hands-on training in AIoT technologies.