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AIoT Archives - EDU Market https://edu-market.net/product-category/engineering/aiot/ Educational Labs & Instruments Sun, 24 Nov 2024 21:38:46 +0000 en-US hourly 1 https://wordpress.org/?v=6.9.4 https://edu-market.net/wp-content/uploads/2023/02/Faveicon-64x62.png AIoT Archives - EDU Market https://edu-market.net/product-category/engineering/aiot/ 32 32 AIoT TestBed Trainer https://edu-market.net/everything/aiot-testbed-trainer/ Fri, 22 Nov 2024 17:10:04 +0000 https://edu-market.net/?post_type=product&p=6510

Features

  • IoT test bed platform based on Open API that can test the interworking of smart home products
  • Divided into door, living room, and kitchen areas, and AI IoT Service for each area is supported
  • Consists of high-performance AIoT server, high-performance access point, IoT nodes (sensors and actuators), display, camera, and audio (microphone, speaker)
  • High-performance IoT server supports AI accelerated computation through CUDA GPGPU
  • GUI-based test bed operation monitoring through 43-inch 4K UHD large monitor including touch screen
  • Provides a large pixel display that can implement emotional lighting and real-time notification services
  • Supports various IoT connectivity through Gigabit Ethernet, Wi-Fi, Bluetooth, and ZigBee Provides high-performance digital microphones and speakers necessary for implementing AI-based voice command services Supports for deep learning-based image recognition services required to implement customized services
  • Soda OS and Pop library, the AIoT dedicated operating system
  • Interpreter-based C/C++ development environments optimized for programming beginners, including Python 3
  • A dedicated web browser-based learning environment for training Python 3 and C/C++ simultaneously on PCs and tablets
  • mDNS/DNS-SD based distributed name resolution, network service publishing and discovery support
  • Open Integrated development environment based on Visual Studio Code for professional application development
  • Provides comprehensive AIoT practical exercise contents in connection with existing AIoT educational equipment

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Training Contents

  • Lab Environment of AIoT Test Bed
  • Low Power Wireless Sensor Network Programming
  • IoT Sensor Control Programming by Voice Command
  • Smartphone Interlocking Programming
  • Cloud Programming
  • Face Recognition Programming Based on Deep Learning

 

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]]> AIoT Home Trainer https://edu-market.net/everything/iot-home-trainer/ Fri, 22 Nov 2024 17:03:58 +0000 https://edu-market.net/?post_type=product&p=6504 Features
  • AI and IoT convergence training equipment using 2D model of living room of home
  • Main module supporting AI acceleration calculation, multimedia and various IoT sensors are integrated into the base board
  • The main module is selectable between a 128-core GPU supercomputer for edge devices or a Cortex-A72 quadcore processor with tensor processor unit
  • 4 inch TFT LCD with 800×480 resolution and 8M pixel high resolution camera
  • Provides Gigabit Ethernet, dual band Wi-Fi(2.4GHz, 5GHz) and Bluetooth 4.2 or 5.0
  • Digital microphones and speakers support cloud-based speech recognition and audio playback
  • 4 dedicated expansion interfaces support various IoT sensor modules
  • Positioning sensors and actuators by creating 2D models of living rooms in real homes to increase immersion
  • Soda OS, the exclusive AIoT operating system, and Pop library
  • Interpreter-based C/C++ development environments optimized for programming beginners, including Python 3
  • A dedicated web browser-based learning environment for training Python 3 and C/C++ simultaneously on PCs and tablets
  • mDNS/DNS-SD based distributed name resolution, network service publishing and discovery support
  • Open Integrated development environment based on Visual Studio Code for professional application development
  • Educational contents for IoT sensor control, multimedia and AI
  • Provides 8 types of IoT sensor modules connected to a dedicated expansion interface
  • Contains a supercomputer up to 21TOPS supporting all AI frameworks.

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]]> Introduction:

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
Camera and Sensor Applications.

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
Theory & Practice for Pop.AI-based Perceptron
Theory & Practice for Pop.AI-based ANN, DNN, and CNN
Theory & Practice for Pop.AI & OpenAI DQN-based Reinforcement Learning
Understanding Tensorflow

 

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]]> AI Autonomous Driving Vehicle Training Equipment AutoCar III G https://edu-market.net/everything/ai-autonomous-driving-vehicle-training-equipment-autocar-iii-g-2/ Fri, 22 Nov 2024 15:15:10 +0000 https://edu-market.net/?post_type=product&p=6482 1- AI Deep Learning platform of NVIDIA.
2- Motor, encoder, and sensor control with a controller.
3- IoT connectivity applications
4- DToF lidar for SLAM.
5- Supports CUDA-based PyTorch and Tensorflow artificial intelligence framework
6- Supports Blockly.
7- Supports ROS2

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  • On-Device AI Autonomous Driving Vehicle Training Equipment
  • High-performance on-device AI platform of NVIDIA is adopted for the Brain Board
  • Built-in high-resolution wide-angle camera for deep learning-based autonomous driving practice
  • Built-in 9-axis high-precision IMU sensor for path tracking and vehicle posture control
  • Built-in high-precision serial servomotor for more accurate steering control
  • Built-in DC motor with encoder and high-efficiency dual motor driver to increase driving accuracy
  • Motor, encoder, and sensor control with a controller equipped with a high-performance MCU for precise control of the driving unit
  • Connect brain board and controller with highly reliable CAN FD communication
  • Built-in Gigabit Ethernet, dual-band Wi-Fi, and Bluetooth for IoT connectivity applications
  • Built-in digital microphone and speaker for voice recognition and audio playback
  • Built-in power path management circuit enabling practice even while the battery is charging
  • Indoor or indoor/outdoor DToF lidar for SLAM and path planning applications
  • Selectable sensor pack with built-in breadboard to use various IoT sensor modules
  • Selectable touch display to implement GUI-based user interface
  • Provides high-level Pop Library to help focus on implementing autonomous driving
  • Supports autonomous vehicle applications based on robot standard middleware ROS2 and Pop Library
  • Supports CUDA-based PyTorch and Tensorflow artificial intelligence framework
  • Supports web browser-based Google block coding platform (Blockly)
  • Supports pre-set integrated development environment based on Visual Studio Code for professional application development
  • Provides learning contents for self-driving cars based on deep learning

    • Operation Program

    List Specifications
    Linux OS Desktop X-Server, Openbox, LightDM, Tint2, blueman, network-manager, conky
    CLI Zsh, Tmux, Peco, powerlevel9k theme, Powerline fonts, Powerline fonts
    Tool Chain GCC, JDK, Node JS, Python3, Clang
    Connectivity Mosquitto(MQTT), Bluez, mtr, nmap, iptraf, Samba, Blynk Server, Remote Desktop Server
    Multimedia portaudio, sox, OpenCV 4, Google Assistant
    Data Science & AI Python3, Numpy, Matplotlib, sympy, Pandas, Seaborn, Scipy, Gym, Scikit-learn, tensorflow, Keras
    Middleware ROS2, Rviz2, RQt, ament, RTPS, Fast DDS, TF2
    Pop Library Output Object Led, Laser, Buzzer, Relay, RGBLed, DCMotor, StepMotor, OLed, PiezoBuzzer, PixelDisplay, TextLCD, FND, Led Bar
    Input Object Switch, Touch, Reed, LimitSwitch, Mercury, Knock, Tilt, Opto, Pir, Flame, LineTrace, TempHumi, UltraSonic, Shock, Sound, Potentiometer, Cds
    SoilMoisture, Thermistor, Temperature, Gas, Dust, Psd, Gesture
    Multimedia AudioPlay, AudioPlayList, AudioRecord, Tone, SoundMeter
    Voice Assistant GAssistant, create_conversation_stream
    AI Linear Regression, Logistic Regression, Perceptron, ANN, DNN, CNN, DQN, Object Follow, Track Driving, YOLO
    PC linkage development environment Jupyter Lab Python3 and Cling support, IPython Widgets, Terminal support, Pop Library support
    Visual Studio Code Insiders Remote SSH, Python3 and Debugging support, Terminal support, Pop Library support

    Block-Based Programming

    Training Contents

    DDS/RTPS Network-Based Autonomous Driving Vehicle Control in ROS2 Environment

    • WSL2-Based Linux Development Environment
    • Understanding Python Syntax for ROS2
    • Understanding Network Programming for ROS2
    • ROS2 Installation and Environment Configuration
    • Understanding Node, Topic, Service, and Parameter Action
    • ROS2 Build Environment
    • Publisher and Subscriber Nodes
    • Services and Customized Interface
    • Actions and Multi-Node
    • Launch and Multi-Execution
    • Advanced ROS2

    Deep Learning-Based Autonomous Driving Technology

    • WSL2-Based Linux Development Environment
    • Supervised Learning and Unsupervised Learning
    • Linear Regression and Logistic Regression
    • ANN, DNN, CNN Basics
    • Understanding Machine Learning Framework
    • High Speed Multidimensional Matrix Library
    • Time Series, Table Data Analysis Library
    • Data Visualization Library
    • Overview of Autonomous Driving Technology
    • Basic Driving and Remote Control
    • Collision Prevention and Follow Object
    • Transfer Learning
    • Advanced Autonomous Driving

    Layout

    Components

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    ]]>         AIoT SerBot G https://edu-market.net/everything/aiot-serbot-g-2/ Fri, 22 Nov 2024 15:07:19 +0000 https://edu-market.net/?post_type=product&p=6476 1- C/C++, Python3.
    2- Supporting all popular AI frameworks.
    3- Lidar and sensors connectivity.

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    ]]>     Product Features

    • AI application practice equipment based on indoor service robot platform
    • Main processor is GPU supercomputer platform for edge device
    • 7-inch touch display with 1024×600 resolution and 8M pixel 160° wide-angle camera
    • Gigabit Ethernet, dual-band Wi-Fi (2.4GHz, 5GHz), and Bluetooth 4.2
    • Voice recognition and audio play through digital microphone and speaker
    • Supports various IoT sensor modules through four exclusive expansion interfaces
    • 3-axis omni wheel to maximize the movement efficiency and minimize the turning radius
    • Multiple PowerPath blocks allowing to practice even while the battery is charging
    • Robot standard middleware ROS2 and Pop library are provided
    • Supports CUDA-based PyTorch and Tensorflow artificial intelligence framework
    • Supports web browser-based Google block coding platform (Blockly)
    • Supports a public integrated development environment based on Visual Studio Code for professional application development
    • Service robot learning content based on artificial intelligence and deep learning is provided

    Software Specifications

    Training Contents

    1. Artificial Intelligence and Autonomous Driving
      • 1.1. Components of Autonomous Driving
      • 1.2. Autonomous Driving Overview
    2. Environment for Experiment
      • 2.1. AIoT SerBot-G
      • 2.2. Communication between PC and AIoT SerBot-G
      • 2.3. Development Environment
    3. Control AIoT SerBot-G
      • 3.1. 3WD Omni Wheel Moving Device
      • 3.2. 9DOF IMU Sensor
      • 3.3. Ultrasonic Sensor
      • 3.4. Psd Sensor
    4. CAN Protocol
      • 4.1. CAN Network
      • 4.2. CAN Communication
      • 4.3. CAN Communication in Linux
      • 4.4. Example of CAN Protocol Application
    5. Moving Device Library based on CAN Communication
      • 5.1. Start Library
      • 5.2. Moving Device Control Library based on CAN Communication
      • 5.3. Implement Sensor Action and Broadcast Reception
    6. MQTT
      • 6.1. MQTT Standard
      • 6.2. MQTT Broker and Client
      • 6.3. Topic
      • 6.4. Session
      • 6.5. MQTT Development Environment
      • 6.6. Remote Control of MQTT Moving Device
    7. LiDAR
      • 7.1. LiDAR Sensor
      • 7.2. LiDAR Control
      • 7.3. Avoidance Driving using LiDAR
    8. Artificial Intelligence
      • 8.1. Machine Learning and Perceptron
      • 8.2. Neural Network and Learning
      • 8.3. Machine Learning Framework
    9. Autonomous Driving
      • 9.1. Vision Processing
      • 9.2. Deep Learning and Convolutional Neural Network
      • 9.3. Lane Recognition based on Deep Learning
      • 9.4. Object Detection and Moving Object Control

    Appendix

    • A. Flame Sensor
    • B. PIR Sensor
    • C. ECO Sensor
    • D. CO2
    • E. Dust Sensor
    • F. Thermopile Sensor
    • G. Micro Wave Sensor
    • H. Peripheral
    • I. Pixel Display

    Layout

    Components

     

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    ]]>         Autofly_Spring https://edu-market.net/everything/autofly_spring/ Wed, 06 Nov 2024 07:02:37 +0000 https://edu-market.net/?post_type=product&p=6340 Drone with Lidar Sensor That You Can Control with GCS & GPS

    • Drone Manufacturing and Control Using Open Platform
    • Mission Flight Autofly Using GPS and GCS
      GPS(Global Positioning System) / GCS(Ground Control System)

    The post Autofly_Spring appeared first on EDU Market.

    ]]>     Features

    • Drone flight and control (driver source code and flight mode source code provided)
    • 4-propeller Quad Copter
    • Learn how to assemble hardware and fly with open source
    • Hovering function using LiDAR
    • Drone flight using controller
    • Support Ground Control System capable of drone control on Windows or Android OS
    • Support auto-flight using GPS and Ground Control System configured in drone
    • Mission planning function : Waypoint routing, event execution
    (Go to designated place and carry out mission (photographing, collecting geographic information, etc.))

    Block Diagram

    Hardware Specifications

    Software Specifications

    Port Driver

    Flight Software

    Training Contents

    • Drone Intro : Definition / Type / Configuration / Principles of Operation / Frame Selection / Flight Controller Selection / Ground Station Selection / Hardware, Firmware, Software Preparation / Additional Hardware / Safety Precautions
    • Drone Manufacturing : H ardware Assembly / Mission Planner Installation / Firmware Programming / Connecting Mission Planner and Ardupilot / Hardware Set-up
    • Flight : Flight Mode Set-up / Safety Inspection Before Motor Operation / Start & Stop Motor / Tips for Beginners / Basic Tuning / Measuring Vibration / Hovering Set-up / Trimming Set-up / Safety Device / Pre-Flight Checklist
    • Advanced Set-up : Auto Tune / Auxiliary Function / Gyro Calibration / Battery Power Limit Set-up / EKF / Flight Time Record / Take-off & Landing Control Set-up / Motor Scaling Ratio
    Set-up / Offset Compensation Set-up for Sensor Location / Sensor Check / Remote Port Configuration / Tuning
    • Flight Controller and Source : Flight Controller Hardware / Source Code
    • GCS-Mission Planner : Mission Planning through Waypoint and Event / Mission Command List / Application
    • GCS-QGROUND CONTROL : Intro / Download and Install App / Menu / Planning / Set-up / Flight / Application
    • LogData : Diagnose problems using Log Data / Analyze Dataflash Log Data / Remote Communication Log Data / Save and Execute Log Data
    • Others : FPV / Indoor Flight Guide / Multi-Flight / Antenna Tracking / Simulation / Referenc

    Practical Exercises

     

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