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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]]>- 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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]]>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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Training Content:
- Overview of SmartFarm
- Training Environment Configuration for Smart Farm II Simulator
- Smart Farm IISimulator Control
- Automatic Smart Farm II Control
- Smart Farm II Control Using LoRa Communication
- Smart Farm II Simulator GUI Application
- Smart Farm II Application Project
- Human Audio Interface Control
- MQTT based SmartFarm II Control
- OpenCV-based SmartFarm II Control Surveillance Camera Control
- Smart Farm II Artificial Intelligence Application
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]]>2- Consists of sensor modules, edge computing modules, and AI computing accelerators
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Training Content:
■ Perception Layer
- Microcontroller Development
(1) GPIO Control LED Blink Experiment(2) UART Communication Experiment
(3) ADC Converter Experiment(4) External Interrupt Experiment
(5) I C Communication Experiment
- Sensing Layer
(1) Gas Measurement Experiment
(2) Hall(Magnetic) Detection Experiment
(3) Photoelectric Switch Experiment
(4) Ultrasonic Distance Experiment
(5) Barometric Pressure Measurement Experiment
(6) Dust Measurement Experiment
(7) Buzzer Control Experiment
(8) IR Experiment
(9) Noise Measurement Experiment
(10) Illuminance Measurement Experiment
(11) Motion Sensing Experiment
(12) 9 DoF Measurement Experiment
(13) Relay Control Experiment
(14) Ultraviolet Measurement Experiment
(15) Temperature and Humidity MeasurementExperiment
(16) Carbon Dioxide Measurement Experiment
■ Network Layer
- Wi-Fi Communication
(1) Wi-Fi AT Command Experiment
(2) Wi-Fi Web Server Experiment
(3) Wi-Fi Request Experiment
- MQTT Communication
(1) MQTT Broker Experiment
(2) MQTT Protocol Introduction Experiment
(3) Sensor Data Transmission Experiment
(4) Actuator Control Experiment
(5) MQTT Programming Experiment
- Web Engineering
(1) IoT Web Server Experiment
(2) IoT SQL Experiment
- Visual Programming Language
(1) VPL Application Experiment
(2) IoT Cloud Experiment
(3) Smart Device Application Experiment
- Experiment List of Edge Computing
- Computer Vision
(1) Computer Vision and Image Processing Experiment
(2) Perform Image Manipulation Experiment
(3) Open and Stream Vidio Experiment
(4) Track Face Detection In Vision Experiment
(5) Train on Images by TensorFlow Experiment
- Edge Computing With IoT
(1) Face Recognition Experiment
(2) Head Pose Experiment
(3) Facial Landmarks Experiment
(4) Emotions Recognition Experiment
(5) Smart Home Experiment
- Experiment List of Zigbee
- Zigbee Create a Network Topology Experiment
- Zigbee Communication Experiment
- Zigbee Sensor Experiment
(1) Blood Oxygen Heart Rate Measurement Experiment
(2) Capacitive Touch Sensing Experiment
- Zigbee VPL Application Experiment (Smart Health Care)
- Experiment List of NB-IoT
- NB-IoT AT Command Parameter Setting Experiment
- NB-IoT Sensors Data Transmission to MQTT
- NB-IoT Sensor Experiment
UV Measurement Experiment
- NB-IoT VPL Application Experiment (Meteorological station)
- Experiment List of LoRa
- LoRa Parameter Setting Experiment
- LoRa Communication Experiment
- LoRa Sensor Experiment
(1) Water Temperature Measurement Experiment
(2) PH Value Measurement Experiment
- LoRa VPL Experiment (Intelligent Fish Farm Monitoring)
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]]>- Raspberry Pi 4.0 based Internet of ThingsExperimental Equipment
- Sensor and actuator operation practice applying C Language
- 5 different Smart IoT convergence system operation practice linked with sensor and actuator
- Healthcare sensor(ECG, temperature, momentum,muscle) operation practice
- Sensor and actuator operation practice with 7 inches touch screen
- Checking data from server and real time streaming image through camera available
- Practice of data output for ECG and muscle sensor utilizing serial chart.
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]]>- Understanding and practice of basic sensor
- Understanding and practice of medicaland muscle sensor
- Understanding and practice of actuator
- Set up server, server demo practice andDB saving practice
- Set up android studio and demo operation practice
- Sensor and actuator practice applying Wi-Fi & Bluetoothcommunication in the App of Android OS base
- Data saving of Sensor and actuator in the server andreal time streaming of image through camera
8. Test experiment linked with sensor and actuator
Training Content:
Chapter 1. Introduction to IoTChapter 2. IoT Hardware Architecture
Chapter 3. Building an Environment for PracticingRaspberry Pi System
Chapter 4. Software InstallationChapter5. Installation and Execution of Android Studio
Chapter6. Practicing GPIO Basics and the DC Fan Operation
Chapter7. Practicing the Siren Operation
Chapter8. Practicing the Sound Sensor Operation
Chapter 9. Practicing the Ultra Sound Sensor Operation
Chapter10. Practicing the LED Operation
Chapter11. Practicing the DC Motor Operation
Chapter12. Practicing the Operation of the Muscle
Chapter13. Practicing the Temperature/HumiditySensor Operation
Chapter14. Practicing the PIR Sensor Operation
Chapter15. Practice the ECG Sensor Operation
Chapter16. Practicing the Database BasicsChapter 17. Operating Raspberry Pi Server
Chapter 18. Practicing the Server BasicsChapter 19. Practicing the App Demo Operation
Chapter20. Practicing App Login and Result Display
Chapter21. Practicing Wi-Fi App Demo Operation
Chapter22. Practicing IoT application System
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]]>Part I: Edge Device Development
• HBE-IoT Smart Server + and Soda OS
• Connecting Host and Edge Device
• Edge Device Support Software
• Python 101
• Desktop Environment
• CLI environment
• Linux 101
Part II: Sensor Programming
• Circuit Basics (charges, voltages, currents, resistors, capacitors, inductors, diodes, transistors, op amps, comparators, integrated circuits, electrical supply devices)
• Hardware Interface (Signal Type, GPIO, PWM, ADC, I2C, SPI, UART)
• WiringPi and Pop Library
• GPIO output device control (Led, Buzzer, DC Motor)
• GPIO input device control (Switch, Pir, Ultrasonic)
• ADC Input device control (Potentiometer, Sound, Cds, Thermistor)
• Reading multiple analog sensor values
Part III: IoT Application Technology
• Save the sensor value as a text file and draw a chart with Excel
• Collect sensor values and run multiple SQL statements
• WAV file playback and waveform output
• Use the microphone as an ambient noise level sensor
• Google text-to-speech converter
• Leverage the Google Assistant API
• GAssistant based user device actions
• Creates Stop Motion with camera and switch module
• Record whenever a human movement is detected by camera and PIR module
• Create a vehicle video recording device with camera and shock module
• Gesture detection system
• OpenCV and Matplotlib Visualization
• Capture video with OpenCV and PiCamera
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]]>IoT Smart Server Plus consists of 30 kinds of sensor modules connected by high-performance edge servers and jumper cables, optimized for IoT application learning, maker and capstone design, and hackathon.
Provides a specially designed Pop library for the 30 sensor modules and Soda OS, which are high performance edge device operating environments.
Soda OS is a Debian-based distribution, with all the tools that Linux professionals love and open software for IoT applications, so they can be used immediately without building a separate system.
The high-level Pop library lets you implement real life ideas without complex hardware knowledge.
Soda OS supports automatic IP address assignment, distributed name resolution using mDNS, and network service publishing / discovery using DNS-SD.
It supports a wide range of languages including C/C++, Java, JavaScript, Node-Js, and more, including Python 3 and extension libraries such as numpy, matplotlib, pandas to extend to data science and AI applications.
It supports Google Cloud-based speech synthesis and voice command function and IoT application service using camera. You can learn Linux operations and Python programming, sensor control, and multimedia applications on a high-performance IoT edge server.
Download Datasheet for XIoT Smart Server Plus • IOT-016
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]]>1. Components and Concepts of Sensor Network
2. Sensor Network Platform
• XNode B Type
• XNode A Type
• Edge Server
3. Sensor Network Protocol
4. Development Environment of Sensor Network
5. Basic Sensor Control
6. Expansion Module Control
7. Zigbee Basic Communication
8. Zigbee Communication Expansion
• Multiple Coordinator Communication
• Network Analysis Tool
9. Zigbee and BLE
10. Lora Communication
11. Sensor Network Application Project I
12. Sensor Network Application Project II
13. Sensor Network Application Project III
14. Sensor Network Application Project IV
Appendix
1. Additional Function on Visual Studio Code
2. Edge Server Initialization
3. Python
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]]>IoT connectivity application training equipment based on wireless personal network (WPAN) and low-power wideband network (LPWAN)
Consist of high-performance edge server with integration of base station and network server, module type sensor node and expansion module
Edge server supports sensor node control and AI fusion programming in a web browser environment through the AIoT dedicated operating system Soda OS and Pop library
Edge server supports mDNS/DNS-SD, SSH, SFTP, SMB/CIFS, MQTT, and NX X Window protocols
Sensor node can be selected between LoRa/Sigfox/Wi-Fi/Bluetooth (Node A) or Zigbee Pro/Bluetooth (Node B)
Provides 2100mA battery, RGB LED for indicator, light sensor based on lux unit and temperature/ humidity sensor for independent operation of sensor node
Sensor node supports interpreter-style Python 3 so that control programs can be easily and concisely written
AIoT dedicated operating system Soda OS and Pop library
Visual Studio Code-based integrated development environment for professional application development
Provides training contents for Python-based edge server and sensor nodes
Download Datasheet for XNode – Prime Edition • IOT-015
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]]>1. Components and Concepts of Sensor Network
2. Sensor Network Platform
3. Sensor Network Protocol
4. Sensor Network Development Environment
5. Basic Sensor Control
6. Extension Module Control
7. Zigbee Basic Communication
8. Zigbee Communication Extension
9. Zigbee and BLE
10. Sensor Network Application Project
Appendix
1. Visual Studio Code Add-on
2. Edge Server Initialization
3. Python
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]]>XNode – Home Edition is an IoT connectivity application training equipment based on wireless personal network (WPAN).
By using the mesh network method, it can be used in large quantities in a wide range of areas such as wireless control and monitoring, and a wide range of communication is possible.
Provides an environment for building a smart home with devices used in real home appliances such as door locks, gas circuit breakers, gas detectors, fans, and LED lights.
DC power is supplied and measured by the XNode Power board, and the measured usage can be monitored remotely.
It provides sensors such as GPS, IrThermo, IMU, and PIR in addition to the Basic Module.
The sensor node provides a 2100mA battery so that it can be operated independently, and also provides an LED for an indicator, and a light sensor and temperature/humidity sensor based on lux units.
The provided sensor node supports interpreter-style Python 3 to write control programs easily and concisely.
Visual Studio Code-based integrated development environment for professional application development.
It also provides training contents for Python-based sensor nodes.
Download Datasheet for XNode – Home Edition • IOT-014
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]]>• Bio-signal Theory
• Arduino Development Environment
• GPIO
• L2C Communication
• UART Communication
• Bluetooth Communication
• A/D Converter
• Theory and Measurement of ECG
• Theory and Measurement of EOG
• Theory and Measurement of EMG
• Theory and Measurement of EEG
• Theory and Measurement of Dust
• Theory and Measurement of PCG
• Theory and Measurement of HHI
• Theory and Measurement of NIBP
• Theory and Measurement of BT
• Theory and Measurement of HR
• Theory and Measurement of Respiration
• Theory and Measurement of SpO2
• Theory and Measurement of Bio-Impedance
• Theory and Measurement of GSR
• Sensor Data Collecting with Raspberry Pi
• Web Server Construction with Lighttpd
• Sensor Monitoring with Javascript
• Alarm Setting with IFTTT
The post IoT Smart Health Lab (Health Care and IoT) • IOT-013 appeared first on EDU Market.
]]>It is possible to learn about implementation and analysis of measurement algorithm based on principle of bio-signal.
Measurement of up to 13 sensors is available including 2 options of GSR and DUST in addition to the basic 11 sensors such as ECG, EEG, EMG+HHI, EOG, PCG, Respiration, NIBP, BT, SpO2, HR and Bio-impedance.
It is convenient to monitor measurement data using 10.1 inch electrostatic touch LCD.
Each sensor module has a rechargeable battery and can be linked with Wi-Fi and Bluetooth.
AVR MCU with Arduino is applied to the sensor module and the receiving module for more various exercises.
In addition to the basic 11 sensor modules, you can select and use additional modules depending on the learning purpose.
Measured sensor values can be monitored on PC and Smart Phone through Hybrid Web.
Provides SMS and e-mail service for specific sensor values by using alarm process and IFTTT.
Supports interface linked with Android and Arduino for comprehensive application practice.
Supports both on-board type and module type at the same time.
Download Datasheet for IoT Smart Health Lab (Health Care and IoT) • IOT-013
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]]>• Overview of Internet of Things
• IoT SMART PIONEER configuration and lab environment configuration
• Practice of smart sensor control using Arduino
• Raspberry Pi
• Practice of smart sensor control using Raspberry Pi
• Smart sensor and cloud interworking
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]]>Sensor data collection is implemented around Open Hardware Platform, so anyone can easily experience IoT service.
Provides 6 basic sensor data bases and application examples.
Provides module practice function using firmware and it is possible to acquire sensor information and practice actuator control to acquire IoT basic technology for each module.
By building a gateway, it is possible to process various projects through sensor information monitoring and remote access control function.
Provides AWS-based cloud services.
Android-based cloud Interworking apps.
Download Datasheet for IoT Smart Pioneer Light (IoT Integrated Platform) • IOT-012
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