About the Program
Internet of Things or IoT in short, is the idea of making devices and objects smarter by linking them to the internet. With IoT, you could for example have a fridge notify you when there’s no milk. IoT has promising applications for smart homes, wearable devices, smart cities, connected cars and more. This course introduces you to the amazing world of IoT and its fascinating applications. Using a Raspberry Pi computer, you will develop an electronic device that streams ultrasonic, IR, fire and gas data over the internet. For example usage of IR Sensor You can program the system in such a way that say whenever the obstacle detects, the device will automatically send an email notification!
Apart from gaining practical skills on the Internet of Things, by doing this project you will learn about the Raspberry Pi development platform which is popular among people who love innovative projects.
Technologies and Tools Covered
- Linux operating system
- Raspberry Pi
- Python Programming & Hardware
- Sensor Interface to Raspberry pi
- Cloud Interface and data upload on cloud using Raspberry pi
LEARNING PATH
1. Raspberry Pi Architecture & Programming
Introduction to the Concept of IoT, Raspberry pi Controller, Python Programming Basics and programs shall be done by the participants. This shall serve as the first step into the entry to the advanced concepts implementation using Raspberry pi
SESSION | CLASS TOPICS |
| 1 | Introduction to IoT and the overview of its Applications |
| 2 | Overview of Raspberry pi Architecture and GPIO Pin Model |
| 3 | Introduction to Python Language |
| 4 | Python Basics (Hands On) |
| 5 | Interfacing led to the Raspberry pi(Hands On) |
| 6 | Interfacing Buzzers to the Raspberry pi (Hands On) |
II. Sensor Interface and Open Source Cloud
Interfacing Sensors like Ultrasonic Sensor, IR Sensors, Fire Sensor to the Raspberry pi Computer, Getting Data from the Sensor, and Interfacing Open Source Cloud with Raspberry pi, and Uploading of Sensor Data to the cloud forms the crux of this day of Learning.
| SESSION | CLASS TOPICS |
| 1 | Introduction to Sensors & Raspberry Pi Interfacing |
| 2 | Working Principle of IR Sensor and Interface with Raspberry pi ( Hands On ) |
| 3 | Working Principle of Ultrasonic Sensor and the interface with Raspberry pi ( Hands On ) |
| 4 | Working Principle of Fire Sensor and the interface with Raspberry pi ( Hands On ) |
| 5 | Upload Sensor data to the Think Speak Cloud ( Hands On ) |
| 6 | Designing web Pages: Basics of HTML |
| 7 | Controlling Devices or appliances using webpage, Reading Input Status from sensor and monitoring on webpage ( Hands On ) |
III. Sensor Interface and OpenCV
Interfacing Sensors like Gas Sensors to the Raspberry pi Computer, Getting Data from the Sensor, Interfacing Open Source Cloud with Raspberry pi, and Uploading of Sensor Data to the cloud , Face Recognition using Raspberry pi and OpenCV forms the crux of this day of Learning.
SESSION | CLASS TOPICS |
| 1 | Working Principle of Gas Sensor and the interface with Raspberry pi |
| 2 | Designing web Pages: Basics of HTML |
| 3 | Controlling Devices or appliances using webpage, Reading Input Status from sensor and monitoring on webpage ( Hands On ) |
| 4 | Introduction to Image Processing Applications using Open CV |
| 5 | Interfacing camera to the Raspberry pi using Open cv |
| 6 | Face Recognition Application using Raspberry Pi |
ML Algorithms on Raspberry Pi
- Introduction to Algorithm
- File handling, Exception handling, Command line arguments, modules and packages
- Functions, String, List
- Python Regular Expressions
- Data types: Array creation, I/O with NumPy, Indexing, Broadcasting, Byte-swapping, Structured arrays, Sub classing array
- Exploring Data with Pandas, Data manipulation with Pandas
- Statistical & Tine Series analysis with Pandas
- Design a Learning System
- Bayesian Learning Techniques
- Linear models for Regression/Classification
- Non-linear Models & Decision Trees
- Instance-based Learning, k-Nearest Neighbour Learning
Projects & Assignments
Hands on Assignments:
Practical 1 : Blinking of LED.
Practical 2 : Turn ON/OFF Buzzer at desired delay.
Practical 3 : Creating the switch and controlling the LEDs.
Practical 4 : Controlling the LED using Web Page
Practical 4: OpenCV Applications on Raspberry Pi
Practical 5: Applying ML Algorithms on Raspberry Pi
Training Methodology
This Training Program is a suitable mix of Theory Sessions, Live Interaction with the technical Industry Experts, Hands on Sessions, Video & Presentations, Quizzes and Assignments. Maximum Impetus is given to Hands on Sessions so as to enable the participants with the maximum knowledge transfer and satisfaction in the ratio 30:70.
pantech
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