Capstone: Autonomous Runway Detection for IoT
- 0.0
-
![]()
![]()
![]()
![]()
![]()
Course Summary
Learn how to detect and avoid runway incursions with autonomous technology. This course covers computer vision and machine learning techniques to identify runways and surrounding areas.Key Learning Points
- Understand the importance of runway safety and the potential dangers of incursions
- Learn how to use computer vision and machine learning to detect runways and surrounding areas
- Gain practical experience with real-world data and tools
Related Topics for further study
Learning Outcomes
- Ability to design and implement computer vision and machine learning algorithms for runway detection
- Understanding of the challenges and potential solutions for improving runway safety
- Experience with real-world data and tools used in autonomous runway detection
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of programming in Python
- Familiarity with computer vision and machine learning concepts
Course Difficulty Level
IntermediateCourse Format
- Self-paced
- Online
- Video lectures
Similar Courses
- Autonomous Navigation for Flying Robots
- Self-Driving Cars
Related Education Paths
Notable People in This Field
- Dr. Andrew Ng
- Dr. Fei-Fei Li
Related Books
Description
This capstone project course ties together the knowledge from three previous courses in IoT though embedded systems: Development of Real-Time Systems, Web Connectivity & Security and Embedded Hardware and Operating Systems. The students will develop a larger system using the learning outcomes from these courses, and the students will evaluate the developed system in a real-world programming environment. This course is a true engineering task in which the student must, not only implement the algorithm code, but also handle the interfaces between many different actors and hardware platforms. The students will learn how to motivate engineering decisions and how to choose implementations to make a system actually running. The students will also learn to evaluate the efficiency and the correctness of their system as well as real-world parameters such as energy consumption and cost.
Outline
- Introduction and methods
- Course introduction & Project overview
- Algorithms and Framework
- Project summary and instructions
- NASA media license
- Capstone quiz
- Implementation and integration
- Implementation
- Deliverables
- FreeRTOS "Hello World" Tutorial
- FreeRTOS API
- Project template
- Canny filter
- RSA encryption
- Project submission and Peer review
- Grading instructions
Summary of User Reviews
Discover the latest Autonomous Runway Detection techniques and technologies with this course. Students love the comprehensive content and hands-on approach in learning how to detect runways with autonomous vehicles.Key Aspect Users Liked About This Course
Comprehensive contentPros from User Reviews
- Hands-on approach to learning
- Up-to-date information on Autonomous Runway Detection
- Engaging and knowledgeable instructors
- Great resource for professionals and students alike
Cons from User Reviews
- Some technical jargon may be difficult for beginners to understand
- Course may be too advanced for those without a background in the field
- Limited opportunities for interaction with other students
Capstone: Autonomous Runway Detection for IoT
- 0.0
-
![]()
![]()
![]()
![]()
![]()
The students will develop a larger system using the learning outcomes from these courses, and the students will evaluate the developed system in a real-world programming environment....
