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Majid Zamani

This course will explain the core structure in any autonomous system which includes sensors, actuators, and potentially communication networks. Then, it will cover different formal modeling frameworks used for autonomous systems including state-space representations (difference or differential equations), timed automata, hybrid automata, and in general transition systems. It will describe solutions and behaviors of systems and different interconnections between systems.

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This course will explain the core structure in any autonomous system which includes sensors, actuators, and potentially communication networks. Then, it will cover different formal modeling frameworks used for autonomous systems including state-space representations (difference or differential equations), timed automata, hybrid automata, and in general transition systems. It will describe solutions and behaviors of systems and different interconnections between systems.

This course can be taken for academic credit as part of CU Boulder’s MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more:

MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder

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What's inside

Syllabus

Course Introduction
In this introductory module, we delve into the world of autonomous and cyber-physical systems, their significance, structure, and applications. By studying real-world examples, such as the Ariane 5 rocket failure, adaptive cruise control, and self-driving cars, we will grasp the foundational understanding of the importance of modeling in autonomous systems. Moreover, we'll discuss key components of these systems, the tight interaction between hardware and software, and the ubiquity of autonomous systems in various sectors.
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Foundations of Autonomous Systems Modeling
In Module 2, we delve into the nuances of system modeling. Through instructional videos, students grapple with system definitions, state diagrams, and transition systems. Relevant assignments further solidify this knowledge. Real-world examples, like the Northeast Blackout of 2003, underscore the importance of precise modeling, while practical systems such as a Beverage Vending Machine and Turnstile illustrate core concepts. A truly academic journey into the essence of system modeling awaits.
Modeling Physical Systems and Hybrid Systems
Module 3 introduces students to the fundamental principles of modeling dynamic systems, focusing on translational mechanical systems, rotational mechanical systems, and analog circuits. Emphasizing the mathematical relationships underlying these systems, the course progresses into more specific examples and dives deep into timed and hybrid automata, providing a comprehensive understanding of the role of timing in systems modeling.
Systems' Behaviors and Composition
Module 4 dives deep into understanding system solutions, behaviors, and various compositions. Learners will be introduced to the mathematical representations of systems and their behaviors. Through a series of engaging video content, learners will explore parallel, serial, and feedback compositions. Additionally, the module provides practical assignments to enhance comprehension and a detailed study of system modeling.

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A specialized course for undergraduate and graduate students in computer science, engineering, or related fields
Teaches the basics of modeling autonomous systems, including state-space representations, timed automata, hybrid automata, and in general transition systems
Covers the core structure of autonomous systems, which includes sensors, actuators, and potentially communication networks
Provides a solid foundation for further study in the field of autonomous systems
Taught by Majid Zamani, an expert in the field of autonomous systems
Requires basic knowledge of mathematics, computer science, and engineering

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Modeling of Autonomous Systems with these activities:
Introductory Tutorial: A Gentle Introduction to Cyber-Physical Systems
Ease yourself into the fascinating realm of cyber-physical systems by engaging with this introductory tutorial.
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  • Explore basic concepts and terminology, laying a solid foundation for your learning journey.
  • Understand the interplay between physical and computational components, expanding your perspective.
  • Open the door to real-world applications of cyber-physical systems, sparking your imagination.
Review Introduction to Automata Theory, Languages, and Computation
Reinforce foundational concepts in automata theory and formal language theory.
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  • Read the first three chapters of the book.
  • Do the practice questions at the end of each chapter.
  • Summarize the main concepts covered in each chapter.
Follow video tutorials on hybrid systems and timed automata
Enhance your understanding of these complex topics through guided explanations.
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  • Find reputable video tutorials on the topics.
  • Watch the videos and take notes.
  • Apply the concepts you learn to solve practice problems.
Six other activities
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Solve practice problems on state-space representations and timed automata
Strengthen your problem-solving skills and improve your understanding of these key concepts.
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  • Identify online resources or textbooks with practice problems.
  • Solve a set of problems regularly.
  • Analyze your solutions and identify areas for improvement.
Join a study group or participate in online forums related to autonomous systems
Engage with peers, share knowledge, and enhance your understanding of the subject.
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  • Find or create a study group with classmates or online community members.
  • Set regular meeting times.
  • Discuss course material, solve problems, and share insights.
Create a simple automaton simulator in Python
Apply concepts of automata theory to build a practical tool.
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  • Design the architecture of the simulator.
  • Implement the basic functionality of a finite automaton.
  • Add features to handle different types of automata, such as pushdown automata and Turing machines.
  • Test the simulator thoroughly.
Attend a workshop on modeling and simulation of autonomous systems
Gain hands-on experience and learn from experts in the field.
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  • Find a relevant workshop in your area.
  • Register for the workshop.
  • Attend the workshop and actively participate in the activities.
  • Follow up with the instructors or attendees for further learning.
Volunteer at a local robotics or autonomous systems lab
Gain practical experience and contribute to real-world projects.
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  • Contact local robotics or autonomous systems labs.
  • Inquire about volunteering opportunities.
  • Attend volunteer orientation and training.
  • Assist with projects and tasks as assigned.
Write a white paper on a specific type of autonomous system
Develop a deep understanding of the design and implementation of a specific type of autonomous system.
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  • Choose a specific type of autonomous system to research.
  • Review existing literature on the topic.
  • Analyze the design and implementation of existing systems.
  • Propose your own design for an improved system.
  • Write a white paper summarizing your findings and proposals.

Career center

Learners who complete Modeling of Autonomous Systems will develop knowledge and skills that may be useful to these careers:
Control Systems Engineer
Control Systems Engineers design and analyze systems that control various processes, including those in autonomous systems. This course provides a foundation in modeling and analyzing autonomous systems, which can be valuable for Control Systems Engineers working on the design and development of these systems. Topics covered in the course, such as state-space representations and hybrid automata, are particularly relevant to this role.
Robotics Engineer
Robotics Engineers design, build, and maintain robots, which are often autonomous systems. This course provides a foundation in modeling and analyzing autonomous systems, which can be valuable for Robotics Engineers working on the design and development of these systems. Topics covered in the course, such as timed automata and hybrid automata, are particularly relevant to this role.
Systems Engineer
Systems Engineers design, develop, and integrate complex systems, including autonomous systems. This course provides a foundation in modeling and analyzing autonomous systems, which can be valuable for Systems Engineers working on the design and development of these systems. Topics covered in the course, such as system composition and behaviors, are particularly relevant to this role.
Software Engineer
Software Engineers design, develop, and maintain software systems, including those used in autonomous systems. This course provides a foundation in modeling and analyzing autonomous systems, which can be valuable for Software Engineers working on the design and development of these systems. Topics covered in the course, such as state-space representations and timed automata, are particularly relevant to this role.
Data Scientist
Data Scientists use data to solve problems and make predictions. This course provides a foundation in modeling and analyzing systems, which can be valuable for Data Scientists working on problems involving autonomous systems. Topics covered in the course, such as system behaviors and compositions, can be helpful for understanding the behavior of autonomous systems and making predictions about their performance.
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve problems in a variety of industries, including those involving autonomous systems. This course provides a foundation in modeling and analyzing systems, which can be valuable for Operations Research Analysts working on problems involving autonomous systems. Topics covered in the course, such as system behaviors and compositions, can be helpful for understanding the behavior of autonomous systems and developing solutions to problems involving these systems.
Management Consultant
Management Consultants advise businesses on how to improve their operations. This course provides a foundation in modeling and analyzing systems, which can be valuable for Management Consultants working on projects involving autonomous systems. Topics covered in the course, such as system behaviors and compositions, can be helpful for understanding the behavior of autonomous systems and developing recommendations for improving their performance.
Financial Analyst
Financial Analysts analyze financial data to make investment recommendations. This course provides a foundation in modeling and analyzing systems, which can be valuable for Financial Analysts working on investments involving autonomous systems. Topics covered in the course, such as system behaviors and compositions, can be helpful for understanding the behavior of autonomous systems and making recommendations on how to invest in these systems.
Market Research Analyst
Market Research Analysts conduct research on markets and consumers to help businesses make informed decisions. This course provides a foundation in modeling and analyzing systems, which can be valuable for Market Research Analysts working on projects involving autonomous systems. Topics covered in the course, such as system behaviors and compositions, can be helpful for understanding the behavior of autonomous systems and developing recommendations for how to market these systems.
Business Analyst
Business Analysts analyze business processes to identify opportunities for improvement. This course provides a foundation in modeling and analyzing systems, which can be valuable for Business Analysts working on projects involving autonomous systems. Topics covered in the course, such as system behaviors and compositions, can be helpful for understanding the behavior of autonomous systems and developing recommendations for how to improve their performance.
Computer Programmer
Computer Programmers write and maintain computer programs. This course provides a foundation in modeling and analyzing systems, which can be valuable for Computer Programmers working on projects involving autonomous systems. Topics covered in the course, such as state-space representations and timed automata, are particularly relevant to this role.
Database Administrator
Database Administrators manage and maintain databases. This course provides a foundation in modeling and analyzing systems, which can be valuable for Database Administrators working on projects involving autonomous systems. Topics covered in the course, such as system behaviors and compositions, can be helpful for understanding the behavior of autonomous systems and developing recommendations for how to manage and maintain their databases.
Technical Writer
Technical Writers write technical documentation, such as user manuals and white papers. This course provides a foundation in modeling and analyzing systems, which can be valuable for Technical Writers working on projects involving autonomous systems. Topics covered in the course, such as system behaviors and compositions, can be helpful for understanding the behavior of autonomous systems and developing documentation for these systems.
Educator
Educators teach students about a variety of subjects. This course provides a foundation in modeling and analyzing systems, which can be valuable for Educators teaching about autonomous systems. Topics covered in the course, such as system behaviors and compositions, can be helpful for understanding the behavior of autonomous systems and developing lesson plans for teaching about these systems.
Librarian
Librarians manage and maintain libraries. This course provides a foundation in modeling and analyzing systems, which may be valuable for Librarians working on projects involving autonomous systems. Topics covered in the course, such as system behaviors and compositions, can be helpful for understanding the behavior of autonomous systems and developing recommendations for how to manage and maintain libraries.

Reading list

We've selected ten books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Modeling of Autonomous Systems.
Introduces the fundamentals of embedded systems, which are the core of autonomous systems, and provides a solid foundation for understanding their modeling aspects.
Provides a comprehensive introduction to embedded systems, including hardware, software, and design techniques, which are essential knowledge for understanding autonomous systems.
Introduces reinforcement learning, a powerful technique for training autonomous systems to perform complex tasks.
Offers a comprehensive overview of computer vision, which key technology for perception in autonomous systems.
While not specific to autonomous systems, this book provides a strong foundation in machine learning, which is increasingly used for perception and decision-making in autonomous systems.
Provides a practical introduction to natural language processing, which is becoming increasingly important for communication and interaction in autonomous systems.
Offers a comprehensive overview of computer networks, which are often used to connect and communicate autonomous systems.
While not directly related to autonomous systems, this book provides a solid foundation in time series analysis, which is often used to model and predict the behavior of autonomous systems.
Provides a comprehensive overview of formal methods for the development of safety-critical systems. It covers a wide range of topics, including system modeling, verification, and testing.
Provides a comprehensive overview of cyber-physical systems from a systems engineering perspective. It covers a wide range of topics, including system modeling, design, and implementation.

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