PHYSICAL AI ENGINEERING

"PHYSICAL AI ENGINEERING" is a comprehensive, practical, and implementation-focused textbook designed for undergraduate (B.Tech) and postgraduate (M.Tech) students of Computer Science and allied engineering disciplines. It serves as a definitive guide to designing, building, and deploying Artificial Intelligence systems that perceive, reason about, and interact with the physical world. The book methodically bridges the gap between software-centric AI and the hardware-centric domains of robotics, IoT, and embedded systems.


Philosophy

The core philosophy of this book is "learning by building." I believe that true understanding in an engineering discipline comes not from passive reading of theory, but from the active process of creation. Theoretical concepts are presented as essential tools, but their value is only fully realized when applied to solve a tangible problem.


Key Features

1. Application-Oriented: More than 80% of the content is focused on implementation, practical applications, and hands-on tutorials.

2. Step-by-Step Guidance: From setting up the development environment to deploying a final application, the book provides clear, sequential instructions.

3. Integrated Capstone Project: The final chapter is a complete DIY project that synthesizes all the concepts from the preceding chapters into a working Physical AI system, including full code and explanations.

4. 4. Industry-Relevant Technologies: The book covers modern tools and frameworks such as TensorFlow Lite, PyTorch Mobile, ROS (Robot Operating System), OpenCV, and MQTT, preparing students for real-world engineering roles.

5. Beginner to Advanced: While starting with the basics, the book gradually introduces more advanced topics like reinforcement learning for control, sensor fusion, and edge computing, making it suitable for a wide range of learners.

6. Comprehensive Coverage: Each chapter includes a thorough exploration of the topic, covering design, architecture, implementation, deployment, functioning, and future scope.


Key Takeaways

Upon completing this book, the reader will be able to:

1. Understand the full stack of a Physical AI system, from hardware sensors to cloud-deployed models.

2. Select appropriate sensors and actuators for a given real-world problem.

3. Process and interpret sensor data using computer vision and signal processing techniques.

4. Design, train, and optimize AI/ML models specifically for resource-constrained edge devices.

5. Implement control systems using both traditional methods and modern reinforcement learning.

6. Integrate various components using frameworks like ROS.

7. Deploy and manage a complete, end-to-end Physical AI application.

8. Build a comprehensive portfolio project that demonstrates practical, industry-relevant skills.


Disclaimer: Earnest request from the Author.

Kindly go through the table of contents and refer kindle edition for a glance on the related contents.

Thank you for your kind consideration!