Embedded Machine Learning with Microcontrollers

Applications on STM32 Development Boards
2024
ISBN: 978-3-031-70911-1
Verlag: Springer International Publishing

Buch, Englisch, 403 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 793 g

This textbook introduces basic embedded machine learning methods by exploring practical applications on STM32 development boards. Covering traditional and neural network-based machine learning methods implemented on microcontrollers, the text is designed for use in courses on microcontrollers, microprocessor systems, and embedded systems. Following the learning by doing approach, the book will enable students to grasp embedded machine learning concepts through real-world examples that will provide them with the design and implementation skills needed for a competitive job market. By utilizing a programming environment that enables students to reach and modify low-level microcontroller properties, the material allows for more control of the developed system. Students will be guided in implementing machine learning methods to be deployed and tested on microcontrollers throughout the book, with the theory behind the implemented methods also emphasized. Sample codes and course slides are available for readers and instructors, and a solutions manual is available to instructors. The book will also be an ideal reference for practicing engineers and electronics hobbyists.

Zielgruppe

Upper undergraduate

Autoren/Hrsg.

Ünsalan, Cem

Atmaca, Eren

Höke, Berkan

Fachgebiete

• Mathematik | Informatik EDV | Informatik Technische Informatik Eingebettete Systeme
• Mathematik | Informatik EDV | Informatik Informatik Rechnerarchitektur
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Bauelemente, Schaltkreise

Inhaltsverzeichnis

Introduction.- Hardware to Be Used in the Book.- Software to Be Used in the Book.- Data Acquisition From Sensors.- Introduction to Machine Learning.- Classification.- Regression.- Clustering.- The Tensorflow Platform and Keras API.- Fundamentals of Neural Networks.- Embedding the Neural Network Model to the Microcontroller.- Multi-layer Neural Networks.- Convolutional Neural Networks.- Recurrent Neural Networks.- ARM CMSIS NN Software Library.- Appendix. STM32 Board Pin Usage Tables.

Über Autor(innen)

Cem Ünsalan is a full professor at the Department of Electrical and Electronics Engineering at Yeditepe University, Turkey, since 2013. He is the Dean of the Faculty of Engineering at the same university. Dr. Ünsalan also worked as a full professor at the Department of Electrical and Electronics Engineering at Marmara University, Turkey,  between 2017 and 2023. He served as the department head for four years there. Dr. Ünsalan received his BSc degree from Hacettepe University, Turkey, his MSc degree from Bogazici University, Turkey, and his Ph.D. from The Ohio State University, USA, in 1995, 1998, and 2003, respectively. His research focuses on embedded systems, computer vision, and remote sensing. He has published extensively on these topics in respected journals and has written several books, including (Springer, 2022).

Berkan Höke is currently working as a senior machine vision engineer at Agsenze Ltd, United Kingdom. He has a diverse professional background including roles as a computer vision engineer at Migros, Turkey (2017–2020), machine learning engineer at Huawei, Turkey (2020–2022), and computer vision engineer at Techsign, Turkey (2022–2023). Mr. Höke received his BSc degree from Bilkent University, Turkey, and his MSc degree from Bogaziçi University, Turkey, in 2014 and 2019, respectively. His research focuses on machine learning, computer vision, and embedded systems.

Eren Atmaca is currently pursuing his master’s degree in communications and electronics engineering at Technical University of Munich, Germany. He received his bachelor's degree from Marmara University, Turkey in 2022. His research focuses on embedded systems, signal processing, and machine learning.