Embedded Machine Learning with Microcontrollers: Applications on Arduino Boards - Tapa dura

Acerca del autor

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 Embedded System Design with ARM Cortex-M Microcontrollers: Applications with C, C++ and MicroPython (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 Boğaziçi 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.

De la contraportada

This textbook introduces basic and advanced embedded machine learning methods by exploring practical applications on Arduino boards. By covering traditional and neural network-based machine learning methods implemented on microcontrollers, the text is designed for use in courses on microcontrollers and embedded machine learning systems. Following the learning-by-doing approach, the book will enable students to grasp embedded machine learning concepts through real-world examples, providing 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 microcontroller properties easily, the material allows for fast implementation of the developed system. Students are 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 real-world projects are available for readers and instructors. The book will also be an ideal reference for practicing engineers and electronics hobbyists.

• Teaches embedded machine learning system design skills needed for today's job market;
• Thoroughly explains each concept and provides illustrated examples and projects;
• Includes sample codes, course slides, and a solutions manual.