Alptekin Erkollar – författare

This book aims to provide a comprehensive exploration of the integration of machine learning and deep learning algorithms into the field of autonomous vehicles and advanced driver assistance systems. It also highlights the use of various sensing technologies such as LiDAR, radar, cameras, and ultrasonic sensors.This book presents machine learning techniques relevant to autonomous systems, with a focus on deep learning, neural networks, and reinforcement learning, providing readers with a solid understanding of these foundational concepts. It further includes real-world applications, offering insights into how these cutting-edge techniques are being employed by industry leaders and startups to improve the perception capabilities of autonomous vehicles.It is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, computer engineering, and automotive engineering.

This book aims to provide a comprehensive exploration of the integration of machine learning and deep learning algorithms into the field of autonomous vehicles and advanced driver assistance systems. It also highlights the use of various sensing technologies such as LiDAR, radar, cameras, and ultrasonic sensors.This book presents machine learning techniques relevant to autonomous systems, with a focus on deep learning, neural networks, and reinforcement learning, providing readers with a solid understanding of these foundational concepts. It further includes real-world applications, offering insights into how these cutting-edge techniques are being employed by industry leaders and startups to improve the perception capabilities of autonomous vehicles.It is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, computer engineering, and automotive engineering.

This book aims to provide a comprehensive exploration of the integration of machine learning and deep learning algorithms into the field of autonomous vehicles and advanced driver assistance systems. It also highlights the use of various sensing technologies such as LiDAR, radar, cameras, and ultrasonic sensors.This book presents machine learning techniques relevant to autonomous systems, with a focus on deep learning, neural networks, and reinforcement learning, providing readers with a solid understanding of these foundational concepts. It further includes real-world applications, offering insights into how these cutting-edge techniques are being employed by industry leaders and startups to improve the perception capabilities of autonomous vehicles.It is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, computer engineering, and automotive engineering.

Organizations have always been dependent on communication, information, technology, and their management. The development of information technology has sped up the importance of business informatics, which is an emerging discipline combining various aspects of informatics, information technology, and business management. Understanding the impact of information on today''s organizations requires technological and managerial views, which are both offered by business informatics. Business management is not only about generating greater returns and using new technologies for developing businesses to reach future goals. Business management also means generating better revenue performance if plans are diligently followed. It is part of business management to have an ear to the ground of global economic trends, changing environmental conditions and preferences, as well as the behavior of value chain partners. While, until now, business management and business informatics are mostly treated as independent fields, this publication takes an interest in the cooperation of the two. Its contributions focus on both research areas and practical approaches, in turn showing novelties in the area of enterprise and business management. Among the other topics covered in this book are strategic management, contact relationship management, corporate social responsibility, corporate blogging, enterprise resource planning, E-business management, E-learning, balanced scorecarding, logistics, operations research, enterprise and software architectures, and social software systems. This book adopts an international view, combines theory and practice, and is authored for researchers and lecturers as well as consultants and practitioners.

Für erfolgreiche Unternehmensführung sind klassische betriebswirtschaftliche Konzepte längst nicht mehr ausreichend - übergreifende Disziplinen wie die Wirtschaftsinformatik als Verwaltungsschnittstelle gewinnen rapide an Bedeutung. In der modernen Arbeitswelt herrscht täglich ein komplexes Zusammenspiel unterschiedlichster Faktoren, die Voraussetzung für Erfolg sind und in wechselseitiger Abhängigkeit stehen. Dieses Lehrbuch veranschaulicht auf verständliche Weise, wie die Faktoren "Management", "Prozesse" und "Mensch" im Unternehmensalltag ineinandergreifen, und welche wachsende Rolle "Informationstechnologie" dabei spielt. Insbesondere die sogenannte "MPI-Wirkung" (Management, Prozesse, Informationstechnologie) wird dabei in den Fokus gestellt. Neben klassischen Themen wie Marketing, Prozess-, Finanz- und strategisches Management, die grundlegend dargestellt und in Fachbeiträgen vertieft werden, kommen hierbei auch zukunftsorientierte Themen wie Social Media Marketing, Corporate Blogging, Corporate Social Responsibility und Cloud Management in Trendbeiträgen zur Geltung. Wiederholungsfragen und Übungen begleiten den Lernprozess und helfen dabei, den Stoff zu vertiefen und anzuwenden.