Michael Stephan – författare

Introduces multiple state-of-the-art deep learning architectures for mmWave radar in a variety of advanced applications

Methods and Techniques in Deep Learning: Advancements in mmWave Radar Solutions provides a timely and authoritative overview of the use of artificial intelligence (AI)-based processing for various mmWave radar applications. Focusing on practical deep learning techniques, this comprehensive volume explains the fundamentals of deep learning, reviews cutting-edge deep metric learning techniques, describes different typologies of reinforcement learning (RL) algorithms, highlights how domain adaptation (DA) can be used for improving the performance of machine learning (ML) algorithms, and more. Throughout the book, readers are exposed to product-ready deep learning solutions while learning skills that are relevant for building any industrial-grade, sensor-based deep learning solution.

A team of authors with more than 70 filed patents and 100 published papers on AI and sensor processing illustrates how deep learning is enabling a range of advanced industrial, consumer, and automotive applications of mmWave radars. In-depth chapters cover topics including multi-modal deep learning approaches, the elemental blocks required to formulate Bayesian deep learning, how domain adaptation (DA) can be used for improving the performance of machine learning algorithms, and geometric deep learning are used for processing point clouds. In addition, the book:

Methods and Techniques in Deep Learning: Advancements in mmWave Radar Solutions is an invaluable resource for industry professionals, researchers, and graduate students working in systems engineering, signal processing, sensors, data science, and AI.

Introduces multiple state-of-the-art deep learning architectures for mmWave radar in a variety of advanced applications

Methods and Techniques in Deep Learning: Advancements in mmWave Radar Solutions provides a timely and authoritative overview of the use of artificial intelligence (AI)-based processing for various mmWave radar applications. Focusing on practical deep learning techniques, this comprehensive volume explains the fundamentals of deep learning, reviews cutting-edge deep metric learning techniques, describes different typologies of reinforcement learning (RL) algorithms, highlights how domain adaptation (DA) can be used for improving the performance of machine learning (ML) algorithms, and more. Throughout the book, readers are exposed to product-ready deep learning solutions while learning skills that are relevant for building any industrial-grade, sensor-based deep learning solution.

A team of authors with more than 70 filed patents and 100 published papers on AI and sensor processing illustrates how deep learning is enabling a range of advanced industrial, consumer, and automotive applications of mmWave radars. In-depth chapters cover topics including multi-modal deep learning approaches, the elemental blocks required to formulate Bayesian deep learning, how domain adaptation (DA) can be used for improving the performance of machine learning algorithms, and geometric deep learning are used for processing point clouds. In addition, the book:

Methods and Techniques in Deep Learning: Advancements in mmWave Radar Solutions is an invaluable resource for industry professionals, researchers, and graduate students working in systems engineering, signal processing, sensors, data science, and AI.

First Published in 1990. How we perceive and respond to the visual image has been a traditional concern of psychologists, philosophers and art historians. Today, where the visual image increasingly permeates our everyday life and consciousness, the question becomes ever more relevant. How do we, for instance, instinctively ‘know’ what it is that a picture represents without having to be taught? How is it that we experience (aesthetic) pleasure in looking at certain pictures? How is it that we often want to talk about the pictures we look at? Such questions are currently asked by a wide range of disciplines, including: semiotics, psychoanalysis, anthropology, neuropsychology, and in general, contemporary critical analysis of the visual arts. In A Transformational Theory of Aesthetics, Michael Stephan breaks new ground by linking the findings of these areas. Drawing on their common area of knowledge, he has developed a radically new theory of picture perception and aesthetic response, arguing that images can generate in us a complex pattern of mental changes, or transformations. This is because the left and right hemispheres of the brain do not always work in harmony, hence the wide-ranging nature of aesthetic response to distinct art forms. A Transformational Theory of Aesthetics is essential reading to those seriously involved in linking the arts and cognitive sciences.

First Published in 1990. How we perceive and respond to the visual image has been a traditional concern of psychologists, philosophers and art historians. Today, where the visual image increasingly permeates our everyday life and consciousness, the question becomes ever more relevant. How do we, for instance, instinctively ‘know’ what it is that a picture represents without having to be taught? How is it that we experience (aesthetic) pleasure in looking at certain pictures? How is it that we often want to talk about the pictures we look at? Such questions are currently asked by a wide range of disciplines, including: semiotics, psychoanalysis, anthropology, neuropsychology, and in general, contemporary critical analysis of the visual arts. In A Transformational Theory of Aesthetics, Michael Stephan breaks new ground by linking the findings of these areas. Drawing on their common area of knowledge, he has developed a radically new theory of picture perception and aesthetic response, arguing that images can generate in us a complex pattern of mental changes, or transformations. This is because the left and right hemispheres of the brain do not always work in harmony, hence the wide-ranging nature of aesthetic response to distinct art forms. A Transformational Theory of Aesthetics is essential reading to those seriously involved in linking the arts and cognitive sciences.

Die 19 Beiträge in dem Herausgeberband analysieren die Zusammenhänge zwischen Innovation und Globalisierung sowie die Chancen und Potenziale dieser beiden Megatrends für Unternehmen. Sie zeigen auf, welche Geschäftsmodelle, Organisations- und Führungskonzepte global tätige Technologieunternehmen realisieren. Rahmenbedingungen und Auswirkungen von Innovationen werden beleuchtet sowie Stand und Perspektiven der Innovationsforschung rezipiert. Die Einzelbeiträge stammen von den führenden Experten und Expertinnen im Innovationsmanagement und Internationalen Management. Anlass für die Publikation ist der 65. Geburtstag von Prof. Dr. Alexander Gerybadze. Ihm und seiner wissenschaftlichen Leistung ist diese Festschrift gewidmet.