95639 - INDUSTRY 4.0

Conoscenze e abilità da conseguire

The general objective of this course is to provide the general conceptual and technological framework that characterise Industry 4.0, focusing in particular on Internet of Things (IoT), Industrial IoT and Computer Vision and their application in digital transformation contexts. At the end of the course, a student: - has a global understanding about the big picture related to Industry 4.0 - knows the main principles, technologies and standards about Internet of Things (IoT) and Industrial IoT, integrated with contents delivered by other courses (e.g. service oriented architectures, API, web, cloud) - knows some main state-of-the art directions in this context. Examples are Web of Things, Digital Twins - knows the main application domains and concrete case studies concerning the application of IoT and Industrial IoT - knows the main topics in the field of computer vision (e.g. object detection and classification) and their applications - knows state-of-the art approaches and technologies in the context of computer vision, with reference to both classic techniques for image representation and deep learning based solutions - is able to analyse and evaluate the application of the models and technologies, as well as to build projects and prototype technologies, given a Digital Transformation context/problem

Contenuti

This course covers fundamental concepts, technologies, drivers, trends, and implications of Ubiquitous Computing in the context of the Industry 4.0 and Digital Transformation. In addition to methods and foundational technologies, we discuss implications of (industrial) Digital Transformation on businesses and on society as a whole.

Overarching Learning Goal: Students know the history, cross‑disciplinary drivers, and current state of the field of (industrial) Digital Transformation. They recognize patterns, can evaluate technology‑driven business models, and can make educated estimates regarding future trends in the post‑PC age.

Overarching Competency Goal: Students are capable of ideating, implementing, and presenting ideas, approaches, and prototypes in the context of Digital Transformation, Industry 4.0, and Ubiquitous Computing.

 

Specific Learning Goals (Knowledge):

• The students know what resource‑constrainedness means in the context of Ubiquitous Computing and Industry 4.0 systems, and can recognize resource‑constrained contexts when they encounter them. They are aware of methods and approaches that are suitable in resource‑constrained contexts, in particular regarding sensing and identification.
  
• Students know about current indoor and outdoor localization systems and the underlying localization principles (triangulation, trilateration, multilateration, fingerprinting).
• Students know about the current scope and limitations of Mixed Reality technologies and of (mobile) gaze tracking, and are aware of the expected developments in these domains.
• Students have a good grasp of the societal and legal context of the development towards Ubiquitous Computing, and can evaluate Ubiquitous Computing systems in this regard.

 

Specific Learning Goals (Competency):

• Students are able to implement functional prototype systems in the domains of low‑power sensing, mobile gaze, mixed reality, and decentralized data storage and processing.
• Students are able to take informed decisions on Digital Transformation projects and can act upon the technology implications of business decisions in this domain, and vice versa.

Testi/Bibliografia

Relevant literature will be announced during the lectures.

Metodi didattici

This course features lectures, exercises, and a multi-week application project. These course elements are designed to convey theory as well as hands‑on experience and are structured in three phases:

• The first phase starts with an introduction to digital transformation and its drivers and then goes in-depth on central aspects of modern computing in digital transformation contexts: Low‑Power Identification and Sensing (including Computer Vision), Context and Location, Human‑in‑the‑Loop Methods, the Internet and the Web of Things, and System Integration. Students gain experience using some of the introduced technologies in the context of practical exercises.
• The second phase brings students to the forefront of current research developments through a seminar where the students present and discuss current published research papers. For higher flexibility, papers will be assigned during the first week of the course, giving students enough time to prepare their presentations.
• The final phase of the course is dedicated to application and creativity, where the students will apply their learnings in the context of Capstone Projects. Following a structured ideation process and based on the methods and technologies covered in the course, the students will submit their project proposals and subsequently implement and demonstrate prototypes in the field of Industry 4.0 and Digital Transformation.

Modalità di verifica e valutazione dell'apprendimento

Available assessment methods are a written exam, implementation exercises, a seminar talk, and a final project presentation. The specific assessment methods will depend on the number of enrolled students and will be announced before the first lecture.

Evaluation criteria and grading:

• 18-23:the student shows sufficient knowledge about the basic concepts and a sufficient technical and methodological preparation;
  
• 24-27: the student shows good knowledge about the conceptual part and adequate capabilities of applying concepts in practice;
  
• 28-30: the student shows good knowledge about the conceptual part, good critical and analytical skills, a good capability of applying concepts in practice by means of a satisfactory technical and methodological preparation;
  
• 30L: the student shows excellent knowledge about the conceptual part, extensive critical and analytical skills, remarkable abilities in applying concepts in practice by means of a robust technical and methodological preparation.

Strumenti a supporto della didattica

All required materials will be made available as part of the lectures and exercises.

Orario di ricevimento

Consulta il sito web di Simon Mayer