Smart Systems

Overview/Introduction

Students who want to make this world a bit smarter are at the right place in this area of specialisation. We want to look at how things work at the lowest level and then make them available through “the cloud”. For this purpose, basic knowledge will be taught in addition to specialist knowledge.

Specialisation 1: System-oriented Programming

Lecturer: Prof. Dr. Arndt Balzer

Contents

Among other things, this course deals with the following:

  • Programming controllers without operating system (bare metal programming).
  • Understanding microcontrollers (possibilities and limitations).
  • Dealing with proprietary development environments, cross compiling.
  • Handling and understanding specifications.
  • Software- and hardware-related addressing of sensors / actuators / memory via standard interfaces with corresponding development of APIs.
  • Software-related addressing and configuration of wireless modules for connecting mobile devices such as smartphones or tablets.
  • Preparing sensor data by using curring methods and hardware.

Learning Outcomes

This specialisation module deals with systems beyond notebooks and PCs. The application scenarios used during the course are entirely relevant to professional practice. In order to implement the tasks successfully, students have to deal with specifications and data sheets. Downloading and coding of appropriate platforms is certainly legitimate in the implementation process, but generally only leads to limited success due to many specifics. However, in the long term, this strengthens the students’ ability to judge.

Specialisation Seminar

Lecturer: Prof. Dr. Arndt Balzer, Prof. Dr. Christian Bachmeir

Description

Every year, the seminar covers a new and up-to-date topic (machine learning, Lidar, Radar etc., Narrowband IoT, blockchain technologies, and more). Participants choose one topic for their team and make themselves familiar with it independently. At the end of the semester, a sample solution has been created which is presented by the team together with the solution’s theoretical principles. In this way, students acquire the ability, not only thematically but also methodically, to familiarise themselves with new topics at short notice in order to follow the short innovation cycles of industry after graduation.

Specialisation 2: Internet of Things

Lecturer: Prof. Dr. Christian Bachmeir

Contents

Among other things, this course deals with the following:

  • Architectures & concepts of IoT systems
  • Hardware platforms and sensors
  • Communication technology for IoT
  • IoT software platforms, cloud integration
  • Security and privacy for IoT
  • Developing an IoT prototype in the lab:
    • design, build and evaluation
    • Demonstration and presentation of the prototype at the end of the course
    • Documentation report

Learning Outcomes

The students know the basic principles, components and processes of IoT systems and are able to apply these to problems. They are able to make statements on individual systems, differentiate between them and make well-founded decisions on their application. Participants will gain insight into: Architectures of IoT systems, the hardware platforms involved, integrated communication technology, protocols, programming an IoT device and in the cloud, and security concepts. Over the course time, students will develop an IoT prototype and thus get a hands-on experience in the topics.

Suitable core electives (FWPM)

Autonomous Cars (winter semester)

This elective is about letting a model-sized vehicle, which is or will be assembled from a given set of components, drive through a given course as quickly as possible using line tracking. For this purpose, software has to be developed that reads sensors, permanently recalculates the steering angle of the front wheels and the friction of the rear wheels and gives commands to the motors. The softwae is executed on a 32-bit µ-controller. Read-out sensor values and calculated control values are transmitted via WLAN module and evaluated stationarily to improve performance. Methods from machine learning (the track corresponds, for example, to the test data) can be used.

Computer Vision: Artificial Intelligence Applied (summer semester)

Have you ever wondered how self-service checkouts scan items, self-driving cars recognize pedestrians, computers detect skin cancer, and 3D models of iconic places like the Colosseum are scanned?

This module aims to answer these questions and many more by

  • Giving an overview of the problems and approaches in computer vision, for applications as diverse as automation, robotics, medical imaging, and photogrammetry.
  • Introducing the fundamentals of neural networks, required for constructing artificial systems with human-level perception capabilities.

The module spans from selecting the appropriate equipment for visual inspection tasks to image classification with convolutional neural networks and image retrieval with bag-of-visual-words models.

This module will be taught in English and delivered online and on campus. All sessions will be recorded. Colloquia can be done in English or German

Topics for bachelor's theses

Complementing the topics of this specialisation, the lecturers offer topics for projects and final theses. If you are interested in a certain topic, please contact the lecturer(s) of this specialisation.

Additional information

The core modules of the bachelor’s programme teach broad and indispensable knowledge of computer science such as techniques, principles and standards which form the basis for numerous applications.