The study of computer engineering prepares you to help shape the Internet of Things and Industry 4.0 through intelligent IT systems. The intelligent processing of information plays a major role in this. That's why you'll be best prepared for your career in the specializations "Autonomous Systems" or "Cyber-physical Systems".
Computer engineering can be found in all areas of today's life!
In automobiles, in consumer products, in manufacturing plants, in computer networks, in space stations, in short: Information is collected, processed and passed on everywhere. The field of technical informatics provides the necessary technologies and develops solutions for the future. The IT sector has the highest growth rates and the career prospects are excellent in the long term.
In the Computer Engineering program, experienced professors from research and practice teach everything necessary for a successful career start. We attach great importance to a sound education in mathematics, software engineering and computer science. This broad education is the basis for further specialization in the field of autonomous systems or cyber-physical systems. The high practical relevance of the study program deepens the acquired knowledge. In well-equipped laboratories, project work is carried out in small groups. An integrated practical semester prepares you for a smooth entry into your profession. There, your previously learned skills will find practical application.
The bachelor's degree program in Computer Engineering is divided into two study concentrations, Autonomous Systems and Cyber-physical Systems, starting in the 6th semester.
SPECIALIZATION IN AUTONOMOUS SYSTEMS
Autonomous systems are capable of solving complex tasks. This capability is based on artificial intelligence algorithms and methods. Autonomous systems learn based on data. They can also act in unknown situations largely without human intervention. To make this possible, autonomous systems must solve a variety of tasks reliably and independently. They must record and process information, make and execute decisions, and communicate with other autonomous systems or humans. The particular challenge is to perform all this even in unfamiliar situations and only poorly structured environments. One example of this is autonomous driving. Therefore, the main contents of the study focus Autonomous Systems are:
Sensors and actuators
Processing of signals
Machine vision and learning
Safety and Security
SPECIALIZATION IN CYBER-PHYSICAL SYSTEMS
Cyber-physical systems consist of mechanical components, software and modern information technology. By networking the individual components via networks such as the Internet, infrastructures can be controlled, regulated and monitored. Cyber-physical systems are based on sensors, actuators, networked software and cloud-based services. Sensors provide measurement data from the physical world. They report these via networks to a service that processes the data. This results in control data, which the software passes on to actuators via the communication network. Cyber-physical systems are characterized by a high level of complexity. They are used, for example, for the realization of intelligent power grids, modern production plants or in medical technology. Therefore, the main contents of the study focus cyber-physical systems are:
For the summer semester: from 24 October to 15 January
For the winter semester: from April 25th to July 15th
Information on admission requirements
Admission limitation, number of places (70 per year).
Specialisation
Autonomous Systems The methods and skills you learn enable you to design and realise smart, autonomous systems. Autonomous systems can also react to unforeseen situations. Students who successfully complete this specialisation have the skill to take methods from the field of artificial intelligence and use them to derive a code of intelligent conduct for an autonomous system based on the machine perception of a physical environment.
Cyber-Physical Systems The methods and skills you learn enable you to design and realise embedded systems which can act autonomously, are connected with other system components via communication networks, and can cope with complex tasks. Students who successfully complete this specialisation have the skill to respond to issues involving the networking of embedded systems and the challenges resulting from this, such as IT security. The knowledge you acquire will allow you to apply sound methodology to create complex, networked, real-time systems.
Prerequisites: Completed internship semester, sound knowledge of own study profile.
Learning Outcomes: Students have the ability to familiarize themselves with engineering issues in the field of media informatics. They are able to understand and follow scientific and technical developments. Students acquire the ability to work scientifically and as engineers, both independently and as part of a project team.
Content: With bachelor thesis the student should demonstrate that the knowledge and skills acquired during their studies can be successfully put into practice. For this purpose, a project-like task is worked on using engineering methods. The supervising professor accompanies the students during the bachelor thesis and guides them to scientific work. The thesis concludes with a written elaboration and a lecture.
Type of teaching and language of instruction: Self-reliant scientific work, German or English
Examination: Report graded, Presentation (20 Min) graded, Participation in IT colloquium attested, Scientific paper attested
Prerequisites: Sound knowledge of own study profile
Learning Outcomes: Students acquire the ability to familiarize themselves with engineering issues in the field of software engineering or media informatics, to understand scientific and technical developments and to be able to follow them in the long term. Students gain detailed insights and comprehensive knowledge in a field of information technology. Based on their own research, the students can analyse problems in information technology and independently find and evaluate solutions to them.
Content: Self-study in the context of the Bachelor's thesis.
Type of teaching and language of instruction: Self-study, German or English
Prerequisites: Basic knowledge of own study profile.
Learning Outcomes: Students acquire a scientific and subject-specific specialisation in the field of their major field of study.es.
Content: The elective module consists of 3 compulsory electives with a total of 6 SWS. The student chooses 3 electives with 2 SWS each to deepen his own study profile. Current and industry-related specialisations are offered as compulsory electives. The electives are announced publicly at the beginning of each semester.
Type of teaching and language of instruction: Lecture with exercises and exam preparation or project work German or English
Prerequisites: Basics in mathematics, physics, electrical engineering and programming, knowledge of real-time operating systems, computer networks.
Learning Outcomes: Students know the basic definitions of safety engineering (safety and security); approach and objectives of risk analysis; strategic concepts for setting up safe systems. The students are able to evaluate a system with regard to its reliability; to develop and select measures to increase safety. Students can formulate requirements for the hardware and software of reliable systems, including the necessary tool chains.
Content: Safety terms and standards (both safety and security); risk analysis; measures to reduce risk; life cycle management for reliable systems; attacks on systems and causes of system failure; architecture concepts of reliable systems; measures in the development of software for reliable systems; qualification of reliable systems.
Type of teaching and language of instruction: Lecture with exercises and exam preparation, Lab work, German
Examination: Exam (90 minutes) graded Lab work attested
Prerequisites: Knowledge of the basics of communication technology, knowledge of the basics of real-time systems, knowledge of computer architectures.
Learning Outcomes: The students are able to realize system networking on the level of distributed, embedded systems. Students acquire knowledge about the working methods of communication systems used in automation technology, automotive engineering, building services engineering and other branches of industry. In addition to knowledge of the basic function, students are able to select a suitable communication system from the available systems for a given task, integrate modules into a given communication system and assess the real-time properties of the system. The students have a sound knowledge of bus systems used in real-time applications. They know the function and properties of the protocols. They are able to select bus systems, implement simple applications and perform error analysis. The students know the connections between communication programs and the resulting fieldbus data traffic. They are able to recognize and analyze error situations. You can independently create communication programs for fieldbus systems.
Content: Basics for communication in distributed embedded systems; Modified OSI model for embedded systems; Functionality of proven communication systems such as AS-I, CAN, Profibus, LIN, and EIB; Functionality of modern communication systems such as FlexRay and Industrial Ethernet; Analysis and calculation of the real-time behavior of communication relationships with regard to latency and synchronicity; Synchronization of network components for real-time applications; Design of communication applications; Application of communication systems (laboratory); Safety-related communication.
Type of teaching and language of instruction: Lecture with exercises and exam preparation, Lab work, German
Examination: Exam (90 minutes) graded Lab work attested
Prerequisites: Mathematics, Physics, Digital Technology 1 and 2, Computer Architecture, Signals and Systems, Control Engineering 1.
Learning Outcomes: Students acquire an understanding of dynamic systems and are able to analyse them. Furthermore, they are able to design control software for technical processes. The students know modelling of control systems and their description by means of block diagrams; design methods for PID-like controllers: Nyquist and root locus curve methods; state controllers and observers; methods of linearization of nonlinear control systems; methods of digital control engineering. Students will be able to select appropriate control techniques for given problems; apply the learned techniques using Matlab/Simulink. The students can implement controllers e.g. in the programming language C on a microcontroller.
Contents: Detailed description and analysis of typical industrial processes as a basis for later controller designs; controller design based on the root locus curve; state representation of linear systems. Control and observability; Introduction to the design of state controllers and the Luenberger Observer; Nonlinear controls: Methods of linearization, stability, investigations in the phase plane; digital control loop; design of digital controllers (algorithms, real-time problems); work with difference equations and sequences; application of the z-transformation; stability of time-discrete systems; design of time-discrete controllers for finite response time; application of the methods considered in the lecture and their deepening in the laboratory using Matlab/Simulink as well as code generation for the controller from the Simulink model.
Type of teaching and language of instruction: Lecture with exercises and exam preparation, Lab work, German
Examination: Exam (90 minutes) graded Lab work attested
Prerequisites: Discrete Fourier Transform, Elementary Statistics, MATLAB Basic Knowledge, Information Technology Basic Knowledge, System Theory Basic Knowledge, Sampling Theorem.
Learning Outcomes: The students know the components required for a problem of machine vision. The students are able to prepare images using a graphically oriented development environment, segment them, extract features and perform a classification. The students can implement a typical problem of digital image processing under the boundary conditions of an embedded system with their own extension of an existing function library.
Content: Visual perception; development, acquisition and digitization of image signals, strategies of 2D and 3D image acquisition; image and image sequence processing in the temporal / spatial domain as well as in the time frequency / spatial frequency domain; morphological image processing, texture analysis, feature extraction; segmentation; object representation, object recognition, classification, neural networks, basics of soft computing; presentation and discussion of realized systems for industrial 2D and 3D inspection, robotics, medicine, traffic, security.
Type of teaching and language of instruction: Lecture with exercises and exam preparation, Lab work, German
Examination: Exam (90 minutes) graded Lab work attested
Prerequisites: Mathematics, signals and systems, control engineering, algorithms and data structures, basic knowledge of C++ and/or Python
Learning Outcomes: Students are enabled to design, evaluate and implement autonomous systems, especially from the field of robotics and self-propelled vehicles. They know basic components of autonomous systems and are able to design autonomous systems. The students know examples of autonomous systems and their fields of application, the most important components of an automated vehicle, their requirements and their mode of operation, exemplary solutions for questions concerning the development and the protection of self-propelled vehicles. The students are able to apply basic procedures of data fusion, decision making, path planning and path following control, Implement and test software components with the Framework Robot Operating System. You can design and implement larger software projects in a team.
Content: Introduction Robotics and automated driving, architectures of automated vehicles, Robot Operating System (ROS), sensors for automated driving, localization and sensor data fusion (especially least squares estimation), situation analysis and behavior planning (especially state charts), motion planning, vehicle dynamics and vehicle control, integration and validation of the overall system.
Type of teaching and language of instruction: Lecture with exercises and exam preparation, Lab work, German
Examination: Exam (90 minutes) graded Lab work attested
Prerequisites: Knowledge of programming languages and methods of software engineering
Learning Outcomes: The students have the ability to familiarize themselves with new engineering questions in the field of media informatics, to understand scientific and technical developments and to follow them in the long run. The students are able to work independently and scientifically.
Content: In the student research project, the student works on an in-house topic in the laboratories of the faculty under the supervision of a professor during the semester. Special emphasis is placed on an engineering approach.
Type of teaching and language of instruction: Self-reliant scientific work German or English
Examination: Report and Presentation (20 Min) graded
Learning Outcomes: Students will be able to design and program embedded systems. Students will know the basics of state machine design using Stateflow as an example; methods for automatic code generation for embedded systems; theory in state machine design; Markov chain building and design; queuing issues. Students will be able to design a software architecture using state machines; set up a Markov chain and its differential equations; solve queuing problems. Students will be able to independently design larger software projects using model-based development for embedded devices.
Content: Overview: Technology Development; Control Systems and Embedded Systems; Process Development; Overview UML. Introduction to Stateflow: Actions, data import, branching, transition, operators and functions; hierarchies and parallel states; update method; exercise examples. Deterministic automata: Autonomous automats; standard automats with different events; input/output automats; Petri nets Non-deterministic vending machines: Probability calculation; Markov chains and queuing problems
Type of teaching and language of instruction: Lecture with exercises and exam preparation, Lab work, German
Examination: Exam (90 minutes) graded Lab work attested
Prerequisites: Fundamental knowledge of computer networks, operating systems and software engineering.
Learning Outcomes: Students can understand the networking of cyber-physical systems. They master the different aspects of networking of cyber-physical systems. They are able to design and operate them. The students know requirements and solutions for real-time communication, essential systems for real-time communication, time-sensitive networking, chances and risks of internet-based networking. Students are able to understand and evaluate cyber-physical networks, configure networks and integrate components into a system. They are able to understand, evaluate and master cyber-physical systems as a whole.
Content: Requirements and concepts of real-time communication, process data exchange and real-time behaviour, time-sensitive networking, Communication via Internet-based protocols, examples of major systems and protocols such as CAN, Industrial and automotive Ethernet, OPC UA, network planning, operation and optimization, edge computing, Wireless networks for the Internet of Things, technologies and standards for network management.
Type of teaching and language of instruction: Lecture with exercises and exam preparation, Lab work, German
Examination: Exam (90 minutes) graded Lab work attested
Prerequisites: Mathematics, Physics, Digital Technology 1 and 2, Computer Architecture, Signals and Systems, Control Engineering 1.
Learning Outcomes: Students acquire an understanding of dynamic systems and are able to analyse them. Furthermore, they are able to design control software for technical processes. The students know modelling of control systems and their description by means of block diagrams; design methods for PID-like controllers: Nyquist and root locus curve methods; state controllers and observers; methods of linearization of nonlinear control systems; methods of digital control engineering. Students will be able to select appropriate control techniques for given problems; apply the learned techniques using Matlab/Simulink. The students can implement controllers e.g. in the programming language C on a microcontroller.
Contents: Detailed description and analysis of typical industrial processes as a basis for later controller designs; controller design based on the root locus curve; state representation of linear systems. Control and observability; Introduction to the design of state controllers and the Luenberger Observer; Nonlinear controls: Methods of linearization, stability, investigations in the phase plane; digital control loop; design of digital controllers (algorithms, real-time problems); work with difference equations and sequences; application of the z-transformation; stability of time-discrete systems; design of time-discrete controllers for finite response time; application of the methods considered in the lecture and their deepening in the laboratory using Matlab/Simulink as well as code generation for the controller from the Simulink model.
Type of teaching and language of instruction: Lecture with exercises and exam preparation, Lab work, German
Examination: Exam (90 minutes) graded Lab work attested
Prerequisites: Good mathematical knowledge, especially in statistics and optimization, knowledge of computer science.
Learning Outcomes: Students know the basics of time series; applications in which time series are generated and recorded; methods of classification of time series data; methods for regression analysis and prediction; basics of artificial neural networks. Students will be able to select and apply appropriate analytical techniques. Students can intelligently analyze time series using algorithms from the fields of data mining and machine learning. The students have basic knowledge in "Data Mining on Time Series" and in handling the software "R". They are able to apply selected methods from the functionalities "Querying", "Classification" and "Prediction" to time series. These techniques are used in many industrial applications, e.g. in a model-based diagnosis of the high-voltage battery of a hybrid vehicle. The methods and concepts learned can also be applied to other data types for data mining purposes.
Content: Introduction to Data Mining with a focus on Time Series Data (Temporal Data Mining); Fundamentals of Time Series Data; Classification, Time Series Querying, Regression/Forecasting; Visualization of Time Series; Artificial Neural Networks; Applied Data Mining for Hybrid Vehicle Powertrain.
Type of teaching and language of instruction: Lecture with exercises and exam preparation, Lab work, German
Examination: Exam (90 minutes) graded Lab work attested
Prerequisites: Fundamentals in mathematics, statistics and stochastics, physics, electrical engineering and software development Knowledge of computer networks and computer architectures.
Learning Outcomes: Students will be able to define qualitative and quantitative design goals for the dependability of Cyber-Physical Systems (CPS), to evaluate characteristics regarding the dependability of a given CPS qualitatively and quantitatively, to understand and design measures to promote dependability. You can analyse and evaluate cyber-physical systems with regard to their dependability and design necessary measures.
Content: Definition of reliability (dependability), availability, dependability, functional security and security, analytical methods for the evaluation of reliability, risk analyses and measures to increase reliability, securing the communication between components and subsystems (confidentiality, integrity, authenticity), attack scenarios on systems and countermeasures.
Type of teaching and language of instruction: Lecture with exercises and exam preparation, Lab work, German
Examination: Exam (90 minutes) graded Lab work attested
Prerequisites: Knowledge of programming languages and methods of software engineering
Learning Outcomes: The students have the ability to familiarize themselves with new engineering questions in the field of media informatics, to understand scientific and technical developments and to follow them in the long run. The students are able to work independently and scientifically.
Content: In the student research project, the student works on an in-house topic in the laboratories of the faculty under the supervision of a professor during the semester. Special emphasis is placed on an engineering approach.
Type of teaching and language of instruction: Self-reliant scientific work German or English
Examination: Report and Presentation (20 Min) graded
Learning Outcomes: In the industrial environment of a company, the students learn to work independently as engineers as well as in a team. They are able to apply the methods of project management. Their awareness of the effects of their own actions is sharpened. Students acquire the engineering skills of working in a project team.
Content: 100 days of operational practice in a company of IT field.
Type of teaching and language of instruction: Internship, German or English
Learning Outcomes: Students acquire the skills of teamwork and methodical work. Students are prepared for a successful career start. They acquire and deepen the ability to record and produce scientific texts and to communicate on technical-scientific topics in English.
Content: Scientific work: Structuring, researching, analysing, scientific writing and quoting; Career start: Career planning, applicant training; Technical English: beginner and advanced level, technical and business English, communication and presentation.
Type of teaching and language of instruction: Lecture with exercises German
Examination: Presentation (20 Min) attested TOEFL test
Prerequisites: Mathematics, Electrical Engineering, Signals and Systems, Physics, Electronics, Digital Technology, Computer Architecture, Programming, Object-Oriented Systems.
Learning Outcomes: Students acquire an understanding of dynamic systems and are able to analyse them. Furthermore, they are able to design control software for technical processes. Students should be able to analyze control systems and to design and implement simple simulation models and controls themselves. The students are able to independently familiarize themselves with more specific problems of system and simulation technology. The students learn the practical application of the concepts of control engineering. Students learn how to model, simulate, control and regulate dynamic systems. This enables them to design and implement simple simulation models and control systems themselves. The students thus acquire the basics to independently familiarize themselves with more specific control engineering problems if required. The students gain first experience with a state-of-the-art design tool for the simulation and implementation of control systems for dynamic systems. They can assess the influence of limitations or interference signals in practical implementation, which are often neglected in theoretical considerations.
Content: Overview of the design and modelling of technical systems; description of the dynamic behaviour of continuous systems by block diagrams and their analysis in the time and frequency domain; properties of control algorithms, stability analysis, important design methods for controllers; implementation of controls in hardware and software; effect of time and value discrete implementation in simulations and control algorithms; design and simulation tool MATLAB/Simulink, real time simulations, automatic code generation.
Type of teaching and language of instruction: Lecture with exercises and exam preparation, Lab work, German
Examination: Exam (90 minutes) graded Lab work attested
Prerequisites: Basic knowledge of physics, elementary statistics, discrete Fourier transform, basic knowledge of MATLAB, basic knowledge of information technology, basic knowledge of systems theory, sampling theorem.
Learning Outcomes: Students acquire an understanding of dynamic systems and are able to analyse them. Furthermore, they are able to design control software for technical processes. The students can assess problems with the passive and active information acquisition of sensor data and independently develop solution strategies. They have a sound knowledge of the most important methods for obtaining information from sensor data of real processes. They master methods for processing, quantitative evaluation and feature extraction. The students understand the concepts of industrial sensor data processing.
Content: Multi-spectral and multi-channel sensors; actuators for information acquisition and output; visual perception; creation, recording and digitisation of image signals; strategies of 2D and 3D image recording; image and image sequence processing in the temporal / spatial domain; as well as in the time-frequency / spatial frequency domain; Morphological image processing, texture analysis, feature extraction, segmentation; object representation, object recognition, classification, neural networks; main features of soft computing; system examples (industrial 2D and 3D inspection, robotics, medicine, traffic, security, environmental monitoring)
Type of teaching and language of instruction: Lecture with exercises and exam preparation, Lab work, German
Examination: Exam (90 minutes) graded Lab work attested
Prerequisites: Fourier and Laplace transform; z-transformation; characteristics and properties of time-continuous, linear systems; sampling and z-transformation; basic knowledge of MATLAB; vectors, polynomials, arithmetic operations.
Learning Outcomes: Students will be able to design linear, time-discrete systems and implement them in digital computers. The students know application fields of digital signal processing; important theories and models of discrete systems as a basis for modern signal processing and control engineering; methods for analysis and design of discrete systems. The students are able to judge the behavior of linear, time-discrete systems in the time and frequency domain; to evaluate sampling processes with regard to the sampling theorem; to design basic digital filters and to realize them with signal processors; to determine and present characteristics of time-discrete signals and systems with the help of the simulation program MATLAB. The students can work on subject-specific tasks in small groups with the help of the MATLAB simulation program, present and defend the results.
Content: Analog filters, standard low-passes; time-discrete systems and their characteristics, such as difference equation, transfer function, frequency response, pole-zero diagram, stability; impulse response, step response, structures; recursive (IIR) and non-recursive (FIR) digital filters; design of digital systems; design and simulation of time-discrete systems with MATLAB; realization of linear, time-discrete systems on a signal processor.
Type of teaching and language of instruction: Lecture with exercises and exam preparation, Lab work, German
Examination: Exam (90 minutes) graded Lab work attested
Learning Outcomes: The lecture introduces the architecture of computer systems with microprocessors and microcontrollers. The students develop a basic understanding of the Instruction Set Architecture of computers and understand how programming constructs of higher programming languages are mapped to the "language of hardware". The understanding should help to better map the interaction of programming language, operating system and hardware. Students acquire a basic understanding of the Instruction Set Architecture of computers and understand how to map the programming constructs of higher programming languages to the "language of hardware". They understand the interaction of programming language, operating system and hardware to develop more efficient software. Students will implement the basics of hardware-related programming in C/C++ and machine language (assembler) in practical exercises.
Content: Structure of computer systems, arithmetic-logical operations, basic tasks of operating systems (repetition); programming model (register set, addressing modes, memory map, instruction set) of an exemplary microprocessor; introduction to machine language, mapping of important high-level language constructs to machine language, estimation of memory requirements and execution speed; Hardware/software interface for typical peripheral components, digital and analog input/output, timer, simple network interfaces; modular programming, interfaces for the interaction of different programming languages; support of operating system mechanisms, e.g.B. Memory protection, virtual memory, through microprocessors; overview of current micro- and signal processor architectures: technology and market significance.
Type of teaching and language of instruction: Lecture with exercises and exam preparation, Lab work, English
Examination: Exam (90 minutes) graded Lab work attested
Prerequisites: Knowlege in Mathematics, Programming, Object-Oriented Systems
Learning Outcomes: Students have an overview of the most important classes of algorithms. Students will be able to assess basic features, performance, similarities and cross-references of different algorithms. Students will be able to correctly apply and assess basic algorithms and data structures in terms of their properties and performance.
Content: Presentation, design and classification of algorithms; Simple and abstract data structures: arrays, lists, sets, directories; complexity, efficiency, computability, O-notation; search and sort; trees and graphs; iterative methods (Gauss, Newton); hash methods; geometric algorithms; string matching algorithms and finite automata; random numbers and Monte Carlo algorithms.
Type of teaching and language of instruction: Lecture with exercises and exam preparation, German
Prerequisites: Knowledge of an object-oriented programming language, knowledge of UML 2
Learning Outcomes: Students are able to implement the requirements in complex software architectures. They can use design and architecture patterns, frameworks and libraries according to their needs. The students acquire competences in the engineering approach to solving problems in the field of software architecture as well as in the assessment and selection of software technologies. Students can select and apply design and architecture patterns. They are able to program components (EJB) and web services (SOA).
Content: Architecture and Architects; Architecture Development Approach; Architecture Views, UML 2 for Architects; Object-Oriented Design Principles; Architecture and Design Patterns; Technical Aspects, Requirements and Constraints Consideration; Middleware, Frameworks, Reference Architectures, Model-Driven Architecture; Components, Component Technologies, Interfaces (API); Architecture Assessment, Refactoring, Reverse Engineering.
Type of teaching and language of instruction: Lecture with exercises and exam preparation, Lab work, English
Examination: Exam (90 minutes) graded Lab work attested
Prerequisites: Digital technology 1, programming 1 - 2.
Learning Outcomes: Students will be able to understand and program the structure and functionality of microprocessors and their peripheral components. Students master the basic concepts of the design and development methods of computer systems with a focus on hardware architecture. The students are able to construct components of simple computer systems and to analyze their interaction. The students acquire the practical conversion of the basic theoretical concepts and methods of simple computer systems in digital hardware in VHDL.
Inhalt: Theory, design and hardware and software realisation of finite automatons; structure, function and interfaces of semiconductor memories; structure and function of bus systems; structure of simple CPUs in Neumann and Harvard architecture; control unit and data path; arithmetic unit and register set; addressing modes, instruction execution; coupling and function of peripheral components such as digital input/output; A/D and D/A conversion; The theoretical part is supplemented by practical laboratory tasks for the design of finite state machines, memory controllers and a CPU in VHDL. The designs are simulated on RTL level and realized with the help of an FPGA.
Type of teaching and language of instruction: Lecture with exercises and exam preparation, Lab work, German
Examination: Exam (90 minutes) graded Lab work attested
Prerequisites: Competences in programming and operating systems
Learning Outcomes: Students acquire knowledge about basic concepts and technologies in computer networks. Students can describe the basic concepts of computer networks. They understand the layer model in communication networks and the basic mechanisms and tasks of communication protocols. The functionality of important standards such as Ethernet and TCP/IP are familiar to the students. This enables them to select and evaluate suitable solutions for various applications. Students can configure network services, use communication protocols, analyze their function and, if necessary, find errors.
Content: Basics and network architectures; communication in local networks; packet switching on the Internet; transport protocols on the Internet; elementary services and applications; network engineering examples.
Type of teaching and language of instruction: Lecture with exercises and exam preparation, Lab work, German
Examination: Exam (90 minutes) graded Lab work attested
Voraussetzungen: Nach Studien- und Prüfungsordnung: Zulassung zum zweiten Studienabschnitt
Inhalte:
a) Einführung - Einführung in zeitkontinuierliche und zeitdiskrete Signale; - Auswirkungen der Quantisierung von Sensoren, A/D-Wandlern und D/A-Wandlern; Zeitkontinuierliche Signale - Fourier-Analyse : Anwendungen zur Fourierreihe ; - Fourier-Transformation und ihre Anwendung zur Fourier-Analyse; Zeitkontinuierliche Systeme - Eigenschaften zeitkoninuierlicher Systeme - Wichtige Anwendungen der Laplace-Transformation; - Stabilität zeitkontinuierlicher Systeme; - Einführung in zeitkonituierliche Filter; Zeitkontinuierliche Filter - Entwurf und Anwendung einfacher Filter : Tiefpass, Hochpass, Bandpass, Bandsperre Zeitdiskrete Signale - Abtast-Haltevorgang und Abtasttheorem nach Shannon; - diskrete Fourier-Transformation , Fast-Fourier-Transformation; Zeitdiskrete Systeme - Differenzengleichung; - diskrete Faltung; - Z-Transformation und Z-Übertragungsfunktion; - Wichtige Anwendungen der Z-Transformation; - Stabilität zeitdiskreter Systeme; - rekursive und nichtrekursive Filter; - Wahl der Abtastzeit; b) Labor Matlab - Einführung in die Bedienoberfläche von Matlab; - Arbeiten mit Matrizen und Vektoren; - Schleifen und Verzweigungen (Kontrollstrukturen); - Speichern von Daten in Dateien, lesen von Daten aus Dateien; - verschiedene Spezialthemen : erzeugen von Bodediagramm; lösen von Gleichungssystemen; - Einführung in die Bedienoberfläche von Simulink; - Simulation von Systemen und Differentialgleichungen;
Prüfungsleistung/Studienleistung: a) Schriftliche Prüfung b) Erfolgreiche Teilnahme an allen Laborübungen und erfolgreiche Bearbeitung des Abschlussprojekts. Das Modul wird benotet. Die Modulnote setzt sich aus den Noten der benoteten Teilmodule, gewichtet mit den zugeordneten Credits zusammen. Alle Teilmodule müssen bestanden sein.
Prerequisites: Knowledge of a higher programming language
Learning Outcomes: The students have knowledge in the areas of engineering software development, requirements analysis and modelling. The students master engineering software engineering. Students can write requirements in English. They can also create a requirement specification. They master the methodical procedure for the creation of software applications. The students learn how to successfully carry out projects. They master the instruments of project management.
Content: Overview of maturity models and process models: project management; configuration management; change management; quality management; requirements engineering; system analysis; system design; system implementation; system integration; system test. Main features of UML 2.x: model elements, classes, artefacts, static Relationships: Dependency, association, generalization, realization, diagram types in UML, use case diagram, activity diagram, state machine, package diagram, class diagram, object diagram, sequence and communication diagrams. Creation of a requirement specification: requirements/requirements (in English), modeling of a software system in UML. Testing: Validation, verification. Acceptance Test Driven Development: Creation of test cases for the requirements.
Type of teaching and language of instruction: Lecture with exercises and exam preparation, Lab work, English
Prerequisites: Direct current and alternating current calculus; mathematical knowledge of differential and integral calculus, complex numbers.
Learning Outcomes: Students acquire knowledge of electrical networks and are able to analyse them. Students will be able to understand the functioning of electronic circuits.
Content: Circuits with diodes; stabilization circuits with Z-diodes; thermal effects; rectifier circuits; voltage multiplication; bipolar transistor and field effect transistors (FET); operational amplifiers; project hardware with changing tasks.
Type of teaching and language of instruction: Lecture with exercises and exam preparation, Lab work, German
Examination: Exam (90 minutes) graded Lab work attested
Prerequisites: Basic mathematical knowledge in algebra and geometry, differential and integral calculus as well as vector calculus.
Learning Outcomes: Students acquire the competence to describe our environment mathematically and to explain various phenomena as a logical consequence of less simple basic facts. Students acquire elementary basic knowledge in the fields of mechanics, electrical engineering, vibrations and waves. Students acquire the ability to recognize physical laws behind technical applications and to apply them to new problems. They learn methods and approaches to approach and solve problems in a structured and goal-oriented way.
Learning Outcomes: Students acquire the competence to describe our environment mathematically and to explain various phenomena from a few simple basic facts. The students have the knowledge to describe real problems with the help of mathematical models and to solve them systematically. Building on this knowledge, students are able to solve simple problems independently. The students can represent functions with the help of power series and Taylor series. They are proficient in dealing with ordinary differential equations and differential equation systems. Students will be able to analyze vibrations using vibration differential equations and Fourier series. Students are able to solve, simulate and visualize mathematical problems with programs on the computer.
Content: Power Series and Taylor Series; Ordinary Differential Equations and Differential Equation Systems; Fourier Series.
Type of teaching and language of instruction: Lecture with exercises and exam preparation, Lab work, German
Examination: Exam (90 minutes) graded Lab work attested
Prerequisites: School knowledge of Boolean algebra, combinatorial circuits and the representation of absolute numbers and integers in computers.
Learning Outcomes: Students will be able to understand and program the structure and functionality of microprocessors and their peripheral components. Students learn the basic structure of digital systems and the methods for developing the hardware of digital systems. Students acquire a basic understanding of combinatorial logic as well as of the structure and functionality of components. They acquire the ability to describe functions. Students get to know the basic structure of digital systems and the methods for developing the hardware of digital systems. Students acquire a basic understanding of combinatorial logic and how simple components work. They will be able to describe logical functions using equations and schematics. The students acquire basic abilities for the practical conversion of the basic theoretical concepts and methods of simple computer systems by means of digital hardware in VHDL.
Content: Basics of Boolean algebra (basic logic functions, De Morgan's laws); description of combinatorial circuits and simplification using Boolean algebra and KV-diagram; basic building blocks of digital systems: gates, flip-flops, multiplexers, registers, counters; coding of numbers and characters in digital systems, dual coding; computing with binary numbers: Amount numbers, integers and floating-point numbers; structure and function of an ALU (arithmetic-logical unit).
Type of teaching and language of instruction: Lecture with exercises and exam preparation, Lab work, German
Examination: Exam (90 minutes) graded Lab work attested
Prerequisites: Mathematical knowledge: Complex numbers, linear differential equations with constant coefficients Knowledge of electrical engineering: Methods for solving DC circuits. Basic knowledge of pointer diagrams for AC circuits.
Learning Outcomes: Sound basic training in electrical engineering and electronics. The following modules contribute to achieving the overall goal: Electrical engineering 1, electrical engineering 2, electronics. Aims of this module: System understanding for linear, dynamic processes and their description in the time and frequency domain by means of alternating current circuits.
Content: Complex alternating current calculation, standardization, transmission factor. Example: Amplitude and phase characteristics of the series resonant circuit; representation and interpretation of the transmission factor with the aid of Bode diagram or locus curve; introduction to computer-aided circuit simulation with LTSpice; power calculation for stationary harmonic excitation: Time domain and with the help of the complex alternating current calculation. Introduction of the active, reactive and apparent power as well as the effective value of periodic signal characteristics; application of the superposition set to the Fourier series representation of periodic signal characteristics; calculation of the transient behaviour of linear, time-invariant circuits with an energy store from the differential equations during switch-on/switch-off processes as well as during harmonic excitation; relationship between compensation processes in the time domain and complex alternating current calculation in the frequency domain using the example of periodic excitations of linear RLC circuits; consolidation of the acquired knowledge in accompanying laboratory exercises.
Type of teaching and language of instruction: Lecture with exercises and exam preparation, Lab work, German
Examination: Exam (90 minutes) graded Lab work attested
Learning Outcomes: The students acquire the competence to use computer hardware and software as well as operating systems and computer networks. The students can describe the basic concepts of operating systems and evaluate the solutions realized in the marketable operating systems. They know the essential functions and services of operating systems and are able to use them interactively or in application programs. The students know the mechanisms of authentication and authorization and are able to regulate the access of users to computers, services and data appropriately.
Content: Introduction to the tasks and structure of operating systems; use of UNIX via command line (shell / script programming) and the most important UNIX commands; processes and threads; memory management; interprocess communication and synchronization; file systems; input and output; security; virtualization and cloud.
Type of teaching and language of instruction: Lecture with exercises and exam preparation, Lab work, German
Examination: Exam (90 minutes) graded Lab work attested
Prerequisites: Knowledge of a programming language
Learning Outcomes: Students acquire a sound basic education in computer science and programming. Students learn object-oriented programming paradigms and their practical application. The students learn the methodical programming of object-oriented systems. The students are able to independently implement object-oriented concepts in programming.
Content: Basic concepts of object-oriented programming are taught. This includes: Class concept (attributes, methods), information hiding (public, private); constructors and destructors; static variables and static methods; operators and overloading; inheritance and polymorphism; abstract classes and their role as interface definitions. Further topics that are important in object-oriented software development are discussed: References, namespaces, handling of strings; definition and handling of exceptions; processing of files with the help of streams; cast operators and type determination at runtime.
Type of teaching and language of instruction: Lecture with exercises and exam preparation, Lab work, German
Examination: Exam (90 minutes) graded Lab work attested
Learning Outcomes: Students will be able to describe, explain and understand random and uncertain phenomena. Students will know the basic combinatorial formulas and their applicability to corresponding questions; the basic probability-theoretical indicators and their calculations or relationships; the basic statistical discrete and continuous distributions; the basics of descriptive statistics and inferential statistics and will be able to apply them to specific situations. Students will be able to describe large datasets and present information; describe events with frequencies, mean and variance or standard deviation; evaluate and classify statements about problems associated with uncertainty. Students can derive, evaluate, classify statements on uncertainty issues; statistics as an important tool to support work with large amounts of data and quality assurance.
Content: Data collection and data cleansing; representation of statistical material (feature types, graphical representation, location parameters of a sample); multidimensional samples (correlation and regression); combinatorics; probability theory (Laplace models; random variables and distribution functions; special distribution functions such as normal or binomial distribution); conclusive statistics, in particular statistical test procedures and confidence intervals; application of statistical methods in quality assurance.
Type of teaching and language of instruction: Lecture with exercises and exam preparation, Lab work, German
Examination: Exam (90 Min) graded Lab work attested
Learning Outcomes: Students acquire the competence to describe our environment mathematically and to explain various phenomena from a few simple basic facts. The students master the handling of differential and integral calculus, consequences, and functions of several real variable. Students are able to solve simple mathematical problems independently and to comprehend logical conclusions. Students can formulate and systematically solve simple engineering and economic problems in mathematical notation.
Content: Differential and integral calculus for functions of a real variable; sequence, series and limit values; functions of several real variable; applications from economics, natural sciences and technology.
Type of teaching and language of instruction: Lecture with exercises and exam preparation, German
Prerequisites: School knowledge about vectors and linear systems of equations
Learning Outcomes: Students acquire the competence to describe our environment mathematically and to explain various phenomena from a few simple basic facts. The students master the handling of linear systems of equations, vectors, matrices and complex numbers. Students are able to solve simple mathematical problems independently and to comprehend logical conclusions. Students are able to formulate and systematically solve simple engineering and economic problems in mathematical notation.
Content: Linear systems of equations; vectors and matrices; linear algebra; complex numbers; applications from economics, natural sciences and technology.
Type of teaching and language of instruction: Lecture with exercises and exam preparation, German
Prerequisites: Mathematical knowledge: functions of a real variable with curve discussion, linear systems of equations, differential and integral calculus.
Learning Outcomes:
Sound basic training in electrical engineering and electronics. System understanding for linear processes and their description in the time domain by means of DC circuits. Introduction to the systematic analysis of linear networks as a prerequisite for a deeper understanding of interfaces and systems.
Content: Basic concepts: charge, current density, current and electrical voltage; simple direct current circuits: Current and voltage sources, Kirchhoff's laws, ohmic resistance, elementary methods for the analysis of plane resistance networks; Gaussian algorithm for the solution of linear systems of equations, power at equal quantities, power adaptation; superposition principle, source equivalences, controlled sources; node voltage system as basis for the numerical description of general electrical circuits; consideration of ideal voltage sources and of controlled sources; Applications: Calculation of short-circuit currents and simple circuits with operational amplifiers as controlled sources; Linear RLC circuits with stationary harmonic excitation: Time domain inductance and capacitance, pointer diagrams for AC circuits.
Type of teaching and language of instruction: Lecture with exercises and exam preparation, German
Learning Outcomes: The students acquire knowledge about work processes in a company. Students will be able to integrate into project teams and act responsibly. Students will have an overview of the different areas of general business administration and will be able to apply their basic instruments and methods. They are also able to understand and describe micro- and macroeconomic aspects of entrepreneurial activity. The students are familiar with the essential subject areas of general business administration and know the functions and interrelations of business structures and processes. They understand the necessity of economics as a basis for entrepreneurial procedures and techniques and are able to assess and apply fundamental methods and instruments of business administration. Students will understand the basic functioning of markets and will be able to apply fundamental methods of economics to microeconomic and macroeconomic issues. They will understand the macroeconomic relationships of goods, labour and money markets.
Content: Companies (legal forms, typology, environment); tasks, measures and methods of the operational functional areas; operational performance and financial processes; basics of accounting; functioning of markets, price formation; role of companies and the state in the market economy; growth and business cycle; monetary and financial systems; project management block seminar.
Type of teaching and language of instruction: Lecture with exercises and exam preparation, German
Overall Objective: Students will acquire a solid foundation in computer science and programming. The following modules contribute to the overall goal: - Programming - Object-oriented systems - Software Engineering
Objective of this module: Students will have the basic understanding of how a computer works and implementation of programming concepts.
Content: Fundamentals: - Operation of a von Neumann calculator. - Representation of numbers in a computer - Memory management, stack and heap - Conversion of tasks into modular programs Introduction to a higher programming language: - Derived and compound data structures (pointers, fields, strings, structures) - High-level file operations - Definition (prototype) and calling of functions (call-by-value and call-by-reference), - Recursive functions - Functions as programming modules and stepwise refinement as a design principle for functions
Type of teaching and language of instruction: Lecture with exercises and exam preparation, Lab work, German
The curriculum for the degree programme and detailed descriptions of the programme modules are contained in the Module Catalogue.
Computer Engineering - A DEGREE PROGRAMME WITH A SMART FUTURE
At the end of the Computer Engineering degree program, you will receive a Bachelor of Engineering degree. Due to the practical experience from the internship semester and the very good engineering education, you have good chances to start your career.
After graduating in Computer Engineering, you will work as an engineer in the position of a specialist or manager. You will work on specific and complex problems in the field of computer science and information technology.
Thanks to the methods and skills you have acquired, you will be able to solve new technically complex problems independently and as part of a team. You will implement software systems with interfaces to machines and systems on the one hand and to the people operating them on the other.
You will find your challenge in the planning, development and programming of embedded systems in areas such as automotive and aerospace engineering, the consumer goods industry or the automation industry.
Specialization AUTONOMOUS SYSTEMS
With this emphasis, you will work on important issues related to the ability to perceive the operational environment of autonomous systems. You will also have to design and develop the action planning and action execution for autonomous systems based on this.
Specialization CYBER-PHYSICAL SYSTEMS
With this focus, you will work on issues regarding the networking of embedded systems and the resulting challenges. For example, security against unauthorized access. You have the necessary knowledge to be able to methodically implement complex distributed real-time systems.
The Bachelor programmes offered by the Faculty of Information Technology have been successfully accredited by the ASIIN accreditation agency.
