Introduction to Artificial Intelligence

Prof. Dr. Yvo Marcelo Chiaradia Masselli

The Introduction to Artificial Intelligence (AI) course aims to present an overview of the AI area, the main techniques and the algorithms used in mobile navigation.

The course combines theoretical sessions, computer simulations and covers the following topics:

• Development of algorithms in Artificial Intelligence.
• Artificial intelligence and image processing.
• Artificial Intelligence and Mobile Navigation Systems.

In the end, the student should be able to develop a solution based on the learned algorithms. These will serve as the base for subsequent lectures in the course lane of AI.

Image Processing Using Artificial Intelligence

Dr. Gábor Kertész

The goal of the lectures is to provide an introduction to image recognition with neural networks, both is theory and in practice.

The course starts with a basic introduction to neural network applications, image representation, and the fundamentals of image processing. On the practical side, Tensorflow and Keras are introduced as the most common frameworks used by machine learning engineers.

During the course the students will get an understanding on how Neural Networks are trained, evaluated, and what common problems could occur. After introducing Convolutional Neural Networks, classical architectures are analysed along with modern, state-of-the-art solutions and techniques, which improve the performance of neural networks.

Course topics:

• Regression and classification in fully connected networks
• Handling image data
• Convolutional Neural Networks
• Classic architectures and techniques
• State-of-the-art solutions and approaches to increase performance
• Hands-on with Tensorflow and Keras

Introduction into Intelligent Mobile Robots

Prof. Dr. Lars Nolle, Christoph Tholen

The objective of this course is to provide students with an overview about the field of intelligent mobile robots, which have become increasingly popular with researchers and practitioners in the recent years. Examples of such systems include the Mars Pathfinder rover, Honda's famous Asimo humanoid robot, and Tesla's Model S car with Autopilot software. This module introduces the concept of autonomy and provides an overview of the building blocks of autonomous mobile systems, such as locomotion, perception, localisation, planning, and navigation. Potential applications of Artificial Intelligence methods (e.g. Fuzzy Logic, Artificial Neural Networks, etc.) within these building blocks will be discussed and real-world examples will be given. Students will get hands-on experience in building and testing autonomous robots.

The course is suitable for undergraduate engineering or computer science students. It combines lectures and practical laboratory work. The lectures provide the theoretical foundations of intelligent mobile robots. The implementation of different AI methods on a mobile robot platform is the focus of the practical work.

Introduction to Project Management

Dr. Miklós Daróczi

The main objective of the course is to deliver some theoretical and practical knowledge related mainly to the project planning, scheduling and budgeting. The module starts by defining the project and differentiating project management from general management. The project manager’s role, the project life cycle and the elements of project plan are also briefly discussed. The common formats of schedule the Gantt-charts and PERT/CPM networks, some methods of budgeting and cost estimating are also covered. Based on these techniques students will be ableto participate in project planning and implementation.

Student teams will prepare a report and give a presentation on a special project. The purpose of this assignment is to deepen your knowledge on planning projects and to share that knowledge with the rest of the group. The topic of the project will be selected by the students.

Biomechanical Modelling of Implanted Bone Systems

Dr. Lucian Rusu, Dr. Dan Ioan Stoia

Keywords: biomechanics, implants, prosthesis, modelling, simulation, FEM.

Knowledge on modelling of biomechanical behaviour of implanted bone systems contributes to the development of new implants/prostheses having a better long-time functionality, development of corresponding surgical instruments, injury prevention, and not at least to manage with ageing.            

The course topics consist in:

• Basic anatomical knowledge - bones, joints, muscles;
• Implants, prostheses, and ortheses – implant design, materials and biomaterials;
• Kinematic and static modelling of biomechanical systems – theory and simulations;
• Numerical analysis - basics of FEM theory.

The course consists of theoretical sessions followed by computer simulations.

Introduction to Medical Robotics

Prof. Dr. Olena Kuzmicheva

The objective of the course is to provide an extensive insight into the rapidly developing topic of medical robotics. The focus is on robot assisted motion therapy, with introduction to other aspects of technically assisted therapy and healthcare robotic systems. The course is directed to undergraduate students in engineering and computer science. Medical background is not required.

The course combines theoretical sessions, computer simulations and experiments with existing systems in the lab and reflects following topics:

• Introduction (applications and paradigms of medical robots, basic kinematic concepts, basic control concepts, safety issues)
• Robot assisted motion therapy (basic aspects of human motion (especially gait), system construction and actuation, control strategies and human-machine interfaces to provide individualized therapy, sensor based assessment)
• Overview of current topics and future directions in medical robotics

Along the course, the students will develop and present a project proposal for a new medical robot or medical technology. The goal is to gain some experience with initiation of a new project. The students (in small teams) will perform a literature review for the selected area of medical robotics (particular topic will be selected by the students), analyse shortcomings and prepare the summary of their proposal. Finally, the students will present the proposals to the other participants. More details on the project proposal will be provided during the course.

Amperometric Biosensors and Related Electronics

Dr. Cengiz Koçum, Dr. Dilek Çökeliler Serdaroğlu

Biosensors are used to detect biological or chemical markers (bio-chemicals, DNA, protein, cells etc.) in order for diagnosis, environmental monitoring and pharmaceutical research. This course represents the fundamental science and engineering concepts of designing biosensors through a common example. Biosensors have three main parts; biorecognition layer, transducers and electronics and they usually produce an electrical signal related to the specific analyte. In this course with lectures and practical exercises students will design and implement the main parts of an amperometric biosensor capable of glucose monitoring. Students will gain an insight of the complexities and design principles of a biosensor from scratch.

Course Outline:

• Introduction to biosensors and nanobiosensors
• Types of biorecognition elements
• Surface functionalizing and modification approaches for biosensors
• Transducer Basics: Sensors and Actuators
• Signal acquisition and conditioning principles
• Amperometric glucose biosensors
• Design and production of glucose sensing layer
• Signal conditioning for the amperometric glucose biosensor
• Electronic circuit and PCB design.

Instrumentation, Acquisition and Signal Processing for Biosignals

Dr. Mehmet Yüksekkaya

Biomedical signals are used to obtain crucial information about human health for diagnosis and treatment follow up. Acquiring biosignals from human body to a computer system is called biosignal acquisition and conditioning. For signal acquisition and conditioning, appropriate electronic instrumentation should be designed. In order to extract the crucial information from the acquired data, it should be processed.

This course is designed to be a practical introduction for all those stages. Students will design a basic instrumentation circuitry to acquire a specific biosignal and learn the fundamentals of signal conditioning. Further, they will learn the essential principles of digital signal processing and will apply those principles in a practical example. After this course, students will get significant theoretical and practical skills for biosignal acquisition and processing.

Course Synopsis: 

• Introduction to the design of analog circuits for signal conditioning.
• Practical design of a biosignal acquisition instrument.
• Introduction to digital signal processing.
• Processing of biosignal for feature extraction. 

Embedded Systems for Developing Biomedical Applications

Dr. Gerardo Marx Chávez-Campos

Nowadays, the “Embedded Systems” and its integration with the “Internet of Things” has become crucial for developing healthcare and industrial applications. Thus, to understand the developing process of apps on the embedded system that uses as the central operative system “Linux” are fundamental. Even more, this first approach to the most used operative system on servers, supercomputers, and single devices will guide the student to understand the complete integration of hardware electronics and code for developing from simple to complex tasks.

During the course, the student will realize by doing the fundamentals of how to work on the embedded board by accessing hardware, input/output digital ports, read the digital to analog converters. Then, the student will create an application to read and log data into structured frameworks. The registered data will be processed and analyzed using Python.

Course Synopsis: 

• Introduction to the beagle bone boards
• The beagle software
• Developing basic applications
• The Linux file system
• Interfacing electronics
• GPIO interfacing
• Internet of things
• A final project

General course fee: To be determined

International students:
All international students have to submit the following documents by e-mail to Iris Wilters:
Statement of motivation, curriculum vitae, passport copy, most recent grades and a letter of recommendation from a professor.
We will inform you at the beginning of June 2021 whether you are accepted or not.

Students from partner universities (see list below):
Ask your local coordinator to sign up. She/he will tell you about the special costs which your group of students will have to pay and what further information we need.

Students from Jade University:
Students from Jade University have to be registered for autumn semester 2021/22.  
Ask the organisation team in Wilhelmshaven for further information and course fees.

Students from abroad may be eligible for a scholarship and will be informed about the possibilities after having handed in their complete application documents.

If you have any questions please contact our International Office, Iris Wilters.

We will book a shared room on a houseboat for you, including breakfast (costs to be determined). If you wish to stay in a single room, we recommend to look for a hotel room.

Please note that there are no cooking facilities on the houseboat. You can obtain a low-cost lunch in the university canteen. For dinner you will have to go to restaurants or buy snacks.

Baskent University Ankara (Turkey)
Dr. Dilek Çökeliler

Instituto Nacional de Telecomunicações - Inatel (Brazil)
Prof. Dr. Yvo Marcelo Chiaradia Masselli

Morelia Institute of Technology (Mexico)
Dr. Gerardo Marx Chávez-Campos

Óbuda University (Hungary)
Dr. Gábor Kertész

St. Istvan University (Hungary)
Dr. Miklós Daróczi

Universitatea "Politehnica" din Timisoara (Romania)
Dr. Lucian Rusu