Biomedical Engineering Summer School

Introduction to Artificial Intelligence

Prof. Dr. Yvo Marcelo Chiaradia Masselli

The Introduction to Artificial Intelligence (AI) course aims to present an overview of the field of AI, the main techniques and algorithms involved in real applications.

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

• Fundamentals of Artificial Intelligence.
• Knowledge representation.
• Informed and uninformed search methods.
• Fuzzy logic and fuzzy controllers.

In the end, the student should be able to understand and develop a solution based on the learned algorithms. These will serve as the basis for subsequent lectures on the AI ​​course track.

Biomedical Image Processing

Dániel Kiss

This is an introductory course in the fundamentals of biological and medical imaging and image processing, including pre-processing and enhancement techniques, filtering, object detection, and image segmentation.

Participants will learn the basics of digital imaging and will gain experience in Python’s image processing abilities. We are also going to meet CellProfiler, an open-source application for batch processing, measuring, and analyzing images. We will see how to use them via real-life examples of various biological experiments covering the following topics.

Course topics:

• Fundamentals of image acquisition and image processing
• Intensity-based methods: histogram correction and thresholding
• Classical filters: smoothing, sharpening, edge enhancement
• Image segmentation and object identification
• Working with multi-channel images, measuring colocalization

Biomedical Applications of Machine Learning

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 the fundamentals of image recognition using neural networks. On the practical side, Tensorflow and Keras is 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

Smart Materials in Biomedical Engineering

Prof. Dr. Hilal Göktaş

Progress in physics and chemistry over the past century has greatly increased the production of new types of synthetic materials. The development of new materials is governed by societal needs and the availability of resources, however, the acceptance and dissemination of a material often depends on its cost. In light of this, the course aims to present an overview of materials/bio-smart materials, their synthesis and their applications in Biomedical Engineering field, and having laboratory work for the characterizations of bio-smart materials with existing analytical devices in the lab (FTIR, UV-visible and SEM). Students will be able to identify materials/bio-smart materials and have hands-on experience on several analytical devices.

Outline of the course:

• Introduction to materials and bio-smart materials
• Synthesis of bio-smart materials
• Characterizations of bio-smart materials
• Application of bio-smart materials in Biomedical Engineering

Brain-Computer-Interfaces

Prof. Dr. Olena Kuzmicheva

Development of assistive robotic devices for persons with motor disabilities is a challenging task. Besides development of autonomous task execution by robotic devices, the problem of robot system control by the user arises, when usual interaction modalities like mouse or joystick cannot be used due to physical restrictions of the person. New modalities become necessary to command the robotic system hands-free. The course introduces different aspects of hands-free control with focus on Brain Computer Interfaces (BCI) as one of possible input modalities. The participants will learn basics of non-invasive BCIs including different aspects of signal processing and directly apply the achieved knowledge in the practice. Therefore, the course combines theoretical sessions with programming units and experiments in the lab and reflects following topics:

• Introduction to hands-free control and different input modalities
• Electroencephalography (EEG), EEG based BCIs as input modality, useful features and BCI paradigms
• Selected topics of signal processing for EEG based BCIs
• Application of BCI to (robotic) system control
• Design and implementation of a small scale BCI controlled application.

The course addresses undergraduate students in engineering and computer science. Medical background is not required.

Biomedical Image Processing Basics and Practical Applications

Prof. Dr. Ziya Telatar

The goal of the course is to introduce some problem domains in imaging, basics of the image analyses and some main solution techniques in spatial or frequency domains.

Course topics:

• Why image processing, problems in image acquisition, understanding the images
• Medelling the image degradation, processing domains for two dimensional signals
• Enhancement and filtering techniques in spatial and spectral domains, applied study in MATLAB
• Feature extraction and segmentation, applied study in MATLAB
• Machine learning techniques in image processing

Amperometric Biosensors and Related Electronics

Dr. Cengiz Koçum, Prof. 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