Summer Semester, VL and UE

Nowadays, digital signal processing is a part of almost every consumer electronics device, vehicle, industrial machinery, etc. This course provides an introduction to the field of digital signal processing. In addition to base-knowledge methods and algorithms, students acquire basic MATLAB® skills. Students are required to complet bi-weekly assignments.

KUSSS course description VL, opens an external URL in a new window, UE, opens an external URL in a new window
KUSSS schedule: VL, opens an external URL in a new window, UE, opens an external URL in a new window

Summer Semester, VL and UE

This course focuses on discrete-time signals and linear time-invariant systems, their representations and mathematical treatment. Students in the course, mainly those in their fourth semester majoring in Electronics and Information Engineering, learn how to work with discrete-time signals and systems in the time and frequency domain in detail. Students also apply what they have learned in the classroom to applications at the MATLAB®.

KUSSS description: VL, opens an external URL in a new window, UE, opens an external URL in a new window
KUSSS schedule: VL, opens an external URL in a new window, UE, opens an external URL in a new window

Winter Semester, VL and UE

This course provides an understanding of the fundamentals of electricity, electronic components, and circuits. In addition, participants are introduced to the fundamentals of computer programming and are introduced to a higher level programming language as well as in interface and microcontroller programming.

KUSSS description: VL, opens an external URL in a new window, UE, opens an external URL in a new window
KUSSS schedule: VL, opens an external URL in a new window, UE, opens an external URL in a new window

Summer Semester, VL and UE

This course provides a deeper understanding of electricity, electronic components, and circuits. Students will expand on their understanding of computer programming on both a higher level programming language and interface and microcontroller programming.

KUSSS description: VL, opens an external URL in a new window, UE, opens an external URL in a new window
KUSSS schedule: VL, opens an external URL in a new window, UE, opens an external URL in a new window

Winter Semester, VL and UE

Statistical and adaptive signal processing algorithms have a vast application range in electronic systems designed to transmit, receive, store or extract information. These systems include communications, radar, speech and image processing, biomedical signal processing, control systems, and many others. The course is especially for graduate students in Electronics and Information Engineering as well as in Mechatronics. The presented theory is applied to many real-world application examples. The accompanying tutorial classes focus on implementing the concepts and algorithms developed in the MATLAB®.

KUSSS description: VL, opens an external URL in a new window , UE, opens an external URL in a new window
KUSSS schedule: VL, opens an external URL in a new window ,  UE, opens an external URL in a new window

Summer Semester, VL and PR

This course is recommended for experienced undergraduate and graduate students. The course focuses on signal processing hardware and implementation. Students learn about a variety of hardware platforms, efficient algorithms, implementation aspects, and pitfalls. The lab course provides constant supervision and gives students an opportunity to implement signal processing algorithms on an FPGA.

KUSSS description: VL, opens an external URL in a new window, PR, opens an external URL in a new window
KUSSS schedule: VL, opens an external URL in a new window, PR, opens an external URL in a new window

Summer Semester, KV

In many practical applications, the task of signal processing systems is to decide whether measurements contain a certain signal or not. The presence of this signal may indicate a fault in a machinery, the presence of objects or obstacles, the reception of bits through a wireless channel, the presence of a speaker, an earthquake, or a new pulsar. 

Detecting the presence of a signal can be formulated as a hypothesis testing problem, or detection problem. Detection theory summarizes optimal and suboptimal approaches to make a well-informed decision between the hypotheses “signal in present” and “measurements contain noise only”, which are taught in this lecture.

Students develop the ability to identify, formulate, and analyze a variety of detection problems, gaining a deep understanding of both theoretical and practical aspects of detection theory. They become proficient in applying core methods to real-world scenarios across diverse domains. Students learn to assess and select appropriate models and detectors, interpret statistical outcomes, and quantify detection performance. Through numerous exercises during the lecture and hands-on applications at home, they build the skills needed to design and evaluate detection systems.

KUSSS schedule: KV Special Topics (Signal Detection) , opens an external URL in a new window