Jurij Kuzmic

• Communication between autonomous vehicles
• Exchange of measured values

• Rescue lane formation on highways
• Image recognition
• Image processing
• Simulations with Unity 3D
• Construction of a test track (highway) with model vehicles

• Track detection with Filtered Canny Edge Detector
• Object detection with TensorFlow and Object Detection API
• Distance measurements in 2D images through approximation of functions and pixel conversion

• Development of software for hardware with limited resources, such as Raspberry Pi 3 B or Raspberry Pi 4 B with and without hardware extension (Intel NCS2)

08/2018 - today Technical University of Dortmund, Computer Science doctoral studies, Dortmund

10/2015 - 08/2018 South Westphalia University of Applied Sciences, M.Sc. Applied Computer Science, Iserlohn

10/2012 - 08/2015 South Westphalia University of Applied Sciences, B.Sc. Applied Computer Science, Iserlohn

10/2009 - 08/2012 Theodor-Reuter-Berufskolleg, advanced technical college entrance qualification, Iserlohn

08/2018 - today Research Assistant, Faculty of Computer Science, TU Dortmund University

03/2016 - 08/2018 Research Assistant, South Westphalia University of Applied Sciences, Iserlohn

04/2013 - 11/2013 System administrator, GDI mbH, Hagen

08/2009 - 07/2012 Apprenticeship as IT systems electronics technician, Theodor-Reuter-Berufskolleg, Iserlohn

• Winter semester 2018/19

  • Introduction to programming (EidP)

• Summer semester 2019

  • Data structures, algorithms and programming 1 (DAP 1)

• Winter semester 2019/20

  • Introduction to programming (EidP)

• Summer semester 2020 (online)

  • Data structures, algorithms and programming 1 (DAP 1)

• Winter semester 2020/21 (online)

  • Introduction to programming (EidP)

• Summer semester 2021 (online)

  • Data structures, algorithms and programming 1 (DAP 1)

• Winter semester 2021/22

  • Introduction to programming (EidP)

• Summer semester 2022

  • Data structures, algorithms and programming 1 (DAP 1)

• Winter semester 2022/23

  • Introduction to programming (EidP)

• Internet of Things (IoT)

  • Web-based control of smart home devices via an MQTT broker

• Artificial intelligence (AI)

  • Person identification using natural language with recurrent neural networks

  • Emotion recognition from portrait images with neural networks and traditional methods

  • Implementation of hybrid methods for instrument recognition in noisy music data

  • Efficient lane detection for model cars with neural networks in simulation and reality

  • Optical distance measurement for objects in road traffic and their classification

  • Comparison of speaker recognition by neural networks as a multiclass problem and multiple two-class problem

  • Suppression of background noise in speech signals with artificial neural networks using the example of keyboard noise

  • Systematic reward study in reinforcement learning of an agent in an avoid & collect environment.

  • Effect of different reward values on agent behavior in an Avoid & Collect environment.

Consultation hours: By appointment