Deutsch Intern
    Lehrstuhl für Informatik III


    Previous Bachelor Theses


    • Hofmann, J.: Deep Reinforcement Learning for Configuration of Time-Sensitive-Networking, (2020).
    • Kargl, J.: Analysis of QoE Aspects of 3D Point Cloud Reduction, (2020).
    • Dworschak, N.-D.: Evaluating Temporal Impairments in Music Streaming, (2020).
    • Fehler, H.: Comparison of Web QoE Algorithms on Different Devices, (2020).
    • Mertinat, N.: Impact of Content Selection on Crowdsourced QoE Studies of HTTP Adaptive Streaming on Mobile Devices, (2020).


    • Leidinger, F.: Studing the Initial Delay of the YouTube Mobile App with TensorFlow, (2019).
    • Simonovski, F.: Studying the Video Segmentation for different Streaming Platforms, (2019).
    • Janiak, T.: Investigating the Influence of Listener Attentiveness on the QoE of Music Streaming, (2019).
    • Wolz, M.: Hit Detection with Asymmetric Latency in UE4 Authoritative Multiplayer Games, (2019).
    • Ewald, M.: Observing Changes in Machine Learning Behavior from Input Latency in Games, (2019).
    • Haberzettl, L.: QoS Assessments of Spotify’s Mobile Application for Audio Streaming, (2019).
    • Hildebrand, K.: Towards a Source Traffic Model for Instant Messaging using WhatsApp, (2019).


    • Wollek, A.: Validierung eines generischen HAS-Modells für unterschiedliche Heuristiken., (2018).
    • Hefter, J.: Analyzing the Streaming Behaviour of a Popular Video-On-Demand Service, (2018).
    • Poignée, F.: Influence of Tension on QoE in Video Streaming, (2018).
    • Gölz, J.: Evaluation of the Movement Synchronization of Unreal Engine 4, (2018).
    • Weber, K.: Machine Learning for Classification of Streaming Data, (2018).
    • Borst, V.: Experimental Evaluation of the Interface Design of Crowdsourcing Tasks, (2018).
    • Vomhoff, V.: Traffic Measurement Study of the Amazon Echo Show, (2018).
    • Bocerov, M.: Videokompressionsverfahren und ihre Eignung für Adaptives Videostreaming, (2018).