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Volker Turau

Picture of Volker Turau
Prof. Dr. rer. nat. Volker Turau
Room 4.088, building E
Am Schwarzenberg-Campus 3
21073 Hamburg
phone+49 40 42878 - 3530
e-mail

I am professor at Hamburg Universtity of Technology since October 2002.


Program Committee Activities | Editorial Activities | CV | Ph.D. students

Books

Algorithmische Graphentheorie - 4., extended and revised edition
De Gruyter Studium, 2015, ISBN 978-3-110-41727-2 (Solutions)

Erdős number

My Erdős number is 3.

Teaching

Publications

Shashini Thamarasie Wanniarachchi and Volker Turau. Dynamic Resource Allocation for 5G Device-to-Device Communication Based on Expected SARSA. In Proceedings of the 12th International Conference on NETworked sYStems (NETYS 2024), May 2024. Rabat, Morocco.
@InProceedings{Telematik_NETYS_2024, author = {Shashini Thamarasie Wanniarachchi and Volker Turau}, title = {Dynamic Resource Allocation for 5G Device-to-Device Communication Based on Expected SARSA}, booktitle = {Proceedings of the 12th International Conference on NETworked sYStems (NETYS 2024)}, day = {29-31}, month = may, year = 2024, location = {Rabat, Morocco}, }
Volker Turau. Counting Fixed Points and Pure 2-Cycles of Tree Cellular Automata. In Proceedings of 16th Latin American Symposium of Theoretical Informatics, IEEE, March 2024, pp. 241–256. Puerto Varas, Chile.
@InProceedings{Telematik_LATIN_2024, author = {Volker Turau}, title = {Counting Fixed Points and Pure 2-Cycles of Tree Cellular Automata}, booktitle = {Proceedings of 16th Latin American Symposium of Theoretical Informatics}, pages = {241-256}, publisher = {IEEE}, day = {18-22}, month = mar, year = 2024, location = {Puerto Varas, Chile}, }
Abstract: Cellular automata are synchronous discrete dynamical systems used to describe complex dynamic behaviors. The dynamic is based on local interactions between the components, these are defined by a finite graph with an initial node coloring with two colors. In each step, all nodes change their current color synchronously to the least/most frequent color in their neighborhood and in case of a tie, keep their current color. After a finite number of rounds these systems either reach a fixed point or enter a 2-cycle. The problem of counting the number of fixed points for cellular automata is #P-complete. In this paper we consider cellular automata defined by a tree. We propose an algorithm with run-time to count the number of fixed points, here is the maximal degree of the tree. We also prove upper and lower bounds for the number of fixed points. Furthermore, we obtain corresponding results for pure cycles, i.e., instances where each node changes its color in every round. We provide examples demonstrating that the bounds are sharp.
Nisal Hemadasa, Marcus Venzke, Volker Turau and Yanqiu Huang. Machine Learning-based Positioning using Multivariate Time Series Classification for Factory Environments. In Proceedings of OkIP International Conference on Automated and Intelligent Systems, CAIS 2023, OkIP Books, October 2023. Oklahoma, USA.
@InProceedings{Telematik_cais_2023, author = {Nisal Hemadasa and Marcus Venzke and Volker Turau and Yanqiu Huang}, title = {Machine Learning-based Positioning using Multivariate Time Series Classification for Factory Environments}, booktitle = {Proceedings of OkIP International Conference on Automated and Intelligent Systems, CAIS 2023}, pages = , publisher = {OkIP Books}, day = {2-5}, month = oct, year = 2023, location = {Oklahoma, USA}, }
Abstract: Indoor Positioning Systems (IPS) gained importance in many industrial applications. State-of-the-art solutions heavily rely on external infrastructures and are subject to potential privacy compromises, external information requirements, and assumptions, that make it unfavorable for environments demanding privacy and prolonged functionality. In certain environments deploying supplementary infrastructures for indoor positioning could be infeasible and expensive. Recent developments in machine learning (ML) offer solutions to address these limitations relying only on the data from onboard sensors of IoT devices. However, it is unclear which model fits best considering the resource constraints of IoT devices. This paper presents a machine learning-based indoor positioning system, using motion and ambient sensors, to localize a moving entity in privacy concerned factory environments. The problem is formulated as a multivariate time series classification (MTSC) and a comparative analysis of different machine learning models is conducted in order to address it. We introduce a novel time series dataset emulating the assembly lines of a factory. This dataset is utilized to assess and compare the selected models in terms of accuracy, memory footprint and inference speed. The results illustrate that all evaluated models can achieve accuracies above 80 %. CNN-1D shows the most balanced performance, followed by MLP. DT was found to have the lowest memory footprint and inference latency, indicating its potential for a deployment in real-world scenarios.

The complete list of publications is available separately.

Supervised Theses

Open Theses

Completed Theses