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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
fax+49 40 427 - 3 - 10456
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

Volker Turau. Synchronous Concurrent Broadcasts for Intermittent Channels with Bounded Capacities. In 28th International Colloquium on Structural Information and Communication Complexity - Sirocco 2021 -, Springer, June 2021, pp. 296–312. virtual.
@InProceedings{Telematik_sirocco_2021, author = {Volker Turau}, title = {Synchronous Concurrent Broadcasts for Intermittent Channels with Bounded Capacities}, booktitle = {28th International Colloquium on Structural Information and Communication Complexity - Sirocco 2021 -}, pages = {296-312}, publisher = {Springer}, day = {28-1}, month = jun, year = 2021, location = {virtual}, }
Abstract: In this work we extend the recently proposed synchronous broadcast algorithm amnesiac flooding to the case of intermittent communication channels. In amnesiac flooding a node forwards a received message in the subsequent round. There are several reasons that render an immediate forward of a message impossible: Higher priority traffic, overloaded channels, etc. We show that postponing the forwarding for one or more rounds prevents termination. Our extension overcomes this shortcoming while retaining the advantages of the algorithm: Nodes don’t need to memorize the reception of a message to guarantee termination and messages are sent at most twice per edge. This extension allows to solve more general broadcast tasks such as multi-source broadcasts and concurrent broadcasts for systems with bounded channel capacities.
Volker Turau. Amnesiac Flooding: Synchronous Stateless Information Dissemination. In Theory and Practice of Computer Science - 47th International Conference on Current Trends in Theory and Practice of Computer Science, SOFSEM 2021, Springer, January 2021, pp. 59–73.
@InProceedings{Telematik_sofsem_2021, author = {Volker Turau}, title = {Amnesiac Flooding: Synchronous Stateless Information Dissemination}, booktitle = {Theory and Practice of Computer Science - 47th International Conference on Current Trends in Theory and Practice of Computer Science, SOFSEM 2021}, pages = {59-73}, publisher = {Springer}, day = {25-29}, month = jan, year = 2021, location = {}, }
Abstract: A recently introduced stateless variant of network flooding for synchronous systems is called amnesiac flooding. Stateless protocols are advantageous in high volume applications, increasing performance by removing the load caused by retention of session information. In this paper we analyze the termination time of multi-source amnesiac flooding. We provide tight upper and lower bounds for the time complexity.
Marcus Venzke, Daniel Klisch, Philipp Kubik, Asad Ali, Jesper Dell Missier and Volker Turau. Artificial Neural Networks for Sensor Data Classification on Small Embedded Systems. Technical Report Report arXiv:2012.08403, arXiv.org e-Print Archive - Computing Research Repository (CoRR), Cornell University, December 2020.
@TechReport{Telematik_Venzke_ANNsES, author = {Marcus Venzke and Daniel Klisch and Philipp Kubik and Asad Ali and Jesper Dell Missier and Volker Turau}, title = {Artificial Neural Networks for Sensor Data Classification on Small Embedded Systems}, number = {Report arXiv:2012.08403}, institution = {arXiv.org e-Print Archive - Computing Research Repository (CoRR)}, address = {Cornell University}, month = dec, year = 2020, }
Abstract: In this paper we investigate the usage of machine learning for interpreting measured sensor values in sensor modules. In particular we analyze the potential of artificial neural networks (ANNs) on low-cost microcontrollers with a few kilobytes of memory to semantically enrich data captured by sensors. The focus is on classifying temporal data series with a high level of reliability. Design and implementation of ANNs are analyzed considering Feed Forward Neural Networks (FFNNs) and Recurrent Neural Networks (RNNs). We validate the developed ANNs in a case study of optical hand gesture recognition on an 8-bit microcontroller. The best reliability was found for an FFNN with two layers and 1493 parameters requiring an execution time of 36 ms. We propose a workflow to develop ANNs for embedded devices.

The complete list of publications is available separately.

Supervised Theses

Open Theses

Ongoing Theses

Completed Theses