Manfred Constapel, M.Sc.

M. Constapel
Position Research Associate
Address Lübeck University of Applied Sciences
Electrical Engineering and Computer Sciences
Mönkhofer Weg 239
D-23562 Lübeck, Deutschland
Room: 18-2.14
Phone +49 (0)451 300-5753


Since August 2017 I am research associate at the Center of Excellence CoSA at the Lübeck University of Applied Sciences. For my Master thesis I worked in the field of sensor fusion and air surveillance and developed a virtual testbed for evaluation of filters and motion models for radar tracking. I'm interested in software development, embedded systems and data science.


1979 born in Norden, Germany
1998 - 2001 Apprenticeship as Specialized Computer Scientist in Application Development
2002 Joining the German Federal Armed Forces (Navy)
2005 - 2008
Study of Compute Science with focus on Business Information at the Stralsund University of Applied Science
2010 - 2015 Submarine officer in operations and nautical service of the German Federal Armed Forces
2014 - 2017
Study of Computer Science with focus on Interactive 3D and Software Technology at the Lübeck University of Applied Science


Propaedeutic Introduction to Algorithms and Data Structures
Fundamentals of Programming
Exercises Theoretical Computer Science
Wireless Sensor Systems
Externships Software Engineering Project I
Software Engineering Project II


DRAISE Wireless, robust, adaptive, industrial Systems
IKARion Intelligent support to improve small group works in online teaching
RoSiE Robust and Safety-relevant Real-Time Localization



Refereed Articles and Book Chapters
[2018] A new localization algorithm based on neural networks (Mathias Pelka, Manfred Constapel, Duc Tu Le Anh, Horst Hellbrück), In Proceedings of the 3rd KuVS/GI Expert Talk on Localization, 2018. [bib] [pdf] [abstract]
Indoor localization plays a major role in a wide range of applications. To determine the location of a tag, localization algorithm is required. In the past, machine learning algorithms were difficult to implement in consumer hardware, but with the advent of tensor processing units, even smartphones are capable to use artificial intelligence to solve complex problems. In this paper, we investigate a machine learning algorithm based on neural networks and compare the result to a linear least squares estimator. We design and evaluate different neural networks. Based on our observation, the neural network delivers poor performance compared to the linear least squares estimator.
[2018] Drahtlose Robuste Adaptive Industrielle Systeme (Manfred Constapel, Leo Krüger, Swen Leugner, Zeynep Vatandas, Koojana Kuladinithi, Andreas Timm-Giel, Horst Hellbrück), In ITG-Fachbericht-Mobilkommunikation VDE VERLAG GmbH, 2018. [bib] [abstract]
Die industrielle Fertigung wandelt sich mit der Digitalisierung. Fertigungsstücke werden individuell durch Industrie 4.0-Technologien; sie sind jederzeit und überall erfassbar. Fertigungsprozesse werden digitalisiert und bilden Struktur und Verhalten einer Fertigungs- und Produktionsstrasse ab, womit sich wichtige Erkenntnisse zum Ablauf gewinnen lassen. Dies setzt eine Vernetzung der Fertigungsstücke voraus, welche die klassische Automatisierung ergänzt. Drahtlose Sensoren sind an Fertigungsstücke gekoppelt und bilden großflächige und dichte Sensornetze. Messgrößen der Sensoren sind beispielsweise Ort, Zeit, Temperatur, Beschleunigung, Geräusch oder Luftfeuchte, welche nach Anforderung der Datenanalyse ausgewählt und ausgewertet werden. Derartige Sensornetze stellen neue Herausforderungen und Forschungsfragen an Industrie 4.0-Anwendungen. Das Projekt DRAISE hat das Ziel, produktionsrelevante Daten in Industrieanlagen zuverlässig zu erfassen und entsprechend aufzubereiten. Hierfür werden Transportwagen einer Fertigung drahtlos vernetzt und Temperatur, Erschütterung, Position und Zeit erfasst. Zusätzlich interagieren die Mitarbeiter in der Produktion über eine definierte Mensch-Maschine-Schnittstelle mit dem System. Zur Minimierung der Auswirkungen durch andere, drahtlose Systeme werden Time Division Multiple Access (TDMA)-basierende Protokolle für die Single-Hop- und Multi-Hop-Kommunikation entwickelt. Die Kommunikation wird durch Lokalisierungskomponenten ergänzt, welche mit einem System auf Basis von Ultra-Wide-Band (UWB) implementiert ist. Für die angestrebte, zuverlässige und robuste Kommunikation werden zukünftig Frequenzsprungverfahren und Spectrum Sensing die entwickelten Protokolle erweitern. Ein großflächig und produktiv eingesetzter Demonstrator dient der ständigen Leistungsbewertung des Systems.
Refereed Conference Papers
[2019] Analysis of Types, Positioning and Appearance of Visualizations in Online Teaching Environments to Improve Learning Experiences (J. Brandenburger, M. Constapel, H. Hellbrück, M. Janneck), In International Conference on Applied Human Factors and Ergonomics, 2019. [bib] [abstract]
In this paper we investigate different visualizations of learners’ data related to collaborative online learning in terms of suitability and attractiveness to students. Furthermore, we analyze whether positioning and color appearance of these data visualizations might have an effect on learners’ behavior. To that end, we conducted an online study (n=120) as well as an eye tracking study (n=20) to compare different types of visualizations. Results show that students prefer classical data visualizations like bar charts. Visualizations placed in the sidebar of a two column web interface get less attention than visualizations in the header of the main content area. Color schemes do not seem to influence the perception of visualizations. We discuss possible explanations and implications for designing data visualizations in learning environments.
[2018] TriClock – Clock Synchronization compensating Drift, Offset and Propagation Delay (Swen Leugner, Manfred Constapel, Horst Hellbrück), In IEEE International Conference on Communications, 2018. [bib] [abstract]
In wireless sensor networks (WSN) precise clock synchronization is still a challenge e.g. for synchronized medium access control (MAC). State of the art solutions require many messages or neglect clock drifts or propagation delay. In multi-hop networks synchronization errors increase with the number of hops because numerous messages increase latency. The latency and clock drift reduces synchronization accuracy. Finally, propagation delay introduces additional synchronization offsets. We introduce a novel synchronization protocol that requires a single message to compensate both clock offset and clock drift and one additional message to account for propagation delay. With this minimal amount of messages, an efficient multi-hop synchronization is practicable. We implement our approach on a DWM1000 hardware and evaluate the protocol in single-hop and multi-hop configuration. In our preliminary experiments, we achieved a synchronization accuracy of 0.46 ns in a single-hop configuration within 3.6 ms and 6 ns in a multi-hop configuration for 5 hops within 11 ms which is appropriate for MAC and time-division multiple access (TDMA) implementations.
Other Publications
[2019] Recurrent Neural Network for Sequence Classification of mmWave Radar Data (T. Geiger, M. Constapel, H. Hellbrück), (T. M. Buzug et. al., ed.), 2019. [bib] [abstract]
This paper unveils the potential and utilization of Recurrent Neural Network (RNN) in radar applications for sequence classification. We focus on human activity recognition, which inputs are multichannel time series signals acquired by a frequency modulated continuous wave (FMCW) millimeter wave (mmWave) radar. We designed a proof of concept implementation for a system where a mmWave radar records the movement of a human body. The proposed RNN predicts and classifies the direction the human is heading. Such systems are valuable in numerous applications ranging from security and safety applications like detection and assessment of human activity at airports to patient monitoring in hospitals. Applied to the dataset recorded by the mmWave radar the implemented RNN provides satisfying classification results.
[2017] A syntactic approach to wreck pattern recognition in sonar images (M. Constapel, T. Teubler, H. Hellbrück), GRIN (T. M. Buzug et. al., ed.), 2017. [bib]
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