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Prof. Dr.-Ing. Guido Dartmann

Hochschule Trier

Publikationen
Ergebnisse pro Seite:  10

Machhamer, Rudiger; Altenhofer, Jannik; Ueding, Kristof et al.

Visual Programmed IoT Beehive Monitoring for Decision Aid by Machine Learning based Anomaly Detection

9th Mediterranean Conference on Embedded Computing (MECO). online: IEEE 2020


Peine, Arne; Hallawa, Ahmed; Schöffski, Oliver et al.

A Deep Learning Approach for Managing Medical Consumable Materials in Intensive Care Units via Convolutional Neural Networks: Technical Proof-of-Concept Study

JMIR Medical Informatics. Bd. 7. H. 4. JMIR Publications Inc. 2019 e14806


Begic Fazlic, Lejla; Hallawa, Ahmed; Schmeink, Anke et al.

A Novel NLP-FUZZY System Prototype for Information Extraction from Medical Guidelines

42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO). online: IEEE 2019


Dartmann, Guido; Song, Houbing; Schmeink, Anke

Big Data Analytics for Cyber-Physical Systems

o. O.: Elsevier 2019


Zamani, Alireza; Klimke, Marvin; Dartmann, Guido et al.

Convolutive Blind Source Separation with Independent Vector Analysis and Beamforming

1st International Conference on Electrical, Control and Instrumentation Engineering (ICECIE). online: IEEE 2019


Ayad, Ahmad; Zamani, Alireza; Schmeink, Anke et al.

Design and Implementation of a Hybrid Anomaly Detection System for IoT

Sixth International Conference on Internet of Things: Systems, Management and Security (IOTSMS). online: IEEE 2019


Gollmer, Klaus-Uwe; Kreten, Sandro; Stolz, Florian et al.

IoT-workshop: Blueprint for pupils education in IoT

Big Data Analytics for Cyber-Physical Systems. o. O.: Elsevier 2019 S. 315 - 344


Zamani, Alireza; Taghizadeh, Omid; Dartmann, Guido et al.

Joint User Association and Robust Beamforming Optimization for C-RANs with Wireless Fronthauls

IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC). online: IEEE 2019


Vieting, Peter M.; de Lamare, Rodrigo C.; Martin, Lukas et al.

Likelihood-Based Adaptive Learning in Stochastic State-Based Models

IEEE Signal Processing Letters. Bd. 26. H. 7. IEEE 2019 S. 1031 - 1035


Hallawa, Ahmed; Zhang, Song; Peine, Arne et al.

Machine Learning in future intensive care: Classification of stochastic Petri Nets via continuous-time Markov chains

Big Data Analytics for Cyber-Physical Systems. o. O.: Elsevier 2019 S. 259 - 273