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Lehrstuhl für Allgemeine Elektrotechnik und Theoretische Nachrichtentechnik


Prof. Dr.- Ing. Anton Kummert

Publikationen

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Jahr:  
Alle :: 1986, ... , 2016, 2017, 2018, 2019
Referenzen
376.
L. Kolonko; J. Velten; A. Kummert
An Improved Multi-Dimensional Approach to Wave Digital Filters with Topology-Related Delay-Free Loops using Automatic Differentiation
2019 IEEE 62nd International Midwest Symposium on Circuits and Systems (MWSCAS) , Seite 1163-1166.
August 2019
DOI: 10.1109/MWSCAS.2019.8885341

Schlüsselwörter: Digital filters;Delays;Topology;Linear systems;Mathematical model;Numerical models;Jacobian matrices;Wave Digital Filter;Automatic Differentiation;Delay-Free Loop;Multi-Dimensional;Contractivity;Bridged-T Model
375.
J. Cao; J. Zhu; W. Hu; A. Kummert
Epileptic Signal Classification with Deep EEG Features by Stacked CNNs
IEEE Transactions on Cognitive and Developmental Systems, :1
2019
ISSN: 2379-8920
DOI: 10.1109/TCDS.2019.2936441

Schlüsselwörter: Electroencephalogram, Epilepsy, Seizure detection, Preictal state classification, Stacked CNNs.
374.
T. Schwerdtfeger; A. Kummert
Nonlinear Circuit Simulation by Means of Alfred Fettweis' Wave Digital Principles
IEEE Circuits and Systems Magazine, 19(1):55-C3
2019
ISSN: 1531-636X
DOI: 10.1109/MCAS.2018.2872666

Schlüsselwörter: circuit simulation;nonlinear network analysis;wave digital filters;Alfred Fettweis wave digital principles;general circuit simulation strategy;digital filter design;analogue reference circuits;accurate digital model;WDFs;Wave Digital Filters;commercial circuit design;dependable circuit simulation;nonlinear circuit simulation;Digital filters;Circuit simulation;Circuit synthesis;Finite element analysis;Nonlinear circuits;SPICE
373.
Lukas Hahn; Lutz Roese-Koerner; Klaus Friedrichs; Anton Kummert
Fast and Reliable Architecture Selection for Convolutional Neural Networks Proceedings of the 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), pages 179-184, Bruges 2019
ArXiv, abs/1905.01924
2019

Schlüsselwörter: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)

Zusammenfassung: The performance of a Convolutional Neural Network (CNN) depends on its hyperparameters, like the number of layers, kernel sizes, or the learning rate for example. Especially in smaller networks and applications with limited computational resources, optimisation is key. We present a fast and efficient approach for CNN architecture selection. Taking into account time consumption, precision and robustness, we develop a heuristic to quickly and reliably assess a network's performance. In combination with Bayesian optimisation (BO), to effectively cover the vast parameter space, our contribution offers a plain and powerful architecture search for this machine learning technique.
372.
K. Kalischewski; D. Wagner; J. Velten; A. Kummert
Spoken Letter Recognition using Deep Convolutional Neural Networks on Sparse and Dissimilar Data
2019 IEEE International Symposium on Circuits and Systems (ISCAS) , Seite 1-5.
2019
DOI: 10.1109/ISCAS.2019.8702300

Schlüsselwörter: Training;Task analysis;Visualization;Spectrogram;Convolutional neural networks;Image recognition
371.
D. Wagner; K. Kalischewski; S. Tilgner; J. Velten; A. Kummert
Automatic Labeling of Industrial Images by using Generative Adversarial Networks
2019 IEEE International Symposium on Circuits and Systems (ISCAS) , Seite 1-5.
2019
DOI: 10.1109/ISCAS.2019.8702195

Schlüsselwörter: Kernel;Generative adversarial networks;Training;Decoding;Generators;Loss measurement;Mutual information
370.
L. Kolonko; J. Velten; A. Kummert
Live Demonstration: A Raspberry Pi Based Video Pipeline for 2-D Wave Digital Filters on Low-Cost FPGA Hardware
2019 IEEE International Symposium on Circuits and Systems (ISCAS) , Seite 1-1.
2019
DOI: 10.1109/ISCAS.2019.8702197

Schlüsselwörter: Field programmable gate arrays;Economic indicators;Liquid crystal displays;Pipeline processing;Digital filters;Hardware;Universal Serial Bus;Wave Digital Filter;Video Pipeline;FPGA;Raspberry Pi
369.
L. Kolonko; J. Velten; A. Kummert
A Raspberry Pi Based Video Pipeline for 2-D Wave Digital Filters on Low-Cost FPGA Hardware
2019 IEEE International Symposium on Circuits and Systems (ISCAS) , Seite 1-5.
2019
DOI: 10.1109/ISCAS.2019.8702369

Schlüsselwörter: Field programmable gate arrays;Streaming media;Pipelines;Economic indicators;Universal Serial Bus;Hardware;Digital filters;Wave Digital Filter;Video Pipeline;FPGA;Raspberry Pi
368.
S. Tilgner; D. Wagner; K. Kalischewski; J. Velten; A. Kummert
Multi-View Fusion Neural Network with Application in the Manufacturing Industry
2019 IEEE International Symposium on Circuits and Systems (ISCAS) , Seite 1-5.
2019
DOI: 10.1109/ISCAS.2019.8702575

Schlüsselwörter: Cameras;Cavity resonators;Biological neural networks;Training;Predictive models;Convolutional neural networks
367.
Matthias Buß; Stephan Benen; D Kraus; Anton Kummert
False Alarm Reduction for Active Sonars using Deep Learning Architectures
2019 UDT, Stockholm
2019
Total:
376