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Lehrstuhl für Allgemeine Elektrotechnik und Theoretische Nachrichtentechnik

Prof. Dr.- Ing. Anton Kummert


Alle :: 1986, ... , 2005, 2006, 2007, ... , 2020


J. Cao; H. Dai; B. Lei; C. Yin; H. Zeng; A. Kummert
Maximum Correntropy Criterion-Based Hierarchical One-Class Classification
IEEE Transactions on Neural Networks and Learning Systems, :1-7
ISSN: 2162-2388

Schlüsselwörter: Hierarchical structure;maximum correntropy criterion (MCC);one-class classification;outlier/anomaly detection.

Zusammenfassung: Due to the effectiveness of anomaly/outlier detection, one-class algorithms have been extensively studied in the past. The representatives include the shallow-structure methods and deep networks, such as the one-class support vector machine (OC-SVM), one-class extreme learning machine (OC-ELM), deep support vector data description (Deep SVDD), and multilayer OC-ELM (ML-OCELM/MK-OCELM). However, existing algorithms are generally built on the minimum mean-square-error (mse) criterion, which is robust to the Gaussian noises but less effective in dealing with large outliers. To alleviate this deficiency, a robust maximum correntropy criterion (MCC)-based OC-ELM (MC-OCELM) is first proposed and then further extended to a hierarchical network to enhance its capability in characterizing complex and large data (named HC-OCELM). The gradient derivation combining with a fixed-point iterative updation scheme is adopted for the output weight optimization. Experiments on many benchmark data sets are conducted for effectiveness validation. Comparisons to many state-of-the-art approaches are provided for the superiority demonstration.

Jessica Malerczyk; Sabine Lerch; Bernd Tibken; Anton Kummert
Impact of intelligent agents on the avoidance of spontaneous traffic jams on two-lane motorways
MATEC Web of Conferences Band 308 , Seite 05003.
EDP Sciences
Jan-Christoph Schmitz; Stephan Tilgner; Kathrin Kalischewski; Daniel Wagner; Anton Kummert
Hands on Wheel Classification Based on Depth Images and Neural Networks
MATEC Web of Conferences Band 308 , Seite 06003.
EDP Sciences
L. Kolonko; J. Velten; A. Kummert
Optimization of Artificial Port Reflectances for Wave Digital Filters with Topology-Related Delay-Free Loops
2020 IEEE 63rd International Midwest Symposium on Circuits and Systems (MWSCAS) , Seite 170-173.

Schlüsselwörter: Wave Digital Filter;Automatic Differentiation;Delay-Free Loop;Multi-Dimensional;Bridged-T Model

Zusammenfassung: In this paper, a generic method for fast determination of all involved optimal artificial port resistances is presented for the realization of Wave Digital Filters (WDFs) containing noncomputable, delay-free loops. Therefore, the concept of Automatic Differentiating WDFs (ADWDFs) is applied to obtain said resistances by minimizing the associated artificial reflectances, which performs significantly faster than empirical approaches, as will be shown in an example. This way, the resulting Wave Digital structure remains completely modular under fixed point iteration schemes achieving optimal convergence speeds. Additionally, contractivity of WDFs is exploited to obtain an optimal operating point from a different perspective.


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

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

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
ISSN: 2379-8920

Schlüsselwörter: Electroencephalogram, Epilepsy, Seizure detection, Preictal state classification, Stacked CNNs.

T. Schwerdtfeger; A. Kummert
Nonlinear Circuit Simulation by Means of Alfred Fettweis' Wave Digital Principles
IEEE Circuits and Systems Magazine, 19(1):55-C3
ISSN: 1531-636X

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

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

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.

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.

Schlüsselwörter: Training;Task analysis;Visualization;Spectrogram;Convolutional neural networks;Image recognition

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.

Schlüsselwörter: Kernel;Generative adversarial networks;Training;Decoding;Generators;Loss measurement;Mutual information

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.

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

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.

Schlüsselwörter: Field programmable gate arrays;Streaming media;Pipelines;Economic indicators;Universal Serial Bus;Hardware;Digital filters;Wave Digital Filter;Video Pipeline;FPGA;Raspberry Pi

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.

Schlüsselwörter: Cameras;Cavity resonators;Biological neural networks;Training;Predictive models;Convolutional neural networks

Matthias Buß; Stephan Benen; D Kraus; Anton Kummert
False Alarm Reduction for Active Sonars using Deep Learning Architectures
2019 UDT, Stockholm


Patrick Weyers; Alexander Barth; Anton Kummert
Driver State Monitoring with Hierarchical Classification
2018 IEEE International Conference on Intelligent Transportation Systems (ITSC)
November 2018
Bartlomiej Sulikowski; Krzysztof Galkowski; Anton Kummert; Eric Rogers
Two-dimensional (2D) systems approach to feedforward/feedback control of a class of spatially interconnected systems
International Journal of Control, 91:1-23
September 2018

Zusammenfassung: Electrical ladder circuits, consisting of a series, or cascade, connection of cells are a class of spatially interconnected systems. These circuits can be modeled as 2D systems, i.e., there exist two directions of information propagation, where one indeterminate is time and the other the number of the current cell (node). In this paper, the recently developed direct (2D) approach to stability analysis and stabilization of these systems is extended to the presence of uncertainty in the models described by the norm bounded structure. The analysis is then further extended to the design of feedforward/feedback control action to track a spatially distributed time invariant reference signal in the presence of disturbances.

Zhu Weimeng; J. Siegemund; Anton Kummert
Dense Spatial Translation Network
2018 IEEE International Conference on Vehicular Electronics and Safety (ICVES)
September 2018
Matthias Buß; Yannik Steiniger; Stephan Benen; Dietmar Stiller; Dieter Kraus; Anton Kummert
Evaluation un terschiedlicher Klassifikationsalgorithmen zur Falschalarmreduktion in der Aktiv-Sonarortung
Jahrestagung für Akustik DAGA 2018
Juli 2018
Farzin Ghorban; Narges Milani; Daniel Schugk; Lutz Roese-Koerner; Yu Su; Dennis M; Anton Kummert
Conditional multichannel generative adversarial networks with an application to traffic signs representation learning
Progress in Artificial Intelligence, 8
April 2018

Zusammenfassung: Generative adversarial networks (GANs) are known to produce photorealistic representations. However, we show in this study that this is only valid when the input channels come from a regular RGB camera sensor. In order to alleviate this shortcoming, we propose a general solution to which we refer to as multichannel GANs (MCGANs). In contrast to the existing approaches, MCGANs can process multiple channels with different textures and resolutions. This is achieved by using known concepts in deep learning such as weight sharing and specially separated convolutions. The proposed pipeline enables particular kernels to learn low-level characteristics from the different channels without the need for exhaustive hyper-parameter tuning. We demonstrate the improved representational ability of the framework on traffic sign samples that are captured by a camera with a so-called red-clear-clear-clear pixel topology. Furthermore, we extend our solution by applying the concept of conditions, that offers a whole spectrum of new features, especially for the generation of traffic signs. Throughout this paper, we further discuss relevant applications for the generated synthetic data.

Farzin Ghorban; Javier Marin; Yu Su; Alessandro Colombo; Anton Kummert
Aggregated channels network for real-time pedestrian detection
, Seite 54.
April 2018
Cao Jiuwen; Anton Kummert; Lin Zhiping; Jörg Velten
Recent Advances in Machine Learning for Signal Analysis and Processing
Journal of The Franklin Institute, Special Issue, 355(4):1513-2066
März 2018
Jörg Velten; Anton Kummert; D. Wagner; K. Galkowski
A k-D Stability Measure for Discrete Roesser-Like System Implementations
Januar 2018
Bartlomiej Sulikowski; Anton Kummert
Feature investigation and control systems design for spatially interconnected systems
L. Kolonko; J. Velten; A. Kummert
Word Length Optimization of 2-D Wave Digital Filters with Weighted Quantization Error Variances
2018 IEEE International Symposium on Circuits and Systems (ISCAS) , Seite 1-5.

Schlüsselwörter: data compression;image coding;image filtering;optimisation;wave digital filters;weighted quantization error variances;finite word length optimization;shared memory bus width;arbitrary bus widths;2D-WDF;2D wave digital filters;magnitude truncation;image sizes;intuitive unbalanced approach;noise figure 23.0 dB;Quantization (signal);Optimization;Transfer functions;Computational modeling;Digital filters;Wave Digital Filter;Quantization;Magnitude Truncation;Optimization

C. Zimmer; N. Theuerkauf; D. Kraus; A. Kummert
Transmitter Pattern Optimization by Conformal Antenna Shape Design
OCEANS 2018 MTS/IEEE Charleston , Seite 1-5.

Schlüsselwörter: acoustic transducer arrays;conformal antennas;optimisation;sonar arrays;transmitters;low ripple characteristics;wide angle transmission characteristics;sonar array;constrainted numerical optimization;conformal antenna shape design;transmitter pattern optimization;radial component;transducer elements;Optimization;Transducers;Frequency measurement;Numerical models;Linear antenna arrays;Array signal processing;Simulation;numerical optimization;conformal transducer design;beamforming

M. Buß; Y. Steiniger; S. Benen; D. Kraus; A. Kummert; D. Stiller
Hand-Crafted Feature Based Classification against Convolutional Neural Networks for False Alarm Reduction on Active Diver Detection Sonar Data
OCEANS 2018 MTS/IEEE Charleston , Seite 1-7.

Schlüsselwörter: convolutional neural nets;feature extraction;image classification;sonar imaging;receiver-operating-characteristic curves;standard active signal processing;two-dimensional sonar images;feed forward neural network;automated feature extraction;contact classification;active diver detection sonar data;false alarm reduction;convolutional neural networks;hand-crafted feature based classification;Feature extraction;Sonar;Signal to noise ratio;Standards;Detectors;Signal processing algorithms;Active Sonar;contact Classification;deep Learning;false Alarm Reduction;neural Networks

T. Grunert; C. Schade; C. Michalik; S. Fielsch; L. Brandes; A. Kummert
ODESCA: A tool for control oriented modeling and analysis in MATLAB
2018 European Control Conference (ECC) , Seite 2959-2964.

Schlüsselwörter: control engineering computing;control system synthesis;nonlinear control systems;object-oriented programming;MATLAB;nonlinear systems;ODESCA tool;control oriented modeling;control oriented analysis;Mathematical model;Tools;Steady-state;Temperature sensors;Matlab;Computational modeling;Analytical models

I. Freeman; L. Roese-Koerner; A. Kummert
Effnet: An Efficient Structure for Convolutional Neural Networks
2018 25th IEEE International Conference on Image Processing (ICIP) , Seite 6-10.

Schlüsselwörter: convolution;feedforward neural nets;mobile hardware;binary networks;revised convolution layers;customer products;embedded hardware;convolutional neural networks;EffNet;MobileNet;ShuffleNet;Convolution;Computational modeling;Optimization;Hardware;Kernel;Data compression;Convolutional neural networks;convolutional neural networks;computational efficiency;real-time inference


Bartlomiej Sulikowski; Krzysztof Galkowski; Anton Kummert; Eric Rogers
Two-dimensional(2D) systems approarch to feedforward/feedback control of a classofspatially interconnected systems
Dezember 2017
Farzin Ghorban; Yu Su; Mirko Meuter; Anton Kummert
Insatiate boosted forest: Towards data exploitation in object detection
2017 13th IEEE International Conference on Intelligent Computer Communication and Processing (ICCP) , Seite 331 - 338.
November 2017
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