Lehrstuhl für Allgemeine Elektrotechnik und Theoretische Nachrichtentechnik
Prof. Dr.- Ing. Anton Kummert
Publikationen
| 366. |
Driver State Monitoring with Hierarchical Classification
2018 IEEE International Conference on Intelligent Transportation Systems (ITSC)
November
2018
|
| 365. |
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. |
| 364. |
Dense Spatial Translation Network
2018 IEEE International Conference on Vehicular Electronics and Safety (ICVES)
September
2018
|
| 363. |
Evaluation un terschiedlicher Klassifikationsalgorithmen zur Falschalarmreduktion in der Aktiv-Sonarortung
Jahrestagung für Akustik DAGA 2018
Juli
2018
|
| 362. |
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. |
| 361. |
Aggregated channels network for real-time pedestrian detection
, Seite 54.
April
2018
DOI: 10.1117/12.2309864
|
| 360. |
Recent Advances in Machine Learning for Signal Analysis and Processing
Journal of The Franklin Institute, Special Issue,
355(4):1513-2066
März
2018
|
| 359. |
A k-D Stability Measure for Discrete Roesser-Like System Implementations
Januar
2018
|
| 358. |
Feature investigation and control systems design for spatially interconnected systems
2018
|
| 357. |
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.
2018
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 |
| 356. |
Transmitter Pattern Optimization by Conformal Antenna Shape Design
OCEANS 2018 MTS/IEEE Charleston
, Seite 1-5.
2018
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 |
| 355. |
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.
2018
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 |
| 354. |
ODESCA: A tool for control oriented modeling and analysis in MATLAB
2018 European Control Conference (ECC)
, Seite 2959-2964.
2018
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 |
| 353. |
Effnet: An Efficient Structure for Convolutional Neural Networks
2018 25th IEEE International Conference on Image Processing (ICIP)
, Seite 6-10.
2018
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 |