@article{LangmannMichael2019, author = {Langmann, Reinhard and Michael, Stiller}, title = {The PLC as a Smart Service in Industry 4.0 Production System}, series = {Applied Sciences}, volume = {9}, journal = {Applied Sciences}, number = {18}, publisher = {MDPI}, issn = {2076-3417}, doi = {10.3390/app9183815}, year = {2019}, language = {en} } @inproceedings{EchternachtSchermuly2019, author = {Echternacht, David and Schermuly, Rainer}, title = {Simulation der Auswirkungen privater Ladeinfrastruktur auf Niederspannungsnetze}, series = {Anlagentechnik 2020}, booktitle = {Anlagentechnik 2020}, editor = {Cichowski, Rolf R{\"u}diger}, edition = {Neuerscheinung}, publisher = {VDE Verlag}, address = {Berlin}, isbn = {978-3-8007-4838-9}, pages = {149 -- 158}, year = {2019}, language = {de} } @article{LehmannWittpahlZakouretal.2019, author = {Lehmann, Matthias and Wittpahl, Christian and Zakour, Hatem Ben and Braun, Alexander}, title = {Resolution and accuracy of nonlinear regression of point spread function with artificial neural networks}, series = {Optical Engineering}, volume = {58}, journal = {Optical Engineering}, number = {4}, publisher = {SPIE}, issn = {0091-3286}, doi = {10.1117/1.oe.58.4.045101}, pages = {045101}, year = {2019}, abstract = {We had already demonstrated a numerical model for the point spread function (PSF) of an optical system that can efficiently model both the experimental measurements and the lens design simulations of the PSF. The novelty lies in the portability and the parameterization of this model, which allow for completely new ways to validate optical systems, which is especially interesting not only for mass production optics such as in the automotive industry but also for ophthalmology. The numerical basis for this model is a nonlinear regression of the PSF with an artificial neural network (ANN). After briefly describing both the principle and the applications of the model, we then discuss two optically important aspects: the spatial resolution and the accuracy of the model. Using mean squared error (MSE) as a metric, we vary the topology of the neural network, both in the number of neurons and in the number of hidden layers. Measurement and simulation of a PSF can have a much higher spatial resolution than the typical pixel size used in current camera sensors. We discuss the influence this has on the topology of the ANN. The relative accuracy of the averaged pixel MSE is below 10  -  4, thus giving confidence that the regression does indeed model the measurement data with good accuracy. This article is only the starting point, and we propose several research avenues for future work.}, language = {en} } @inproceedings{MuellerLehmannBraun2019, author = {M{\"u}ller, Patrick and Lehmann, Matthias and Braun, Alexander}, title = {Optical quality metrics for image restoration}, series = {Digital Optical Technologies 2019}, volume = {Proceedings, Vol. 11062}, booktitle = {Digital Optical Technologies 2019}, editor = {Kress, Bernard C. and Schelkens, Peter}, address = {Munich}, organization = {International Society for Optics and Photonics}, doi = {10.1117/12.2528100}, pages = {1106214}, year = {2019}, abstract = {Image restoration is a process used to remove blur (from different sources like object motion or aberrations) from images by either non-blind or blind-deconvolution. The metrics commonly used to quantify the restoration process are peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). Often only a small sample of test images are used (like Lena or the camera guy). In optical design research PSNR and SSIM are not normally used, here image quality metrics based on linear system theory (e.g. modulation transfer function, MTF) are used to quantify optical errors like spherical or chromatic aberration. In this article we investigate how different image restoration algorithms can be quantified by applying image quality metrics. We start with synthetic image data that is used in camera test stands (e.g. Siemens star etc.), apply two different spatially variant degradation algorithms, and restore the original image by a direct method (Wiener filtering within sub-images), and by an iterative method (alternating direction method of multipliers, ADMM). Afterwards we compare the quality metrics (like MTF curves) for the original, the degraded and the restored image. As a first result we show that restoration algorithms sometimes fail in dealing with non-natural scenes, e.g. slanted-edge targets. Further, these first results indicate a correlation between degradation and restoration, i.e. the restoration algorithms are not capable of removing the optically relevant errors introduced by the degradation, a fact neither visible nor available from the PSNR values. We discuss the relevance in the context of the automotive industry, where image restoration may yield distinct advantages for camera-based applications, but testing methods rely on the used image quality metrics.}, language = {en} } @article{RichterSteenmannSchellscheidtetal.2019, author = {Richter, Jessica and Steenmann, Anna and Schellscheidt, Benjamin and Licht, Thomas}, title = {On-Chip Diffusion Bonding creates Stable Interconnections Usable at Temperatures over 300°C}, series = {International Symposium on Microelectronics}, volume = {2019}, journal = {International Symposium on Microelectronics}, number = {1}, publisher = {IMAPS}, organization = {IMAPS}, doi = {10.4071/2380-4505-2019.1.000530}, pages = {000530 -- 000534}, year = {2019}, language = {en} } @article{LehmannWittpahlZakouretal.2019, author = {Lehmann, Matthias and Wittpahl, Christian and Zakour, Hatem Ben and Braun, Alexander}, title = {Modeling realistic optical aberrations to reuse existing drive scene recordings for autonomous driving validation}, series = {Journal of Electronic Imaging}, volume = {28}, journal = {Journal of Electronic Imaging}, number = {1}, publisher = {SPIE}, issn = {1560-229X}, doi = {10.1117/1.JEI.28.1.013005}, pages = {013005}, year = {2019}, abstract = {Training autonomous vehicles requires lots of driving sequences in all situations. Collecting and labeling these drive scenes is a very time-consuming and expensive process. Currently, it is not possible to reuse these drive scenes with different optical properties, because there exists no numerically efficient model for the transfer function of the optical system. We present a numerical model for the point spread function (PSF) of an optical system that can efficiently model both experimental measurements and lens design simulations of the PSF. The numerical basis for this model is a nonlinear regression of the PSF with an artificial neural network. The novelty lies in the portability and the parameterization of this model. We present a lens measurement series, yielding a numerical function for the PSF that depends only on the parameters defocus, field, and azimuth. By convolving existing images and videos with this PSF, we generate images as if seen through the measured lens. The methodology applies to any optical scenario, but we focus on the context of autonomous driving, where the quality of the detection algorithms depends directly on the optical quality of the used camera system. With this model, it is possible to reuse existing recordings, with the potential to avoid millions of test drive miles. The parameterization of the optical model allows for a method to validate the functional and safety limits of camera-based advanced driver assistance systems based on the real, measured lens actually used in the product.}, language = {en} } @misc{OPUS4-2098, title = {687 - Zweite Satzung zur {\"A}nderung der Pr{\"u}fungsordnung f{\"u}r den Masterstudiengang Elektro- und Informationstechnik an der Hochschule D{\"u}sseldorf vom 19.12.2019}, address = {D{\"u}sseldorf}, organization = {Hochschule D{\"u}sseldorf}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-20988}, pages = {3}, year = {2019}, abstract = {Aufgrund der \S\S 2 Abs. 4, 64 Abs. 1 des Gesetzes {\"u}ber die Hochschulen des Landes Nordrhein-Westfalen (Hochschulgesetz - HG) vom 16.09.2014 (GV. NRW. S. 547) in der aktuell g{\"u}ltigen Fassung hat die Hochschule D{\"u}sseldorf die folgende Ordnung als Satzung erlassen.}, subject = {Pr{\"u}fungsrecht}, language = {de} } @misc{OPUS4-2097, title = {686 - Zweite Satzung zur {\"A}nderung der Pr{\"u}fungsordnung f{\"u}r den Bachelorstudiengang Wirtschaftsingenieurwesen Elektrotechnik an der Hochschule D{\"u}sseldorf vom 19.12.2019}, address = {D{\"u}sseldorf}, organization = {Hochschule D{\"u}sseldorf}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-20970}, pages = {3}, year = {2019}, abstract = {Aufgrund der \S\S 2 Abs. 4, 64 Abs. 1 des Gesetzes {\"u}ber die Hochschulen des Landes Nordrhein-Westfalen (Hochschulgesetz - HG) vom 16.09.2014 (GV. NRW. S. 547) in der aktuell g{\"u}ltigen Fassung hat die Hochschule D{\"u}sseldorf die folgende Ordnung als Satzung erlassen.}, subject = {Pr{\"u}fungsrecht}, language = {de} } @misc{OPUS4-2096, title = {685 - F{\"u}nfte Satzung zur {\"A}nderung der Pr{\"u}fungsordnung f{\"u}r den Bachelor-Studiengang „Wirtschaftsingenieur Elektrotechnik" (WIE) an der Hochschule D{\"u}sseldorf vom 19.12.2019}, address = {D{\"u}sseldorf}, organization = {Hochschule D{\"u}sseldorf}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-20964}, pages = {3}, year = {2019}, abstract = {Aufgrund der \S\S 2 Abs. 4, 64 Abs. 1 des Gesetzes {\"u}ber die Hochschulen des Landes Nordrhein-Westfalen (Hochschulgesetz - HG) vom 16.09.2014 (GV. NRW. S. 547) in der aktuell g{\"u}ltigen Fassung hat die Hochschule D{\"u}sseldorf die folgende Ordnung als Satzung erlassen.}, subject = {Pr{\"u}fungsrecht}, language = {de} } @misc{OPUS4-2095, title = {684 - Zweite Satzung zur {\"A}nderung der Pr{\"u}fungsordnung f{\"u}r den dualen Bachelorstudiengang „Kommunikations- und Informationstechnik" an der Hochschule D{\"u}sseldorf vom 19.12.2019}, address = {D{\"u}sseldorf}, organization = {Hochschule D{\"u}sseldorf}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-20959}, pages = {3}, year = {2019}, abstract = {Aufgrund der \S\S 2 Abs. 4, 64 Abs. 1 des Gesetzes {\"u}ber die Hochschulen des Landes Nordrhein-Westfalen (Hochschulgesetz - HG) vom 16.09.2014 (GV. NRW. S. 547) in der aktuell g{\"u}ltigen Fassung hat die Hochschule D{\"u}sseldorf die folgende Ordnung als Satzung erlassen.}, subject = {Pr{\"u}fungsrecht}, language = {de} }