Browsing by Author "Frenner, Karsten"

Now showing 1 - 4 of 4

Open Access
Data-driven development of sparse multi-spectral sensors for urological tissue differentiation
(2023) Fischer, Felix; Frenner, Karsten; Granai, Massimo; Fend, Falko; Herkommer, Alois
Open Access
Evaluation of a time-gated-single-pixel-camera as a promising sensor for autonomous vehicles in harsh weather conditions
(2023) Bett, Claudia Monika; Daiber-Huppert, Max; Frenner, Karsten; Osten, Wolfgang
Open Access
FeaSel-Net : a recursive feature selection callback in neural networks
(2022) Fischer, Felix; Birk, Alexander; Somers, Peter; Frenner, Karsten; Tarín, Cristina; Herkommer, Alois
Selecting only the relevant subsets from all gathered data has never been as challenging as it is in these times of big data and sensor fusion. Multiple complementary methods have emerged for the observation of similar phenomena; oftentimes, many of these techniques are superimposed in order to make the best possible decisions. A pathologist, for example, uses microscopic and spectroscopic techniques to discriminate between healthy and cancerous tissue. Especially in the field of spectroscopy in medicine, an immense number of frequencies are recorded and appropriately sized datasets are rarely acquired due to the time-intensive measurements and the lack of patients. In order to cope with the curse of dimensionality in machine learning, it is necessary to reduce the overhead from irrelevant or redundant features. In this article, we propose a feature selection callback algorithm (FeaSel-Net) that can be embedded in deep neural networks. It recursively prunes the input nodes after the optimizer in the neural network achieves satisfying results. We demonstrate the performance of the feature selection algorithm on different publicly available datasets and compare it to existing feature selection methods. Our algorithm combines the advantages of neural networks’ nonlinear learning ability and the embedding of the feature selection algorithm into the actual classifier optimization.
Open Access
Polarization scramblers with plasmonic meander-type metamaterials
(2012) Schau, Philipp; Fu, Liwei; Frenner, Karsten; Schäferling, Martin; Schweizer, Heinz; Giessen, Harald; Gaspar Venancio, Luis Miguel; Osten, Wolfgang
Due to plasmonic excitations, metallic meander structures exhibit an extraordinarily high transmission within a well-defined pass band. Within this frequency range, they behave like almost ideal linear polarizers, can induce large phase retardation between s- and p-polarized light and show a high polarization conversion efficiency. Due to these properties, meander structures can interact very effectively with polarized light. In this report, we suggest a novel polarization scrambler design using spatially distributed metallic meander structures with random angular orientations. The whole device has an optical response averaged over all pixel orientations within the incident beam diameter. We characterize the depolarizing properties of the suggested polarization scrambler with the Mueller matrix and investigate both single layer and stacked meander structures at different frequencies. The presented polarization scrambler can be flexibly designed to work at any wavelength in the visible range with a bandwidth of up to 100 THz. With our preliminary design, we achieve depolarization rates larger than 50% for arbitrarily polarized monochromatic and narrow-band light. Circularly polarized light could be depolarized by up to 95% at 600 THz.