Background: For a precise description of the emission situation of the anaerobic digestion (AD) of the separately collected organic fraction of household waste (bio-waste), only a few data are available. The paper presents the greenhouse gas (GHG) emissions measured at 12 representative AD plants treating bio-waste. The results of the emission measurements were used to assess the ecological impact of bio-waste digestion and to describe possible mitigation measures to reduce the occurring GHG emissions. With respect to the climate protection, a quantitative assessment of the emissions of energy generation from biomass and biological waste treatment is important. Biogas plants need to be operated in a way that negative environmental effects are avoided and human health is not compromised. Methods: GHG balances were calculated based on the measured emissions of the gases methane, nitrous oxide, and ammonia of bio-waste AD plants. The emission analysis supports the reduction of GHGs in biogas production and contributes to a climate-efficient technology. Results: The results show that GHG emissions can be minimized, if the technology and operation of the plant are adjusted accordingly. The open storage of active material (e.g., insufficient fermented residues from batch fermentation systems), open digestate storage tanks, missing acidic scrubbers in front of bio-filters, or insufficient air supply during the post-composting of digestate can cause relevant GHG emissions. Conclusions: Consequently avoiding open storage of insufficient fermented residues and using aerated post-composting with short turnover periods, smaller heaps, and an optimized amount of structure (woody) material can reduce GHG emissions.

Millimeterwaves offer the capability to look through dielectric material. Wound healing processes often have to take place under a cover and especially in the case of broken limbs plaster of Paris bandages have to be removed for inspection. Investigations were done to evaluate the possibility of wound monitoring through plasters

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Cameras operating in the visual range of the electromagnetic spectrum are central to advanced driver assistance systems (ADAS). Front cameras, analyzing traffic, are often located behind the windshield to detect and classify objects.Thus, the area of the windshield within the camera’s field of view is a part of the optical system. Simple windshields consist of two curved glass surfaces connected by a thermoplastic interlayer. Due to defects present in the raw glass, as well as those introduced during the bending and lamination process, windshields will have optical aberrations. While optical quality may be suitable for human vision, it can fall short of what is needed for machine vision. In this article we investigate how the optical aberrations generated by laminated safety glass (LSG) influence the optical performance of a camera system and based on this, how the classification of image content by a convolutional neural network (CNN) is affected. A method for wavefront measurements of LSG samples is presented, which allows us to parameterize a linear optical model in Zernike Space. From this, we derive space-variant point spread functions (PSFs) and apply those to the dataset to simulate the windshield’s impact on the camera image. As a use case, a CNN was trained on the unmodified dataset and compared to the modified versions with the LSG models applied. We measured and modelled two different LSG samples, one with high and the other one with low optical quality. We compare the prediction accuracy of the classification with the unmodified data. The highquality sample had negligible effect on the overall classification accuracy, while the low-quality sample lowered the prediction accuracy by up to ten percentage points due to the optical aberrations.

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