Contemporary motion picture content, consisting of scenes with different amounts of visual complexity or camera noise, represents demanding input for video encoders operating in rate control (RC) modes. This paper presents improvements to the 2-pass RC method integrated into VVenC, an open VVC encoder implementation, outlined in previous publications. We specifically introduce three extensions to our RC solution: first, frame type adaptation operating near scene cuts, along with an associated simple detector; second, rate stabilization means to allow for more reliable lookahead based 2-pass RC operation in on-the-fly encoding applications; and third, a low-complexity approach for estimating the instantaneous intensity of camera noise or film grain to avoid large variations in bit consumption when encoding individual frames in the final RC pass. Experimental evaluation confirms that these extensions significantly improve both the objective (BD rate) and subjective (visual) RC performance of VVenC especially on challenging video content.

Versatile Video Coding (VVC) is the most recent international video coding standard jointly developed by ITU-T and ISO/IEC, which has been finalized in July 2020. VVC allows for significant bit-rate reductions around 50% for the same subjective video quality compared to its predecessor, High Efficiency Video Coding (HEVC). One year after finalization, VVC support in devices and chipsets is still under development, which is aligned with the typical development cycles of new video coding standards. This paper presents open-source software packages that allow building a complete VVC end-to-end toolchain already one year after its finalization. This includes the Fraunhofer HHI VVenC library for fast and efficient VVC encoding as well as HHI's VVdeC library for live decoding. An experimental integration of VVC in the GPAC software tools and FFmpeg media framework allows packaging VVC bitstreams, e.g. encoded with VVenC, in MP4 file format and using DASH for content creation and streaming. The integration of VVdeC allows playback on the receiver. Given these packages, step-by-step tutorials are provided for two possible application scenarios: VVC file encoding plus playback and adaptive streaming with DASH.

Versatile Video Coding (VVC) is the emerging video coding standard to be finalized by the Joint Video Experts Team in July 2020. Compared to its predecessor, the High Efficiency Video Coding (HEVC) standard, VVC provides 50% bit-rate reduction at comparable visual quality for natural video content in high-definition (HD) and ultra high-definition (UHD) resolution. To achieve this, the standard incorporates more advanced and generalized algorithms, leading to an increase in computational complexity. This includes for example additional in-loop filters, decoder-side motion refinement and search as well as an increased number of transforms, which creates a grand challenge for implementers to achieve live decoding on general-purpose CPUs. In this paper, the work on an efficient software decoder implementation for the upcoming VVC standard is described, including optimization of sample operations using single instruction multiple data (SIMD) instructions and parallelization approaches with multithreading. As a result, the presented decoder can perform live decoding of 10bit HD video at 60 frames per second (fps) and 10bit UHD video at 30fps on modern mobile consumer hardware, showcasing that VVC live decoding is possible already right before finalization of the standard.

An emerging field in medical diagnostics is the study of micro-circulations in blood vessels. Several characteristics of the micro-circulations in blood vessels have been shown to predict inflammation in a patient's tissue. The characteristics are video recorded via a camera inserted into the subject. At present, the analysis of the videos are done manually by visual inspection to determine inflammation. In our paper, we propose a technique to automatically classify the videos as containing inflammation or not. Our technique uses a convolutional neural network which classifies many different segments of images from a video and averages the predictions. Our network achieves an accuracy of 83%. We further divide inflammation into extreme and moderate inflammation and our network achieves an accuracy of 80%. This is the first step in developing methods that can perform a better quantitative analysis of inflammation to speed up medical diagnosis.

Understanding the differences underlying the scope, usage and content of language data requires the provision of a clarifying terminological basis which is integrated in the metadata describing a particular language resource. While terminological resources such as the SIL Glossary of Linguistic Terms, ISOcat or the GOLD ontology provide a considerable amount of linguistic terms, their practical usage is limited to a look up of a defined term whose relation to other terms is unspecified or insufficient. Therefore, in this paper we propose an ontology for linguistic terminology, called OnLiT. It is a data model which can be used to represent linguistic terms and concepts in a semantically interrelated data structure and, thus, overcomes prevalent isolating definition-based term descriptions. OnLiT is based on the LiDo Glossary of Linguistic Terms and enables the creation of RDF datasets, that represent linguistic terms and their meanings within the whole or a subdomain of linguistics.

In this paper the developmental process of a flexible robot-based assembly cell by a research institute in cooperation with the cells end-user is described. The developed cell contains novel processes-such as the assembly of parts with only minimal geometrical clearance-and is capable of producing a large number of product variations. Additionally a lean feeding method to process loosely supplied components was implemented as part of the setup. At the end the validation of the prototype cell within the real production environment is discussed. To fulfill cycle time constraints some changes to the cell concept were necessary for the final production cell, which therefore lacks some of the flexibility of the initial prototype.

Industrial robots offer a good basis for machining from a conceptual point of view. Still they are rarely utilized for machining applications in industry compared to CNC machines due to their low stiffness and the bad achievable work piece quality. Available compensation approaches, like online compensation approaches to increase position accuracy using costly additional hardware and measurement equipment as well as offline compensation approaches using a set of empirical measurement data and models to predict deviation, try to compensate errors whether to already avoid them if possible. In this paper milling and robot strategies are proposed to increase work piece quality without additional hardware or models. Experimental validations of the results have been performed for different kinds of shapes and materials.

Industrial robots offer a good basis for machining from a conceptual point of view. However they are rarely utilized for machining applications in industry due to their low stiffness and the bad achievable work piece quality. Available solutions using position control of the tool require costly additional hardware and measurement equipment; force controlled solutions depend on low level controller access that is not commonly available for generic cell setups. This paper proposes a three-step approach to compensate for process force induced accuracy errors: (1) selection of appropriate milling strategies and cutting parameters, (2) an offline compensation of the force induced deviations and (3) a respective online compensation method. Experimental validation of the results has been performed for the first two steps.

Neural networks require VLSI implementations for on-board systems. Size and real-time considerations show that on-chip learning is necessary for a large range of applications. A flexible digital design is preferred here to more compact analog or optical realizations. As opposed to many current implementations, the two-dimensional systolic array system presented is an attempt to define a novel computer architecture inspired by neurobiology. It is composed of generic building blocks for basic operations rather than predefined neural models. A full custom VLSI design of a first prototype has demonstrated the efficacy of this design. A complete board dedicated to Hopfield's model has been designed using these building blocks. Beyond the very specific application presented, the underlying principles can be used for designing efficient hardware for most neural network models.