Due to the steadily increasing number of decentralized generation units, the upcoming smart meter rollout and the expected electrification of the transport sector (e-mobility), grid planning and grid operation at low-voltage (LV) level are facing major challenges. Therefore, many studies, research and demonstration projects on the above topics have been carried out in recent years, and the results and the methods developed have been published. However, the published methods usually cannot be replicated or validated, since the majority of the examination models or the scenarios used are incomprehensible to third parties. There is a lack of uniform grid models that map the German LV grids and can be used for comparative investigations, which are similar to the example of the North American distribution grid models of the IEEE. In contrast to the transmission grid, whose structure is known with high accuracy, suitable grid models for LV grids are difficult to map because of the high number of LV grids and distribution system operators. Furthermore, a detailed description of real LV grids is usually not available in scientific publications for data privacy reasons. For investigations within a research project, the most characteristic synthetic LV grid models have been created, which are based on common settlement structures and usual grid planning principles in Germany. In this work, these LV grid models, and their development are explained in detail. For the first time, comprehensible LV grid models for the middle European area are available to the public, which can be used as a benchmark for further scientific research and method developments. This document is an English version of the paper which was originally written in German1. In addition, this paper discusses a few more aspects especially on the planning process of distribution grids in Germany.

Beamforming performs spatial filtering to preserve the signal from given directions of interest while suppressing interfering signals and noise arriving from other directions. For example, a microphone array equipped with beamforming algorithm could preserve the sound coming from a target speaker and suppress sounds coming from other speakers. Beamformer has been widely used in many applications such as radar, sonar, communication, and acoustic systems. A data-independent beamformer is the beamformer whose coefficients are independent on sensor signals, it normally uses less computation since the coefficients are computed once. Moreover, its coefficients are derived from the well-defined statistical models, then it produces less artifacts. The major drawback of this beamforming class is its limitation to the interference suppression. On the other hand, an adaptive beamformer is a beamformer whose coefficients depend on or adapt to sensor signals. It is capable of suppressing the interference better than a data-independent beamforming but it suffers from either too much distortion of the signal of interest or less noise reduction when the updating rate of coefficients does not synchronize with the changing rate of the noise model. Besides, it is computationally intensive since the coefficients need to be updated frequently. In acoustic applications, the bandwidth of signals of interest extends over several octaves, but we always expect that the characteristic of the beamformer is invariant with regard to the bandwidth of interest. This can be achieved by the so-called broadband beamforming. Since the beam pattern of conventional beamformers depends on the frequency of the signal, it is common to use a dense and uniform array for the broadband beamforming to guarantee some essential performances together, such as frequency-independence, less sensitive to white noise, high directivity factor or high front-to-back ratio. In this dissertation, we mainly focus on the sparse array of which the aim is to use fewer sensors in the array, while simultaneously assuring several important performances of the beamformer. In the past few decades, many design methodologies for sparse arrays have been proposed and were applied in a variety of practical applications. Although good results were presented, there are still some restrictions, such as the number of sensors is large, the designed beam pattern must be fixed, the steering ability is limited and the computational complexity is high. In this work, two novel approaches for the sparse array design taking a hypothesized uniform array as a basis are proposed, that is, one for data-independent beamformers and the another for adaptive beamformers. As an underlying component of the proposed methods, the dissertation introduces some new insights into the uniform array with broadband beamforming. In this context, a function formulating the relations between the sensor coefficients and its beam pattern over frequency is proposed. The function mainly contains the coordinate transform and inverse Fourier transform. Furthermore, from the bijection of the function and broadband beamforming perspective, we propose the lower and upper bounds for the inter-distance of sensors. Within these bounds, the function is a bijective function that can be utilized to design the uniform array with broadband beamforming. For data-independent beamforming, many studies have focused on optimization procedures to seek the sparse array deployment. This dissertation presents an alternative approach to determine the location of sensors. Starting with a weight spectrum of a virtual dense and uniform array, some techniques are used, such as analyzing a weight spectrum to determine the critical sensors, applying the clustering technique to group the sensors into different groups and selecting representative sensors for each group. After the sparse array deployment is specified, the optimization technique is applied to find the beamformer coefficients. The proposed method helps to save the computation time in the design phase and its beamformer performance outperforms other state-of-the-art methods in several aspects such as the higher white noise gain, higher directivity factor or more frequency-independence. For adaptive beamforming, the dissertation attempts to design a versatile sparse microphone array that can be used for different beam patterns. Furthermore, we aim to reduce the number of microphones in the sparse array while ensuring that its performance can continue to compete with a highly dense and uniform array in terms of broadband beamforming. An irregular microphone array in a planar surface with the maximum number of distinct distances between the microphones is proposed. It is demonstrated that the irregular microphone array is well-suited to sparse recovery algorithms that are used to solve underdetermined systems with subject to sparse solutions. Here, a sparse solution is the sound source's spatial spectrum that need to be reconstructed from microphone signals. From the reconstructed sound sources, a method for array interpolation is presented to obtain an interpolated dense and uniform microphone array that performs well with broadband beamforming. In addition, two alternative approaches for generalized sidelobe canceler (GSC) beamformer are proposed. One is the data-independent beamforming variant, the other is the adaptive beamforming variant. The GSC decomposes beamforming into two paths: The upper path is to preserve the desired signal, the lower path is to suppress the desired signal. From a beam pattern viewpoint, we propose an improvement for GSC, that is, instead of using the blocking matrix in the lower path to suppress the desired signal, we design a beamformer that contains the nulls at the look direction and at some other directions. Both approaches are simple beamforming design methods and they can be applied to either sparse array or uniform array. Lastly, a new technique for direction-of-arrival (DOA) estimation based on the annihilating filter is also presented in this dissertation. It is based on the idea of finite rate of innovation to reconstruct the stream of Diracs, that is, identifying an annihilating filter/locator filter for a few uniform samples and the position of the Diracs are then related to the roots of the filter. Here, an annihilating filter is the filter that suppresses the signal, since its coefficient vector is always orthogonal to every frame of signal. In the DOA context, we regard an active source as a Dirac associated with the arrival direction, then the directions of active sources can be derived from the roots of the annihilating filter. However, the DOA obtained by this method is sensitive to noise and the number of DOAs is limited. To address these issues, the dissertation proposes a robust method to design the annihilating filter and to increase the degree-of-freedom of the measurement system (more active sources can be detected) via observing multiple data frames. Furthermore, we also analyze the performance of DOA with diffuse noise and propose an extended multiple signal classification algorithm that takes diffuse noise into account. In the simulation, it shows, that in the case of diffuse noise, only the extended multiple signal classification algorithm can estimate the DOAs properly.

We introduce Property-Driven Design, a tool-flow that guarantees formal soundness be- tween ESL and RTL and thus enables a shift-left of general functional verification by moving HW verification to higher abstraction layers. In addition, by generating a formal Verification IP (VIP) automatically from ESL descriptions, the entry hurdle to formal methods is reduced considerably, opening them to a wider audience, which effectively ‘democratizes’ them. Short feedback cycles reduce time spent on RTL verification and lead to higher-quality designs.

Bees are recognized as an indispensable link in the human food chain and general ecological system. Numerous threats, from pesticides to parasites, endanger bees and frequently lead to hive collapse. The varroa destructor mite is a key threat to bee keeping and the monitoring of hive infestation level is of major concern for effective treatment. Sensors and automation, e.g., as in condition-monitoring and Industry 4.0, with machine learning offer help. In numerous activities a rich variety of sensors have been applied to apiary/hive instrumentation and bee monitoring. Quite recent activities try to extract estimates of varroa infestation level by hive air analysis based on gas sensing and gas sensor systems. In our work in the IndusBee4.0 project [8, 11], an hive-integrated, compact autonomous gas sensing system for varroa infestation level estimation based on low- cost highly integrated gas sensors was conceived and applied. This paper adds to [11] with the first results of a mid-term duration investigation from July to September 2020 until formic acid treatment. For the regarded hive more than 79 % of detection probability based on the SGP30 gas sensor readings have been achieved.

Code coverage analysis plays an important role in the software testing process. More recently, the remarkable effectiveness of coverage feedback has triggered a broad interest in feedback-guided fuzzing. In this work, we discuss static instrumentation techniques for binary-level coverage analysis without compiler support. We show that the proposed techniques are precise, efficient, and transparent significantly beyond the state of the art. We implement these techniques into two tools, namely, Spedi and bcov. Both tools are open source and publicly available. Spedi shows that the disassembly and function identification of stripped binaries can be highly accurate without resort to any external information. We build on these important results in bcov where we statically instrument x86-64 ELF binaries to track code coverage. However, improving efficiency and scaling to large real-world software required an orchestrated effort combining several techniques. First, we bring a well-known probe pruning technique, for the first time, to binary-level instrumentation and effectively leverage its notion of superblocks to reduce overhead. Second, we introduce sliced microexecution, a robust technique for jump table analysis which improves CFG precision and enables us to instrument jump table entries. Additionally, smaller instructions in x86-64 pose a challenge for inserting detours. To address this challenge, we aggressively exploit padding bytes. Also, we introduce a greedy scheme to systematically host detours in neighboring basic blocks. We evaluate bcov on a corpus of 95 binaries compiled from eight popular and well-tested packages like FFmpeg and LLVM. Two instrumentation policies, with different edge-level precision, are used to patch all functions in this corpus - over 1.6 million functions. Our precise policy has average performance and memory overheads of 14% and 22%, respectively. Instrumented binaries do not introduce any test regressions. The reported coverage is highly accurate with an average F-score of 99.86%. Finally, our jump table analysis is comparable to that of IDA Pro on gcc binaries and outperforms it on clang binaries. Our work demonstrates that static instrumentation can offer unique advantages in comparison to established methods like compiler instrumentation and dynamic binary instrumentation. It also opens the door for many interesting applications of static instrumentation, which can go well beyond coverage analysis.