@article{19313, abstract = {{The increasingly simulation-driven design process of ultrasonic transducers requires several reliable parameters for the description of the material behaviour. Exact results can only be achieved when a single specimen is used in the identification process, which typically is prone to the problem of low sensitivities to certain material parameters and thus high uncertainties. Therefore, a custom electrode topology for increased sensitivity is proposed for a piezoceramic disc. The thereupon conducted measurements of the electric impedance can be used as a starting point for an inverse approach where an equivalent simulation model is used to identify fitting material parameters. An optimisation strategy based on a preliminary sensitivity analysis is presented that leads to a good agreement between measurement and simulation. Furthermore, the proposed measurement procedure is able to evaluate the quality of the simulation model. Hence, different frequency-dependent damping models are presented and evaluated.}}, author = {{Feldmann, Nadine and Schulze, Veronika and Claes, Leander and Jurgelucks, Benjamin and Walther, Andrea and Henning, Bernd}}, issn = {{2196-7113}}, journal = {{tm - Technisches Messen}}, pages = {{50--55}}, title = {{{Inverse piezoelectric material parameter characterization using a single disc-shaped specimen}}}, doi = {{10.1515/teme-2020-0012}}, year = {{2020}}, } @inproceedings{24053, abstract = {{We overview the 3-year Meteracom project which will provide traceability to the SI for THz communication measurement parameters. The key objectives are to develop new metrological methods to characterize the measurement systems, system components and propagation channels. The final objective is to develop metrology for functionality and signal integrity of THz communication systems; particularly device discovery and beam tracking, determination of physical layer parameters for digital transmission and real-time performance evaluation.}}, author = {{Humphreys, David and Berekovic, Mladen and Kallfass, Ingmar and Scheytt, Christoph and Kuerner, Thomas and Jukan, Admela and Schneider, Thomas and Kleine-Ostmann, Thomas and Koch, Martin and Thomae, Reiner}}, booktitle = {{Proc. 43-nd Meeting of the Wireless World Research Forum (WWRF)",}}, title = {{{An overview of the Meteracom Project}}}, year = {{2019}}, } @inproceedings{24058, abstract = {{Embedded systems require a high energy efficiency in combination with an optimized performance. As such, Bit Manipulation Instructions (BMIs) were introduced for x86 and ARMv8 to improve the runtime efficiency and power dissipation of the compiled software for various applications. Though the RISC-V platform is meanwhile widely accepted for embedded systems application, its instruction set architecture (ISA) currently still supports only two basic BMIs.We introduce ten advanced BMIs for the RISC-V ISA and implemented them on Berkeley's Rocket CPU [1], which we synthesized for the Artix-7 FPGA and the TSMC 65nm cell library. Our RISC-V BMI definitions are based on an analysis and combination of existing x86 and ARMv8 BMIs. Our Rocket CPU hardware extensions show that RISC-V BMI extensions have no negative impact on the critical path of the execution pipeline. Our software evaluations show that we can, for example, expect a significant impact for time and power consuming cryptographic applications.}}, author = {{Koppelmann, Bastian and Adelt, Peer and Müller, Wolfgang and Scheytt, Christoph}}, booktitle = {{29th International Symposium on Power and Timing Modeling, Optimization and Simulation (PATMOS)}}, title = {{{RISC-V Extensions for Bit Manipulation Instructions}}}, doi = {{10.1109/PATMOS.2019.8862170}}, year = {{2019}}, } @inproceedings{24060, abstract = {{In diesem Artikel stellen wir eine Methode zur nicht-invasiven dynamischen Speicher- und IO-Analyse mit QEMU für sicherheitskritische eingebettete Software für die RISC-V Befehlssatzarchitektur vor. Die Implementierung basiert auf einer Erweiterung des Tiny Code Generator (TCG) des quelloffenen CPU-Emulators QEMU um die dynamische Identifikation von Zugriffen auf Datenspeicher sowie auf an die CPU angeschlossene IO-Geräte. Wir demonstrieren die Funktionalität der Methode anhand eines Versuchsaufbaus, bei dem eine Schließsystemkontrolle mittels serieller UART-Schnittstelle an einen RISC-V-Prozessor angebunden ist. Dieses Szenario zeigt, dass ein unberechtigter Zugriff auf die UART-Schnittstelle frühzeitig aufgedeckt und ein Angriff auf eine Zugangskontrolle somit endeckt werden kann. }}, author = {{Adelt, Peer and Koppelmann, Bastian and Müller, Wolfgang and Scheytt, Christoph}}, booktitle = {{MBMV 2019-22.Workshop Methoden und Beschreibungssprachen zur Modellierung und Verifikation von Schaltungen und Systemen (MBMV 2019)}}, isbn = {{978-3-8007-4945-4}}, title = {{{Analyse sicherheitskritischer Software für RISC-V Prozessoren}}}, year = {{2019}}, } @inproceedings{24061, author = {{Adelt, Peer and Koppelmann, Bastian and Müller, Wolfgang and Scheytt, Christoph and Driessen, Benedikt}}, booktitle = {{ 2nd International Workshop on Embedded Software for Industrial IoT in conjunction with DATE 2019}}, pages = {{32--34}}, title = {{{QEMU for Dynamic Memory Analysis of Security Sensitive Software}}}, year = {{2019}}, } @article{24063, abstract = {{It its current Version 3.1.0 QEMU supports RISC-V RV32GC and RV64GC software emulation in user and full system mode. We will first give an overview of the current state of the QEMU RISC-V implementation. Thereafter, we will present the DecodeTree tool, which will be available with the next QEMU release. DecodeTree is a code generator included in QEMU that can generate the program logic for extracting and decoding opcodes and operands from a formal instruction list of the target architecture. This enables the structured implementation of just-in-time compilations to guarantee that the QEMU implementation meets the ISA specification. As such, we completely replaced the existing RISC-V RV32GC and RV64GC implementations by DecodeTree generations in the next official QEMU release, which is expected in spring 2019. We will demonstrate the DecodeTree applications by the example of RISC-V ISA subset configurations.}}, author = {{Adelt, Peer and Koppelmann, Bastian and Müller, Wolfgang and Scheytt, Christoph}}, journal = {{2nd International Workshop on RISC-V Research Activities}}, title = {{{QEMU Support for RISC-V: Current State and Future Releases}}}, volume = {{(Presentation)}}, year = {{2019}}, } @inproceedings{21524, abstract = {{For the measurement of process data in bioreactors, very small wireless sensors are currently under development to replace the conventional rod probes. The so-called Sens-o-Spheres measure the temperature and in future the oxygen content and the pH of fluids. In order to evaluate the distribution of the measured values within the process, it is necessary to locate the wireless sensors. Because of the small size of the sphere (diameter 8 mm), inhomogeneous ambient media and the size of the reactor (less than 2 m), an inductive locating by magnetic fields with a frequency of f = 13.56 MHz is necessary. Since the behaviour of the magnetic field is very different from that of the electromagnetic wave, new locating methods are required, which are presented in this paper.}}, author = {{Lange, Sven and Schröder, Dominik and Hedayat, Christian and Otto, Thomas and Hilleringmann, Ulrich}}, booktitle = {{2019 17th IEEE International New Circuits and Systems Conference (NEWCAS)}}, isbn = {{9781728110318}}, keywords = {{oxygen content, inhomogeneous ambient media, magnetic field, inductive locating method, miniaturized wireless sensors, inhomogeneous dielectrics, conventional rod probes, Sens-o-Spheres measure, frequency 13.56 MHz}}, location = {{Munich, Germany}}, title = {{{Inductive Locating Method to Locate Miniaturized Wireless Sensors within Inhomogeneous Dielectrics}}}, doi = {{10.1109/newcas44328.2019.8961227}}, year = {{2019}}, } @article{17762, abstract = {{Abstract Wenn akustische Signalverarbeitung mit automatisiertem Lernen verknüpft wird: Nachrichtentechniker arbeiten mit mehreren Mikrofonen und tiefen neuronalen Netzen an besserer Spracherkennung unter widrigsten Bedingungen. Von solchen Sensornetzwerken könnten langfristig auch digitale Sprachassistenten profitieren.}}, author = {{Haeb-Umbach, Reinhold}}, journal = {{forschung}}, number = {{1}}, pages = {{12--15}}, title = {{{Lektionen für Alexa \& Co?!}}}, doi = {{10.1002/fors.201970104}}, volume = {{44}}, year = {{2019}}, } @article{19446, abstract = {{We present a multi-channel database of overlapping speech for training, evaluation, and detailed analysis of source separation and extraction algorithms: SMS-WSJ -- Spatialized Multi-Speaker Wall Street Journal. It consists of artificially mixed speech taken from the WSJ database, but unlike earlier databases we consider all WSJ0+1 utterances and take care of strictly separating the speaker sets present in the training, validation and test sets. When spatializing the data we ensure a high degree of randomness w.r.t. room size, array center and rotation, as well as speaker position. Furthermore, this paper offers a critical assessment of recently proposed measures of source separation performance. Alongside the code to generate the database we provide a source separation baseline and a Kaldi recipe with competitive word error rates to provide common ground for evaluation.}}, author = {{Drude, Lukas and Heitkaemper, Jens and Boeddeker, Christoph and Haeb-Umbach, Reinhold}}, journal = {{ArXiv e-prints}}, title = {{{SMS-WSJ: Database, performance measures, and baseline recipe for multi-channel source separation and recognition}}}, year = {{2019}}, } @misc{8112, author = {{Maaz, Mohammad Urf and Sprenger, Alexander and Hellebrand, Sybille}}, keywords = {{WORKSHOP}}, publisher = {{31. Workshop "Testmethoden und Zuverlässigkeit von Schaltungen und Systemen" (TuZ'19)}}, title = {{{A Hybrid Space Compactor for Varying X-Rates}}}, year = {{2019}}, } @article{8667, author = {{Sprenger, Alexander and Hellebrand, Sybille}}, issn = {{0218-1266}}, journal = {{Journal of Circuits, Systems and Computers}}, number = {{1}}, pages = {{1--23}}, publisher = {{World Scientific Publishing Company}}, title = {{{Divide and Compact - Stochastic Space Compaction for Faster-than-At-Speed Test}}}, doi = {{10.1142/s0218126619400012}}, volume = {{28}}, year = {{2019}}, } @article{8872, abstract = {{We consider light scattering from a new type of model particle whose shape is represented in the form of a generalized ellipsoid having N foci, where N is greater than two. Such particles can be convex as well as concave. We use the geometrical optics approximation to study the light scattering from 3-foci particles. Non-zero elements of the scattering matrix are calculated for ensembles of randomly oriented independent transparent particles, m = n + i0. Several internal reflection orders are considered separately. It was found that the transmission-transmission (TT) and transmission-reflectance-transmission (TRT) components dominate in the formation of intensity of scattered light at large and small phase angles, respectively. We found a significant role of the total internal reflections of the TRT in the middle portion of the phase angle range. The main factors in the formation of positive linear polarization are the R and TRT component. The TT component is responsible for the formation of negative polarization branch at large phase angles.}}, author = {{Stankevich, Dmitriy and Hradyska, Larissa and Shkuratov, Yuriy and Grynko, Yevgen and Videen, Gorden and Förstner, Jens}}, issn = {{0022-4073}}, journal = {{Journal of Quantitative Spectroscopy and Radiative Transfer}}, keywords = {{tet_topic_scattering}}, pages = {{49}}, title = {{{Light scattering by 3-Foci convex and concave particles in the geometrical optics approximation}}}, doi = {{10.1016/j.jqsrt.2019.04.016}}, volume = {{231}}, year = {{2019}}, } @inproceedings{9718, author = {{Johannesmann, Sarah and Webersen, Manuel and Düchting, Julia and Claes, Leander and Henning, Bernd}}, booktitle = {{45th Annual Review of Progress in Quantitative Nondestructive Evaluation }}, location = {{Burlington}}, title = {{{Characterization of the linear-acoustic material behavior of fiber-reinforced composites using lamb waves}}}, doi = {{10.1063/1.5099742}}, volume = {{38}}, year = {{2019}}, } @inproceedings{11965, abstract = {{We present an unsupervised training approach for a neural network-based mask estimator in an acoustic beamforming application. The network is trained to maximize a likelihood criterion derived from a spatial mixture model of the observations. It is trained from scratch without requiring any parallel data consisting of degraded input and clean training targets. Thus, training can be carried out on real recordings of noisy speech rather than simulated ones. In contrast to previous work on unsupervised training of neural mask estimators, our approach avoids the need for a possibly pre-trained teacher model entirely. We demonstrate the effectiveness of our approach by speech recognition experiments on two different datasets: one mainly deteriorated by noise (CHiME 4) and one by reverberation (REVERB). The results show that the performance of the proposed system is on par with a supervised system using oracle target masks for training and with a system trained using a model-based teacher.}}, author = {{Drude, Lukas and Heymann, Jahn and Haeb-Umbach, Reinhold}}, booktitle = {{INTERSPEECH 2019, Graz, Austria}}, title = {{{Unsupervised training of neural mask-based beamforming}}}, year = {{2019}}, } @inproceedings{12874, abstract = {{We propose a training scheme to train neural network-based source separation algorithms from scratch when parallel clean data is unavailable. In particular, we demonstrate that an unsupervised spatial clustering algorithm is sufficient to guide the training of a deep clustering system. We argue that previous work on deep clustering requires strong supervision and elaborate on why this is a limitation. We demonstrate that (a) the single-channel deep clustering system trained according to the proposed scheme alone is able to achieve a similar performance as the multi-channel teacher in terms of word error rates and (b) initializing the spatial clustering approach with the deep clustering result yields a relative word error rate reduction of 26% over the unsupervised teacher.}}, author = {{Drude, Lukas and Hasenklever, Daniel and Haeb-Umbach, Reinhold}}, booktitle = {{ICASSP 2019, Brighton, UK}}, title = {{{Unsupervised Training of a Deep Clustering Model for Multichannel Blind Source Separation}}}, year = {{2019}}, } @inproceedings{12875, abstract = {{Signal dereverberation using the Weighted Prediction Error (WPE) method has been proven to be an effective means to raise the accuracy of far-field speech recognition. First proposed as an iterative algorithm, follow-up works have reformulated it as a recursive least squares algorithm and therefore enabled its use in online applications. For this algorithm, the estimation of the power spectral density (PSD) of the anechoic signal plays an important role and strongly influences its performance. Recently, we showed that using a neural network PSD estimator leads to improved performance for online automatic speech recognition. This, however, comes at a price. To train the network, we require parallel data, i.e., utterances simultaneously available in clean and reverberated form. Here we propose to overcome this limitation by training the network jointly with the acoustic model of the speech recognizer. To be specific, the gradients computed from the cross-entropy loss between the target senone sequence and the acoustic model network output is backpropagated through the complex-valued dereverberation filter estimation to the neural network for PSD estimation. Evaluation on two databases demonstrates improved performance for on-line processing scenarios while imposing fewer requirements on the available training data and thus widening the range of applications.}}, author = {{Heymann, Jahn and Drude, Lukas and Haeb-Umbach, Reinhold and Kinoshita, Keisuke and Nakatani, Tomohiro}}, booktitle = {{ICASSP 2019, Brighton, UK}}, title = {{{Joint Optimization of Neural Network-based WPE Dereverberation and Acoustic Model for Robust Online ASR}}}, year = {{2019}}, } @inproceedings{12876, abstract = {{In this paper, we present libDirectional, a MATLAB library for directional statistics and directional estimation. It supports a variety of commonly used distributions on the unit circle, such as the von Mises, wrapped normal, and wrapped Cauchy distributions. Furthermore, various distributions on higher-dimensional manifolds such as the unit hypersphere and the hypertorus are available. Based on these distributions, several recursive filtering algorithms in libDirectional allow estimation on these manifolds. The functionality is implemented in a clear, well-documented, and object-oriented structure that is both easy to use and easy to extend.}}, author = {{Kurz, Gerhard and Gilitschenski, Igor and Pfaff, Florian and Drude, Lukas and Hanebeck, Uwe D. and Haeb-Umbach, Reinhold and Siegwart, Roland Y.}}, booktitle = {{Journal of Statistical Software 89(4)}}, title = {{{Directional Statistics and Filtering Using libDirectional}}}, year = {{2019}}, } @article{12890, abstract = {{We formulate a generic framework for blind source separation (BSS), which allows integrating data-driven spectro-temporal methods, such as deep clustering and deep attractor networks, with physically motivated probabilistic spatial methods, such as complex angular central Gaussian mixture models. The integrated model exploits the complementary strengths of the two approaches to BSS: the strong modeling power of neural networks, which, however, is based on supervised learning, and the ease of unsupervised learning of the spatial mixture models whose few parameters can be estimated on as little as a single segment of a real mixture of speech. Experiments are carried out on both artificially mixed speech and true recordings of speech mixtures. The experiments verify that the integrated models consistently outperform the individual components. We further extend the models to cope with noisy, reverberant speech and introduce a cross-domain teacher–student training where the mixture model serves as the teacher to provide training targets for the student neural network.}}, author = {{Drude, Lukas and Haeb-Umbach, Reinhold}}, issn = {{1941-0484}}, journal = {{IEEE Journal of Selected Topics in Signal Processing}}, title = {{{Integration of Neural Networks and Probabilistic Spatial Models for Acoustic Blind Source Separation}}}, doi = {{10.1109/JSTSP.2019.2912565}}, year = {{2019}}, } @article{12908, author = {{Hammer, Manfred and Ebers, Lena and Förstner, Jens}}, issn = {{0740-3224}}, journal = {{Journal of the Optical Society of America B}}, keywords = {{tet_topic_waveguides}}, pages = {{2395}}, title = {{{Oblique quasi-lossless excitation of a thin silicon slab waveguide: a guided-wave variant of an anti-reflection coating}}}, doi = {{10.1364/josab.36.002395}}, volume = {{36}}, year = {{2019}}, } @inproceedings{15488, abstract = {{The continuous refinement of sensor technologies enables the manufacturing industry to capture increasing amounts of data during the production process. As processes take time to complete, sensors register large amounts of time-series-like data for each product. In order to make this data usable, a feature extraction is mandatory. In this work, we discuss and evaluate different network architectures, input pre-processing and cost functions regarding, among other aspects, their suitability for time series of different lengths.}}, author = {{Thiel, Christian and Steidl, Carolin and Henning, Bernd}}, booktitle = {{20. GMA/ITG-Fachtagung. Sensoren und Messsysteme 2019}}, isbn = {{978-3-9819376-0-2}}, keywords = {{Dynamic Time Warping, Feature Extraction, Masking, Neural Networks}}, title = {{{P2.9 Comparison of deep feature extraction techniques for varying-length time series from an industrial piercing press}}}, doi = {{10.5162/SENSOREN2019/P2.9}}, year = {{2019}}, }