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PublicationChallenges and perspectives in continuous glucose monitoring( 2018)
;Enter, Benjamin Jasha vanHauff, Elizabeth vonDiabetes is a global epidemic that threatens the health and well-being of hundreds of millions of people. The first step in patient treatment is to monitor glucose levels. Currently this is most commonly done using enzymatic strips. This approach suffers from several limitations, namely it requires a blood sample and is therefore invasive, the quality and the stability of the enzymatic strips vary widely, and the patient is burdened by performing the measurement themselves. This results in dangerous fluctuations in glucose levels often going undetected. There is currently intense research towards new approaches in glucose detection that would enable non-invasive continuous glucose monitoring (CGM). In this review, we explore the state-of-the-art in glucose detection technologies. In particular, we focus on the physical mechanisms behind different approaches, and how these influence and determine the accuracy and reliability of glucose detection. We begin by reviewing the basic physical and chemical properties of the glucose molecule. Although these play a central role in detection, especially the anomeric ratio, they are surprisingly often overlooked in the literature. We then review state-of-the art and emerging detection methods. Finally, we survey the current market for glucometers. Recent results show that past challenges in glucose detection are now being overcome, thereby enabling the development of smart wearable devices for non-invasive continuous glucose monitoring. These new directions in glucose detection have enormous potential to improve the quality of life of millions of diabetics, as well as offer insight into the development, treatment and even prevention of the disease. -
PublicationIdentification of a novel aminopolycarboxylic acid siderophore gene cluster encoding the biosynthesis of ethylenediaminesuccinic acid hydroxyarginine (EDHA)( 2018)
;Spohn, Marius ;Edenhart, Simone ;Alanjary, Mohammad ;Ziemert, Nadine ;Wibberg, Daniel ;Kalinowski, Jörn ;Niedermeyer, Timo H.J. ;Stegmann, EviWohlleben, WolfgangThe mechanism of siderophore-mediated iron supply enhances fitness and survivability of microorganisms under iron limited growth conditions. One class of naturally occurring ionophores is the small aminopolycarboxylic acids (APCAs). Although they are structurally related to the most famous anthropogenic chelating agent, ethylenediaminetetraacetate (EDTA), they have been largely neglected by the scientific community. Here, we demonstrate the detection of APCA gene clusters by a computational screening of a nucleotide database. This genome mining approach enabled the discovery of a yet unknown APCA gene cluster in well-described actinobacterial strains, either known for their potential to produce valuable secondary metabolites (Streptomyces avermitilis) or for their pathogenic lifestyle (Streptomyces scabies, Corynebacterium pseudotuberculosis, Corynebacterium ulcerans and Nocardia brasiliensis). The herein identified gene cluster was shown to encode the biosynthesis of APCA, ethylenediaminesuccinic acid hydroxyarginine (EDHA). Detailed and comparatively performed production and transcriptional profiling of EDHA and its biosynthesis genes showed strict iron-responsive biosynthesis. -
PublicationA novel method for antibiotic detection in milk based on competitive magnetic immunodetection( 2020)
;Pietschmann, Jan ;Dittmann, Dominik ;Krause, Hans-JoachimSchröper, FlorianThe misuse of antibiotics as well as incorrect dosage or insufficient time for detoxification can result in the presence of pharmacologically active molecules in fresh milk. Hence, in many countries, commercially available milk has to be tested with immunological, chromatographic or microbiological analytical methods to avoid consumption of antibiotic residues. Here a novel, sensitive and portable assay setup for the detection and quantification of penicillin and kanamycin in whole fat milk (WFM) based on competitive magnetic immunodetection (cMID) is described and assay accuracy determined. For this, penicillin G and kanamycin-conjugates were generated and coated onto a matrix of immunofiltration columns (IFC). Biotinylated penicillin G or kanamycin-specific antibodies were pre-incubated with antibiotics-containing samples and subsequently applied onto IFC to determine the concentration of antibiotics through the competition of antibody-binding to the antibiotic-conjugate molecules. Bound antibodies were labeled with streptavidin-coated magnetic particles and quantified using frequency magnetic mixing technology. Based on calibration measurements in WFM with detection limits of 1.33 ng·mL-1 for penicillin G and 1.0 ng·mL-1 for kanamycin, spiked WFM samples were analyzed, revealing highly accurate recovery rates and assay precision. Our results demonstrate the suitability of cMID-based competition assay for reliable and easy on-site testing of milk. -
PublicationEffect of power ultrasonic on the viscosity of anhydride epoxy resin system( 2020)Kronseder, MaximilianThe effect of power ultrasonic (US) on the viscosity of an anhydride epoxy resin system has been investigated. Especially, the potential reduction of the viscosity could be used prospectively for the production process pultrusion in the variation of closed injection pultrusion. An ultrasonic sonotrode is integrated into a rheometer to directly determine the influence of US on the viscosity of the epoxy resin Biresin® CR141. The temperature is determined during the ultrasonic excitation to consider the effect of US and temperature on the viscosity separately. The results demonstrate that the viscosity can be reduced by at least 80% points within 5 s using US. The temperature of the sonicated resin system increases from 30 °C to a maximum of 63-71 °C. In terms of the viscosity of the resin system, the result is that the decrease in viscosity is only due to the increase in temperature.
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PublicationDefending Against Adversarial Denial-of-Service Data Poisoning Attacks( 2020)
;Müller, Nicolas ;Roschmann, SimonData poisoning is one of the most relevant security threats against machine learning and data-driven technologies. Since many applications rely on untrusted training data, an attacker can easily craft malicious samples and inject them into the training dataset to degrade the performance of machine learning models. As recent work has shown, such Denial-of-Service (DoS) data poisoning attacks are highly effective. To mitigate this threat, we propose a new approach of detecting DoS poisoned instances. In comparison to related work, we deviate from clustering and anomaly detection based approaches, which often suffer from the curse of dimensionality and arbitrary anomaly threshold selection. Rather, our defence is based on extracting information from the training data in such a generalized manner that we can identify poisoned samples based on the information present in the unpoisoned portion of the data. We evaluate our defence against two DoS poisoning attacks and seven datasets, and find that it reliably identifies poisoned instances. In comparison to related work, our defence improves false positive / false negative rates by at least 50%, often more. -
PublicationTransfer of automated grid planning methods from power to gas distribution grids( 2021)
;Klaaßen, ChristophKneiske, Tanja M.In the context of distribution grid planning, automated approaches from the electricity sector have already been developed. They focus on removing voltage violations and line overloading, which stem from new installations of PV and wind systems while minimizing the resulting capital costs. The idea is to develop a similar approach for automated gas grid planning. Two methods are studied in this paper. First, a transfer of a capital cost approach from existing power grid planning was implemented. Second, a new operational cost approach in the context of randomized grid defection is introduced. The first method can only be applied in the presence of violated restrictions while the second method can also be used for cost reduction when all parameters are within their limits. The results have shown that the second approach is better suited for the gas grid development in the context of decreasing demand, since the focus lies on minimizing operational costs. In the future a combination of both methods would lead to a generalised framework for minimizing the total cost for strategic gas grid planning. -
PublicationA Study of 5G Edge-Central Core Network Split Options( 2021)
;Chakraborty, PousaliWith the wide adoption of edge compute infrastructures, an opportunity has arisen to deploy part of the functionality at the edge of the network to enable a localized connectivity service. This development is also supported by the adoption of "on-premises" local 5G networks addressing the needs of different vertical industries and by new standardized infrastructure services such as Mobile Edge Computing (MEC). This article introduces a comprehensive set of deployment options for the 5G network and its network management, complementing MEC with the connectivity service and addressing different classes of use cases and applications. We have also practically implemented and tested the newly introduced options in the form of slices within a standard-based testbed. Our performed validation proved their feasibility and gave a realistic perspective on their impact. The qualitative assessment of the connectivity service gives a comprehensive overview on which solution would be viable to be deployed for each vertical market and for each large-scale operator situation, making a step forward towards automated distributed 5G deployments. -
PublicationDesign and modeling of a novel piezoresistive microphone for aero acoustic measurements in laminar boundary layers using FEM and LEM( 2021)
;Erbacher, Kolja ;Ngo, Ha-Duong ;Wu, Lixiang ;Julliard, EmmanuelSpehr, CarstenIn this paper the modeling and simulation results of a piezo-resistive microphone are presented and a possible fabrication process flow and characterization concept of the sensor are described. The main objective in this funded AEROMIC project is to develop a thin and small in size microphone, which can be integrated into a flexible array, that can be mounted onto an airplane hull for flight tests. The microphone array should be no thicker than 2 mm and should contain more than 80 flush mounted single microphones, allowing acoustic measurement without disturbance of the laminar boundary layer. The pitch of the microphone sensors in the array enable high spatial resolution of the pressure fluctuation. The optimization of geometry of single sensor microphone has been done using FEA (Finite Element Analysis). For the optimization of the geometry of the single microphone chip, FEA of the air damped dynamic behavior of the diaphragm is modeled in Ansys Harmonic Response Analyses with Acoustics ACT package. To model the array on system level, a lumped-element model (LEM) is set up to predict spatial resolution and signal to noise ratio. Derived from the FEA results, a sensor chip layout with three membrane sizes is presented. -
PublicationRandom and Systematic Variation in Nanoscale Hf0.5Zr0.5O2 Ferroelectric FinFETs: Physical Origin and Neuromorphic Circuit Implications( 2021)
;De, Sourav ;Baig, M.A. ;Qiu, B.-H. ;Le, H.-H. ;Lederer, Maximilian ;Sung, P.-J. ;Su, C.-J. ;Lee, Y.-J.Lu, D.D.This work presents 2-bits/cell operation in deeply scaled ferroelectric finFETs (Fe-finFET) with a 1 µs write pulse of maximum ±5 V amplitude and WRITE endurance above 109 cycles. Fe-finFET devices with single and multiple fins have been fabricated on an SOI wafer using a gate first process, with gate lengths down to 70 nm and fin width 20 nm. Extrapolated retention above 10 years also ensures stable inference operation for 10 years without any need for re-training. Statistical modeling of device-to-device and cycle-to-cycle variation is performed based on measured data and applied to neural network simulations using the CIMulator software platform. Stochastic device-to-device variation is mainly compensated during online training and has virtually no impact on training accuracy. On the other hand, stochastic cycle-to-cycle threshold voltage variation up to 400 mV can be tolerated for MNIST handwritten digits recognition. A substantial inference accuracy drop with systematic retention degradation was observed in analog neural networks. However, quaternary neural networks (QNNs) and binary neural networks (BNNs) with Fe-finFETs as synaptic devices demonstrated excellent immunity toward the cumulative impact of stochastic and systematic variations. -
PublicationDesign of dual-frequency piezoelectric MEMS microphones for wind tunnel testing( 2021)
;Wu, Lixiang ;Chen, Xuyuan ;Julliard, EmmanuelSpehr, CarstenThe demand for aeroacoustic measurement microphones is surging in recent years as new rules on noise reduction and environmental compliance are getting tougher. However, the state-of-the-art microphones including classical measurement microphones and micro-electro-mechanical systems (MEMS) microphones cannot fully meet the strict requirements for wind tunnel testing (WTT) in terms of form factor, acoustic performance, and product price. To break through the bottleneck, a new type of piezoelectric MEMS microphones with dual frequency bands was designed as key part of a dedicate WTT solution, which aims to capture the unsteady pressure fluctuations underneath the turbulent boundary layer and predict the cabin noise excitation. The finite element method (FEM) was applied to analyze and optimize the MEMS design at the system level. The feasibility of the new MEMS design has been preliminarily verified by characterizing the mechanical and electrical properties of first batch of dual-frequency piezoelectric MEMS microphones. The acoustic characterization was conducted to evaluate the overall performance and the system-level FEM model was refined based on the measurement results. -
PublicationAdaptive and Automated Protection Settings for Over-Current Relays in Radial Grid Configuration( 2021)
;Lytaev, Pawel ;Banerjee, GourabHaack, JonasIn this paper, the challenges related to power system protection for medium voltage distribution grids (focus is Germany) are addressed considering a future power system with a high share of distributed energy resources, grid configuration changes, and digitization. These make the conventional protection design unselective, and thus there is the requirement for improvement. An automated adaptive non-directional over-current protection scheme using a centralized communication architecture is proposed as a solution here. Within a supervisory control and data acquisition system, both the tripping time and the threshold current settings of each relay in the grid are calculated. The unselective behavior of the existing protection scheme is first evaluated during a grid reconfiguration in different radial topologies after a fault incident, and then a smart protection design method is introduced to solve the issues of relay selectivity and sensitivity. It is shown with a few case studies in a medium-voltage distribution grid that the proposed scheme is resolving the challenges and ensuring an efficient and flexible protection operation, that will be practically useful by system operators and protection engineers. -
PublicationSampling criteria for mutual over-the-air synchronisation of radar sensors( 2021)Synchronisation of radar systems can be used to suppress interference and enable communication between radars. In this paper, a generalised Kuramoto model is proposed, which can be used to synchronise pulse repetition frequencies of two radars over-the-air. The generalised ap-proach allows the adjustment of a sampling factor which influences the convergence of the two pulse repetition frequencies. Furthermore, sampling criteria are derived, which can be used to estimate whether convergence occurs. Finally, it is shown by means of simulation that the model together with the criteria allows to reliably predict convergence and to perform synchronisation.
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PublicationCognitive Load Monitoring with Wearables - Lessons Learned from a Machine Learning Challenge( 2021)
;Gjoreski, Martin ;Mahesh, Bhargavi ;Kolenik, Tine ;Garbas, Jens Uwe ;Gjoreski, Hristijan ;Lustrek, Mitja ;Gams, MatjazPejovic, VeljkoTo further extend the applicability of wearable sensors, methods for accurately extracting subtle psychological information from the sensor data are required. However, accessing subjective information in everyday life, such as cognitive load, remains challenging. To bring consensus on methods for cognitive load monitoring, a machine learning challenge is organized. The participants developed machine learning methods for cognitive load classification using wrist-worn physiological sensors' data, namely heart rate, R-R intervals, skin conductance, and skin temperature. The data from subjects solving cognitive tasks of varying difficulty is used for the challenge. This article presents a systematic comparison and multi-strategic performance evaluation of the thirteen methods submitted to this challenge. A systematic comparison of preprocessing techniques, classification algorithms, and implementation techniques is presented. Performance variations for different task difficulty levels, different subjects, and different experiment periods are evaluated. The results indicate that the most robust methods used multimodal sensor data, classical classification approaches such as decision trees and support vector machines or their ensembles, and Bayesian hyperparameter optimization for hyperparameter tuning. The most accurate models used handcrafted features that are further selected using sequential backward floating search and evaluated using stratified person-aware cross-validation strategy. Moreover, the results indicated better classification performance for specific test subjects, the tasks with the highest difficulty, and in some cases, the time elapsed since the start of the experiment. This dependency is likely due to model overfitting or due to the subjective nature of the psychophysiological process. The intersubject variability in responses is challenging to be captured through objective binary labels for cognitive load, thereby warranting more sophisticated annotation approaches. -
PublicationImplementation of a Line Search Algorithm to determine the optimal Trade-off between Grid Operation and Grid Expansion in the German Transmission Grid( 2021)
;Damm, NicolaiBraun, MartinThe ongoing decarbonization of the power sector evokes a structural change of the electrical power system, especially the transmission grid, making the expansion of the grid unavoidable and a complex task. The expansion measures should be well chosen due to their long operating period and to avoid misinvestments. This paper raises the question of how the grid operational flexibilities can be considered in determining future grid expansion measures to minimize the total cost. For this, an existing sequential structure to determine grid expansion measures at minimal expansion cost and a grid operation optimization is extended with an iterative loop to determine the optimal trade-off between grid expansion and grid operation measures using a line search algorithm. The presented approach is used on a study case of the German transmission grid. The simulation results show that considering operational flexibilities in the TEP can significantly reduce the total cost generated by grid expansion measures and grid operational measures by using the presented approach. -
PublicationCompression efficiency analysis of AV1, VVC, and HEVC for random access applications( 2021)
;Nguyen, Hoang TungAOM Video 1 (AV1) and Versatile Video Coding (VVC) are the outcome of two recent independent video coding technology developments. Although VVC is the successor of High Efficiency Video Coding (HEVC) in the lineage of international video coding standards jointly developed by ITU-T and ISO/IEC within an open and public standardization process, AV1 is a video coding scheme that was developed by the industry consortium Alliance for Open Media (AOM) and that has its technological roots in Google's proprietary VP9 codec. This paper presents a compression efficiency evaluation for the AV1, VVC, and HEVC video coding schemes in a typical video compression application requiring random access. The latter is an important property, without which essential functionalities in digital video broadcasting or streaming could not be provided. For the evaluation, we employed a controlled experimental environment that basically follows the guidelines specified in the Common Test Conditions of the Joint Video Experts Team. As representatives of the corresponding video coding schemes, we selected their freely available reference software implementations. Depending on the application-specific frequency of random access points, the experimental results show averaged bit-rate savings of about 10-15% for AV1 and 36-37% for the VVC reference encoder implementation (VTM), both relative to the HEVC reference encoder implementation (HM) and by using a test set of video sequences with different characteristics regarding content and resolution. A direct comparison between VTMand AV1 reveals averaged bit-rate savings of about 25-29% for VTM, while the averaged encoding and decoding run times of VTM relative to those of AV1 are around 300% and 270%, respectively. -
PublicationComparison of iPad Pro®’s LiDAR and TrueDepth Capabilities with an Industrial 3D Scanning Solution( 2021)
;Vogt, Maximilian ;Rips, AdrianEmmelmann, ClausToday’s smart devices come equipped with powerful hard- and software-enabling professional use cases. The latest hardware by Apple utilizes LiDAR and TrueDepth, which offer the capability of 3D scanning. Devices equipped with these camera systems allow manufacturers to obtain 3D data from their customers at low costs, which potentially enables time-efficient mass customization and product differentiation strategies. However, the utilization is limited by the scanning accuracy. To determine the potential application of LiDAR and TrueDepth as a 3D scanning solution, in this paper an evaluation was performed. For this purpose, different Lego bricks were scanned with the technologies and an industrial 3D scanner. The results were compared according to shape and position tolerances. Even though the industrial 3D scanner consistently delivered more accurate results, the accuracy of the smart device technologies may already be sufficient, depending on the application. -
PublicationA Conceptual Framework for Biointelligent Production - Calling for Systemic Life Cycle Thinking in Cellular Units( 2021)
;Buckreus, LorenaA sustainable design of production systems is essential for the future viability of the economy. In this context, biointelligent production systems (BIS) are currently considered one of the most innovative paths for a comprehensive reorientation of existing industrial patterns. BIS are intended to enable a highly localized on-demand production of personalized goods via stand-alone non-expert systems. Recent studies in this field have primarily adopted a technical perspective; this paper addresses the larger picture by discussing the essential issues of integrated production system design. Following a normative logic, we introduce the basic principle of systemic life cycle thinking in cellular units as the foundation of a management framework for BIS. Thereupon, we develop a coherent theoretical model of a future decentralized production system and derive perspectives for future research and development in key areas of management. -
PublicationSimulative investigation of hybrid force and position control for electromechanical feed axes in production machines( 2022)
;Sewohl, André ;Norberger, Manuel ;Schlegel, HolgerPutz, MatthiasIn the area of production engineering, there are several ongoing efforts to improve manufacturing strategies and processes in terms of stability, quality, and efficiency. Control of process forces is one such appropriate measure for ensuring stable process conditions. This can also ensure in reducing the number of parts rejected due to bad quality and thus aiding as a significant economic benefit. However, control of process forces in production machines with electromechanical feed axes is still a developing field and offers space for potential improvement. Control concepts at the process level, which enable a combination of force control and position control still need to be developed. The concept of hybrid force and position control is presented in this article as a possible approach. Different methods of implementation along with the effects are examined in the simulation. A reduced-order model of an electromechanical feed axis is used for this purpose. The potential of the hybrid force and position control as well as the limits and possible applications are explained based on the simulation results. -
PublicationAl2O3-HfO2 mixed high-k dielectrics for MIM decoupling capacitors in the BEOL( 2022)
;Mertens, KonstantinHeitmann, J.An experimental study of MIM decoupling capacitors placed in the BEOL of 300mm wafers using Al2O3 within HfO2 dielectric thin films is reported. By increasing aluminum concentration (7.9%-14.3%) within the dielectric insulator, a capacitance density of up to 27.6 fF/µm2 with linearity of 1610 ppm/(MV/cm)2 at 10kHz was achieved. J-E and dielectric breakdown characteristics at temperatures from -50°C to +150°C were analyzed. Low leakage current (<0.1µA/cm2) was measured for up to 100°C. Further, time-dependent dielectric breakdown reliability measurements under constant field stress were investigated over temperature (25-150°C). Capacitors reached 1000 years of extrapolated lifetime for all Al concentrations (7.9%-14.3%) at 25°C. -
PublicationDesign of Cognitive Assistance Systems in Manual Assembly Based on Quality Function Deployment( 2022)
;Popescu, D.Increasing volatility and product individualization are leading to higher complexity in manual assembly. At the same time, production and processes must become more flexible, and humans have to adapt to new products more often and even faster. Industry 5.0 will increasingly focus on human-centric approaches, on the collaboration of humans and machines intensively using cognitive assistance systems. The design of an innovative cognitive assistance system is a complex task due to the many technological opportunities and their interrelationships. In the framework of this research, a method was developed enabling the systematic design of cognitive assistance systems that integrates business and worker requirements aiming at improving productivity, quality, worker satisfaction and well-being. The research question was approached by design science research having, as the main output, a systematic and innovative method for the design of cognitive assistance systems based on quality function deployment (QFD), referred to as cognitive assistance system-QFD (CAS-QFD). The developed methodology is divided into six phases and includes the iterative design of a cognitive assistance system starting from the assembly process. The method considers the information needs of the workers, the definition of the appropriate assistance functions and the selection of the interaction technologies. The exemplarily industrial evaluation highlighted the relevance of CAS-QFD for systematically designing cognitive assistance systems based on holistic requirements, identified at the worker, workplace, production area and, finally, at the enterprise level.