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Sweaty has already participated four times in RoboCup soccer competitions (Adult Size) and came second three times. While 2016 Sweaty needed a lot of luck to be finalist, 2017 Sweaty was a serious adversary in the preliminary rounds. In 2018 Sweaty showed up in the final with some lack of experience and room for improvements, but not without any chance. This paper describes the intended improvements of the humanoid adult size robot Sweaty in order to qualify for the RoboCup 2019 adult size competition.
Hochspannungs-Mischstrom-Übertragung (HMÜ) - Eine Ergänzung zu bestehenden Übertragungstechnologien?
(2019)
Bei der Mischstromübertragung wird einem Wechselstrom direkt ein Gleichstrom überlagert. Wechselstrom und Gleichstrom werden also auf dem gleichen Seil geführt.
Dadurch könnten die bereits bestehenden Drehstrom-Übertragungs-Strecken des Übertragungsnetzes genutzt werden.
Durch eine Aufschaltung des Gleichstromes auf vorhandene Freileitungen kann theoretisch bei kurzen Leitungen (<150km) bis zu 50% mehr Wirkleistung und bei großen Übertragungsstrecken (>300km) in etwa eine Verdopplung der übertragbaren Wirkleistung erwartet werden.
Theoretisch betrachtet ist die Mischstrom-Übertragung eine geometrische Addition aller Strom- und Spannungskomponenten, was zu einer Erhöhung der Leiter-Erde-Spannung führt, ohne dabei Einfluss auf die verkettete Spannung zu nehmen.
Außerdem wird die Übertragung von Blindströmen unnötig, da ein natürlicher Betrieb von Leitungen des HDÜ-Netzes empfehlenswert ist.
Die theoretischen Betrachtungen konnten mathematisch bewiesen und die technische Umsetzung mit einem 1:1000-Modellsystem demonstriert und bestätigt werden.
Wireless sensor networks have found their way into a wide range of applications, among which environmental monitoring systems have attracted increasing interests of researchers. Main challenges for these applications are scalability of the network size and energy efficiency of the spatially distributed nodes. Nodes are mostly battery-powered and spend most of their energy budget on the radio transceiver module. In normal operation modes most energy is spent waiting for incoming frames. A so-called Wake-On-Radio (WOR) technology helps to optimize trade-offs between energy consumption, communication range, complexity of the implementation and response time. We already proposed a new protocol called SmartMAC that makes use of such WOR technology. Furthermore, it gives the possibility to balance the energy consumption between sender and receiver nodes depending on the use case. Based on several calculations and simulations, it was predicted that the SmartMAC protocol was significantly more efficient than other schemes being proposed in recent publications, while preserving a certain backward compatibility with standard IEEE802.15.4 transceivers. To verify this prediction, we implemented the SmartMAC protocol for a given hardware platform. This paper compares the realtime performance of the SmartMAC protocol against simulation results, and proves the measured values are very close to the estimated values. Thus we believe that the proposed MAC algorithms outperforms all other Wake-on-Radio MACs.
Amongst all the major hazard aspects for the health of people in big conglomerates is the increase of the particulate matter concentration. Traditional systems for particulate matter (PM) monitoring have a great number of drawbacks but the main issues are economical and are related to the installation costs and never ending periodical maintenance expenses. After all there are installations of such systems but their number is limited and having in mind the growth of population, cities and industry areas, there is even a bigger need for more information on air quality because PM changes non-linearly, has a wide range and different sources. In this paper, we propose an approach, based on low-cost sensor nodes, for real-time measuring and obtaining information about the PM concentration. The adoption of that approach allows for a detailed study of the intensities of pollution and its sources. The system power supply is powered by a PV module. The power supply unit is designed using a model-based design that is a new approach to prototyping power-operated electronic devices with guaranteed performance.
This paper presents an approach for implementing an automated hit detection and score calculation system for a steel dartboard using a standard webcam. First, the rectilinear field separations of the dartboard are described mathematically by means of line slopes and are than stored. These slopes serve as a basis for later score calculation. In addition, thrown darts have to be detected and the pixel at which the dart cuts the dartboard has to be determined. When this information is known, a comparison is made using the line slopes, allowing the field number of the hit to be detected. The decision for single, double or triple hit is made by evaluating the defined colors on the dartboard. All these functions are then packaged in a Matlab GUI.
The paper describes a systematic approach for a precise short-time cloud coverage prediction based on an optical system. We present a distinct pre-processing stage that uses a model based clear sky simulation to enhance the cloud segmentation in the images. The images are based on a sky imager system with fish-eye lens optic to cover a maximum area. After a calibration step, the image is rectified to enable linear prediction of cloud movement. In a subsequent step, the clear sky model is estimated on actual high dynamic range images and combined with a threshold based approach to segment clouds from sky. In the final stage, a multi hypothesis linear tracking framework estimates cloud movement, velocity and possible coverage of a given photovoltaic power station. We employ a Kalman filter framework that efficiently operates on the rectified images. The evaluation on real world data suggests high coverage prediction accuracy above 75%.
The fast and cost-effective manufacturing of tools for thermoforming is an essential requirement to shorten the development time of products. Thus, additive processes are used increasingly in tooling for thermoforming of plastic sheets. However, a disadvantage of many additive methods is that they are highly cost-intensive, since complex systems based on laser technology and expensive metal powders are needed. Therefore, this paper examines how to work with favorable additive methods, e.g. Binder Jetting, to manufacture tools, which provide sufficient strength for thermoforming. The use of comparatively low-priced inkjet technology for the layer construction and a polymer plaster as material can be expected to result in significant cost reductions. Based on a case study using a cowling (engine bonnet) for an Unmanned Aerial Vehicle (UAV), the development of a complex tool for thermoforming is demonstrated. The object in this study is to produce a tool for a complex-shaped component in small numbers and high quality in a short time and at reasonable costs. Within the tooling process, integrated vacuum channels are implemented in additive tooling without the need for additional post-processing (for example, drilling). In addition, special technical challenges, such as the demolding of undercuts or the parting of the tool are explained. All process steps from tool design to the use of the additively manufactured tool are analyzed. Based on the manufacturing of a small series of cowlings for a UAV made of plastic sheets (ABS), it is shown, that the Binder Jetting offers sufficient mechanical and thermal strength for additive tooling. In addition, an economic evaluation of the tool manufacturing and a detailed consideration of the required manufacturing times for the different process steps are carried out. Finally, a comparison is made with conventional and alternative additive methods of tooling.
When designing and installing Indoor Positioning Systems, several interrelated tasks have to be solved to find an optimum placement of the Access Points. For this purpose, a mathematical model for a predefined number of access points indoors is presented. Two iterative algorithms for the minimization of localization error of a mobile object are described. Both algorithms use local search technique and signal level probabilities. Previously registered signal strengths maps were used in computer simulation.
Narrowband IoT (NB-IoT) as a radio access technology for the cellular Internet of Things (cIoT) is getting more traction due to attractive system parameters, new proposals in the 3 rd Generation Partnership Project (3GPP) Release 14 for reduced power consumption and ongoing world-wide deployment. As per 3GPP, the low-power and wide-area use cases in 5G specification will be addressed by the early NB-IoT and Long-Term Evolution for Machines (LTE-M) based technologies. Since these cIoT networks will operate in a spatially distributed environment, there are various challenges to be addressed for tests and measurements of these networks. To meet these requirements, unified emulated and field testbeds for NB-IoT-networks were developed and used for extensive performance measurements. This paper analyses the results of these measurements with regard to RF coverage, signal quality, latency, and protocol consistency.
The monitoring of industrial environments ensures that highly automated processes run without interruption. However, even if the industrial machines themselves are monitored, the communication lines are currently not continuously monitored in todays installations. They are checked usually only during maintenance intervals or in case of error. In addition, the cables or connected machines usually have to be removed from the system for the duration of the test. To overcome these drawbacks, we have developed and implemented a cost-efficient and continuous signal monitoring of Ethernet-based industrial bus systems. Several methods have been developed to assess the quality of the cable. These methods can be classified to either passive or active. Active methods are not suitable if interruption of the communication is undesired. Passive methods, on the other hand, require oversampling, which calls for expensive hardware. In this paper, a novel passive method combined with undersampling targeting cost-efficient hardware is proposed.
Enabling ultra-low latency is one of the major drivers for the development of future cellular networks to support delay sensitive applications including factory automation, autonomous vehicles and tactile internet. Narrowband Internet of Things (NB-IoT) is a 3 rd Generation Partnership Project (3GPP) Release 13 standardized cellular network currently optimized for massive Machine Type Communication (mMTC). To reduce the latency in cellular networks, 3GPP has proposed some latency reduction techniques that include Semi Persistent Scheduling (SPS) and short Transmission Time Interval (sTTI). In this paper, we investigate the potential of adopting both techniques in NB-IoT networks and provide a comprehensive performance evaluation. We firstly analyze these techniques and then implement them in an open-source network simulator (NS3). Simulations are performed with a focus on Cat-NB1 User Equipment (UE) category to evaluate the uplink user-plane latency. Our results show that SPS and sTTI have the potential to greatly reduce the latency in NB-IoT systems. We believe that both techniques can be integrated into NB-IoT systems to position NB-IoT as a preferred technology for low data rate Ultra-Reliable Low-Latency Communication (URLLC) applications before 5G has been fully rolled out.
With the surge in global data consumption with proliferation of Internet of Things (IoT), remote monitoring and control is increasingly becoming popular with a wide range of applications from emergency response in remote regions to monitoring of environmental parameters. Mesh networks are being employed to alleviate a number of issues associated with single-hop communication such as low area coverage, reliability, range and high energy consumption. Low-power Wireless Personal Area Networks (LoWPANs) are being used to help realize and permeate the applicability of IoT. In this paper, we present the design and test of IEEE 802.15.4-compliant smart IoT nodes with multi-hop routing. We first discuss the features of the software stack and design choices in hardware that resulted in high RF output power and then present field test results of different baseline network topologies in both rural and urban settings to demonstrate the deployability and scalability of our solution.
Low latency communication is essential to enable mission-critical machine-type communication (mMTC) use cases in cellular networks. Factory and process automation are major areas that require such low latency communication. In this paper, we investigate the potential of adopting the semi-persistent scheduling (SPS) latency reduction technique in narrowband LTE (NB-LTE) networks and provide a comprehensive performance evaluation. First, we investigate and implement SPS in an open-source network simulator (NS3). We perform simulations with a focus on LTE-M and Narrowband IoT (NB-IoT) systems and evaluate the impact of the SPS technique on the uplink latency of these narrowband systems in real industrial automation scenarios. The performance gain of adopting SPS is analyzed and the results is compared with the legacy dynamic scheduling. Our results show that SPS has the potential to reduce the latency of cellular Internet of Things (cIoT) networks. We believe that SPS can be integrated into LTE-M and NB-IoT systems to support low-latency industrial applications.
The Internet of Things (IoT) application has becoming progressively in-demand, most notably for the embedded devices (ED). However, each device has its own difference in computational capabilities, memory usage, and energy resources in connecting to the Internet by using Wireless Sensor Networks (WSNs). In order for this to be achievable, the WSNs that form the bulk of the IoT implementation requires a new set of technologies and protocol that would have a defined area, in which it addresses. Thus, IPv6 Low Power Area Network (6LoWPAN) was designed by the Internet Engineering Task Force (IETF) as a standard network for ED. Nevertheless, the communication between ED and 6LoWPAN requires appropriate routing protocols for it to achieve the efficient Quality of Service (QoS). Among the protocols of 6LoWPAN network, RPL is considered to be the best protocol, however its Energy Consumption (EC) and Routing Overhead (RO) is considerably high when it is implemented in a large network. Therefore, this paper would propose the HRPL to enchance the RPL protocol in reducing the EC and RO. In this study, the researchers would present the performance of RPL and HRPL in terms of EC, Control traffic Overhead (CTO) and latency based on the simulation of the 6LoWPAN network in fixed environment using COOJA simulator. The results show HRPL protocol achieves better performance in all the tested topology in terms of EC and CTO. However, the latency of HRPL only improves in chain topology compared with RPL. We found that further research is required to study the relationship between the latency and the load of packet transmission in order to optimize the EC usage.
Formal Description of Use Cases for Industry 4.0 Maintenance Processes Using Blockchain Technology
(2019)
Maintenance processes in Industry 4.0 applications try to achieve a high degree of quality to reduce the downtime of machinery. The monitoring of executed maintenance activities is challenging as in complex production setups, multiple stakeholders are involved. So, full transparency of the different activities and of the state of the machine can only be supported, if these stakeholders trust each other. Therefore, distributed ledger technologies, like Blockchain, can be promising candidates for supporting such applications. The goal of this paper is a formal description of business and technical interactions between non-trustful stakeholders in the context of Industry 4.0 maintenance processes using distributed ledger technologies. It also covers the integration of smart contracts for automated triggering of activities.
This paper presents the use of model predictive control (MPC) based approach for peak shaving application of a battery in a Photovoltaic (PV) battery system connected to a rural low voltage gird. The goals of the MPC are to shave the peaks in the PV feed-in and the grid power consumption and at the same time maximize the use of the battery. The benefit to the prosumer is from the maximum use of the self-produced electricity. The benefit to the grid is from the reduced peaks in the PV feed-in and the grid power consumption. This would allow an increase in the PV hosting and the load hosting capacity of the grid.
The paper presents the mathematical formulation of the optimal control problem
along with the cost benefit analysis. The MPC implementation scheme in the
laboratory and experiment results have also been presented. The results show
that the MPC is able to track the deviation in the weather forecast and operate
the battery by solving the optimal control problem to handle this deviation.
Printed electronics (PE) is a fast growing technology with promising applications in wearables, smart sensors and smart cards since it provides mechanical flexibility, low-cost, on-demand and customizable fabrication. To secure the operation of these applications, True Random Number Generators (TRNGs) are required to generate unpredictable bits for cryptographic functions and padding. However, since the additive fabrication process of PE circuits results in high intrinsic variation due to the random dispersion of the printed inks on the substrate, constructing a printed TRNG is challenging. In this paper, we exploit the additive customizable fabrication feature of inkjet printing to design a TRNG based on electrolyte-gated field effect transistors (EGFETs). The proposed memory-based TRNG circuit can operate at low voltages (≤ 1 V ), it is hence suitable for low-power applications. We also propose a flow which tunes the printed resistors of the TRNG circuit to mitigate the overall process variation of the TRNG so that the generated bits are mostly based on the random noise in the circuit, providing a true random behaviour. The results show that the overall process variation of the TRNGs is mitigated by 110 times, and the simulated TRNGs pass the National Institute of Standards and Technology Statistical Test Suite.
Printed Electronics is perceived to have a major impact in the fields of smart sensors, Internet of Things and wearables. Especially low power printed technologies such as electrolyte gated field effect transistors (EGFETs) using solution-processed inorganic materials and inkjet printing are very promising in such application domains. In this paper, we discuss a modeling approach to describe the variations of printed devices. Incorporating these models and design flows into our previously developed printed design system allows for robust circuit design. Additionally, we propose a reliability-aware routing solution for printed electronics technology based on the technology constraints in printing crossovers. The proposed methodology was validated on multiple benchmark circuits and can be easily integrated with the design automation tools-set.
Smart Home or Smart Building applications are a growing market. An increasing challenge is to design energy efficient Smart Home applications to achieve sustainable and green homes. Using the example of the development of an Indoor Smart Gardening system with wireless monitoring and automated watering this paper is discussing in particular the design issue of energy autonomous working sensors and actuators for home automation. Most important part of the presented Smart Gardening system is a 3D printed smart flower pot for single plants. The smart flower pot has integrated a water reservoir for automated plant irrigation and an electronic for monitoring important plant parameters and the water level of the water reservoir. Energy harvesting with solar cells enables energy autonomous working of the flower pot. A low-power wireless interface also integrated in the flowerpot and an external gateway based on a Raspberry Pi 3 enables wireless networking of multiple of those flower pots. The gateway is used for evaluating the plant parameters and as a user interface. Particularly the architecture of the energy autonomous wireless flower pot will be considered, because fully energy autonomous sensors and actuators for home automation could not be implemented without special concepts for the energy supply and the overall electronic.
Radio frequency identification (RFID) antennas are popular for high frequency (HF) RFID, energy transfer and near field communication (NFC) applications. Particularly for wireless measurement systems the RFID/NFC technology is a good option to implement a wireless communication interface. In this context, the design of corresponding reader and transmitter antennas plays a major role for achieving suitable transmission quality. This work proves the feasibility of the rapid prototyping of a RFID/NFC antenna, which is used for the wireless communication and energy harvesting at the required frequency of 13.56 MHz. A novel and low-cost direct ink writing (DIW) technology utilizing highly viscous silver nanoparticle ink is used for this process. This paper describes the development and analysis of low-cost printed flexible RFID/NFC antennas on cost-effective substrates for a microelectronic vital parameter measurement system. Furthermore, we compare the measured technical parameters with existing copper-based counterparts on a FR4 substrate.
Smart Home-/Smart-Building-Anwendungen sind ein stetig wachsender Markt. Smart Gardening ist ein Beispiel dafür, Nutzern mehr Komfort und eine bessere Lebensqualität zu Hause oder in Bürogebäuden zu ermöglichen. Im Rahmen dieses Beitrags wird die Entwicklung eines Indoor-Smart-Gardening-Systems mit dem Fokus auf energieautarkes Arbeiten vorgestellt. Herzstück des Systems ist ein 3D-gedruckter Blumentopf für einzelne Pflanzen mit integrierter Elektronik zum Monitoring der wichtigsten Pflanzenparameter und einem integrierten Wasserreservoir mit Tauchpumpe für das automatisierte Bewässern der Pflanze. Energy Harvesting per Solarzellen ermöglicht ein energieautarkes Arbeiten des Blumentopfes. Eine selbstentwickelte Low-Power-Funkschnittstelle im Blumentopf und ein externes Gateway ermöglichen die drahtlose Vernetzung mehrerer Pflanzen. Das Gateway dient zur Auswertung der Pflanzenparameter, der Ansteuerung der im Netzwerk vorhandenen Blumentöpfe und als Benutzerinterface.
Kleinstlebewesen vorgestellt, das Vitalparameter erfasst und diese in einem FRAM-Speicher bis zum Auslesen abspeichert. Durch eine drahtlose RFID-/NFC-Ausleseschnittstelle kann die erfasste Körpertemperatur und der Puls der letzten Wochen ausgelesen werden. Alle Einstellungen des Messsystems können durch einen geeigneten RFID-Reader für Laptops oder durch Smartphones über die NFC-Schnittstelle geändert werden. Das vollständige Aufladen des nur 3 g leichten und 15 mm x 25 mm großen Messsystems erfolgt durch eine selbstgedruckte RFID-Reader-Antenne in Verbindung mit einem RFID-Reader und benötigt hierzu weniger als 21 Stunden. Bei vollständig aufgeladenem Energiespeicher ist ein Betrieb von 47 Tagen möglich. Dies wird durch ein speziell für das Messsystem konzipiertes Lade- und Powermanagement erreicht. Neben der Auswahl von energiesparenden Komponenten für die Hardware und deren bestmöglichen Nutzung, wurde die Software so optimiert, dass das Programm schnell und stromsparend abgearbeitet wird. Die Erweiterbarkeit und Anpassung wird durch das modulare Konzept auch in anderen Bereichen gewährleistet.
The high peak power in comparison to the average transmit power is one of the major long-standing problems in multicarrier modulation and is known as the PAPR (peak to average power ratio) problem. Many PAPR reduction methods have been devised and their comparison is usually based on the complementary cumulative distribution function (CCDF) of the PAPR. While this comparison is straightforward and easy to compute, its relationship with system performance metrics like the (uncoded) BER or the word error rate (WER) for coded systems is considerably more involved. We evaluate the impact of the PAPR on performance metrics like uncoded BER, EVM (error vector magnitude), mutual information and the WER for soft decoding. In this context, we find that system performance is not necessarily degraded by an increasing PAPR. We show that a high number of subcarriers, despite the corresponding high PAPR, is actually not a problem for the system performance and provide a simple explanation for this seemingly counter-intuitive fact.
One of the challenges for autonomous driving in general is to detect objects in the car's camera images. In the Audi Autonomous Driving Cup (AADC), among those objects are other cars, adult and child pedestrians and emergency vehicle lighting. We show that with recent deep learning networks we are able to detect these objects reliably on the limited Hardware of the model cars. Also, the same deep network is used to detect road features like mid lines, stop lines and even complete crossings. Best results are achieved using Faster R-CNN with Inception v2 showing an overall accuracy of 0.84 at 7 Hz.
Most machine learning methods require careful selection of hyper-parameters in order to train a high performing model with good generalization abilities. Hence, several automatic selection algorithms have been introduced to overcome tedious manual (try and error) tuning of these parameters. Due to its very high sample efficiency, Bayesian Optimization over a Gaussian Processes modeling of the parameter space has become the method of choice. Unfortunately, this approach suffers from a cubic compute complexity due to underlying Cholesky factorization, which makes it very hard to be scaled beyond a small number of sampling steps. In this paper, we present a novel, highly accurate approximation of the underlying Gaussian Process. Reducing its computational complexity from cubic to quadratic allows an efficient strong scaling of Bayesian Optimization while outperforming the previous approach regarding optimization accuracy. First experiments show speedups of a factor of 162 in single node and further speed up by a factor of 5 in a parallel environment.
Current training methods for deep neural networks boil down to very high dimensional and non-convex optimization problems which are usually solved by a wide range of stochastic gradient descent methods. While these approaches tend to work in practice, there are still many gaps in the theoretical understanding of key aspects like convergence and generalization guarantees, which are induced by the properties of the optimization surface (loss landscape). In order to gain deeper insights, a number of recent publications proposed methods to visualize and analyze the otimization surfaces. However, the computational cost of these methods are very high, making it hardly possible to use them on larger networks. In this paper, we present the GradVis Toolbox, an open source library for efficient and scalable visualization and analysis of deep neural network loss landscapes in Tesorflow and PyTorch. Introducing more efficient mathematical formulations and a novel parallelization scheme, GradVis allows to plot 2d and 3d projections of optimization surfaces and trajectories, as well as high resolution second order gradient information for large networks.
Recent deep learning based approaches have shown remarkable success on object segmentation tasks. However, there is still room for further improvement. Inspired by generative adversarial networks, we present a generic end-to-end adversarial approach, which can be combined with a wide range of existing semantic segmentation networks to improve their segmentation performance. The key element of our method is to replace the commonly used binary adversarial loss with a high resolution pixel-wise loss. In addition, we train our generator employing stochastic weight averaging fashion, which further enhances the predicted output label maps leading to state-of-the-art results. We show, that this combination of pixel-wise adversarial training and weight averaging leads to significant and consistent gains in segmentation performance, compared to the baseline models.
Apache Hadoop is a well-known open-source framework for storing and processing huge amounts of data. This paper shows the usage of the framework within a project of the university in cooperation with a semiconductor company. The goal of this project was to supplement the existing data landscape by the facilities of storing and analyzing the data on a new Apache Hadoop based platform.
Spinal cord stimulation (SCS) is the most commonly used technique of neurostimulation. It involves the stimulation of the spinal cord and is therefore used to treat chronic pain. The existing esophageal catheters are used for temperature monitoring during an electrophysiology study with ablation and transesophageal echocardiography. The aim of the study was to model the spine and new esophageal electrodes for the transesophageal electrical pacing of the spinal cord, and to integrate them in the Offenburg heart rhythm model for the static and dynamic simulation of transesophageal neurostimulation. The modeling and simulation were both performed with the electromagnetic and thermal simulation software CST (Computer Simulation Technology, Darmstadt). Two new esophageal catheters were modelled as well as a thoracic spine based on the dimensions of a human skeleton. The simulation of directed transesophageal neurostimulation is performed using the esophageal balloon catheter with an electric pacing potential of 5 V and a trapezoidal signal. A potential of 4.33 V can be measured directly at the electrode, 3.71 V in the myocardium at a depth of 2 mm, 2.68 V in the thoracic vertebra at a depth of 10 mm, 2.1 V in the thoracic vertebra at a depth of 50 mm and 2.09 V in the spinal cord at a depth of 70 mm. The relation between the voltage delivered to the electrodes and the voltage applied to the spinal cord is linear. Virtual heart rhythm and catheter models as well as the simulation of electrical pacing fields and electrical sensing fields allow the static and dynamic simulation of directed transesophageal electrical pacing of the spinal cord. The 3D simulation of the electrical sensing and pacing fields may be used to optimize transesophageal neurostimulation.
The visualization of heart rhythm disturbance and atrial fibrillation therapy allow the optimization of new cardiac catheter ablations. With the simulation software CST (Computer Simulation Technology, Darmstadt) electromagnetic and thermal simulations can be carried out to analyze and optimize different heart rhythm disturbance and cardiac catheters for pulmonary vein isolation. Another form of visualization is provided by haptic, three-dimensional print models. These models can be produced using an additive manufacturing method, such as a 3D printer. The aim of the study was to produce a 3D print of the Offenburg heart rhythm model with a representation of an atrial fibrillation ablation procedure to improve the visualization of simulation of cardiac catheter ablation.
The basis of 3D printing was the Offenburg heart rhythm model and the associated simulation of cryoablation of the pulmonary vein. The thermal simulation shows the pulmonary vein isolation of the left inferior pulmonary vein with the cryoballoon catheter Arctic Front AdvanceTM from Medtronic. After running through the simulation, the thermal propagation during the procedure was shown in the form of different colors. The three-dimensional print models were constructed on the base of the described simulation in a CAD program. Four different 3D printers are available for this purpose in a rapid prototyping laboratory at the University of Applied Science Offenburg. Two different printing processes were used: 1. a binder jetting printer with polymer gypsum and 2. a multi-material printer with photopolymer. A final print model with additional representation of the esophagus and internal esophagus catheter was also prepared for printing.
With the help of the thermal simulation results and the subsequent evaluation, it was possible to make a conclusion about the propagation of the cold emanating from the catheter in the myocardium and the surrounding tissue. It could be measured that already 3 mm from the balloon surface into the myocardium the temperature drops to 25 °C. The simulation model was printed using two 3D printing methods. Both methods as well as the different printing materials offer different advantages and disadvantages. While the first model made of polymer gypsum can be produced quickly and cheaply, the second model made of photopolymer takes five times longer and was twice as expensive. On the other hand, the second model offers significantly better properties and was more durable overall. All relevant parts, especially the balloon catheter and the conduction, are realistically represented. Only the thermal propagation in the form of different colors is not shown on this model.
Three-dimensional heart rhythm models as well as virtual simulations allow a very good visualization of complex cardiac rhythm therapy and atrial fibrillation treatment methods. The printed models can be used for optimization and demonstration of cryoballoon catheter ablation in patients with atrial fibrillation.
A Novel Approach of High Dynamic Current Control of Interior Permanent Magnet Synchronous Machines
(2019)
Harmonic-afflicted effects of permanent magnet synchronous machines with high power density are hardly faced by traditional current PI controllers, due to limited controller bandwidth. As a consequence, currents and lastly torque ripples appear. In this paper, a new deadbeat current controller architecture has been presented, which is capable to encounter the effects of these harmonics. This new control algorithm, here named “Hybrid-Deadbeat-Controller”, combines the stability and the low steady-state errors offered by common PI regulators with the high dynamic offered by the deadbeat control. Therefore, a novel control algorithm is proposed, capable of either compensating the current harmonics in order to get smoother currents or to control a varying reference value to achieve a smoother torque. The information needed to calculate the optimal reference currents is based on an online parameter estimation feeding an optimization algorithm to achieve an optimal torque output and will be investigated in future research. In order to ensure the stability of the controller over the whole area of operation even under the influence of effects changing the system’s parameter, this work as well focusses on the robustness of the “hybrid” dead beat controller.
Hintergrund: Die Pulmonalvenenisolation (PVI) mit Hilfe von Kryoballonkathetern ist eine anerkannte Methode zur Behandlung von Vorhofflimmern (AF). Diese Methode bietet eine kürzere Behandlungsdauer als die klassische Therapie durch die Hochfrequenzablation (HF). Ziel dieser Studie war es, verschiedene Kryoballonkatheter, HF-Katheter und Ösophaguskatheter in ein Herzrhythmusmodell zu integrieren und mittels statischer und dynamischer Simulation elektrische und thermische Felder bei PVI unter Vorhofflimmern zu untersuchen.
Methodik: Die Modellierung und Simulation erfolgte mit der elektromagnetischen und thermischen Simulationssoftware CST (CST Darmstadt). Zwei Kryoballons, ein HF-Ablationskatheter und ein Ösophaguskatheter wurden auf der Grundlage der technischen Handbücher der Hersteller Medtronic und Osypka modelliert. Der 23 mm Kryoballon und ein kreisförmiger Mappingkatheter wurden in das Offenburger Herzrhythmusmodell integriert, insbesondere die left inferior pulmonary vein (LIPV) zur Simulation der thermischen Feldausbreitung während einer PVI. Die Simulation einer PVI mit HF-Energie wurde mit dem integrierten HF-Ablationskatheter in der Nähe der LIPV durchgeführt. Der im Herzrhythmusmodell platzierte TO8 Ösophaguskatheter ermöglichte die Ableitung linksatrialer elektrischer Felder bei AF und die Analyse thermischer Felder während PVI.
Ergebnisse: Elektrische Felder konnten bei Sinusrhythmus und AF mit einem AF-Fokus in der LIVP statisch und dynamisch im Herzen und Ösophagus simuliert werden. Bei einer simulierten 20 Sekunden Applikation eines Kryoballon-Katheters bei -50°C wurde eine Temperatur von -24°C in einer Tiefe von 0,5 mm im Myokard gemessen. In einer Tiefe von 1 mm betrug die Temperatur -3°C, bei 2 mm Tiefe 18°C und bei 3 mm Tiefe 29°C. Unter der 15 sekündigen Anwendung eines HF-Katheters mit einer 8-mm-Elektrode und einer Leistung von 5 W bei 420 kHz betrug die Temperatur an der Spitze der Elektrode 110°C. In einer Tiefe von 0,5 mm im Myokard betrug die Temperatur 75°C, in einer Tiefe von 1 mm 58°C, in einer Tiefe von 2 mm 45°C und in einer Tiefe von 3 mm 38°C. Im Ösophagus konnte bei den meisten Simulationen eine konstante Temperatur von 37°C gemessen und die Gefahr einer Ösophagus-Fistel ausgeschlossen werden. Bei Kryoablation der LIPV wurde eine Abkühlung des Ösophagus auf 30°C gemessen.
Schlussfolgerungen: Die Herzrhythmussimulation elektrischer und thermaler Felder ermöglichen mit Anwendung unterschiedlicher Herzkatheter eine statische und dynamische Simulation von PVI durch Kryoablation, HF-Ablation und Temperaturanalyse im Ösophagus. Unter Einbeziehung von MRT- oder CT-Daten können elektrische und thermale Simulationen möglicherweise zur Optimierung von PVIs genutzt werden.
Background: The application of high-frequency ablation is used for the treatment of tachycardia arrhythmias and is a respected method. Ablation with high frequency current leads to the targeted heat destruction of myocardial tissue at specific sites and thus prevents the pathological propagation of excitation through these structures.
Purpose: The aim of this study was to simulate heat propagation during RF ablation with modeled electrodes in different sizes and materials. The simulation was performed on atrioventricular node re-entry tachycardia (AVNRT), atrioventricular re-entry tachycardia (AVRT) and atrial flutter (AFL).
Methods: Using the modeling and simulation software CST, ablation catheters with 4 mm and 8 mm tip electrodes were modeled from gold and platinum for each. The designed catheters correspond to the manufacturer"s specifications of Medtronic, Biotronik and Osypka. The catheters were integrated into the Offenburg heart rhythm model to simulate and compare the heat propagation during an ablation application, which also takes into account the blood flow in the four heart chambers. A power of 5 W - 40 W was simulated for the 4 mm electrodes and a power of 50 W - 80 W for the 8 mm electrodes.
Results: During the simulated HF ablation application, the temperature at the ablation electrode was measured at different powers. This is 40.67°C at 5 W, 44.34°C at 10 W, 51.76°C at 20 W, 59.0°C at 30 W, and 66.33°C at 40 W. The measured temperature during 40 W application is 39.5°C at 0,5 mm depth in the myocardium and 37.5°C at 2 mm depth.
In the simulation, the 8 mm platinum electrode reached an ablation temperature of 72.85°C at its tip during an applied power of 60 W. In contrast, the 8 mm platinum electrode reached a depth of 5 mm at 39.5 C° and at a depth of 2 mm at 37.5 °C. In contrast, the 8 mm gold electrode reached a temperature of 64.66°C with the same performance. This is due to the thermal properties of gold, which has a better thermal conductivity than platinum.
Conclusions: CST offers the possibility to carry out a static and dynamic simulation of a heart model and the ablation electrodes integrated in it during an HF ablation. In variation with different electrode sizes and materials, therapy methods for the treatment of AVNRT, AVRT and AFL can be optimized
Background: Pulmonary vein isolation (PVI) using cryoballoon catheters are a recognized method for the treatment of atrial fibrillation (AF). This method offers shorter treatment duration in contrast to the classical therapy with high-frequency (HF) ablation.
Purpose: The aim of this study was to integrate different cryoballoon catheters and a HF catheter into a heart rhythm model and to compare them by means of static and dynamic electromagnetic and thermal simulation in use under AF.
Methods: The cryoballoon catheters from Medtronic and the HF ablation catheter from Osypka were modelled virtually with the aid of manufacturer specifications and the CST (Computer Simulation Technology, Darmstadt) simulation program. The cryoballoon catheter was located in the lower left pulmonary vein of the virtual heart rhythm model for the realization of pulmonary vein isolation (PVI) by cryoenergy. The simulated temperature at the balloon surface was -50°C during the simulation.
Results: During a simulated 20 second application of a cryoballoon catheter at -50°C, a temperature of -24°C was measured at a depth of 0.5 mm in the myocardium. At a depth of 1 mm the temperature was -3°C, at 2 mm depth 18°C and at 3 mm depth 29°C. Under the 15 second application of a RF catheter with a 8 mm electrode and a power of 5 W at 420 kHz, the temperature at the tip of the electrode was 110°C. At a depth of 0.5 mm in the myocardium, the temperature was 75°C, at a depth of 1 mm 58°C, at 2 mm depth 45°C and at 3 mm depth 38°C.
Conclusions: The simulation of temperature profiles during the virtual application of several catheter models in the heart rhythm model allows the static and dynamic simulation of PVI by cryoballoon ablation and RF ablation. The three-dimensional simulation can be used to improve ablation applications by creating a model in personalized cardiac rhythm therapy from MRI or CT data of a heart and finding a favourable position for ablation of AF.
Background: Transesophageal left atrial (LA) pacing and transesophageal LA ECG recording are semi-invasive techniques for diagnostic and therapy of supraventricular rhythm disturbance. Cardiac resynchronization therapy (CRT) with right atrial (RA) sensed biventricular pacing is an established therapy for heart failure patients with reduced left ventricular (LV) ejection fraction, sinus rhythm and interventricular electrical desynchronization.
Purpose: The aim of the study was to evaluate electromagnetic and voltage pacing fields of the combination of RA pacing, LA pacing and biventricular pacing in patients with long interatrial and interventricular electrical desynchronization.
Methods: The modelling and electromagnetic simulations of transesophageal LA pacing in combination with RA pacing and biventricular pacing would be staged and analyzed with the CST (Computer Simulation Technology) software. Different electrodes were modelled in order to simulate different types of bipolar pacing in the 3D-CAD Offenburg heart rhythm model: The bipolar Solid S (Biotronik) electrode where modelled for RA pacing and right ventricular (RV) pacing, Attain 4194 (Medtronic) for LV pacing and TO8 (Osypka) multipolar esophageal electrode with hemispheric electrodes for LA pacing.
Results: The pacemaker amplitudes for the electromagnetic pacing simulations were performed with 3 V for RA pacing, 1.5 V for RV pacing, 50 V for LA pacing and 3V for LV pacing with pacing impulse duration of 0.5 ms for RA, RV and LV pacing and 10 ms for LA pacing. The atrioventricular pacing delay after RA pacing was 140 ms. The different pacing modes AAI, VVI, DDD, DDD0V and DDD0D were evaluated for the analysis of the electric pacing field propagation of pacemaker, CRT and LA pacing. The pacing results were compared at minimum (LOW) and maximum (HIGH) parameter settings. While the LOW setting produced fewer tetrahedral and more inaccurate results, the HIGH setting produced many tetrahedral and therefore more accurate results.
Conclusions: The simulation of the combination of transesophageal LA pacing with RA sensed biventricular pacing is possible with the Offenburg heart rhythm model. The new temporary 4-chamber pacing method may be additional useful method in CRT non-responders with long interatrial electrical delay.
This paper describes the concept and some results of the project "Menschen Lernen Maschinelles Lernen" (Humans Learn Machine Learning, ML2) of the University of Applied Sciences Offenburg. It brings together students of different courses of study and practitioners from companies on the subject of Machine Learning. A mixture of blended learning and practical projects ensures a tight coupling of machine learning theory and application. The paper details the phases of ML2 and mentions two successful example projects.
Diffracted waves carry high resolution information that can help interpreting fine structural details at a scale smaller than the seismic wavelength. Because of the low signal-to-noise ratio of diffracted waves, it is challenging to preserve them during processing and to identify them in the final data. It is, therefore, a traditional approach to pick manually the diffractions. However, such task is tedious and often prohibitive, thus, current attention is given to domain adaptation. Those methods aim to transfer knowledge from a labeled domain to train the model, and then infer on the real unlabeled data. In this regard, it is common practice to create a synthetic labeled training dataset, followed by testing on unlabeled real data. Unfortunately, such procedure may fail due to the existing gap between the synthetic and the real distribution since quite often synthetic data oversimplifies the problem, and consequently the transfer learning becomes a hard and non-trivial procedure. Furthermore, deep neural networks are characterized by their high sensitivity towards cross-domain distribution shift. In this work, we present deep learning model that builds a bridge between both distributions creating a semi-synthetic datatset that fills in the gap between synthetic and real domains. More specifically, our proposal is a feed-forward, fully convolutional neural network for imageto-image translation that allows to insert synthetic diffractions while preserving the original reflection signal. A series of experiments validate that our approach produces convincing seismic data containing the desired synthetic diffractions.
Generative convolutional deep neural networks, e.g. popular GAN architectures, are relying on convolution based up-sampling methods to produce non-scalar outputs like images or video sequences. In this paper, we show that common up-sampling methods, i.e. known as up-convolution or transposed convolution, are causing the inability of such models to reproduce spectral distributions of natural training data correctly. This effect is independent of the underlying architecture and we show that it can be used to easily detect generated data like deepfakes with up to 100% accuracy on public benchmarks. To overcome this drawback of current generative models, we propose to add a novel spectral regularization term to the training optimization objective. We show that this approach not only allows to train spectral consistent GANs that are avoiding high frequency errors. Also, we show that a correct approximation of the frequency spectrum has positive effects on the training stability and output quality of generative networks.
This paper presents a novel low-jitter interface between a low-cost integrated IEEE802.11 chip and a FPGA. It is designed to be part of system hardware for ultra-precise synchronization between wireless stations. On physical level, it uses Wi-Fi chip coexistence signal lines and UART frame encoding. On its basis, we propose an efficient communication protocol providing precise timestamping of incoming frames and internal diagnostic mechanisms for detecting communication faults. Meanwhile it is simple enough to be implemented both in low-cost FPGA and commodity IEEE802.11 chip firmware. The results of computer simulation shows that developed FPGA implementation of the proposed protocol can precisely timestamp incoming frames as well as detect most of communication errors even in conditions of high interference. The probability of undetected errors was investigated. The results of this analysis are significant for the development of novel wireless synchronization hardware.
The recent successes and wide spread application of compute intensive machine learning and data analytics methods have been boosting the usage of the Python programming language on HPC systems. While Python provides many advantages for the users, it has not been designed with a focus on multiuser environments or parallel programming - making it quite challenging to maintain stable and secure Python workflows on a HPC system. In this paper, we analyze the key problems induced by the usage of Python on HPC clusters and sketch appropriate workarounds for efficiently maintaining multi-user Python software environments, securing and restricting resources of Python jobs and containing Python processes, while focusing on Deep Learning applications running on GPU clusters.
The development of Internet of Things (IoT) embedded devices is proliferating, especially in the smart home automation system. However, the devices unfortunately are imposing overhead on the IoT network. Thus, the Internet Engineering Task Force (IETF) have introduced the IPv6 Low-Power Wireless Personal Area Network (6LoWPAN) to provide a solution to this constraint. 6LoWPAN is an Internet Protocol (IP) based communication where it allows each device to connect to the Internet directly. As a result, the power consumption is reduced. However, the limitation of data transmission frame size of the IPv6 Routing Protocol for Low-power and Lossy Network’s (RPL’s) had made it to be the running overhead, and thus consequently degrades the performance of the network in terms of Quality of Service (QoS), especially in a large network. Therefore, HRPL was developed to enhance the RPL protocol to minimize redundant retransmission that causes the routing overhead. We introduced the T-Cut Off Delay to set the limit of the delay and the H field to respond to actions taken within the T-Cut Off Delay. Thus, this paper presents the comparison performance assessment of HRPL between simulation and real-world scenarios (6LoWPAN Smart Home System (6LoSH) testbed) in validating the HRPL functionalities. Our results show that HRPL had successfully reduced the routing overhead when implemented in 6LoSH. The observed Control Traffic Overhead (CTO) packet difference between each experiment is 7.1%, and the convergence time is 9.3%. Further research is recommended to be conducted for these metrics: latency, Packet Delivery Ratio (PDR), and throughput.
The authentication method of electronic devices, based on individual forms of correlograms of their internal electric noises, is well-known. Specific physical differences in the components – for example, caused by variations in production quality – cause specific electrical signals, i.e. electric noise, in the electronic device. It is possible to obtain this information and to identify the specific differences of the individual devices using an embedded analog-to-digital converter (ADC). These investigations confirm the possibility to identify and authenticate electronic devices using bit templates, calculated from the sequence of values of the normalized autocorrelation function of noise. Experiments have been performed using personal computers. The probability of correct identification and authentication increases with increasing noise recording duration. As a result of these experiments, an accuracy of 98.1% was achieved for a 1 second-long registration of EM for a set of investigated computers.
Time Sensitive Networking (TSN) provides mechanisms to enable deterministic and real-time networking in industrial networks. Configuration of these mechanisms is key to fully deploy and integrate TSN in the networks. The IEEE 802.1 Qcc standard has proposed different configuration models to implement a TSN configuration. Up until now, TSN and its configuration have been explored mostly for Ethernet-based industrial networks. However, they are still considered “work-in-progress” for wireless networks. This work focuses on the fully centralized model and describes a generic concept to enable the configuration of TSN mechanisms in wireless industrial networks. To this end, a configuration entity is implemented to conFigure the wireless end stations to satisfy their requirements. The proposed solution is then validated with the Digital Enhanced Cordless Telecommunication ultra-low energy (DECT ULE) wireless communication protocol.
Analysis of Amplitude and Phase Errors in Digital-Beamforming Radars for Automotive Applications
(2020)
Fundamentally, automotive radar sensors with Digital-Beamforming (DBF) use several transmitter and receiver antennas to measure the direction of the target. However, hardware imperfections, tolerances in the feeding lines of the antennas, coupling effects as well as temperature changes and ageing will cause amplitude and phase errors. These errors can lead to misinterpretation of the data and result in hazardous actions of the autonomous system. First, the impact of amplitude and phase errors on angular estimation is discussed and analyzed by simulations. The results are compared with the measured errors of a real radar sensor. Further, a calibration method is implemented and evaluated by measurements.
With the increasing degree of interconnectivity in industrial factories, security becomes more and more the most important stepping-stone towards wide adoption of the Industrial Internet of Things (IIoT). This paper summarizes the most important aspects of one keynote of DESSERT2020 conference. It highlights the ongoing and open research activities on the different levels, from novel cryptographic algorithms over security protocol integration and testing to security architectures for the full lifetime of devices and systems. It includes an overview of the research activities at the authors' institute.
RETIS – Real-Time Sensitive Wireless Communication Solution for Industrial Control Applications
(2020)
Ultra-Reliable Low Latency Communications (URLLC) has been always a vital component of many industrial applications. The paper proposes a new wireless URLLC solution called RETIS, which is suitable for factory automation and fast process control applications, where low latency, low jitter, and high data exchange rates are mandatory. In the paper, we describe the communication protocol as well as the hardware structure of the network nodes for implementing the required functionality. Many techniques enabling fast, reliable wireless transmissions are used – short Transmission Time Interval (TTI), Time-Division Multiple Access (TDMA), MIMO, optional duplicated data transfer, Forward Error Correction (FEC), ACK mechanism. Preliminary tests show that reliable end-to-end latency down to 350 μs and packet exchange rate up to 4 kHz can be reached (using quadruple MIMO and standard IEEE 802.15.4 PHY at 250 kbit/s).
Many different methods, such as screen printing, gravure, flexography, inkjet etc., have been employed to print electronic devices. Depending on the type and performance of the devices, processing is done at low or high temperature using precursor- or particle-based inks. As a result of the processing details, devices can be fabricated on flexible or non-flexible substrates, depending on their temperature stability. Furthermore, in order to reduce the operating voltage, printed devices rely on high-capacitance electrolytes rather than on dielectrics. The printing resolution and speed are two of the major challenging parameters for printed electronics. High-resolution printing produces small-size printed devices and high-integration densities with minimum materials consumption. However, most printing methods have resolutions between 20 and 50 μm. Printing resolutions close to 1 μm have also been achieved with optimized process conditions and better printing technology.
The final physical dimensions of the devices pose severe limitations on their performance. For example, the channel lengths being of this dimension affect the operating frequency of the thin-film transistors (TFTs), which is inversely proportional to the square of channel length. Consequently, short channels are favorable not only for high-frequency applications but also for high-density integration. The need to reduce this dimension to substantially smaller sizes than those possible with today’s printers can be fulfilled either by developing alternative printing or stamping techniques, or alternative transistor geometries. The development of a polymer pen lithography technique allows scaling up parallel printing of a large number of devices in one step, including the successive printing of different materials. The introduction of an alternative transistor geometry, namely the vertical Field Effect Transistor (vFET), is based on the idea to use the film thickness as the channel length, instead of the lateral dimensions of the printed structure, thus reducing the channel length by orders of magnitude. The improvements in printing technologies and the possibilities offered by nanotechnological approaches can result in unprecedented opportunities for the Internet of Things (IoT) and many other applications. The vision of printing functional materials, and not only colors as in conventional paper printing, is attractive to many researchers and industries because of the added opportunities when using flexible substrates such as polymers and textiles. Additionally, the reduction of costs opens new markets. The range of processing techniques covers laterally-structured and large-area printing technologies, thermal, laser and UV-annealing, as well as bonding techniques, etc. Materials, such as conducting, semiconducting, dielectric and sensing materials, rigid and flexible substrates, protective coating, organic, inorganic and polymeric substances, energy conversion and energy storage materials constitute an enormous challenge in their integration into complex devices.
Machine learning (ML) has become highly relevant in applications across all industries, and specialists in the field are sought urgently. As it is a highly interdisciplinary field, requiring knowledge in computer science, statistics and the relevant application domain, experts are hard to find. Large corporations can sweep the job market by offering high salaries, which makes the situation for small and medium enterprises (SME) even worse, as they usually lack the capacities both for attracting specialists and for qualifying their own personnel. In order to meet the enormous demand in ML specialists, universities now teach ML in specifically designed degree programs as well as within established programs in science and engineering. While the teaching almost always uses practical examples, these are somewhat artificial or outdated, as real data from real companies is usually not available. The approach reported in this contribution aims to tackle the above challenges in an integrated course, combining three independent aspects: first, teaching key ML concepts to graduate students from a variety of existing degree programs; second, qualifying working professionals from SME for ML; and third, applying ML to real-world problems faced by those SME. The course was carried out in two trial periods within a government-funded project at a university of applied sciences in south-west Germany. The region is dominated by SME many of which are world leaders in their industries. Participants were students from different graduate programs as well as working professionals from several SME based in the region. The first phase of the course (one semester) consists of the fundamental concepts of ML, such as exploratory data analysis, regression, classification, clustering, and deep learning. In this phase, student participants and working professionals were taught in separate tracks. Students attended regular classes and lab sessions (but were also given access to e-learning materials), whereas the professionals learned exclusively in a flipped classroom scenario: they were given access to e-learning units (video lectures and accompanying quizzes) for preparation, while face-to-face sessions were dominated by lab experiments applying the concepts. Prior to the start of the second phase, participating companies were invited to submit real-world problems that they wanted to solve with the help of ML. The second phase consisted of practical ML projects, each tackling one of the problems and worked on by a mixed team of both students and professionals for the period of one semester. The teams were self-organized in the ways they preferred to work (e.g. remote vs. face-to-face collaboration), but also coached by one of the teaching staff. In several plenary meetings, the teams reported on their status as well as challenges and solutions. In both periods, the course was monitored and extensive surveys were carried out. We report on the findings as well as the lessons learned. For instance, while the program was very well-received, professional participants wished for more detailed coverage of theoretical concepts. A challenge faced by several teams during the second phase was a dropout of student members due to upcoming exams in other subjects.
Novel manufacturing technologies, such as printed electronics, may enable future applications for the Internet of Everything like large-area sensor devices, disposable security, and identification tags. Printed physically unclonable functions (PUFs) are promising candidates to be embedded as hardware security keys into lightweight identification devices. We investigate hybrid PUFs based on a printed PUF core. The statistics on the intra- and inter-hamming distance distributions indicate a performance suitable for identification purposes. Our evaluations are based on statistical simulations of the PUF core circuit and the thereof generated challenge-response pairs. The analysis shows that hardware-intrinsic security features can be realized with printed lightweight devices.
Mit der Implementierung sowie einer anschließenden aussagekräftigen Evaluierung, soll das, visuelle-inertiale Kartierungs- und Lokalisierungssystem maplab analysiert werden. Hierbei basiert die Kartierung bzw. Lokalisierung auf der Detektion von Umgebungsmerkmalen. Neben der Möglichkeit der Kartenerstellung besteht ferner die Option, mehrere Karten zu fusionieren und somit weitreichende Gebiete zu kartieren sowie für weitere Datenauswertungen zu nutzen. Aufgrund der Durchführung und Bewertung der Ergebnisse in unterschiedlichen Anwendungsszenarien zeigt sich, dass maplab besonders zur Kartierung von Räumen bzw. kleinen Gebäudekomplexen geeignet ist. Die Möglichkeit der Kartenfusionierung bietet weiterhin die Option, den Informationsgehalt von Karten zu erhöhen, welches die Effektivität für eine anschließende Lokalisierung steigert. Bei wachsender Kartierungsgröße hingegen zeigt sich jedoch eine Vergrößerung geometrischer Inkonsistenzen.
In this contribution, we propose an system setup for the detection andclassification of objects in autonomous driving applications. The recognition algo-rithm is based upon deep neural networks, operating in the 2D image domain. Theresults are combined with data of a stereo camera system to finally incorporatethe 3D object information into our mapping framework. The detection systemis locally running upon the onboard CPU of the vehicle. Several network archi-tectures are implemented and evaluated with respect to accuracy and run-timedemands for the given camera and hardware setup.
Multiple Object Tracking (MOT) is a long-standing task in computer vision. Current approaches based on the tracking by detection paradigm either require some sort of domain knowledge or supervision to associate data correctly into tracks. In this work, we present a self-supervised multiple object tracking approach based on visual features and minimum cost lifted multicuts. Our method is based on straight-forward spatio-temporal cues that can be extracted from neighboring frames in an image sequences without supervision. Clustering based on these cues enables us to learn the required appearance invariances for the tracking task at hand and train an AutoEncoder to generate suitable latent representations. Thus, the resulting latent representations can serve as robust appearance cues for tracking even over large temporal distances where no reliable spatio-temporal features can be extracted. We show that, despite being trained without using the provided annotations, our model provides competitive results on the challenging MOT Benchmark for pedestrian tracking.
Bei dem vorgestellten Ansatz soll der Auftreffpunkt des Pfeils durch die Kreuzkorrelation von Audio-Signalen bestimmt werden. Das Auftreffen des Pfeils erzeugt ein charakteristisches Geräusch, welches von mehreren Mikrofonen in bestimmter Anordnung um die Dartscheibe herum in elektrische Signale umgewandelt wird. Mithilfe der Schallgeschwindigkeit und den Zeitdifferenzen, welche die Schallwelle zu den einzelnen Mikrofonen benötigt soll dann der Auftreffpunkt berechnet werden.
An Empirical Study of Explainable AI Techniques on Deep Learning Models For Time Series Tasks
(2021)
Decision explanations of machine learning black-box models are often generated by applying Explainable AI (XAI) techniques. However, many proposed XAI methods produce unverified outputs. Evaluation and verification are usually achieved with a visual interpretation by humans on individual images or text. In this preregistration, we propose an empirical study and benchmark framework to apply attribution methods for neural networks developed for images and text data on time series. We present a methodology to automatically evaluate and rank attribution techniques on time series using perturbation methods to identify reliable approaches.
The precise positioning of mobile systems is a prerequisite for any autonomous behavior, in an industrial environment as well as for field robotics. The paper describes the set up for an experimental platform and its use for the evaluation of simultaneous localization and mapping (SLAM) algorithms. Two approaches are compared. First, a local method based on point cloud matching and integration of inertial measurement units is evaluated. Subsequent matching makes it possible to create a three-dimensional point cloud that can be used as a map in subsequent runs. The second approach is a full SLAM algorithm, based on graph relaxation models, incorporating the full sensor suite of odometry, inertial sensors, and 3D laser scan data.
Neuromorphic computing systems have demonstrated many advantages for popular classification problems with significantly less computational resources. We present in this paper the design, fabrication and training of a programmable neuromorphic circuit, which is based on printed electrolytegated field-effect transistor (EGFET). Based on printable neuron architecture involving several resistors and one transistor, the proposed circuit can realize multiply-add and activation functions. The functionality of the circuit, i.e. the weights of the neural network, can be set during a post-fabrication step in form of printing resistors to the crossbar. Besides the fabrication of a programmable neuron, we also provide a learning algorithm, tailored to the requirements of the technology and the proposed programmable neuron design, which is verified through simulations. The proposed neuromorphic circuit operates at 5V and occupies 385mm 2 of area.
A novel approach for synchronization and calibration of a camera and an inertial measurement unit (IMU) in the research-oriented visual-inertial mapping-and localization-framework maplab is presented. Mapping and localization are based on detecting different features in the environment. In addition to the possibility of creating single-case maps, the included algorithms allow merging maps to increase mapping accuracy and obtain large-scale maps. Furthermore, the algorithms can be used to optimize the collected data. The preliminary results show that after appropriate calibration and synchronization maplab can be used efficiently for mapping, especially in rooms and small building environments.
Background: A disturbed synchronization of the ventricular contraction can cause a highly developed systolic heart failure in affected patients, which can often be explained by a diseased left bundle branch block (LBBB). If medication remains unresponsive, the concerned patients will be treated with a cardiac resynchronization therapy (CRT) system. The aim of this study was to integrate His bundle pacing into the Offenburg heart rhythm model in order to visualize the electrical pacing field generated by His bundle pacing.
Methods: Modelling and electrical field simulation activities were performed with the software CST (Computer Simulation Technology) from Dessault Systèms. CRT with biventricular pacing is to be achieved by an apical right ventricular electrode and an additional left ventricular electrode, which is floated into the coronary vein sinus. This conventional type of biventricular pacing leads to a reduction of the left ventricular ejection fraction. Furthermore, the non-responder rate of the CRT therapy is about one third of the CRT patients.
Results: His bundle pacing represents a physiological alternative to conventional cardiac pacing and cardiac resynchronization. An electrode implanted in the His bundle emits a stronger electrical pacing field than the electrical pacing field of conventional cardiac pacemakers. The pacing of the His bundle was performed by the Medtronic Select Secure 3830 electrode with pacing voltage amplitudes of 3 V, 2 V and 1.5 V in combination with a pacing pulse duration of 1 ms.
Conclusions: Compared to conventional cardiac pacemaker pacing, His bundle pacing is capable of bridging LBBB conduction disorders in the left ventricle. The His bundle pacing electrical field is able to spread via the physiological pathway in the right and left ventricles for CRT with a narrow QRS-complex in the surface ECG.
During the day-to-day exploitation of localization systems in mines, the technical staff tends to incorrectly rearrange radio equipment: positions of devices may not be accurately marked on a map or their positions may not correspond to the truth. This situation may lead to positioning inaccuracies and errors in the operation of the localization system.This paper presents two Bayesian algorithms for the automatic corrections of positions of the equipment on the map using trajectories restored by the inertial measurement units mounted to mobile objects, like pedestrians and vehicles. As a basis, a predefined map of the mine represented as undirected weighted graph was used as input. The algorithms were implemented using the Simultaneous Localization and Mapping (SLAM) approach.The results prove that both methods are capable to detect misplacement of access points and to provide corresponding corrections. The discrete Bayesian filter outperforms the unscented Kalman filter, which, however, requires more computational power.
In this work, we evaluate two different image clustering objectives, k-means clustering and correlation clustering, in the context of Triplet Loss induced feature space embeddings. Specifically, we train a convolutional neural network to learn discriminative features by optimizing two popular versions of the Triplet Loss in order to study their clustering properties under the assumption of noisy labels. Additionally, we propose a new, simple Triplet Loss formulation, which shows desirable properties with respect to formal clustering objectives and outperforms the existing methods. We evaluate all three Triplet loss formulations for K-means and correlation clustering on the CIFAR-10 image classification dataset.
Due to the rapidly increasing storage consumption worldwide, as well as the expectation of continuous availability of information, the complexity of administration in today’s data centers is growing permanently. Integrated techniques for monitoring hard disks can increase the reliability of storage systems. However, these techniques often lack intelligent data analysis to perform predictive maintenance. To solve this problem, machine learning algorithms can be used to detect potential failures in advance and prevent them. In this paper, an unsupervised model for predicting hard disk failures based on Isolation Forest is proposed. Consequently, a method is presented that can deal with the highly imbalanced datasets, as the experiment on the Backblaze benchmark dataset demonstrates.
Als Einstieg in den Diskurs über zivile Netzwerktechnologien, mobile Geräte, Onlinedienste und die Frage, wie sich die „Kirche der Zukunft“ (zumindest aus medienwissenschaftlicher Sicht) positionieren kann, dienen drei Zitate. Die Gegenüberstellung der darin vertretenen Positionen soll den Nutzen und die Folgen der zunehmend vollständigen Durchdringung (fast) aller Lebensbereiche mit Digitaltechnik für den Einzelnen wie für die Gesellschaft aufzeigen.
In this work a method for the estimation of current slopes induced by inverters operating interior permanent magnet synchronous machines is presented. After the derivation of the estimation algorithm, the requirements for a suitable sensor setup in terms of accuracy, dynamic and electromagnetic interference are discussed. The boundary conditions for the estimation algorithm are presented with respect to application within high power traction systems. The estimation algorithm is implemented on a field programmable gateway array. This moving least-square algorithm offers the advantage that it is not dependent on vectors and therefore not every measured value has to be stored. The summation of all measured values leads to a significant reduction of the required storage units and thus decreases the hardware requirements. The algorithm is designed to be calculated within the dead time of the inverter. Appropriate countermeasures for disturbances and hardware restrictions are implemented. The results are discussed afterwards.
Wireless synchronization of industrial controllers is a challenging task in environments where wired solutions are not practical. The best solutions proposed so far to solve this problem require pretty expensive and highly specialized FPGA-based devices. With this work we counter the trend by introducing a straightforward approach to synchronize a fairly cheap IEEE 802.11 integrated wireless chip (IWC) with external devices. More specifically we demonstrate how we can reprogram the software running in the 802.11 IWC of the Raspberry Pi 3B and transform the receiver input potential of the wireless transceiver into a triggering signal for an external inexpensive FPGA. Experimental results show a mean-square synchronization error of less than 496 ns, while the absolute synchronization error does not exceed 6 μs. The jitter of the output signal that we obtain after synchronizing the clock of the external device did not exceed 5.2 μs throughout the whole measurement campaign. Even though we do not score new records in term of accuracy, we do in terms of complexity, cost, and availability of the required components: all these factors make the proposed technique a very promising of the deployment of large-scale low-cost automation solutions.
Wireless communication technologies play a major role to enable megatrends like Internet of Things (IoT) and Industry 4.0. The Narrowband Wireless WAN (NBWWAN) introduced to meet the long range and low power requirements of spatially distributed wireless communication use cases. These networks introduce additional challenges in testing because the network topology and RF characteristics become particularly complex and thus a multitude of different scenarios must be tested. This paper describes the infrastructure for automated testing of radio communication and for systematic measurements of the network performance of NBWWAN.
One of the main requirements of spatially distributed Internet of Things (IoT) solutions is to have networks with wider coverage to connect many low-power devices. Low-Power Wide-Area Networks (LPWAN) and Cellular IoT(cIOT) networks are promising candidates in this space. LPWAN approaches are based on enhanced physical layer (PHY) implementations to achieve long range such as LoRaWAN, SigFox, MIOTY. Narrowband versions of cellular network offer reduced bandwidth and, simplified node and network management mechanisms, such as Narrow Band IoT (NB-IoT) and Long-Term Evolution for Machines (LTE-M). Since the underlying use cases come with various requirements it is essential to perform a comparative analysis of competing technologies. This article provides systematic performance measurement and comparison of LPWAN and NB-IoT technologies in a unified testbed, also discusses the necessity of future fifth generation (5G) LPWAN solutions.
Modeling of Random Variations in a Switched Capacitor Circuit based Physically Unclonable Function
(2020)
The Internet of Things (IoT) is expanding to a wide range of fields such as home automation, agriculture, environmental monitoring, industrial applications, and many more. Securing tens of billions of interconnected devices in the near future will be one of the biggest challenges. IoT devices are often constrained in terms of computational performance, area, and power, which demand lightweight security solutions. In this context, hardware-intrinsic security, particularly physically unclonable functions (PUFs), can provide lightweight identification and authentication for such devices. In this paper, random capacitor variations in a switched capacitor PUF circuit are used as a source of entropy to generate unique security keys. Furthermore, a mathematical model based on the ordinary least square method is developed to describe the relationship between random variations in capacitors and the resulting output voltages. The model is used to filter out systematic variations in circuit components to improve the quality of the extracted secrets.
To demonstrate how deep learning can be applied to industrial applications with limited training data, deep learning methodologies are used in three different applications. In this paper, we perform unsupervised deep learning utilizing variational autoencoders and demonstrate that federated learning is a communication efficient concept for machine learning that protects data privacy. As an example, variational autoencoders are utilized to cluster and visualize data from a microelectromechanical systems foundry. Federated learning is used in a predictive maintenance scenario using the C-MAPSS dataset.
The number of use cases for autonomous vehicles is increasing day by day especially in commercial applications. One important application of autonomous vehicles can be found within the parcel delivery section. Here, autonomous cars can massively help to reduce delivery efforts and time by supporting the courier actively. One important component of course is the autonomous vehicle itself. Nevertheless, beside the autonomous vehicle, a flexible and secure communication architecture also is a crucial key component impacting the overall performance of such system since it is required to allow continuous interactions between the vehicle and the other components of the system. The communication system must provide a reliable and secure architecture that is still flexible enough to remain practical and to address several use cases. In this paper, a robust communication architecture for such autonomous fleet-based systems is proposed. The architecture provides a reliable communication between different system entities while keeping those communications secure. The architecture uses different technologies such as Bluetooth Low Energy (BLE), cellular networks and Low Power Wide Area Network (LPWAN) to achieve its goals.
Investigation of the Angle Dependency of Self-Calibration in Multiple-Input-Multiple-Output Radars
(2021)
Multiple-Input-Multiple-Output (MIMO) is a key technology in improving the angular resolution (spatial resolution) of radars. In MIMO radars the amplitude and phase errors in antenna elements lead to increase in the sidelobe level and a misalignment of the mainlobe. As the result the performance of the antenna channels will be affected. Firstly, this paper presents analysis of effect of the amplitude and phase errors on angular spectrum using Monte-Carlo simulations. Then, the results are compared with performed measurements. Finally, the error correction with a self-calibration method is proposed and its angle dependency is evaluated. It is shown that the values of the errors change with an incident angle, which leads to a required angle-dependent calibration.
Estimation of Scattering and Transfer Parameters in Stratified Dispersive Tissues of the Human Torso
(2021)
The aim of this study is to understand the effect of the various layers of biological tissues on electromagnetic radiation in a certain frequency range. Understanding these effects could prove crucial in the development of dynamic imaging systems under operating environments during catheter ablation in the heart. As the catheter passes through some arterial paths in the region of interest inside the heart through the aorta, a three-dimensional localization of the catheter is required. In this paper, a study is given on the detection of the catheter by using electromagnetic waves. Therefor, an appropriate model for the layers of the human torso is defined and simulated without and with an inserted electrode.
Elektronische Türschilder zur Darstellung von Informationen sind insbesondere in öffentlichen Gebäuden zwischenzeitlich weit verbreitet. Die Varianz dieser elektronischen Türschilder reicht vom Tablet-basierten Türschild bis hin zum PC-basierten Türschild mit externem Bildschirm. Zumeist werden die Systeme mit 230 V betrieben. Bei einer großen Summe von Türschildern in öffentlichen Gebäuden kann dies zu einem signifikanten Umsatz an Energie führen. Im Rahmen dieses Papers wird die Entwicklung eines energieautarken arbeiten Türschildes vorgestellt, bei dem ein E-Paper-Display zum Einsatz kommt. Das Türschild lässt sich per Smartphone-App und NFC-Schnittstelle konfigurieren. Es wird insbesondere auf das Low-Power-Hardware-Design der Elektronik und energetische Aspekte eingegangen.
Artificial intelligence (AI), and in particular machine learning algorithms, are of increasing importance in many application areas but interpretability and understandability as well as responsibility, accountability, and fairness of the algorithms' results, all crucial for increasing the humans' trust into the systems, are still largely missing. Big industrial players, including Google, Microsoft, and Apple, have become aware of this gap and recently published their own guidelines for the use of AI in order to promote fairness, trust, interpretability, and other goals. Interactive visualization is one of the technologies that may help to increase trust in AI systems. During the seminar, we discussed the requirements for trustworthy AI systems as well as the technological possibilities provided by interactive visualizations to increase human trust in AI.
IoT networks are increasingly used as entry points for cyberattacks, as often they offer low-security levels, as they may allow the control of physical systems and as they potentially also open the access to other IT networks and infrastructures. Existing intrusion detection systems (IDS) and intrusion prevention systems (IPS) mostly concentrate on legacy IT networks. Nowadays, they come with a high degree of complexity and adaptivity, including the use of artificial intelligence. It is only recently that these techniques are also applied to IoT networks. In this paper, we present a survey of machine learning and deep learning methods for intrusion detection, and we investigate how previous works used federated learning for IoT cybersecurity. For this, we present an overview of IoT protocols and potential security risks. We also report the techniques and the datasets used in the studied works, discuss the challenges of using ML, DL and FL for IoT cybersecurity and provide future insights.
Active participation of industrial enterprises in electricity markets - a generic modeling approach
(2021)
Industrial enterprises represent a significant portion of electricity consumers with the potential of providing demand-side energy flexibility from their production processes and on-site energy assets. Methods are needed for the active and profitable participation of such enterprises in the electricity markets especially with variable prices, where the energy flexibility available in their manufacturing, utility and energy systems can be assessed and quantified. This paper presents a generic model library equipped with optimal control for energy flexibility purposes. The components in the model library represent the different technical units of an industrial enterprise on material, media, and energy flow levels with their process constraints. The paper also presents a case study simulation of a steel-powder manufacturing plant using the model library. Its energy flexibility was assessed when the plant procured its electrical energy at fixed and variable electricity prices. In the simulated case study, flexibility use at dynamic prices resulted in a 6% cost reduction compared to a fixed-price scenario, with battery storage and the manufacturing system making the largest contributions to flexibility.
The Go programming language is an increasingly popular language but some of its features lack a formal investigation. This article explains Go's resolution mechanism for overloaded methods and its support for structural subtyping by means of translation from Featherweight Go to a simple target language. The translation employs a form of dictionary passing known from type classes in Haskell and preserves the dynamic behavior of Featherweight Go programs.
We describe a prototype for power line communi- cation for grid monitoring. The PLC receiver is used to gain information about the PLC channel and the current state of the power grid. The PLC receiver uses the communication signal to obtain an accurate estimate of the current channel and provides information which can be used as a basis for further processing with the aim to detect partial discharges and other anomalies in the grid. This monitoring of the power grid takes advantage of existing PLC infrastructure and uses the data signals, which are transmitted anyway to obtain a real-time measurement of the channel transfer function and the received noise signal. Since this signal is sampled at a high sampling rate compared to simpler measurement sensors, it contains valuable information about possible degradations in the grid which need to be addressed. While channel measurements are based on a received PLC signal, information about partial discharges or other sources of interference can be gathered by a PLC receiver in the absence of a transmit signal. A prototype based on Software Defined Radio has been developed, which implements the simultaneous communication and sensing for a power grid.
The term “attribute transfer” refers to the tasks of altering images in such a way, that the semantic interpretation of a given input image is shifted towards an intended direction, which is quantified by semantic attributes. Prominent example applications are photo realistic changes of facial features and expressions, like changing the hair color, adding a smile, enlarging the nose or altering the entire context of a scene, like transforming a summer landscape into a winter panorama. Recent advances in attribute transfer are mostly based on generative deep neural networks, using various techniques to manipulate images in the latent space of the generator. In this paper, we present a novel method for the common sub-task of local attribute transfers, where only parts of a face have to be altered in order to achieve semantic changes (e.g. removing a mustache). In contrast to previous methods, where such local changes have been implemented by generating new (global) images, we propose to formulate local attribute transfers as an inpainting problem. Removing and regenerating only parts of images, our “Attribute Transfer Inpainting Generative Adversarial Network” (ATI-GAN) is able to utilize local context information to focus on the attributes while keeping the background unmodified resulting in visually sound results.
The following describes a new method for estimating the parameters of an interior permanent magnet synchronous machine (IPMSM). For the estimation of the parameters the current slopes caused by the switching of the inverter are used to determine the unknowns of the system equations of the electrical machine. The angle and current dependence of the machine parameters are linearized within a PWM cycle. By considering the different switching states of the inverter, several system equations can be derived and a solution can be found within one PWM cycle. The use of test signals and filter-based approaches is avoided. The derived algorithm is explained and validated with measurements on a test bench.
The nonlinear behavior of inverters is mainly influenced by the interlocking and switching times of the semiconductors. In the following work, a method is presented that enables the possibility of an online identification of the switching times of the semiconductors. This information allows a compensation of the non-linear behavior, a reduction of the locking time and can be used for diagnostic purposes. First, a theoretical derivation of the method is made by considering different cases when switching of the inverter and deriving identification possibilities. The method is then extended so that the entire module is taken into account. Furthermore, a possible theoretical implementation is shown. After the methodology has been investigated with possible limitations, boundary conditions and with respect to real hardware, an implementation in the FPGA is performed. Finally, the results are presented, discussed
and further improvements are presented in an outlook.
Patients with focal ventricular tachycardia are at risk of hemodynamic failure and if no treatment is provided the mortality rate can exceed 30%. Therefore, medical professionals must be adequately trained in the management of these conditions. To achieve the best treatment, the origin of the abnormality should be known, as well as the course of the disease. This study provides an opportunity to visualize various focal ventricular tachycardias using the Offenburg cardiac rhythm model.
Disturbances of the cardiac conduction system causing reentry mechanisms above the atrioventricular (AV) node are induced by at least one accessory pathway with different conducting properties and refractory periods. This work aims to further develop the already existing and continuously expanding Offenburg heart rhythm model to visualise the most common supraventricular reentry tachycardias to provide a better understanding of the cause of the respective reentry mechanism.
Printed electronics (PE) offers flexible, extremely low-cost, and on-demand hardware due to its additive manufacturing process, enabling emerging ultra-low-cost applications, including machine learning applications. However, large feature sizes in PE limit the complexity of a machine learning classifier (e.g., a neural network (NN)) in PE. Stochastic computing Neural Networks (SC-NNs) can reduce area in silicon technologies, but still require complex designs due to unique implementation tradeoffs in PE. In this paper, we propose a printed mixed-signal system, which substitutes complex and power-hungry conventional stochastic computing (SC) components by printed analog designs. The printed mixed-signal SC consumes only 35% of power consumption and requires only 25% of area compared to a conventional 4-bit NN implementation. We also show that the proposed mixed-signal SC-NN provides good accuracy for popular neural network classification problems. We consider this work as an important step towards the realization of printed SC-NN hardware for near-sensor-processing.
Physically Unclonable Functions (PUFs) are hardware-based security primitives, which allow for inherent device fingerprinting. Therefore, intrinsic variation of imperfect manufactured systems is exploited to generate device-specific, unique identifiers. With printed electronics (PE) joining the internet of things (IoT), hardware-based security for novel PE-based systems is of increasing importance. Furthermore, PE offers the possibility for split-manufacturing, which mitigates the risk of PUF response readout by third parties, before commissioning. In this paper, we investigate a printed PUF core as intrinsic variation source for the generation of unique identifiers from a crossbar architecture. The printed crossbar PUF is verified by simulation of a 8×8-cells crossbar, which can be utilized to generate 32-bit wide identifiers. Further focus is on limiting factors regarding printed devices, such as increased parasitics, due to novel materials and required control logic specifications. The simulation results highlight, that the printed crossbar PUF is capable to generate close-to-ideal unique identifiers at the investigated feature size. As proof of concept a 2×2-cells printed crossbar PUF core is fabricated and electrically characterized.
Das hier vorgestellte System verbindet das neue Konzept der Peer-to-Peer-Navigation mit dem Einsatz von Augmented Reality zur Unterstützung von bettseitig durchgeführten externen Ventrikeldrainagen. Das sehr kompakte und genaue Gesamtsystem beinhaltet einen Patiententracker mit integrierter Kamera, eine Augmented-Reality-Brille mit Kamera und eine Punktionsnadel bzw. einen Pointer mit zwei Trackern, mit dessen Hilfe die Anatomie des Patienten aufgenommen wird. Die exakte Position und Richtung der Punktionsnadel wird unter Zuhilfenahme der aufgenommenen Landmarken berechnet und über die Augmented-Reality-Brille für den Chirurgen sichtbar auf dem Patienten dargestellt. Die Methode zur Kalibrierung der statischen Transformationen zwischen Patiententracker und daran befestigter Kamera beziehungsweise zwischen den Trackern der Punktionsnadel sind für die Genauigkeit sehr wichtig und werden hier vorgestellt. Das Gesamtsystem konnte in vitro erfolgreich getestet werden und bestätigt den Nutzen eines Peer-to-Peer-Navigationssystems.
The twin concept is increasingly used for optimization tasks in the context of Industry 4.0 and digitization. The twin concept can also help small and medium-sized enterprises (SME) to exploit their energy flexibility potential and to achieve added value by appropriate energy marketing. At the same time, this use of flexibility helps to realize a climate-neutral energy supply with high shares of renewable energies. The digital twin reflects real production, power flows and market influences as a computer model, which makes it possible to simulate and optimize on-site interventions and interactions with the energy market without disturbing the real production processes. This paper describes the development of a generic model library that maps flexibility-relevant components and processes of SME, thus simplifying the creation of a digital twin. The paper also includes the development of an experimental twin consisting of SME hardware components and a PLC-based SCADA system. The experimental twin provides a laboratory environment in which the digital twin can be tested, further developed and demonstrated on a laboratory scale. Concrete implementations of such a digital twin and experimental twin are described as examples.
In bimodal cochlear implant (CI) / hearing aid (HA) users a constant interaural time delay in the order of several milliseconds occurs due to differences in signal processing of the devices. For MED-EL CI systems in combination with different HA types, we have quantified the respective device delay mismatch (Zirn et al. 2015). In the current study, we investigate the effect of the device delay mismatch in simulated and actual bimodal listeners on sound localization accuracy.
To deal with the device delay mismatch in actual bimodal listeners we delayed the CI stimulation according to the measured HA processing delay and two other values. With all delay values highly significant improvements of the rms error in the localization task were observed compared to the test without the delay. The results help to narrow down the optimal patient-specific delay value.
Zeitliche Anpassung führt zu verbesserter Schalllokalisation bei bimodal versorgten CI-/HG-Trägern
(2021)
Bei bimodal versorgten Cochlea-Implantaten (CI) / Hörgerät (HG)-Trägern entsteht durch die unterschiedliche Signalverarbeitung der Geräte eine konstante interaurale Zeitverzögerung in der Größenordnung von mehreren Millisekunden. Für MED-EL CI-Systeme in Kombination mit verschiedenen HG-Typen haben wir den jeweiligen Device-Delay-Mismatch quantifiziert. In der aktuellen Studie untersuchen wir den Einfluss der Device-Delay-Mismatch bei simulierten und tatsächlichen bimodalen Hörern auf die Genauigkeit der Schalllokalisation.
Um den Device-Delay-Mismatch bei bimodal versorgten Patienten zu verringern, haben wir die CI-Stimulation um die gemessene HG-Signallaufzeit und zwei weitere Werte verzögert. Nach einer Angewöhnungsphase war der effektive Winkelfehler bei Verzögerung um die HG-Signallaufzeit hochsignifikant reduziert im Vergleich zu der Testkondition ohne CI-Verzögerung (mittlere Verbesserung: 11 % ; p < .01, Wilcoxon Signed Rank Test). Aber auch mit den beiden weiteren Verzögerungswerten wurden Verbesserungen erreicht. Anhand der Ergebnisse lässt sich der optimale patientenspezifische Verzögerungswert näher eingrenzen.
Recently, adversarial attacks on image classification networks by the AutoAttack (Croce and Hein, 2020b) framework have drawn a lot of attention. While AutoAttack has shown a very high attack success rate, most defense approaches are focusing on network hardening and robustness enhancements, like adversarial training. This way, the currently best-reported method can withstand about 66% of adversarial examples on CIFAR10. In this paper, we investigate the spatial and frequency domain properties of AutoAttack and propose an alternative defense. Instead of hardening a network, we detect adversarial attacks during inference, rejecting manipulated inputs. Based on a rather simple and fast analysis in the frequency domain, we introduce two different detection algorithms. First, a black box detector that only operates on the input images and achieves a detection accuracy of 100% on the AutoAttack CIFAR10 benchmark and 99.3% on ImageNet, for epsilon = 8/255 in both cases. Second, a whitebox detector using an analysis of CNN feature maps, leading to a detection rate of also 100% and 98.7% on the same benchmarks.