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Printed systems spark immense interest in industry, and for several parts such as solar cells or radio frequency identification antennas, printed products are already available on the market. This has led to intense research; however, printed field-effect transistors (FETs) and logics derived thereof still have not been sufficiently developed to be adapted by industry. Among others, one of the reasons for this is the lack of control of the threshold voltage during production. In this work, we show an approach to adjust the threshold voltage (Vth) in printed electrolyte-gated FETs (EGFETs) with high accuracy by doping indium-oxide semiconducting channels with chromium. Despite high doping concentrations achieved by a wet chemical process during precursor ink preparation, good on/off-ratios of more than five orders of magnitude could be demonstrated. The synthesis process is simple, inexpensive, and easily scalable and leads to depletion-mode EGFETs, which are fully functional at operation potentials below 2 V and allows us to increase Vth by approximately 0.5 V.
Harnessing the overall benefits of the latest advancements in artificial intelligence (AI) requires the extensive collaboration of academia and industry. These collaborations promote innovation and growth while enforcing the practical usefulness of newer technologies in real life. The purpose of this article is to outline the challenges faced during cross-collaboration between academia and industry. These challenges are also inspected with the help of an ongoing project titled “Quality Assurance of Machine Learning Applications” (Q-AMeLiA), in which three universities cooperate with five industry partners to make the product risk of AI-based products visible. Further, we discuss the hurdles and the key challenges in machine learning (ML) technology transformation from academia to industry based on robustness, simplicity, and safety. These challenges are an outcome of the lack of common standards, metrics, and missing regulatory considerations when state-of-the-art (SOTA) technology is developed in academia. The use of biased datasets involves ethical concerns that might lead to unfair outcomes when the ML model is deployed in production. The advancement of AI in small and medium sized enterprises (SMEs) requires more in terms of common tandardization of concepts rather than algorithm breakthroughs. In this paper, in addition to the general challenges, we also discuss domain specific barriers for five different domains i.e., object detection, hardware benchmarking, continual learning, action recognition, and industrial process automation, and highlight the steps necessary for successfully managing the cross-sectoral collaborations between academia and industry.
Spatially Distributed Wireless Networks (SDWN) are one of the basic technologies for the Internet of Things (IoT) and (Industrial) Internet of Things (IIoT) applications. These SDWN for many of these applications has strict requirements such as low cost, simple installation and operations, and high potential flexibility and mobility. Among the different Narrowband Wireless Wide Area Networking (NBWWAN) technologies, which are introduced to address these categories of wireless networking requirements, Narrowband Internet of Things (NB-IoT) is getting more traction due to attractive system parameters, energy-saving mode of operation with low data rates and bandwidth, and its applicability in 5G use cases. Since several technologies are available and because the underlying use cases come with various requirements, it is essential to perform a systematic comparative analysis of competing technologies to choose the right technology. It is also important to perform testing during different phases of the system development life cycle. This paper describes the systematic test environment for automated testing of radio communication and systematic measurements of the performance of NB-IoT.
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.
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.
Die Erfindung betrifft ein Verfahren zur Synchronisation eines Netzwerkgeräts für die drahtlose Kommunikation, insbesondere eines Netzwerk-Endgeräts, in einem Drahtlosnetzwerk, wobei das Netzwerkgerät einen integrierten Schaltkreis für die drahtlose Kommunikation (IWC), eine Synchronisationsevent-Detektoreinrichtung (SED) für das Detektieren von Synchronisationsevents, einen steuerbaren Clock-Generator (CCG) für das Erzeugen eines synchronisierten Zeitsignals TCCGund eine Synchronisationssteuereinrichtung (SCD) zur Steuerung des Synchronisationsvorgangs des Netzwerkgeräts umfasst. In dem Netzwerkgerät werden während einer Synchronisationsphase folgende Verfahrensschritte durchgeführt: Zunächst wird ein Synchronisations-Frame empfangen und ein Synchronisations-Timestamp TAPdetektiert. Anschließend wird ein Timestamp TBmittels einer im IWC enthaltenen IWC-Clock erzeugt, der die Empfangszeit des Synchronisations-Frames definiert. In einem weiteren Schritt wird an einem Port des IWC ein Potenzialwechsel erzeugt, der einen Synchronisationsevent darstellt. Weiterhin wird ein Timestamp TSEmittels der IWC-Clock erzeugt, der den Zeitpunkt des Synchronisationsevents definiert. Die SED detektiert den Synchronisationsevent durch Auswerten der zeitlichen Länge des Potenzialwechsels des Ports des IWC und erzeugt einen Timestamp TSunter Verwendung des synchronisierten Zeitsignals TCCG, wobei der Timestamp TSdenselben Zeitpunkt des Synchronisationsevents definiert wie der Timestamp TSE. Die Timestamps TAP, TB, TSEund TS, die mittels Verarbeitung von ein oder mehreren Synchronisationsevent-Frames gemäß den Schritten (a) bis (d) ermittelt wurden, werden dann zur Synchronisierung des vom CCG erzeugten synchronisierten Zeitsignals TCCGauf das Master-Zeitsignal verwendet.
Die vorliegende Erfindung betrifft Steuer- und Regeleinheiten für eine extrakorporale Kreislaufunterstützung sowie Systeme, umfassend eine solche Steuer- und Regeleinheit und entsprechende Verfahren. Entsprechend wird eine Steuer- und Regeleinheit Steuer- und Regeleinheit (10) für eine extrakorporale Kreislaufunterstützung vorgeschlagen, welche dazu eingerichtet ist eine Messung eines EKG-Signals (12) eines unterstützten Patienten über einen vorgegebenen Zeitraum zu empfangen, wobei das EKG-Signal (12) für jeden Zeitpunkt innerhalb eines Herzzyklus mehrere Datenpunkte umfasst. Die Steuer- und Regeleinheit (10) umfasst eine Auswerteeinheit (100), welche dazu eingerichtet ist, die Datenpunkte für mindestens einen Zeitpunkt räumlich und/oder zeitlich auszuwerten und aus den ausgewerteten Datenpunkten mindestens eine Amplitudenänderung (14) innerhalb des Herzzyklus zu bestimmen. Die Steuer- und Regeleinheit (10) ist weiterhin dazu eingerichtet, ein Steuer- und/oder Regelsignal (16) für die extrakorporale Kreislaufunterstützung an einem vorgegebenen Zeitpunkt nach der mindestens einen Amplitudenänderung (14) auszugeben.
Die vorliegende Erfindung betrifft Steuer- und Regeleinheiten für eine extrakorporale Kreislaufunterstützung sowie Systeme, umfassend eine solche Steuer- und Regeleinheit und entsprechende Verfahren. Entsprechend wird eine Steuer- und Regeleinheit (10) für eine extrakorporale Kreislaufunterstützung vorgeschlagen, welche dazu eingerichtet ist eine Messung eines EKG-Signals (12) eines unterstützten Patienten über einen vorgegebenen Zeitraum zu empfangen und für die extrakorporale Kreislaufunterstützung bereitzustellen, wobei das EKG-Signal (12) für jeden Zeitpunkt innerhalb eines Herzzyklus eine Signalhöhe aus mindestens einer EKG-Ableitung (14A, 14B) umfasst. Die Steuer- und Regeleinheit (10) umfasst eine Auswerteeinheit (16), welche dazu eingerichtet ist, eine Signaldifferenz (18) einer Signalhöhe eines aktuellen Zeitpunkts (12A) und einer Signalhöhe des vorhergehenden Zeitpunkts (12B) zu bestimmen und die Signaldifferenz (18) mit einem vorgegebenen Schwellenwert (20) zu vergleichen. Die Steuer- und Regeleinheit (10) ist weiterhin dazu eingerichtet, das EKG-Signal (22) beim Überschreiten des Schwellenwerts (20) für den aktuellen Zeitpunkt und eine vorgegebene Anzahl von nachfolgenden Zeitpunkten (28) mit einer vorgegebenen Signalhöhe (30) bereitzustellen.
In this preliminary report, we present a simple but very effective technique to stabilize the training of CNN based GANs. Motivated by recently published methods using frequency decomposition of convolutions (e.g. Octave Convolutions), we propose a novel convolution scheme to stabilize the training and reduce the likelihood of a mode collapse. The basic idea of our approach is to split convolutional filters into additive high and low frequency parts, while shifting weight updates from low to high during the training. Intuitively, this method forces GANs to learn low frequency coarse image structures before descending into fine (high frequency) details. Our approach is orthogonal and complementary to existing stabilization methods and can simply plugged into any CNN based GAN architecture. First experiments on the CelebA dataset show the effectiveness of the proposed method.
In this preliminary report, we present a simple but very effective technique to stabilize the training of CNN based GANs. Motivated by recently published methods using frequency decomposition of convolutions (eg Octave Convolutions), we propose a novel convolution scheme to stabilize the training and reduce the likelihood of a mode collapse. The basic idea of our approach is to split convolutional filters into additive high and low frequency parts, while shifting weight updates from low to high during the training. Intuitively, this method forces GANs to learn low frequency coarse image structures before descending into fine (high frequency) details. Our approach is orthogonal and complementary to existing stabilization methods and can simply plugged into any CNN based GAN architecture. First experiments on the CelebA dataset show the effectiveness of the proposed method.
Im Beitrag wird für lineare, zeitinvariante, zeitdiskrete und stabile Regelstrecken beschrieben, wie zwei bekannte Zustandsraumverfahren zur Windup-Vermeidung so miteinander kombiniert werden können, dass dadurch für sämtliche PI-Zustandsregler Strecken- und Regler-Windup verhindert wird, sofern diese Regler im unbegrenzten Fall stabil sind. Zurückgegriffen wird hierbei auf das „Additional Dynamic Element“ (ADE) von Hippe zur Vermeidung von Strecken-Windup [Hippe, P.: Windup in control – Its effects and their prevention, 2006; at – Automatisierungstechnik, 2007], dessen Übertragung auf zeitdiskrete Systeme im Beitrag kurz skizziert wird, sowie auf das Verfahren der Führungsgrößenkorrektur [Nuß, U.: at – Automatisierungstechnik, 2017] zur Vermeidung von Regler-Windup. Das vorgestellte Kombinationsverfahren setzt für die jeweilige Regelstrecke lediglich die Einbeziehung eines bereits existierenden P-Zustandsreglers voraus, der Strecken-Windup vermeidet. Die Bereitstellung eines möglichst einfachen und dennoch nicht allzu einschränkenden Kriteriums zur Überprüfung, ob ein P-Zustandsregler diese Eigenschaft besitzt, ist ebenfalls ein Anliegen des Beitrags. Diesbezüglich wird auf der Basis einer geeigneten Ljapunow-Funktion ein hinreichendes Kriterium angegeben, das umfassender ist als das in [Nuß, U.: at – Automatisierungstechnik, 2017] verwendete. Ein Beispiel aus der elektrischen Antriebstechnik demonstriert die Leistungsfähigkeit der vorgestellten Methode.
Despite the success of convolutional neural networks (CNNs) in many computer vision and image analysis tasks, they remain vulnerable against so-called adversarial attacks: Small, crafted perturbations in the input images can lead to false predictions. A possible defense is to detect adversarial examples. In this work, we show how analysis in the Fourier domain of input images and feature maps can be used to distinguish benign test samples from adversarial images. We propose two novel detection methods: Our first method employs the magnitude spectrum of the input images to detect an adversarial attack. This simple and robust classifier can successfully detect adversarial perturbations of three commonly used attack methods. The second method builds upon the first and additionally extracts the phase of Fourier coefficients of feature-maps at different layers of the network. With this extension, we are able to improve adversarial detection rates compared to state-of-the-art detectors on five different attack methods. The code for the methods proposed in the paper is available at github.com/paulaharder/SpectralAdversarialDefense
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.
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.
Die transösophageale Neurostimulation ist eine neue Therapieform und könnte unter anderem zur Schmerzlinderung während einer transösophagealen Linksherzstimulation angewendet werden. Sie ist in die Kategorie der Rückenmarksstimulation (SCS) einzuordnen, die die meist verwendete Technik der Neurostimulation ist. Die derzeit auf dem Markt vorhandenen Ösophaguskatheter werden bei einer elektrophysiologischen Untersuchung mit Ablation und transösophagealer Echokardiographie zur Temperaturüberwachung eingesetzt. Das Ziel dieser Arbeit war, das vorhandene Offenburger Herzrhythmusmodell, um die Wirbelsäule zu erweitern, einen neuen Ösophagus-Elektroden- Katheter für die transösophageale elektrische Stimulation des Rückenmarks zu modellieren und mittels 3D-Computer-Simulationen auf Ihre Wirksamkeit zu untersuchen.
Die Katheterablation mit Hochfrequenzstrom (HF) ist der Goldstandard für die Therapie vieler kardi-aler Tachyarrhythmien. Bei der HF-Ablation entstehen Temperaturen zwischen 50 °C und 70 °C, wo-durch bestimmte Strukturen im Herzgewebe gezielt zerstört werden können. Ziel der Studie ist, die HF-Ablation und deren Wärmeausbreitung in Bezug auf die zugeführte Leistung mit unterschiedli-chem Elektrodenmaterial und Elektrodengröße bei supraventrikülären Tachykardien zu simulieren.
Erlang is a functional programming language with dynamic typing. The language offers great flexibility for destructing values through pattern matching and dynamic type tests. Erlang also comes with a type language supporting parametric polymorphism, equi-recursive types, as well as union and a limited form of intersection types. However, type signatures only serve as documentation; there is no check that a function body conforms to its signature.
Set-theoretic types and semantic subtyping fit Erlang’s feature set very well. They allow expressing nearly all constructs of its type language and provide means for statically checking type signatures. This article brings set-theoretic types to Erlang and demonstrates how existing Erlang code can be statically type checked without or with only minor modifications to the code. Further, the article formalizes the main ingredients of the type system in a small core calculus, reports on an implementation of the system, and compares it with other static type checkers for Erlang.
Sequenzielle Schaltungen
(2022)
Recent studies have shown remarkable success in image-to-image translation for attribute transfer applications. However, most of existing approaches are based on deep learning and require an abundant amount of labeled data to produce good results, therefore limiting their applicability. In the same vein, recent advances in meta-learning have led to successful implementations with limited available data, allowing so-called few-shot learning.
In this paper, we address this limitation of supervised methods, by proposing a novel approach based on GANs. These are trained in a meta-training manner, which allows them to perform image-to-image translations using just a few labeled samples from a new target class. This work empirically demonstrates the potential of training a GAN for few shot image-to-image translation on hair color attribute synthesis tasks, opening the door to further research on generative transfer learning.
Featherweight Go (FG) is a minimal core calculus that includes essential Go features such as overloaded methods and interface types. The most straightforward semantic description of the dynamic behavior of FG programs is to resolve method calls based on run-time type information. A more efficient approach is to apply a type-directed translation scheme where interface-values are replaced by dictionaries that contain concrete method definitions. Thus, method calls can be resolved by a simple lookup of the method definition in the dictionary. Establishing that the target program obtained via the type-directed translation scheme preserves the semantics of the original FG program is an important task.
To establish this property we employ logical relations that are indexed by types to relate source and target programs. We provide rigorous proofs and give a detailed discussion of the many subtle corners that we have encountered including the need for a step index due to recursive inter- faces and method definitions.
Seismic data processing relies on multiples attenuation to improve inversion and interpretation. Radon-based algorithms are often used for multiples and primaries discrimination. Deep learning, based on convolutional neural networks (CNNs), has shown encouraging applications for demultiple that could mitigate Radon-based challenges. In this work, we investigate new strategies to train a CNN for multiples removal based on different loss functions. We propose combined primaries and multiples labels in the loss for training a CNN to predict primaries, multiples, or both simultaneously. Moreover, we investigate two distinctive training methods for all the strategies: UNet based on minimum absolute error (L1) training, and adversarial training (GAN-UNet). We test the trained models with the different strategies and methods on 400 synthetic data. We found that training to predict multiples, including the primaries …
Printed electronics can add value to existing products by providing new smart functionalities, such as sensing elements over large-areas on flexible or non-conformal surfaces. Here we present a hardware concept and prototype for a thinned ASIC integrated with an inkjet-printed temperature sensor alongside in-built additional security and unique identification features. The hybrid system exploits the advantages of inkjet-printable platinum-based sensors, physically unclonable function circuits and a fluorescent particle-based coating as a tamper protection layer.
One of the most important questions about smart metering systems for the end users is their data privacy and security. Indeed, smart metering systems provide a lot of advantages for distribution system operators (DSO), but functionalities offered to users of existing smart meters are still limited and society is becoming increasingly critical. Smart metering systems are accused of interfering with personal rights and privacy, providing unclear tariff regulations which not sufficiently encourage households to manage their electricity consumption in advance. In the specific field of smart grids, data security appears to be a necessary condition for consumer confidence without which they will not be able to give their consent to the collection and use of personal data concerning them.
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.
Die Erfindung betrifft eine Schaltungsanordnung (10) für ein Kraftfahrzeug, mit einer Hochvolt-Batterie (12) zum Speichern von elektrischer Energie, mit wenigstens einer elektrischen Maschine (14) zum Antreiben des Kraftfahrzeugs, mit einem Stromrichter (16), mittels welchem von der Hochvolt-Batterie (12) bereitstellbare Hochvolt-Gleichspannung in Hochvolt-Wechselspannung zum Betreiben der elektrischen Maschine (14) umwandelbar ist, und mit einem Ladeanschluss (20) zum Bereitstellen von elektrischer Energie zum Laden der Hochvolt-Batterie (12), wobei der Stromrichter (16) als ein Drei-Stufen-Stromrichter ausgebildet ist und wenigstens eine einer Phase (u) der elektrischen Maschine (14) zugeordnete Schaltereinheit (46) aufweist, welche zwei in Reihe geschaltete Schaltergruppen (52, 54) umfasst, die jeweils zwei in Reihe geschaltete IGBTs (T11, T12, T13, T14) aufweisen, wobei zwischen den IGBTs (T11, T12) einer der Schaltergruppen (52, 54) ein Anschluss (64) angeordnet ist, welcher direkt mit einer Leitung (34) des Ladeanschlusses (20) elektrisch verbunden ist.
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.
Engineering, construction and operation of complex machines involves a wide range of complicated, simultaneous tasks, which potentially could be automated. In this work, we focus on perception tasks in such systems, investigating deep learning approaches for multi-task transfer learning with limited training data. We show an approach that takes advantage of a technical systems’ focus on selected objects and their properties. We create focused representations and simultaneously solve joint objectives in a system through multi-task learning with convolutional autoencoders. The focused representations are used as a starting point for the data-saving solution of the additional tasks. The efficiency of this approach is demonstrated using images and tasks of an autonomous circular crane with a grapple.
Despite the success of convolutional neural networks (CNNs) in many academic benchmarks for computer vision tasks, their application in the real-world is still facing fundamental challenges. One of these open problems is the inherent lack of robustness, unveiled by the striking effectiveness of adversarial attacks. Current attack methods are able to manipulate the network's prediction by adding specific but small amounts of noise to the input. In turn, adversarial training (AT) aims to achieve robustness against such attacks and ideally a better model generalization ability by including adversarial samples in the trainingset. However, an in-depth analysis of the resulting robust models beyond adversarial robustness is still pending. In this paper, we empirically analyze a variety of adversarially trained models that achieve high robust accuracies when facing state-of-the-art attacks and we show that AT has an interesting side-effect: it leads to models that are significantly less overconfident with their decisions, even on clean data than non-robust models. Further, our analysis of robust models shows that not only AT but also the model's building blocks (like activation functions and pooling) have a strong influence on the models' prediction confidences. Data & Project website: https://github.com/GeJulia/robustness_confidences_evaluation
Despite the success of convolutional neural networks (CNNs) in many academic benchmarks for computer vision tasks, their application in the real-world is still facing fundamental challenges. One of these open problems is the inherent lack of robustness, unveiled by the striking effectiveness of adversarial attacks. Adversarial training (AT) is often considered as a remedy to train more robust networks. In this paper, we empirically analyze a variety of adversarially trained models that achieve high robust accuracies when facing state-of-the-art attacks and we show that AT has an interesting side-effect: it leads to models that are significantly less overconfident with their decisions even on clean data than non-robust models. Further, our analysis of robust models shows that not only AT but also the model's building blocks (like activation functions and pooling) have a strong influence on the models' prediction confidences.
This paper presents the new Deep Reinforcement Learning (DRL) library RL-X and its application to the RoboCup Soccer Simulation 3D League and classic DRL benchmarks. RL-X provides a flexible and easy-to-extend codebase with self-contained single directory algorithms. Through the fast JAX-based implementations, RL-X can reach up to 4.5x speedups compared to well-known frameworks like Stable-Baselines3.
Printed electronics (PE) circuits have several advantages over silicon counterparts for the applications where mechanical flexibility, extremely low-cost, large area, and custom fabrication are required. The custom (personalized) fabrication is a key feature of this technology, enabling customization per application, even in small quantities due to low-cost printing compared with lithography. However, the personalized and on-demand fabrication, the non-standard circuit design, and the limited number of printing layers with larger geometries compared with traditional silicon chip manufacturing open doors for new and unique reverse engineering (RE) schemes for this technology. In this paper, we present a robust RE methodology based on supervised machine learning, starting from image acquisition all the way to netlist extraction. The results show that the proposed RE methodology can reverse engineer the PE circuits with very limited manual effort and is robust against non-standard circuit design, customized layouts, and high variations resulting from the inherent properties of PE manufacturing processes.
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).
Modern CNNs are learning the weights of vast numbers of convolutional operators. In this paper, we raise the fundamental question if this is actually necessary. We show that even in the extreme case of only randomly initializing and never updating spatial filters, certain CNN architectures can be trained to surpass the accuracy of standard training. By reinterpreting the notion of pointwise ($1\times 1$) convolutions as an operator to learn linear combinations (LC) of frozen (random) spatial filters, we are able to analyze these effects and propose a generic LC convolution block that allows tuning of the linear combination rate. Empirically, we show that this approach not only allows us to reach high test accuracies on CIFAR and ImageNet but also has favorable properties regarding model robustness, generalization, sparsity, and the total number of necessary weights. Additionally, we propose a novel weight sharing mechanism, which allows sharing of a single weight tensor between all spatial convolution layers to massively reduce the number of weights.
Entity Matching (EM) defines the task of learning to group objects by transferring semantic concepts from example groups (=entities) to unseen data. Despite the general availability of image data in the context of many EM-problems, most currently available EM-algorithms solely rely on (textual) meta data. In this paper, we introduce the first publicly available large-scale dataset for "visual entity matching", based on a production level use case in the retail domain. Using scanned advertisement leaflets, collected over several years from different European retailers, we provide a total of ~786k manually annotated, high resolution product images containing ~18k different individual retail products which are grouped into ~3k entities. The annotation of these product entities is based on a price comparison task, where each entity forms an equivalence class of comparable products. Following on a first baseline evaluation, we show that the proposed "visual entity matching" constitutes a novel learning problem which can not sufficiently be solved using standard image based classification and retrieval algorithms. Instead, novel approaches which allow to transfer example based visual equivalent classes to new data are needed to address the proposed problem. The aim of this paper is to provide a benchmark for such algorithms.
Information about the dataset, evaluation code and download instructions are provided under https://www.retail-786k.org/.
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.
Fünf Jahre vor seinem Tod, im Jahr 1932, wurde der berühmte französische Komponist Maurice Ravel (1875–1937), der an einer frontotemporalen Demenz (M. Pick) mit primär progressiver Aphasie litt, bei einem Unfall verletzt, als er in einem Pariser Taxi saß. In diesem Fallbericht wird der Unfallmechanismus unter bestimmten Annahmen dargestellt und diskutiert. Ausgehend von diesen Überlegungen ist ein Unfall bei geringer Kollisionsgeschwindigkeit wahrscheinlich. Trotz eines Unfalls mit nur geringer Geschwindigkeit ist nicht von der Hand zu weisen, dass dieser Unfall zumindest zu einer deutlichen Verschlimmerung der Krankheitssymptome geführt haben könnte, da Ravel seit diesem Taxiunfall bis zu seinem Tod keine weiteren Kompositionen mehr vollendet hat.
Elektrische Antriebe sind ein innovationstreibender Kernbestandteil vieler industrieller Anlagen und Einrichtungen. Damit sie diese herausragende Rolle mit den daran geknüpften Erwartungen erfüllen können, ist neben optimierten Elektromotoren und sie speisenden, schnell schaltenden Stromrichterstellgliedern auch eine hochdynamische Regelung erforderlich. Diesem Umstand wird mit der angebotenen Veranstaltung in Form einer detaillierten Einführung in die Thematik der Regelung elektrischer Antriebe Rechnung getragen.
In users of a cochlear implant (CI) together with a contralateral hearing aid (HA), so-called bimodal listeners, differences in processing latencies between digital HA and CI up to 9 ms constantly superimpose interaural time differences. In the present study, the effect of this device delay mismatch on sound localization accuracy was investigated. For this purpose, localization accuracy in the frontal horizontal plane was measured with the original and minimized device delay mismatch. The reduction was achieved by delaying the CI stimulation according to the delay of the individually worn HA. For this, a portable, programmable, battery-powered delay line based on a ring buffer running on a microcontroller was designed and assembled. After an acclimatization period to the delayed CI stimulation of 1 hr, the nine bimodal study participants showed a highly significant improvement in localization accuracy of 11.6% compared with the everyday situation without the delay line (p < .01). Concluding, delaying CI stimulation to minimize the device delay mismatch seems to be a promising method to increase sound localization accuracy in bimodal listeners.
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.
Der verstärkte Einsatz von Wärmepumpen bei der Realisierung einer klimaneutralen Wärmeversorgung führt zu einer signifikanten Zunahme und Änderung der elektrischen Lasten in den Verteilnetzen. Daher gilt es, Wärmepumpen so zu steuern, dass sie Verteilnetze wenig belasten oder sogar unterstützen.
Inhalt des Projekts „PV²WP - PV Vorhersage für die netzdienliche Steuerung von Wärmepumpen“ (Projektlaufzeit 1.07.2018 – 30.06.2021) war die Demonstration eines neuen Ansatzes zur Steuerung von Heizungssystemen, die auf Wärmepumpen und thermischen Speichern basieren und in Kombination mit einer Photovoltaikanlage betrieben werden. Das übergeordnete Ziel war dabei die Verbesserung der Netzintegration und Smart-Grid-Tauglichkeit entsprechender Heizungssysteme durch eine kostengünstige Technologie bei gleichzeitiger Erhöhung der Wirtschaftlichkeit.
Dabei wurden drei zukunftsweisende Technologien in Kombination genutzt und demonstriert: wolkenkamerabasierte Kurzfristprognosen, prädiktive Steuerung und Regelung sowie machinelearning-basierte Systemmodellierung als Basis für die Optimierung. Als Demonstrationsumgebung diente mit dem Projekthaus Ulm ein real bewohntes Einfamilienhaus.Umweltforschung
Das Ziel des Projekts PRYSTINE war es, eine fehlertolerante 360°-Rundumwahrnehmung für das hochautomatisierte Fahren in städtischen und ländlichen Umgebungen, auf Basis einer robusten Radar- und Lidar-Sensorfusion sowie Kontrollfunktionen, zu realisieren.
Im Teilvorhaben "Entwurf der Systemarchitektur von Radarsensoren auf Grundlage identifizierter Szenarien" stand die Entwicklung eines zukunftsfähigen RF-CMOS basierten Radarsystems im Fokus, das sich durch eine hohe Robustheit und Fehlertoleranz bei gleichzeitiger Reduktion der Kosten, Chipfläche und Leistungsaufnahme auszeichnet.
Darin war die Hochschule Offenburg sowohl an der Spezifizierung und am Entwurf einer Systemarchitektur für einen neuartigen RF-CMOS basierten Radarchip als auch an der anschließenden Untersuchung und Validierung des im Projekt realisierten hochauflösenden Radarsensors beteiligt.
Printed electrolyte-gated oxide electronics is an emerging electronic technology in the low voltage regime (≤1 V). Whereas in the past mainly dielectrics have been used for gating the transistors, many recent approaches employ the advantages of solution processable, solid polymer electrolytes, or ion gels that provide high gate capacitances produced by a Helmholtz double layer, allowing for low-voltage operation. Herein, with special focus on work performed at KIT recent advances in building electronic circuits based on indium oxide, n-type electrolyte-gated field-effect transistors (EGFETs) are reviewed. When integrated into ring oscillator circuits a digital performance ranging from 250 Hz at 1 V up to 1 kHz is achieved. Sequential circuits such as memory cells are also demonstrated. More complex circuits are feasible but remain challenging also because of the high variability of the printed devices. However, the device inherent variability can be even exploited in security circuits such as physically unclonable functions (PUFs), which output a reliable and unique, device specific, digital response signal. As an overall advantage of the technology all the presented circuits can operate at very low supply voltages (0.6 V), which is crucial for low-power printed electronics applications.
Autonomous driving is disrupting the automotive industry as we know it today. For this, fail-operational behavior is essential in the sense, plan, and act stages of the automation chain in order to handle safety-critical situations on its own, which currently is not reached with state-of-the-art approaches.The European ECSEL research project PRYSTINE realizes Fail-operational Urban Surround perceptION (FUSION) based on robust Radar and LiDAR sensor fusion and control functions in order to enable safe automated driving in urban and rural environments. This paper showcases some of the key exploitable results (e.g., novel Radar sensors, innovative embedded control and E/E architectures, pioneering sensor fusion approaches, AI-controlled vehicle demonstrators) achieved until its final year 3.
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.
An international study summarizes the threat situation in the OT environment under the heading "Growing security threats" [1]. According to this study, attacks on automation systems are likely to increase in the future. Accordingly, an automation system must be able to protect the integrity of the transmitted information in the future. This requirement is motivated, among other things, by the fact that the network-side isolation of industrial communication systems is no longer considered sufficient as the sole protective measure. This paper uses the example of PROFINET to show how the future requirements for a real-time communication protocol can be met and how they can be derived from the IEC 62443 standard.