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One of the major challenges impeding the energy transition is the intermittency of solar and wind electricity generation due to their dependency on weather changes. The demand-side energy flexibility contributes considerably to mitigate the energy supply/demand imbalances resulting from external influences such as the weather. As one of the largest electricity consumers, the industrial enterprises present a high demand-side flexibility potential from their production processes and on-site energy assets. In this direction, methods are needed with a focus on enabling the energy flexibility and ensure an active participation of such enterprises in the electricity markets especially with variable prices of electricity. This paper presents a generic model library for an industrial enterprise implemented with optimal control for energy flexibility purposes. The components in the model library represent the typical technical units of an industrial enterprise on material, media, and energy flow levels with their operative constraints. A case study of a plastic manufacturing plant using the generic model library is also presented, in which the results of two simulation with different electricity prices are compared and the behavior of the model can be assessed. The results show that the model provides an optimal scheduling of the manufacturing system according to the variations in the electricity prices, and ensures an optimal control for utilities and energy systems needed for the production.
Solar energy plays a central role in the energy transition. Clouds generate locally large fluctuations in the generation output of photovoltaic systems, which is a major problem for energy systems such as microgrids, among others. For an optimal design of a power system, this work analyzed the variability using a spatially distributed sensor network at Stuttgart Airport. It has been shown that the spatial distribution partially reduces the variability of solar radiation. A tool was also developed to estimate the output power of photovoltaic systems using irradiation time series and assumptions about the photovoltaic sites. For days with high fluctuations of the estimated photovoltaic power, different energy system scenarios were investigated. It was found the approach can be used to have a more realistic representation of aggregated PV power taking spatial smoothing into account and that the resulting PV power generation profiles provide a good basis for energy system design considerations like battery sizing.
In recent years, light-weight cryptography has received a lot of attention. Many primitives suitable for resource-restricted hardware platforms have been proposed. In this paper, we present a cryptanalysis of the new stream cipher A2U2 presented at IEEE RFID 2011 [9] that has a key length of 56 bit. We start by disproving and then repairing an extremely efficient attack presented by Chai et al. [8], showing that A2U2 can be broken in less than a second in the chosen-plaintext case. We then turn our attention to the more challenging known-plaintext case and propose a number of attacks. A guess-and-determine approach combined with algebraic cryptanalysis yields an attack that requires about 249 internal guesses. We also show how to determine the 5-bit counter key and how to reconstruct the 56-bit key in about 238 steps if the attacker can freely choose the IV. Furthermore, we investigate the possibility of exploiting the knowledge of a “noisy keystream” by solving a Max-PoSSo problem. We conclude that the cipher needs to be repaired and point out a number of simple measures that would prevent the above attacks.
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.
The desire to connect more and more devices and to make them more intelligent and more reliable, is driving the needs for the Internet of Things more than ever. Such IoT edge systems require sound security measures against cyber-attacks, since they are interconnected, spatially distributed, and operational for an extended period of time. One of the most important requirements for the security in many industrial IoT applications is the authentication of the devices. In this paper, we present a mutual authentication protocol based on Physical Unclonable Functions, where challenge-response pairs are used for both device and server authentication. Moreover, a session key can be derived by the protocol in order to secure the communication channel. We show that our protocol is secure against machine learning, replay, man-in-the-middle, cloning, and physical attacks. Moreover, it is shown that the protocol benefits from a smaller computational, communication, storage, and hardware overhead, compared to similar works.
In recent years, Physical Unclonable Functions (PUFs) have gained significant attraction in the Internet of Things (IoT) for security applications such as cryptographic key generation and entity authentication. PUFs extract the uncontrollable production characteristics of physical devices to generate unique fingerprints for security applications. One common approach for designing PUFs is exploiting the intrinsic features of sensors and actuators such as MEMS elements, which typically exist in IoT devices. This work presents the Cantilever-PUF, a PUF based on a specific MEMS device – Aluminum Nitride (AlN) piezoelectric cantilever. We show the variations of electrical parameters of AlN cantilevers such as resonance frequency, electrical conductivity, and quality factor, as a result of uncontrollable manufacturing process variations. These variations, along with high thermal and chemical stability, and compatibility with silicon technology, makes AlN cantilever a decent candidate for PUF design. We present a cantilever design, which magnifies the effect of manufacturing process variations on electrical parameters. In order to verify our findings, the simulation results of the Monte Carlo method are provided. The results verify the eligibility of AlN cantilever to be used as a basic PUF device for security applications. We present an architecture, in which the designed Cantilever-PUF is used as a security anchor for PUF-enabled device authentication as well as communication encryption.
Most recently, the federal government in Germany published new climate goals in order reach climate neutrality by 2045. This paper demonstrates a path to a cost optimal energy supply system for the German power grid until the year 2050. With special regard to regionality, the system is based on yearly myopic optimization with the required energy system transformation measures and the associated system costs. The results point out, that energy storage systems (ESS) are fundamental for renewables integration in order to have a feasible energy transition. Moreover, the investment in storage technologies increased the usage of the solar and wind technologies. Solar energy investments were highly accompanied with the installation of short-term battery storage. Longer-term storage technologies, such as H2, were accompanied with high installations of wind technologies. The results pointed out that hydrogen investments are expected to overrule short-term batteries if their cost continues to decrease sharply. Moreover, with a strong presence of ESS in the energy system, biomass energy is expected to be completely ruled out from the energy mix. With the current emission reduction strategy and without a strong presence of large scale ESS into the system, it is unlikely that the Paris agreement 2° C target by 2050 will be achieved, let alone the 1.5° C.
With recent developments in the Ukrainian-Russian conflict, many are discussing about Germany’s dependency on fossil fuel imports in its energy system, and how can the country proceed with reducing that dependency. With its wide-ranging consumption sectors, the electricity sector comes as the perfect choice to start with. Recent reports showed that the German federal government is already intending to have a fully renewable electricity by 2035 while exploiting all possible clean power options. This was published in the federal government’s climate emergency program (Easter Package) in early 2022. The aim of this package is to initiate a rapid transition and decarbonization of the electricity sector. The Easter Package expects an enormous growth of renewable energies to a completely new level, with already at least 80% renewable gross energy consumption, with extensive and broad deployment of different generation technologies on various scales. This paper will discuss this ambitious plan and outline some insights into this huge and rapidly increasing step, and show how much will Germany need in order to achieve this huge milestone towards a fully green supply of the electricity sector. Different scenarios and shares of renewables will be investigated in order to elaborate on preponed climate-neutral goal of the electricity sector by 2035. The results pointed out some promising aspects in achieving a 100% renewable power, with massive investments in both generation and storage technologies.
An import ban of Russian energy sources to Germany is currently being increasingly discussed. We want to support the discussion by showing a way how the electricity system in Germany can manage low energy imports in the short term and which measures are necessary to still meet the climate protection targets. In this paper, we examine the impact of a complete stop of Russian fossil fuel imports on the electricity sector in Germany, and how this will affect the climate coals of an earlier coal phase-out and climate neutrality by 2045.
Following a scenario-based analysis, the results gave a point of view on how much would be needed to completely rely on the scarce non-renewable energy resources in Germany. Huge amounts of investments would be needed in order to ensure a secure supply of electricity, in both generation energy sources (RES) and energy storage systems (ESS). The key findings are that a rapid expansion of renewables and storage technologies will significantly reduce the dependence of the German electricity system on energy imports. The huge integration of renewable energy does not entail any significant imports of the energy sources natural gas, hard coal, and mineral oil, even in the long term. The results showed that a ban on fossil fuel imports from Russia outlines huge opportunities to go beyond the German government's climate targets, where the 1.5-degree-target is achieved in the electricity system.
To deal with frequent power outages in developing countries, people turn to solutions like uninterruptible power supply (UPS), which stores electric energy during normal operating hours and use it to meet energy needs during rolling blackout intervals. Locally produced UPSs of poorer power quality are widely accessible in the marketplaces, and they have a negative impact on power quality. The charging and discharging of the batteries in these UPSs generate significant amount of power losses in weak grid environments. The Smart-UPS is our proposed smart energy metering (SEM) solution for low voltage consumers that is provided by the distribution company. It does not require batteries, therefore there is no power loss or harmonic distortion due to corresponding charging and discharging. Through load flow and harmonic analysis of both traditional UPS and Smart-UPS systems on ETAP, this paper examines their impact on the harmonics and stability of the distribution grid. The simulation results demonstrate that Smart-UPS can assist fixing power quality issues in a developing country like Pakistan by providing cleaner energy than the battery-operated traditional UPSs.
Due to its potential in improving the efficiency of energy supply, smart energy metering (SEM) has become an area of interest with the surge in Internet of Things (IoT). SEM entails remote monitoring and control of the sensors and actuators associated with the energy supply system. This provides a flexible platform to conceive and implement new data driven Demand Side Management (DSM) mechanisms. The IoT enablement allows the data to be gathered and analyzed at requisite granularity. In addition to efficient use of energy resources and provisioning of power, developing countries face an additional challenge of temporal mismatch in generation capacity and load factors. This leads to widespread deployment of inefficient and expensive Uninterruptible Power Supply (UPS) solutions for limited power provisioning during resulting blackouts. Our proposed “Soft-UPS” allows dynamic matching of load and generation through a combination of managed curtailment. This eliminates inefficiencies in the energy and power value chain and allows a data-driven approach to solving a widespread problem in developing countries, simultaneously reducing both upfront and running costs of conventional UPS and storage. A scalable and modular platform is proposed and implemented in this paper. The architecture employs “WiMODino” using LoRaWAN with a “Lite Gateway” and SQLite repository for data storage. Role based access to the system through an android application has also been demonstrated for monitoring and control.
Following their success in visual recognition tasks, Vision Transformers(ViTs) are being increasingly employed for image restoration. As a few recent works claim that ViTs for image classification also have better robustness properties, we investigate whether the improved adversarial robustness of ViTs extends to image restoration. We consider the recently proposed Restormer model, as well as NAFNet and the "Baseline network" which are both simplified versions of a Restormer. We use Projected Gradient Descent (PGD) and CosPGD for our robustness evaluation. Our experiments are performed on real-world images from the GoPro dataset for image deblurring. Our analysis indicates that contrary to as advocated by ViTs in image classification works, these models are highly susceptible to adversarial attacks. We attempt to find an easy fix and improve their robustness through adversarial training. While this yields a significant increase in robustness for Restormer, results on other networks are less promising. Interestingly, we find that the design choices in NAFNet and Baselines, which were based on iid performance, and not on robust generalization, seem to be at odds with the model robustness.
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.
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.
This paper describes the Sweaty II humanoid adult size robot trying to qualify for the RoboCup 2018 adult size humanoid competition. Sweaty came 2nd in RoboCup 2017 adult size league. The main characteristics of Sweaty are described in the Team Description Paper 2017. The improvements that have been made or are planned to be implemented for RoboCup 2018 are described in this paper.
Soccer simulation league is one of the founding leagues of RoboCup. In this paper we discuss the past, present and planned future achievements and changes. Also we summarize the connections and inter-league achievements of this league and provide an overview of the community contributions that made this league successful.
Soiling is an important issue in the renewable energy sector since it can result in significant yield losses, especially in regions with higher pollution or dust levels. To mitigate the impact of soiling on photovoltaic (PV) plants, it is essential to regularly monitor and clean the panels, as well as develop accurate soiling predictions that can affect cleaning strategies and enhance the overall performance of PV power plants. This research focuses on the problem of soiling loss in photovoltaic power plants and the potential to improve the accuracy of soiling predictions. The study examines how soiling can affect the efficiency and productivity of the modules and how to measure and predict soiling using machine learning (ML) algorithms. The research includes analyzing real data from large-scale ground-mounted PV sites and comparing different soiling measurement methods. It was observed that there were some deviations in the real soiling loss values compared to the expected values for some projects in southern Spain, thus, the main goal of this work is to develop machine learning models that could predict the soiling more accurately. The developed models have a low mean square error (MSE), indicating the accuracy and suitability of the models to predict the soiling rates. The study also investigates the impact of different cleaning strategies on the performance of PV power plants and provides a powerful application to predict both the soiling and the number of cleaning cycles.
The central purpose of this paper is to present a novel framework supporting the specification and the implementation of media streaming services using XML and Java Media Framework (JMF). It provides an integrated service development environment comprising of a streaming service model, a service specification language and several implementation and retrieval tools. Our approach is based on a clear separation of a streaming service specification, and its implementation by a distributed JMF application and can be used for different streaming paradigms, e.g. push and pull services.
The central purpose of this paper is to present a novel framework supporting the specification, the implementation and retrieval of media streaming services. It provides an integrated service development environment comprising of a streaming service model, a service specification language and several implementation and retrieval tools. Our approach is based on a clear separation of a streaming service specification, and its implementation by a distributed application and can be used for different streaming paradigms, e.g. push and pull services.
The excessive control signaling in Long Term Evolution networks required for dynamic scheduling impedes the deployment of ultra-reliable low latency applications. Semi-persistent scheduling was originally designed for constant bit-rate voice applications, however, very low control overhead makes it a potential latency reduction technique in Long Term Evolution. In this paper, we investigate resource scheduling in narrowband fourth generation Long Term Evolution networks through Network Simulator (NS3) simulations. The current release of NS3 does not include a semi-persistent scheduler for Long Term Evolution module. Therefore, we developed the semi-persistent scheduling feature in NS3 to evaluate and compare the performance in terms of uplink latency. We evaluate dynamic scheduling and semi-persistent scheduling in order to analyze the impact of resource scheduling methods on up-link latency.
Vehicle-to-Everything (V2X) communication promises improvements in road safety and efficiency by enabling low-latency and reliable communication services for vehicles. Besides using Mobile Broadband (MBB), there is a need to develop Ultra Reliable Low Latency Communications (URLLC) applications with cellular networks especially when safety-related driving applications are concerned. Future cellular networks are expected to support novel latencysensitive use cases. Many applications of V2X communication, like collaborative autonomous driving requires very low latency and high reliability in order to support real-time communication between vehicles and other network elements. In this paper, we classify V2X use-cases and their requirements in order to identify cellular network technologies able to support them. The bottleneck problem of the medium access in 4G Long Term Evolution(LTE) networks is random access procedure. It is evaluated through simulations to further detail the future limitations and requirements. Limitations and improvement possibilities for next generation of cellular networks are finally detailed. Moreover, the results presented in this paper provide the limits of different parameter sets with regard to the requirements of V2X-based applications. In doing this, a starting point to migrate to Narrowband IoT (NB-IoT) or 5G - solutions is given.
The next generation cellular networks are expected to improve reliability, energy efficiency, data rate, capacity and latency. Originally, Machine Type Communication (MTC) was designed for low-bandwidth high-latency applications such as, environmental sensing, smart dustbin, etc., but there is additional demand around applications with low latency requirements, like industrial automation, driver-less cars, and so on. Improvements are required in 4G Long Term Evolution (LTE) networks towards the development of next generation cellular networks for providing very low latency and high reliability. To this end, we present an in-depth analysis of parameters that contribute to the latency in 4G networks along with a description of latency reduction techniques. We implement and validate these latency reduction techniques in the open-source network simulator (NS3) for narrowband user equipment category Cat-Ml (LTE-M) to analyze the improvements. The results presented are a step towards enabling narrowband Ultra Reliable Low Latency Communication (URLLC) networks.
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.
Although short range wireless communication explicitly targets local and very regional applications, range continues to be an extremely important issue. The range directly depends on the so called link budget, which can be increased by the choice of modulation and coding schemes. Especially, the recent transceiver generation comes with extensive and flexible support for Software Defined Radio (SDR). The SX127x family from Semtech Corp. is a member of this device class and promises significant benefits for range, robust performance, and battery lifetime compared to competing technologies. This contribution gives a short overview into the technologies to support Long Range (LoRa ™), describes the outdoor setup at the Laboratory Embedded Systems and Communication Electronics of Offenburg University of Applied Sciences, shows detailed measurement results and discusses the strengths and weaknesses of this technology.
Ripple: Overview and Outlook
(2015)
Ripple is a payment system and a digital currency which evolved completely independently of Bitcoin. Although Ripple holds the second highest market cap after Bitcoin, there are surprisingly no studies which analyze the provisions of Ripple.
In this paper, we study the current deployment of the Ripple payment system. For that purpose, we overview the Ripple protocol and outline its security and privacy provisions in relation to the Bitcoin system. We also discuss the consensus protocol of Ripple. Contrary to the statement of the Ripple designers, we show that the current choice of parameters does not prevent the occurrence of forks in the system. To remedy this problem, we give a necessary and sufficient condition to prevent any fork in the system. Finally, we analyze the current usage patterns and trade dynamics in Ripple by extracting information from the Ripple global ledger. As far as we are aware, this is the first contribution which sheds light on the current deployment of the Ripple system.
Investigation on Bowtie Antennas Operating at Very Low Frequencies for Ground Penetrating Radar
(2023)
The efficiency of Ground Penetrating Radar (GPR) systems significantly depends on the antenna performance as the signal has to propagate through lossy and inhomogeneous media. GPR antennas should have a low operating frequency for greater penetration depth, high gain and efficiency to increase the receiving power and should be compact and lightweight for ease of GPR surveying. In this paper, two different designs of Bowtie antennas operating at very low frequencies are proposed and analyzed.
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.
This paper presents a system that uses a multi-stage AI analysis method for determining the condition and status of bicycle paths using machine learning methods. The approach for analyzing bicycle paths includes three stages of analysis: detection of the road surface, investigation of the condition of the bicycle paths, and identification of substrate characteristics. In this study, we focus on the first stage of the analysis. This approach employs a low-threshold data collection method using smartphone-generated video data for image recognition, in order to automatically capture and classify surface condition and status.
For the analysis convolutional neural networks (CNN) are employed. CNNs have proven to be effective in image recognition tasks and are particularly well-suited for analyzing the surface condition of bicycle paths, as they can identify patterns and features in images. By training the CNN on a large dataset of images with known surface conditions, the network can learn to identify common features and patterns and reliably classify them.
The results of the analysis are then displayed on digital maps and can be utilized in areas such as bicycle logistics, route planning, and maintenance. This can improve safety and comfort for cyclists while promoting cycling as a mode of transportation. It can also assist authorities in maintaining and optimizing bicycle paths, leading to more sustainable and efficient transportation system.
NEXCODE is a project promoted by the European Space Agency aimed at research design development and demonstration of a receiver chain for telecomm and links in space missions including the presence of new short low-density parity-check codes for error correction. These codes have excellent performance from the error rate viewpoint but also put new challenges as regards synchronization issues and implementation. In this paper after a short review of the results obtained through numerical simulations we present an overview of the breadboard designed for practical testing and the test-plan proposed for the verification of the breadboard and the validation of the new codes and novel synchronization techniques under relevant operation conditions.
Synthesizing voice with the help of machine learning techniques has made rapid progress over the last years. Given the current increase in using conferencing tools for online teaching, we question just how easy (i.e. needed data, hardware, skill set) it would be to create a convincing voice fake. We analyse how much training data a participant (e.g. a student) would actually need to fake another participants voice (e.g. a professor). We provide an analysis of the existing state of the art in creating voice deep fakes and align the identified as well as our own optimization techniques in the context of two different voice data sets. A user study with more than 100 participants shows how difficult it is to identify real and fake voice (on avg. only 37% can recognize a professor’s fake voice). From a longer-term societal perspective such voice deep fakes may lead to a disbelief by default.
Im Rahmen einer Master Thesis wurde ausgehend von einem vorhandenen System On Chip Design, welches eingehende EKG-Datensignale verarbeitet, das bestehende System so erweitert dass es komplett über den standardisierten SPI-Bus steuerbar und auslesbar ist.
Im ASIC Design Center der Hochschule Offenburg wird ein Design Kit für die UMC 0.18μm Faraday Technologie aufbereitet. Dabei werden alle benötigten Dateien, welche für einen zunächst rein digitalen Chipentwurf unter Verwendung der Synopsys, Cadence und Mentor Tools benötigt werden, für den UMC 0.18μm Prozess zusammengestellt.
In this TDP we describe a new tool created for testing the strategy layer of our soccer playing agents. It is a complete 2D simulator that simulates the games based on the decisions of 22 agents. With this tool, debugging the decision and strategy layer of our agents is much more efficient than before due to various interaction methods and complete control over the simulation.
In the future, the tool could also serve as a measure to run simulations of game series much faster than with the 3D simulator. This way, the impact of different play strategies could be evaluated much faster than before.
An der FH Offenburg arbeiten seit Ende 1989 in einem Team die Professoren Dr. Jansen, Dr. Schüssele, die wissenschaftlichen Mitarbeiter Bernd Reinke, Martin Jörger und die Diplomanden Hans Fiesel, Otmar Feißt an dem Entwurf eines Nachrichtenempfängers. Im Rahmen dieses Projekts, genannt GPS-Projekt (GPS = Global Positioning System), wurde im Herbst 1990 ein experimenteller Empfänger in Betrieb genommen. Nachdem die Testergebnisse gezeigt hatten,daß das Konzept der Anlage stimmte, ging es nun um die Miniaturisieriung, Integration und Optimierung der Schaltung. Außerdem sollte der bisher verwendete PC durch einen auf der Platine befindlichen Mikroprozessor ersetzt werden. Im Zusammenhang mit dem GPS-Projekt wurden bisher im Offenburger ASIC-Labor eine Analogschaltung auf einem B500, drei LCA Designs und diverse GAL's entwickelt.
Zur Zeit arbeiten mehrere Diplomanden an der zweiten Generation des Empfängers. Meine Aufgabe besteht darin, die dort noch in drei LCA's untergebrachte digitale Logik sowie einen Teil des bisherigen PC-Interface in einem IMS Gate Forrest zu integrieren. Außerdem muß die Logik von 8 Bit auf einen 16 Bit breiten Datenbus umgestellt und an die neue Peripherie des Mikroprozessors angepasst werden. Damit soll die jetzige Digital-Platine noch weiter verkleinert werden. Wesentlich ist dabei die Umsetzung der zahlreichen Zähler- und Registerstrukturen in einem Gate Forrest. Als Arbeitsmittel stehen Apollo Workstations mit Mentor Software zur Verfügung.
Accelerated transformation of the society and industry through digi-talization, artificial intelligence and other emerging technologies has intensified the need for university graduates that are capable of rapidly finding breakthrough solutions to complex problems, and can successfully implement innovation con-cepts. However, there are only few universities making significant efforts to com-prehensively incorporate creative and systematic tools of TRIZ (theory of in-ventive problem solving) and KBI (knowledge-based innovation) into their de-gree structure. Engineering curricula offer little room for enhancing creativity and inventiveness by means of discipline‐specific subjects. Moreover, many ed-ucators mistakenly believe that students are either inherently creative, or will in-evitably obtain adequate problem-solving skills as a result of their university study. This paper discusses challenges of intelligent integration of TRIZ and KBI into university curricula. It advocates the need for development of standard guidelines and best-practice recommendations in order to facilitate sustainable education of ambitious, talented, and inventive specialists. Reflections of educa-tors that teach TRIZ and KBI to students from mechanical, electrical, process engineering, and business administration are presented.
The design of control systems of concentrator photovoltaic power plants will be more challenging in the future. Reasons are cost pressure, the increasing size of power plants, and new applications for operation, monitoring and maintenance required by grid operators, manufacturers and plant operators. Concepts and products for fixed-mounted photovoltaic can only partly be adapted since control systems for concentrator photovoltaic are considerable more complex due to the required high accurate sun-tracking. In order to assure reliable operation during a lifetime of more than 20 years, robustness of the control system is one crucial design criteria. This work considers common engineering technics for robustness, safety and security. Potential failures of the control system are identified and their effects are analyzed. Different attack scenarios are investigated. Outcomes are design criteria that encounter both: failures of system components and malicious attacks on the control system of future concentrator photovoltaic power plants. Such design criteria are a transparent state management through all system layers, self-tests and update capabilities for security concerns. The findings enable future research to develop a more robust and secure control system for concentrator photovoltaics when implementing new functionalities in the next generation.
The communication system of a large-scale concentrator photovoltaic power plant is very challenging. Manufacturers are building power plants having thousands of sun tracking systems equipped with communication and distributed over a wide area. Research is necessary to build a scalable communication system enabling modern control strategies. This poster abstract describes the ongoing work on the development of a simulation model of such power plants in OMNeT++. The model uses the INET Framework to build a communication network based on Ethernet. First results and problems of timing and data transmission experiments are outlined. The model enables research on new communication and control approaches to improve functionality and efficiency of power plants based on concentrator photovoltaic technology.
Die direkte Vermarktung von Strom aus Wind und Sonne stellt einen wichtigen Schritt der Energiewende dar. Einerseits kann durch die Marktintegration die Unabhängigkeit von EEG-Subventionen gelingen. Andererseits wird über diese Mechanismen die Stromerzeugung an der Nachfrage orientiert, wodurch zur Stabilität des Stromnetzes beigetragen wird. Ein Beispiel dafür ist die lokale Vermarktung von PV-Strom in einem Mietshaus. Für deren Umsetzung benötigen die Akteure ein Mess- und Steuerungssystem, dass vor Ort Zähler- und Anlagendaten erfasst und die Abrechnung der Mieter vereinfacht. Außerdem sollte es Kennwerte wie beispielsweise den PV-Anteil berechnen und gegebenenfalls ein Blockheizkraftwerk steuern. Weder die Zählersysteme der Messstellenbetreiber noch die Steuerungssysteme von PV- oder Blockheizkraftwerken erfüllen diese Anforderungen ausreichend. In der Forschung ist man währenddessen bereits einen Schritt weiter und arbeitet an technischen Systemen, die für wesentlich komplexere Energiesystem- und Markttopologien ausgelegt werden. In dieser Arbeit werden die neuen technischen Anforderungen der Direktvermarktung in einem Mietshaus identifiziert und mit dem Stand aktueller Marktprodukte sowie dem System »OpenMUC« aus der Forschung verglichen.
Simulation-based degradation assessment of lithium-ion batteries in a hybrid electric vehicle
(2017)
Covert and Side-Channels have been known for a long time due to their versatile forms of appearance. For nearly every technical improvement or change in technology, such channels have been (re-)created or known methods have been adapted. For example the introduction of hyperthreading technology has introduced new possibilities for covert communication between malicious processes because they can now share the arithmetic logical unit (ALU) as well as the L1 and L2 cache which enables establishing multiple covert channels. Even virtualization which is known for its isolation of multiple machines is prone to covert and side-channel attacks due to the sharing of resources. Therefore itis not surprising that cloud computing is not immune to this kind of attacks. Even more, cloud computing with multiple, possibly competing users or customers using the same shared resources may elevate the risk of unwanted communication. In such a setting the ”air gap” between physical servers and networks disappears and only the means of isolation and virtual separation serve as a barrier between adversary and victim. In the work at hand we will provide a survey on weak spots an adversary trying to exfiltrate private data from target virtual machines could exploit in a cloud environment. We will evaluate the feasibility of example attacks and point out possible mitigation solutions if they exist.
Several cloud schedulers have been proposed in the literature with different optimization goals such as reducing power consumption, reducing the overall operational costs or decreasing response times. A less common goal is to enhance the system security by applying specific scheduling decisions. The security risk of covert channels is known for quite some time, but is now back in the focus of research because of the multitenant nature of cloud computing and the co-residency of several per-tenant virtual machines on the same physical machine. Especially several cache covert channels have been identified that aim to bypass a cloud infrastructure's sandboxing mechanism. For instance, cache covert channels like the one proposed by Xu et. al. use the idealistic scenario with two alternately running colluding processes in different VMs accessing the cache to transfer bits by measuring cache access time. Therefore, in this paper we present a cascaded cloud scheduler coined C 3 -Sched aiming at mitigating the threat of a leakage of customers data via cache covert channels by preventing processes to access cache lines alternately. At the same time we aim at maintaining the cloud performance and minimizing the global scheduling overhead.
In this paper an RFID/NFC (ISO 15693 standard) based inductively powered passive SoC (system on chip) for biomedical applications is presented. A brief overview of the system design, layout techniques and verification method is dis-cussed here. The SoC includes an integrated 32 bit microcontroller, sensor interface circuit, analog to digital converter, integrated RAM, ROM and some other peripherals required for the complete passive operation. The entire chip is realized in CMOS 0.18 μm technology with a chip area of 1.52mm x 3.24 mm.
A new RFID/NFC (ISO 15693 standard) based inductively powered passive SoC (System on chip) for biomedical applications is presented here. The proposed SOC consists of an integrated 32 bit microcontroller, RFID/NFC frontend, sensor interface circuit, analog to digital converter and some peripherals such as timer, SPI interface and memory devices. An energy harvesting unit supplies the power required for the entire system for complete passive operation. The complete chip is realized on CMOS 0.18 μm technology with a chip area of 1.5 mm × 3.0 mm.
In this paper, a complete passive transponder device has been discussed which is meant to monitor leakage in silicone breast implants. The passive tag operates in the HF frequency range of 13.56MHz using RFID ISO 15693 standard. The complete system consists of the transponder, reader and a PC. This paper focusses on the development of such a state of the art passive RFID transponder to monitor the wellness of the silicone breast implants periodically in order to detect leakage in the same. Keyword: RFID (Radio frequency identification device), EM (Electromagnetic) field, Passive Transponder, Silicone breast implants.
Team description papers of magmaOffenburg are incremental in the sense that each year we address a different topic of our team and the tools around our team. In this year’s team description paper we focus on the architecture of the software. It is a main factor for being able to keep the code maintainable even after 15 years of development. We also describe how we make sure that the code follows this architecture.
The majority of anterior cruciate ligament (ACL) injuries in team sports are non-contact injuries, with cutting maneuvers identified as high-risk tasks. Young female handball players have been shown to be at greater risk for ACL injuries than males. One risk factor for ACL injuries is the magnitude of the knee abduction moment (KAM). Cutting technique variables on foot placement, overall approach and knee kinematics have been shown to influence the KAM. Since injury risk is believed to increase with increasing task complexity, the purpose of the study was to test the effect of task complexity on technique variables that influence the KAM in female handball players during fake-and-cut tasks.
Landing heel first has been associated with elevated external knee abduction moments (KAM), thereby potentially increasing the risk of sustaining a non-contact ACL injury. Apart from the foot strike angle, knee valgus angle (VAL) and vertical center of mass velocity at initial ground contact (IC) have been associated with increased KAM in females across different sidestep cuts. While real-time biofeedback training has been proven effective for gait retraining [4], the highly dynamic, non-cyclical nature of cutting maneuvers makes real-time feedback unsuitable and alternative approaches necessary. This study aimed at assessing the efficacy of immediate software-aided feedback on cutting technique in reducing KAM during handball-specific cutting maneuvers.
The purpose of this study was to 1) compare knee joint kinematics and kinetics of fake-and-cut tasks of varying complexity in 51 female handball players and 2) present a case study of one athlete who ruptured her ACL three weeks post data collection. External knee joint moments and knee joint angles in all planes at the instance of the peak external knee abduction moment (KAM) as well as moment and angle time curves were analyzed. Peak KAMs and knee internal rotation moments were substantially higher than published values obtained during simple change-of-direction tasks and, along with flexion angles, differed significantly between the tasks. Introducing a ball reception and a static defender increased joint loads while they partially decreased again when anticipation was lacking. Our results suggest to use game-specific assessments of injury risk while complexity levels do not directly increase knee loading. Extreme values of several risk factors for a post-test injured athlete highlight the need and usefulness of appropriate screenings.
In 2015, Google engineer Alexander Mordvintsev presented DeepDream as technique to visualise the feature analysis capabilities of deep neural networks that have been trained on image classification tasks. For a brief moment, this technique enjoyed some popularity among scientists, artists, and the general public because of its capability to create seemingly hallucinatory synthetic images. But soon after, research moved on to generative models capable of producing more diverse and more realistic synthetic images. At the same time, the means of interaction with these models have shifted away from a direct manipulation of algorithmic properties towards a predominance of high level controls that obscure the model's internal working. In this paper, we present research that returns to DeepDream to assess its suit-ability as method for sound synthesis. We consider this research to be necessary for two reasons: it tackles a perceived lack of research on musical applications of DeepDream, and it addresses DeepDream's potential to combine data driven and algorithmic approaches. Our research includes a study of how the model architecture, choice of audio data-sets, and method of audio processing influence the acoustic characteristics of the synthesised sounds. We also look into the potential application of DeepDream in a live-performance setting. For this reason, the study limits itself to models consisting of small neural networks that process time-domain representations of audio. These models are resource-friendly enough to operate in real time. We hope that the results obtained so far highlight the attractiveness of Deep-Dream for musical approaches that combine algorithmic investigation with curiosity driven and open ended exploration.
This paper describes the authors' first experiments in creating an artificial dancer whose movements are generated through a combination of algorithmic and interactive techniques with machine learning. This approach is inspired by the time honoured practice of puppeteering. In puppeteering, an articulated but inanimate object seemingly comes to live through the combined effects of a human controlling select limbs of a puppet while the rest of the puppet's body moves according to gravity and mechanics. In the approach described here, the puppet is a machine-learning-based artificial character that has been trained on motion capture recordings of a human dancer. A single limb of this character is controlled either manually or algorithmically while the machine-learning system takes over the role of physics in controlling the remainder of the character's body. But rather than imitating physics, the machine-learning system generates body movements that are reminiscent of the particular style and technique of the dancer who was originally recorded for acquiring training data. More specifically, the machine-learning system operates by searching for body movements that are not only similar to the training material but that it also considers compatible with the externally controlled limb. As a result, the character playing the role of a puppet is no longer passively responding to the puppeteer but makes movement decisions on its own. This form of puppeteering establishes a form of dialogue between puppeteer and puppet in which both improvise together, and in which the puppet exhibits some of the creative idiosyncrasies of the original human dancer.
Generative machine learning models for creative purposes play an increasingly prominent role in the field of dance and technology. A particularly popular approach is the use of such models for generating synthetic motions. Such motions can either serve as source of ideation for choreographers or control an artificial dancer that acts as improvisation partner for human dancers. Several examples employ autoencoder-based deep-learning architectures that have been trained on motion capture recordings of human dancers. Synthetic motions are then generated by navigating the autoencoder's latent space. This paper proposes an alternative approach of using an autoencoder for creating synthetic motions. This approach controls the generation of synthetic motions on the level of the motion itself rather than its encoding. Two different methods are presented that follow this principle. Both methods are based on the interactive control of a single joint of an artificial dancer while the other joints remain under the control of the autoencoder. The first method combines the control of the orientation of a joint with iterative autoencoding. The second method combines the control of the target position of a joint with forward kinematics and the application of latent difference vectors. As illustrative example of an artistic application, this latter method is used for an artificial dancer that plays a digital instrument. The paper presents the implementation of these two methods and provides some preliminary results.
Strings P
(2021)
Strings is an audiovisual performance for an acoustic violin and two generative instruments, one for creating synthetic sounds and one for creating synthetic imagery. The three instruments are related to each other conceptually , technically, and aesthetically by sharing the same physical principle, that of a vibrating string. This submission continues the work the authors have previously published at xCoAx 2020. The current submission briefly summarizes the previous publication and then describes the changes that have been made to Strings. The P in the title emphasizes, that most of these changes have been informed by experiences collected during rehearsals (in German Proben). These changes have helped Strings to progress from a predominantly technical framework to a work that is ready for performance.
In thin-layer chromatography, fiber-bundle arrays have been introduced for spectral absorption measurements in the UV-region. Using all-silica fiber bundles, the exciting light will be detected after re-emission on the plate with a fiberoptic spectrometer. In addition, fluorescence light can be detected which will be masked by the re-emitted light. Therefore, it is helpful to separate the absorption and fluorescence on the TLC-plate. A modified three-array assembly has been developed: using one array for detection, the two others are used for excitation with broadband band deuterium-light and with UV-LEDs adjusted to the substances under test. As an example, the quantification of glucosamine in nutritional supplements or spinach leaf extract will be described. Using simply heating of the amino-plate for derivation, the reaction product of Glucosamine can be detected sensitively either by light absorption or by fluorescence, using the new fiber-optic assembly. In addition, the properties of the new 3-row fiber-optic array and the commercially available UV-LEDs will be shown, in the interesting wavelength region for excitation of fluorescence, from 260 nm to 360 nm. The squint angle having an influence on coupling efficiency and spatial resolution will be measured with the inverse farfield method. Some properties of UV-LEDs for analytical applications will be described and discussed, too.
Die Hochschule Offenburg begleitet seit Juli 2006 in Zusammenarbeit mit dem Fraunhofer ISE in Freiburg, die solar unterstützte Klimatisierung der Festo AG & Co. KG in Esslingen im Rahmen des Forschungsvorhabens Solarthermie2000plus. Dabei wurde die bereits bestehende Adsorptionskälteanlage, die bisher mit Gaskesseln und Kompressorenabwärme betrieben wurde, durch eine Solaranlage als dritte Wärmequelle ergänzt.
Eine neue Prozessidee zur Auftrennung racemischer Wirkstoffe unter Verwendung nanoskaliger AlO(OH)‐Hohlkugeln als Adsorbens und überkritischen Kohlenstoffdioxides (sc‐CO2) als Lösungsmittel wird vorgestellt. Zur Auslegung des Prozesses werden Untersuchungen zur Abscheidung der racemischen Wirkstoffe (RS)‐Flurbiprofen, (RS)‐Ibuprofen, (RS)‐Ketoprofen und den reinen Enantiomeren (R)‐Flurbiprofen, (S)‐Ibuprofen und (S)‐Ketoprofen an AlO(OH)‐Hohlkugeln präsentiert und bewertet. Zudem werden Adsorptionsdaten von gasförmigem CO2 an den Hohlkugeln und kommerziellen AlO(OH)‐Partikeln, die mit einer Magnetschwebewaage ermittelt wurden, verglichen. Abschließend werden erste Ergebnisse von orientierenden Versuchen zur Adsorption von racemischem Flurbiprofen aus sc‐CO2 an den Hohlkugeln vorgestellt.
The isolation measures adopted during the COVID-19 pandemic brought light to discussions related to the importance of meaningful social relationships as a basic need to human well-being. But even before the pandemic outbreak in the years 2020 and 2021, organizations and scholars were already drawing attention to the growing numbers related to lonely people in the world (World Economic Forum, 2019). Loneliness is an emotional distress caused by the lack of meaningful social connections, which affects people worldwide across all age groups, mainly young adults (Rook, 1984). The use of digital technologies has gained prominence as a means of alleviating the distress. As an example, studies have shown the benefits of using digital games both to stimulate social interactions (Steinfield, Ellison & Lampe, 2008) and to enhance the effects of digital interventions for mental health treatments, through gamification (Fleming et al., 2017). It is with these aspects in mind that the gamified app Noneliness was designed with the intention of reducing loneliness rates among young students at a German university. In addition to sharing the related works that supported the application development, this chapter also presents the aspects considered for the resource's design, its main functionalities, and the preliminary results related to the reduction of loneliness in the target audience.
The transition from college to university can have a variety of psychological effects on students who need to cope with daily obligations by themselves in a new setting, which can result in loneliness and social isolation. Mobile technology, specifically mental health apps (MHapps), have been seen as promising solutions to assist university students who are facing these problems, however, there is little evidence around this topic. My research investigates how a mobile app can be designed to reduce social isolation and loneliness among university students. The Noneliness app is being developed to this end; it aims to create social opportunities through a quest-based gamified system in a secure and collaborative network of local users. Initial evaluations with the target audience provided evidence on how an app should be designed for this purpose. These results are presented and how they helped me to plan the further steps to reach my research goals. The paper is presented at MobileHCI 2020 Doctoral Consortium.
Loneliness, an emotional distress caused by the lack of meaningful social connections, has been increasingly affecting university students who need to deal with everyday situations in a new setting, especially those who have come from abroad. Currently there is little work on digital solutions to reduce loneliness. Therefore, this work describes the general design considerations for mobile apps in this context and outlines a potential solution. The mobile app Noneliness is used to this end: it aims to reduce loneliness by creating social opportunities through a quest-based gamified system in a secure and collaborative network of local users. The results of initial evaluations with the target audience are described. The results informed a user interface redesign as well as a review of the features and the gamification principles adopted.
During the periods of social isolation to contain the advance of COVID-19 in 2020 and 2021, educational institutions have had the challenge to adopt technological strategies not only to ensure continuity in students’ classes, but also to support their mental health in a period of uncertainty and health risks. Loneliness is an emotional distress caused by the lack of meaningful social connections; it has increasingly affected young adults worldwide during the pandemic's social isolation and still bears psychological effects in the current post-pandemic period. In the light of this challenge, the Nonenliness App was developed as a way to bring together university communities to address issues related to loneliness and mental health disorders through a gamified and social online environment. In this paper, we present the app and its main functionalities (Beta version) and discuss the preliminary results of a pilot clinical study conducted with university students in Germany (N = 12) to verify the app's efficacy and usability, alongside the challenges faced and the next steps to be taken regarding the platform's improvement.
Digital libraries are providing an increasing amount of data, which is normally structured in a classical way by documents and described by metadata as keywords. The data, even in scientific systems such as digital libraries and virtual research environments, will contain a great amount of noise or information unnecessary for our personal interests. Although there has been a lot of progress in the field of information retrieval, search techniques and other content finding methods, there is still much to be done in the field of information retrieval based on user behavior. This paper presents an approach deployed in the Humboldt Digital Library (HDL) to facilitate the retrieval of relevant information to the users of the system, making recommendations of paragraphs based on their profile and the behavior of other users who share similar profiles. The Humboldt digital library represents an innovative system of open access to the legacy of Alexander von Humboldt in a digital form on the Internet (www.avhumboldt.net). It contributes to the key question, how to present interconnected data in a proper form using information technologies.
Duplikaterkennung, -suche und -konsolidierung für Kunden- und Geschäftspartnerdaten, sog. „Identity Resolution“, ist die Voraussetzung für erfolgreiches Customer Relationship Management und Customer Experience Management, aber auch für das Risikomanagement zur Minimierung von Betrugsrisiken und Einhaltung regulatorischer Vorschriften und viele weitere Anwendungsfälle. Diese Systeme sind jedoch hochkomplex und müssen individuell an die kundenspezifischen Anforderungen angepasst werden. Der Einsatz lernbasierter Verfahren bietet großes Potenzial zur automatisierten Anpassung. In diesem Beitrag präsentieren wir für ein KMU praxisfähige, lernbasierte Verfahren zur automatischen Konfiguration von Business-Regeln in Duplikaterkennungssystemen. Dabei wurden für Fachanwender Möglichkeiten entwickelt, um beispielgetrieben das Match-System an individuelle Business-Regeln (u.a. Umzugserkennung, Sperrlistenabgleich) anzupassen und zu konfigurieren. Die entwickelten Verfahren wurden evaluiert und in einer prototypischen Lösung integriert. Wir konnten zeigen, dass unser Machine-Learning-Verfahren, die von einem Domainexperten erstellten Business-Regeln für das Duplikaterkennungssystem „identity“ verbessern konnte. Zudem konnte der hierzu erforderliche Zeitaufwand verkürzt werden.
In this paper we present the concept of the "KI-Labor Südbaden" to support regional companies in the use of AI technologies. The approach is based on the "Periodic Table of AI" and is extended with both new dimensions for sustainability, and the impact of AI on the working environment. It is illustrated on the basis of three real-world use cases: 1. The detection of humans with lowresolution infrared (IR) images for collaborative robotics; 2. The use of machine data from specifically designed vehicles; 3. State-of-the-art Large Language Models (LLMs) applied to internal company documents. We explain the use cases, thereby demonstrating how to apply the Periodic Table of AI to structure AI applications.
Kundendaten im E-Commerce – Optimierungspotenzial im Checkout-Prozess des deutschen Online-Handels
(2023)
Die Gestaltung eines benutzungsfreundlichen Checkout-Prozesses ist für den Erfolg des E-Commerce von großer Bedeutung. Die Abfrage der Kundendaten bildet einen wichtigen Teil der Customer Journey. Auf der einen Seite wollen die Handelsunternehmen so viel wie möglich über ihre Kundschaft erfahren, um möglichst zielgenaue Angebote und Marketingmaßnahmen ausspielen und das perfekte Einkaufserlebnis generieren zu können. Auf der anderen Seite möchten sich die Kundinnen und Kunden beim Online-Shopping auf den Kauf konzentrieren und erwarten einen reibungslosen Ablauf. Der Checkout-Prozess ist in diesem Zusammenhang ein kritischer Punkt. Dies spiegelt sich auch in den hohen Warenkorbabbruchraten wider. Um Online-Shoppende nachhaltig zu begeistern, gibt es noch viel Raum für Verbesserungen. Mit dem Ziel, den Status quo im deutschen Online-Handel besser zu verstehen und Usability und User Experience für eine höhere Konvertierungsrate zu optimieren, untersuchte die hier vorgestellte Forschungsarbeit den Anmelde- und Checkout-Prozess der 100 umsatzstärksten Online-Shops in Deutschland. Es werden die Ergebnisse der Studie präsentiert und aufgezeigt, an welchen Stellen Optimierungspotenzial besteht – bspw. bei zu komplizierten Formularen, unnötigen Datenabfragen oder erzwungenen Registrierungen – sowie Vorschläge für die Praxis des Online-Handels diskutiert.
We aim to debate and eventually be able to carefully judge how realistic the following statement of a young computer scientist is: “I would like to become an ethical correctly acting offensive cybersecurity expert”. The objective of this article is not to judge what is good and what is wrong behavior nor to present an overall solution to ethical dilemmas. Instead, the goal is to become aware of the various personal moral dilemmas a security expert may face during his work life. For this, a total of 14 cybersecurity students from HS Offenburg were asked to evaluate several case studies according to different ethical frameworks. The results and particularities are discussed, considering different ethical frameworks. We emphasize, that different ethical frameworks can lead to different preferred actions and that the moral understanding of the frameworks may differ even from student to student.
Thin-layer chromatography is a rapid and reliable working method for quantification of mycotoxins which is suitable for checking EC legislation aflatoxin limits for dried figs without an RP-18 pre-column cleaning step. We describe normal-phase chromatography on silica gel plates with 2.4:0.05:0.1:0.05 ( v/v ) methyl t -butyl ether-water-methanol-cyclohexane as mobile phase and reversed-phase chromatography on RP-18 plates with methanol-4% aqueous ZnSO 4 solution-ethyl methyl ketone 15:15:3 ( v/v ) as mobile phase. Sample pretreatment was by modified QuEChERS (Quick, Easy, Cheap, Effective, Rugged, Safe) extraction with tetrahydrofuran or acetone. NaCl was used as QuEChERS salt. Response was a linear function of amount chromatographed in the ranges 3 to 100 pg per zone for aflatoxins B 2 and G 2 , 10 to 350 pg per zone for the aflatoxins B 1 and G 1 , and 0.25 to 2.5 ng per zone for ochratoxin A. Quantification limits for the aflatoxins were between 13 and 35 pg per zone (equivalent to 1.5 and 2.4 ppb, taking the pre-treatment procedure into account). Ochratoxin A was detectable with a limit of quantification of 970 pg per zone, corresponding to 56 ppb in the sample. Normal phase and RP-18 separations work rapidly, reliably, and at low cost. They are also suitable for checking the content of the mycotoxins patulin, penicillic acid, zearalenone, and deoxynivalenol.
Grey-box modelling combines physical and data-driven models to benefit from their respective advantages. Neural ordinary differential equations (NODEs) offer new possibilities for grey-box modelling, as differential equations given by physical laws and neural networks can be combined in a single modelling framework. This simplifies the simulation and optimization and allows to consider irregularly-sampled data during training and evaluation of the model. We demonstrate this approach using two levels of model complexity; first, a simple parallel resistor-capacitor circuit; and second, an equivalent circuit model of a lithium-ion battery cell, where the change of the voltage drop over the resistor-capacitor circuit including its dependence on current and State-of-Charge is implemented as NODE. After training, both models show good agreement with analytical solutions respectively with experimental data.
Lithium-ion batteries show strongly nonlinear behaviour regarding the battery current and state of charge. Therefore, the modelling of lithium-ion batteries is complex. Combining physical and data-driven models in a grey-box model can simplify the modelling. Our focus is on using neural networks, especially neural ordinary differential equations, for grey-box modelling of lithium-ion batteries. A simple equivalent circuit model serves as a basis for the grey-box model. Unknown parameters and dependencies are then replaced by learnable parameters and neural networks. We use experimental full-cycle data and data from pulse tests of a lithium iron phosphate cell to train the model. Finally, we test the model against two dynamic load profiles: one consisting of half cycles and one dynamic load profile representing a home-storage system. The dynamic response of the battery is well captured by the model.
Nowadays, the wide majority of Europeans uses smartphones. However, touch displays are still not accessible by everyone. Individuals with deafblindness, for example, often face difculties in accessing vision-based touchscreens. Moreover, they typically have few fnancial resources which increases the need for customizable, low-cost assistive devices. In this work-in-progress, we present four prototypes made from low-cost, every-day materials, that make modern pattern lock mechanisms more accessible to individuals with vision impairments or even with deafblindness. Two out of four prototypes turned out to be functional tactile overlays for accessing digital 4-by-4 grids that are regularly used to encode dynamic dot patterns. In future work, we will conduct a user study investigating whether these two prototypes can make dot-based pattern lock mechanisms more accessible for individuals with visual impairments or deafblindness.
In short-reach connections, large-diameter multimode fibres allow for robust and easy connections. Unfortunately, their propagation properties depend on the excitation conditions. We propose a launching technique using a fibre stub that can tolerate fabrication tolerances in terms of tilts and off-sets to a large extent. A study of the influence of displaced connectors along the transmission link shows that the power distributions approach a steady-state power distribution very similar to the initial distribution established by the proposed launching scheme.
Die Möglichkeit zur digitalen Verbindung geographischer Orte mit Aufgaben, Herausforderungen oder Lernmaterialien hat eine Vielzahl von Anwendungen auch außerhalb der Mathematikbildung inspiriert. Dieser Beitrag stellt eine exemplarische Auswahl solcher Applikationen vor und versucht, die technischen, organisatorischen und konzeptionellen Gestaltungselemente zu systematisieren. Die Ausführungen sollen als Anregung bei der Anlage von Mathematiktrails sowie bei der Weiterentwicklung technischer Lösungen für den Lehreinsatz dienen.