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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.
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.
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.
Astronomical phenomena fascinate people from the very beginning of mankind up to today. In this paper the authors will present their experience with photography of astronomical events. The main focus will be on aurora borealis, comet Neowise, total lunar eclipses and how mobile devices open up new possibilities to observe the green flash. Our efforts were motivated by the great impact and high number of viewers of these events. Visitors from over a hundred countries watched our live broadcasts.
Furthermore, we report on our experiences with the photography of optical phenomena such as polar lights Fig. 1, comet Neowise with a Delta Aquariids meteor Fig. 11, and lunar eclipses Fig. 12.
The PHOTOPUR project aims to develop a photocatalytic process as a type of AOPs (Advanced Oxidation Processes) for the elimination of plant protection products (PPP) of the cleaning water used to wash sprayers. At INES a PV based energy supply for the photocatalytic cleaning system was developed within the framework of two bachelor theses and assembled as a demonstration unit. Then the system was step by step extended with further process automation features and pushed to a remote operating device. The final system is now available as a mobile unit mounted on a lab table. The latest step was the photocatalytic reactor module which completed the first PHOTOPUR prototype. The system is actually undergoing an intensive testing phase with performance checks at the consortium partners. First results give an overview about the successful operation.
In this work a method for the estimation of current slopes induced by inverters operating interior permanent magnet synchronous machines is presented. After the derivation of the estimation algorithm, the requirements for a suitable sensor setup in terms of accuracy, dynamic and electromagnetic interference are discussed. The boundary conditions for the estimation algorithm are presented with respect to application within high power traction systems. The estimation algorithm is implemented on a field programmable gateway array. This moving least-square algorithm offers the advantage that it is not dependent on vectors and therefore not every measured value has to be stored. The summation of all measured values leads to a significant reduction of the required storage units and thus decreases the hardware requirements. The algorithm is designed to be calculated within the dead time of the inverter. Appropriate countermeasures for disturbances and hardware restrictions are implemented. The results are discussed afterwards.
Wireless communication technologies play a major role to enable megatrends like Internet of Things (IoT) and Industry 4.0. The Narrowband Wireless WAN (NBWWAN) introduced to meet the long range and low power requirements of spatially distributed wireless communication use cases. These networks introduce additional challenges in testing because the network topology and RF characteristics become particularly complex and thus a multitude of different scenarios must be tested. This paper describes the infrastructure for automated testing of radio communication and for systematic measurements of the network performance of NBWWAN.
One of the main requirements of spatially distributed Internet of Things (IoT) solutions is to have networks with wider coverage to connect many low-power devices. Low-Power Wide-Area Networks (LPWAN) and Cellular IoT(cIOT) networks are promising candidates in this space. LPWAN approaches are based on enhanced physical layer (PHY) implementations to achieve long range such as LoRaWAN, SigFox, MIOTY. Narrowband versions of cellular network offer reduced bandwidth and, simplified node and network management mechanisms, such as Narrow Band IoT (NB-IoT) and Long-Term Evolution for Machines (LTE-M). Since the underlying use cases come with various requirements it is essential to perform a comparative analysis of competing technologies. This article provides systematic performance measurement and comparison of LPWAN and NB-IoT technologies in a unified testbed, also discusses the necessity of future fifth generation (5G) LPWAN solutions.
Partial substitution of Al atoms with Sc in wurtzite AlN crystals increases the piezoelectric constants. This leads to an increased electromechanical coupling, which is required for high bandwidths in piezo-acoustic filters. The crystal bonds in Ah-xScxN (AlScN) are softened as function of Sc atomic percentage x, leading to reduction of phase velocity in the film. Combining high Sc content AlScN films with high velocity substrates favors higher order guided surface acoustic wave (SAW) modes [1]. This study investigates higher order SAW modes in epitaxial AlScN on sapphire (Al2O3). Their dispersion for Pt metallized epitaxial AlScN films on Al2O3was computed for two different propagation directions. Computed phase velocity dispersion branches were experimentally verified by the characterization of fabricated SAW resonators. The results indicated four wave modes for the propagation direction (0°, 0°, 0°), featuring 3D polarized displacement fields. The sensitivity of the wave modes to the elastic constants of AlScN was investigated. It was shown that due to the 3D polarization of the waves, all elastic constants have an influence on the phase velocity and can be measured by suitable weighting functions in material constant extraction procedures.
Deafblindness, a form of dual sensory impairment, signifcantly impacts communication, access to information and mobility. Inde- pendent navigation and wayfnding are main challenges faced by individuals living with combined hearing and visual impairments. We developed a haptic wearable that provides sensory substitution and navigational cues for users with deafblindness by conveying vibrotactile signals onto the body. Vibrotactile signals on the waist area convey directional and proximity information collected via a fisheye camera attached to the garment, while semantic informa- tion is provided with a tapping system on the shoulders. A playful scenario called “Keep Your Distance” was designed to test the navigation system: individuals with deafblindness were “secret agents” that needed to follow a “suspect”, but they should keep an opti- mal distance of 1.5 meters from the other person to win the game. Preliminary fndings suggest that individuals with deafblindness enjoyed the experience and were generally able to follow the directional cues.
A Gamified and Adaptive Learning System for Neurodivergent Workers in Electronic Assembling Tasks
(2020)
Learning and work-oriented assistive systems are often designed to fit the workflow of neurotypical workers. Neurodivergent workers and individuals with learning disabilities often present cognitive and sensorimotor characteristics that are better accommodated with personalized learning and working processes. Therefore, we designed an adaptive learning system that combines an augmented interaction space with user-sensitive virtual assistance to support step-by-step guidance for neurodivergent workers in electronic assembling tasks. Gamified learning elements were also included in the interface to provide self-motivation and praise whenever users progress in their learning and work achievements.
Machine learning (ML) has become highly relevant in applications across all industries, and specialists in the field are sought urgently. As it is a highly interdisciplinary field, requiring knowledge in computer science, statistics and the relevant application domain, experts are hard to find. Large corporations can sweep the job market by offering high salaries, which makes the situation for small and medium enterprises (SME) even worse, as they usually lack the capacities both for attracting specialists and for qualifying their own personnel. In order to meet the enormous demand in ML specialists, universities now teach ML in specifically designed degree programs as well as within established programs in science and engineering. While the teaching almost always uses practical examples, these are somewhat artificial or outdated, as real data from real companies is usually not available. The approach reported in this contribution aims to tackle the above challenges in an integrated course, combining three independent aspects: first, teaching key ML concepts to graduate students from a variety of existing degree programs; second, qualifying working professionals from SME for ML; and third, applying ML to real-world problems faced by those SME. The course was carried out in two trial periods within a government-funded project at a university of applied sciences in south-west Germany. The region is dominated by SME many of which are world leaders in their industries. Participants were students from different graduate programs as well as working professionals from several SME based in the region. The first phase of the course (one semester) consists of the fundamental concepts of ML, such as exploratory data analysis, regression, classification, clustering, and deep learning. In this phase, student participants and working professionals were taught in separate tracks. Students attended regular classes and lab sessions (but were also given access to e-learning materials), whereas the professionals learned exclusively in a flipped classroom scenario: they were given access to e-learning units (video lectures and accompanying quizzes) for preparation, while face-to-face sessions were dominated by lab experiments applying the concepts. Prior to the start of the second phase, participating companies were invited to submit real-world problems that they wanted to solve with the help of ML. The second phase consisted of practical ML projects, each tackling one of the problems and worked on by a mixed team of both students and professionals for the period of one semester. The teams were self-organized in the ways they preferred to work (e.g. remote vs. face-to-face collaboration), but also coached by one of the teaching staff. In several plenary meetings, the teams reported on their status as well as challenges and solutions. In both periods, the course was monitored and extensive surveys were carried out. We report on the findings as well as the lessons learned. For instance, while the program was very well-received, professional participants wished for more detailed coverage of theoretical concepts. A challenge faced by several teams during the second phase was a dropout of student members due to upcoming exams in other subjects.
The precise positioning of mobile systems is a prerequisite for any autonomous behavior, in an industrial environment as well as for field robotics. The paper describes the set up for an experimental platform and its use for the evaluation of simultaneous localization and mapping (SLAM) algorithms. Two approaches are compared. First, a local method based on point cloud matching and integration of inertial measurement units is evaluated. Subsequent matching makes it possible to create a three-dimensional point cloud that can be used as a map in subsequent runs. The second approach is a full SLAM algorithm, based on graph relaxation models, incorporating the full sensor suite of odometry, inertial sensors, and 3D laser scan data.
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.
Efficient collaborative robotic applications need a combination of speed and separation monitoring, and power and force limiting operations. While most collaborative robots have built-in sensors for power and force limiting operations, there are none with built-in sensor systems for speed and separation monitoring. This paper proposes a system for speed and separation monitoring directly from the gripper of the robot. It can monitor separation distances of up to three meters. We used single-pixel Time-of-Flight sensors to measure the separation distance between the gripper and the next obstacle perpendicular to it. This is the first system capable of measuring separation distances of up to three meters directly from the robot's gripper.
Well-designed and informative product presentations can support consumers in making purchase decisions. There are plenty of facts and details about a product of interest. However, also emotions are an important aspect for the purchase decision. The unique visualization opportunities of virtual reality (VR) can give users of VR applications the feeling of being there (telepresence). The applications can intensely engage them in a flow experience, comprising the four dimensions of enjoyment, curiosity, focused attention and control. In this work, we claim that VR product presentations can create subjective product experiences for consumers and motivate them to reuse this innovative type of product presentation in the future, by immersing them in a virtual world and causing them to interact with it. To verify the conceptual model a study was conducted with 551 participants who explored a VR hotel application. The results indicate that VR product presentations evoke positive emotions among consumers. The virtual experience made potential customers focus their attention on the virtual world and aroused their curiosity about getting more information about the product in an enjoyable way. In contrast to the theoretical assumption, control did not influence the users’ behavioral intentions to reuse VR product presentation. We conclude that VR product presentations create a feeling of telepresence, which leads to a flow experience that contributes to the behavioral intention of users to reuse VR product presentations in the future.
Additive manufacturing (AM) or 3D printing (3DP) has become a widespread new technology in recent years and is now used in many areas of industry. At the same time, there is an increasing need for training courses that impart the knowledge required for product development in 3D printing. In this article, a workshop on “Rapid Prototyping” is presented, which is intended to provide students with the technical and creative knowledge for product development in the field of AM. Today, additive manufacturing is an important part of teaching for the training of future engineers. In a detailed literature review, the advantages and disadvantages of previous approaches to training students are examined and analyzed. On this basis, a new approach is developed in which the students analyze and optimize a given product in terms of additivie manufacturing. The students use two different 3D printers to complete this task. In this way, the students acquire the skills to work independently with different processes and materials. With this new approach, the students learn to adapt the design to different manufacturing processes and to observe the restrictions of different materials. The results of these courses are evaluated through feedback in a presentation and a questionnaire.
Live streaming of events over an IP network as a catalyst in media technology education and training
(2020)
The paper describes how students are involved in applied research when setting up the technology and running a live event. Real-time IP transmission in broadcast environments via fiber optics will become increasingly important in the future. Therefore, it is necessary to create a platform in this area where students can learn how to handle IP infrastructure and fiber optics. With this in mind, we have built a fully functional TV control room that is completely IP-based. The authors present the steps in the development of the project and show the advantages of the proposed digital solutions. The IP network proves to be a synergy between the involved teams: participants of the robot competition and the members of the media team. These results are presented in the paper. Our activities aim to awaken enthusiasm for research and technology in young people. Broadcasts of live events are a good opportunity for "hands on" activities.
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.