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Artificial intelligence (AI), and in particular machine learning algorithms, are of increasing importance in many application areas but interpretability and understandability as well as responsibility, accountability, and fairness of the algorithms' results, all crucial for increasing the humans' trust into the systems, are still largely missing. Big industrial players, including Google, Microsoft, and Apple, have become aware of this gap and recently published their own guidelines for the use of AI in order to promote fairness, trust, interpretability, and other goals. Interactive visualization is one of the technologies that may help to increase trust in AI systems. During the seminar, we discussed the requirements for trustworthy AI systems as well as the technological possibilities provided by interactive visualizations to increase human trust in AI.
The use of architectural models is a long-proven method for the visualization of designs. More recently, powerful 3D printers have enabled the rapid and cost-effective additive manufacturing (AM) of textured architectural models. The use of AM technology to sample terraced houses in a specific use case (sampling center with more than 1200 customers per year) is examined within this contribution. The aim is to offer customers with limited spatial imagination assistance in the form of detailed architectural models of the whole house, which are divided into different modules. For this purpose, the structure of the terraced house is first analysed and examined for flexible design elements. The implementation of different variants of each floor should serve as a basis for the customer's decision on design and equipment. Thus, the architectural models are additively manufactured using Polyjet modeling. The necessary CAAD-data and interfaces, the technical possibilities and limits of this approach as well as the resulting costs are analyzed. The results of the AM process are evaluated to determine their applicability for the sampling of terraced houses. In addition, the evaluation will show that the additively manufactured architectural models will allow a more precise visualization of the building and thus a faster understanding of the design choices.
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.
Social Haptic Communication (SHC) is one of the many tactile modes of communication used by persons with deafblindness to access information about their surroundings. SHC usually involves an interpreter executing finger and hand signs on the back of a person with multi-sensory disabilities. Learning SHC, however, can become challenging and time-consuming, particularly to those who experience deafblindness later in life. In this work, we present PatRec: a mobile game for learning SHC concepts. PatRec is a multiple-choice quiz game connected to a chair interface that contains a 3x3 array of vibration motors emulating different SHC signs. Players collect scores and badges whenever they guess the right SHC vibration pattern, leading to continuous engagement and a better position on a leaderboard. The game is also meant for family members to learn SHC. We report the technical implementation of PatRec and the findings from a user evaluation.
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.
Das hier vorgestellte System verbindet das neue Konzept der Peer-to-Peer-Navigation mit dem Einsatz von Augmented Reality zur Unterstützung von bettseitig durchgeführten externen Ventrikeldrainagen. Das sehr kompakte und genaue Gesamtsystem beinhaltet einen Patiententracker mit integrierter Kamera, eine Augmented-Reality-Brille mit Kamera und eine Punktionsnadel bzw. einen Pointer mit zwei Trackern, mit dessen Hilfe die Anatomie des Patienten aufgenommen wird. Die exakte Position und Richtung der Punktionsnadel wird unter Zuhilfenahme der aufgenommenen Landmarken berechnet und über die Augmented-Reality-Brille für den Chirurgen sichtbar auf dem Patienten dargestellt. Die Methode zur Kalibrierung der statischen Transformationen zwischen Patiententracker und daran befestigter Kamera beziehungsweise zwischen den Trackern der Punktionsnadel sind für die Genauigkeit sehr wichtig und werden hier vorgestellt. Das Gesamtsystem konnte in vitro erfolgreich getestet werden und bestätigt den Nutzen eines Peer-to-Peer-Navigationssystems.
Physically Unclonable Functions (PUFs) are hardware-based security primitives, which allow for inherent device fingerprinting. Therefore, intrinsic variation of imperfect manufactured systems is exploited to generate device-specific, unique identifiers. With printed electronics (PE) joining the internet of things (IoT), hardware-based security for novel PE-based systems is of increasing importance. Furthermore, PE offers the possibility for split-manufacturing, which mitigates the risk of PUF response readout by third parties, before commissioning. In this paper, we investigate a printed PUF core as intrinsic variation source for the generation of unique identifiers from a crossbar architecture. The printed crossbar PUF is verified by simulation of a 8×8-cells crossbar, which can be utilized to generate 32-bit wide identifiers. Further focus is on limiting factors regarding printed devices, such as increased parasitics, due to novel materials and required control logic specifications. The simulation results highlight, that the printed crossbar PUF is capable to generate close-to-ideal unique identifiers at the investigated feature size. As proof of concept a 2×2-cells printed crossbar PUF core is fabricated and electrically characterized.
Printed electronics (PE) offers flexible, extremely low-cost, and on-demand hardware due to its additive manufacturing process, enabling emerging ultra-low-cost applications, including machine learning applications. However, large feature sizes in PE limit the complexity of a machine learning classifier (e.g., a neural network (NN)) in PE. Stochastic computing Neural Networks (SC-NNs) can reduce area in silicon technologies, but still require complex designs due to unique implementation tradeoffs in PE. In this paper, we propose a printed mixed-signal system, which substitutes complex and power-hungry conventional stochastic computing (SC) components by printed analog designs. The printed mixed-signal SC consumes only 35% of power consumption and requires only 25% of area compared to a conventional 4-bit NN implementation. We also show that the proposed mixed-signal SC-NN provides good accuracy for popular neural network classification problems. We consider this work as an important step towards the realization of printed SC-NN hardware for near-sensor-processing.
Sustainable chemical processes should be designed to combine the technological advantages and progress with lower safety risks and minimization of environmental impact such as, for example, reduction of raw materials, energy and water consumption, and avoidance of hazardous waste and pollution with toxic chemical agents. A number of novel eco-friendly chemical technologies have been developed in the recent decades with the help of the eco-innovations approaches and methods such as Life Cycle Analysis, Green Process Engineering, Process Intensification, Process Design for Sustainability, and others. An emerging approach to the sustainable process design in process engineering builds on the innovative solutions inspired from nature. However, the implementation of the eco-friendly technologies often faces secondary ecological problems. The study postulates that the eco-inventive principles identified in natural systems allow to avoid secondary eco-problems and proposes to apply these principles for sustainable design in chemical process engineering. The research work critically examines how this approach differs from the biomimetics, as it is commonly used for copying natural systems. The application of nature-inspired eco-design principles is illustrated with an example of a sustainable technology for extraction of nickel from pyrophyllite.
The proposed method includes identification and documentation of the elementary TRIZ inventive principles from the TRIZ body of knowledge, extension and enhancement of inventive principles by patents and technologies analysis, avoiding overlapping and redundant principles, classification and adaptation of principles to at least following categories such as working medium, target object, useful action, harmful effect, environment, information, field, substance, time, and space, assignment of the elementary inventive principles to the at least following underlying engineering domains such as universal, design, mechanical, acoustic, thermal, chemical, electromagnetic, intermolecular, biological, and data processing. The method includes classification of abstraction level of the elementary principles, definition of the statistical ranking of principles for different problem types, and specific engineering or non-technical domains, definition of strategies for selection of principles sets with high solution potential for predefined problems, automated semantic transformation of the elementary inventive principles into solution ideas, evaluation of automatically generated ideas and transformation of ideas to innovation or inventive concepts.
The paper describes the implementation of practical laboratory settings in a virtual environment. With the entry of VR glasses into the mass market, there is a chance to establish educational and training applications for displaying some teaching materials and practical works. Therefore our project focuses on the realization of virtual experiments and environments, which gives users a deep insight into selected subfields of Optics and Photonics. Our goal is not to substitute the hand on experiments rather to extend them. By means of VR glasses, the user is offered the possibility to view the experiment from several angles and to make changes through interactive control functions. During the VR application, additional context-related information is displayed. By using object recognition, the specific graphics and texts for the respective object are loaded and supplemented at the appropriate place. Thus, complex facts are supported in an informative way. The prototype is developed using the Unity Engine and can thus be exported to different platforms and end devices. Another major advantage of virtual simulations to the real situation is the high degree of controllability as well as the easy repeatability. With slight modifications, entire experiments can be reused. Our research aims to acquire new knowledge in the field of e-learning in association with VR technology. Here we try to answer a core question of the compatibility of the individual media components.
As engineering graduates and specialists frequently lack the advanced skills and knowledge required to run eco-innovation systematically, the paper proposes a new learning materials and educational tools in the field of eco-innovation and evaluates the learning experience and outcomes. This programme is aimed at strengthening student’s skills and motivation to identify and creatively overcome secondary eco-contradictions in case if additional environmental problems appear as negative side effects of eco-friendly solutions. The paper evaluates the efficiency of the proposed interdisciplinary tool for systematic eco-innovation including creative semi-automatic knowledge-based idea generation and concept development. It analyses the learning experience and identifies the factors that impact the eco-innovation performance of the students.
Increasing power density causes increased self-generation of harmonics and intermodulation. As this leads to violations of the strict linearity requirements, especially for carrier aggregation (CA), the nonlinearity must be considered in the design process of RF devices. This raises the demand of accurate simulation models. Linear and nonlinear P-Matrix/COM models are used during the design due to their fast simulation times and accurate results. However, the finite element method (FEM) is useful to get a deeper insight in the device's nonlinearities, as the total field distributions can be visualized. The FE method requires complete sets of material tensors, which are unknown for most relevant materials in nonlinear micro-acoustics. In this work, we perform nonlinear FEM simulations, which allow the calculation of nonlinear field distributions of a lithium tantalate based layered SAW system up to third order. We aim at achieving good correspondence to measured data and determine the contributions of each material layer to the nonlinear signals. Therefore, we use approximations circumventing the issue of limited higher order tensor data. Experimental data for the third order nonlinearity is shown to validate the presented approach.
Cryptographic protection of messages requires frequent updates of the symmetric cipher key used for encryption and decryption, respectively. Protocols of legacy IT security, like TLS, SSH, or MACsec implement rekeying under the assumption that, first, application data exchange is allowed to stall occasionally and, second, dedicated control messages to orchestrate the process can be exchanged. In real-time automation applications, the first is generally prohibitive, while the second may induce problematic traffic patterns on the network. We present a novel seamless rekeying approach, which can be embedded into cyclic application data exchanges. Although, being agnostic to the underlying real-time communication system, we developed a demonstrator emulating the widespread industrial Ethernet system PROFINET IO and successfully use this rekeying mechanism.
To demonstrate how deep learning can be applied to industrial applications with limited training data, deep learning methodologies are used in three different applications. In this paper, we perform unsupervised deep learning utilizing variational autoencoders and demonstrate that federated learning is a communication efficient concept for machine learning that protects data privacy. As an example, variational autoencoders are utilized to cluster and visualize data from a microelectromechanical systems foundry. Federated learning is used in a predictive maintenance scenario using the C-MAPSS dataset.
It seems to be a widespread impression that the use of strong cryptography inevitably imposes a prohibitive burden on industrial communication systems, at least inasmuch as real-time requirements in cyclic fieldbus communications are concerned. AES-GCM is a leading cryptographic algorithm for authenticated encryption, which protects data against disclosure and manipulations. We study the use of both hardware and software-based implementations of AES-GCM. By simulations as well as measurements on an FPGA-based prototype setup we gain and substantiate an important insight: for devices with a 100 Mbps full-duplex link, a single low-footprint AES-GCM hardware engine can deterministically cope with the worst-case computational load, i.e., even if the device maintains a maximum number of cyclic communication relations with individual cryptographic keys. Our results show that hardware support for AES-GCM in industrial fieldbus components may actually be very lightweight.