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Multiple Object Tracking (MOT) is a long-standing task in computer vision. Current approaches based on the tracking by detection paradigm either require some sort of domain knowledge or supervision to associate data correctly into tracks. In this work, we present a self-supervised multiple object tracking approach based on visual features and minimum cost lifted multicuts. Our method is based on straight-forward spatio-temporal cues that can be extracted from neighboring frames in an image sequences without supervision. Clustering based on these cues enables us to learn the required appearance invariances for the tracking task at hand and train an AutoEncoder to generate suitable latent representations. Thus, the resulting latent representations can serve as robust appearance cues for tracking even over large temporal distances where no reliable spatio-temporal features can be extracted. We show that, despite being trained without using the provided annotations, our model provides competitive results on the challenging MOT Benchmark for pedestrian tracking.
Diffracted waves carry high resolution information that can help interpreting fine structural details at a scale smaller than the seismic wavelength. Because of the low signal-to-noise ratio of diffracted waves, it is challenging to preserve them during processing and to identify them in the final data. It is, therefore, a traditional approach to pick manually the diffractions. However, such task is tedious and often prohibitive, thus, current attention is given to domain adaptation. Those methods aim to transfer knowledge from a labeled domain to train the model, and then infer on the real unlabeled data. In this regard, it is common practice to create a synthetic labeled training dataset, followed by testing on unlabeled real data. Unfortunately, such procedure may fail due to the existing gap between the synthetic and the real distribution since quite often synthetic data oversimplifies the problem, and consequently the transfer learning becomes a hard and non-trivial procedure. Furthermore, deep neural networks are characterized by their high sensitivity towards cross-domain distribution shift. In this work, we present deep learning model that builds a bridge between both distributions creating a semi-synthetic datatset that fills in the gap between synthetic and real domains. More specifically, our proposal is a feed-forward, fully convolutional neural network for imageto-image translation that allows to insert synthetic diffractions while preserving the original reflection signal. A series of experiments validate that our approach produces convincing seismic data containing the desired synthetic diffractions.
This paper describes a comparative study of two tactile systems supporting navigation for persons with little or no visual and auditory perception. The efficacy of a tactile head-mounted device (HMD) was compared to that of a wearable device, a tactile belt. A study with twenty participants showed that the participants took significantly less time to complete a course when navigating with the HMD, as compared to the belt.
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.
Short-term load forecasting (STLF) has been playing a key role in the electricity sector for several decades, due to the need for aligning energy generation with the demand and the financial risk connected with forecasting errors. Following the top-down approach, forecasts are calculated for aggregated load profiles, meaning the sum of singular loads from consumers belonging to a balancing group. Due to the emerging flexible loads, there is an increasing relevance for STLF of individual factories. These load profiles are typically more stochastic compared to aggregated ones, which imposes new requirements to forecasting methods and tools with a bottom-up approach. The increasing digitalization in industry with enhanced data availability as well as smart metering are enablers for improved load forecasts. There is a need for STLF tools processing live data with a high temporal resolution in the minute range. Furthermore, behin-the-meter (BTM) data from various sources like submetering and production planning data should be integrated in the models. In this case, STLF is becoming a big data problem so that machine learning (ML) methods are required. The research project “GaIN” investigates the improvement of the STLF quality of an energy utility using BTM data and innovative ML models. This paper describes the project scope, proposes a detailed definition for a benchmark and evaluates the readiness of existing STLF methods to fulfil the described requirements as a reviewing paper.
The review highlights that recent STLF investigations focus on ML methods. Especially hybrid models gain more and more importance. ML can outperform classical methods in terms of automation degree and forecasting accuracy. Nevertheless, the potential for improving forecasting accuracy by the use of ML models depends on the underlying data and the types of input variables. The described methods in the analyzed publications only partially fulfil the tool requirements for STLF on company level. There is still a need to develop suitable ML methods to integrate the expanded data base in order to improve load forecasts on company level.
The interaction between agents in multiagent-based control systems requires peer to peer communication between agents avoiding central control. The sensor nodes represent agents and produce measurement data every time step. The nodes exchange time series data by using the peer to peer network in order to calculate an aggregation function for solving a problem cooperatively. We investigate the aggregation process of averaging data for time series data of nodes in a peer to peer network by using the grouping algorithm of Cichon et al. 2018. Nodes communicate whether data is new and map data values according to their sizes into a histogram. This map message consists of the subintervals and vectors for estimating the node joining and leaving the subinterval. At each time step, the nodes communicate with each other in synchronous rounds to exchange map messages until the network converges to a common map message. The node calculates the average value of time series data produced by all nodes in the network by using the histogram algorithm. The relative error for comparing the output of averaging time series data, and the ground truth of the average value in the network will decrease as the size of the network increases. We perform simulations which show that the approximate histograms method provides a reasonable approximation of time series data.
We propose in this work to solve privacy preserving set relations performed by a third party in an outsourced configuration. We argue that solving the disjointness relation based on Bloom filters is a new contribution in particular by having another layer of privacy on the sets cardinality. We propose to compose the set relations in a slightly different way by applying a keyed hash function. Besides discussing the correctness of the set relations, we analyze how this impacts the privacy of the sets content as well as providing privacy on the sets cardinality. We are in particular interested in how having bits overlapping in the Bloom filters impacts the privacy level of our approach. Finally, we present our results with real-world parameters in two concrete scenarios.
The Effect of Gamification on Emotions - The Potential of Facial Recognition in Work Environmentsns
(2015)
Gamification means using video game elements to improve user experience and user engagement in non-game services and applications. This article describes the effects when gamification is used in work contexts. Here we focus on industrial production. We describe how facial recognition can be employed to measure and quantify the effect of gamification on the users’ emotions.
The quantitative results show that gamification significantly reduces both task completion time and error rate. However, the results concerning the effect on emotions are surprising. Without gamification there are not only more unhappy expressions (as to expect) but surprisingly also more happy expressions. Both findings are statistically highly significant.
We think that in redundant production work there are generally more (negative) emotions involved. When there is no gamification happy and unhappy balance each other. In contrast gamification seems to shift the spectrum of moods towards “relaxed”. Especially for work environments such a calm attitude is a desirable effect on the users. Thus our findings support the use of gamification.
Video game developers continuously increase the degree of details and realism in games to create more human-like characters. But increasing the human-likeness becomes a problem in regard to the Uncanny Valley phenomenon that predicts negative feelings of people towards artificial entities. We developed an avatar creation system to examine preferences towards parametrized faces and explore in regard to the Uncanny Valley phenomenon how people design faces that they like or reject. Based on the 3D model of the Caucasian average face, 420 participants generate 1341 faces of positively and negatively associated concepts of both gender. The results show that some characteristics associated with the Uncanny Valley are used to create villains or repulsive faces. Heroic faces get attractive features but are rarely and little stylized. A voluntarily designed face is very similar to the heroine. This indicates that there is a tendency of users to design feminine and attractive but still credible faces.
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.
In this contribution, we propose an system setup for the detection andclassification of objects in autonomous driving applications. The recognition algo-rithm is based upon deep neural networks, operating in the 2D image domain. Theresults are combined with data of a stereo camera system to finally incorporatethe 3D object information into our mapping framework. The detection systemis locally running upon the onboard CPU of the vehicle. Several network archi-tectures are implemented and evaluated with respect to accuracy and run-timedemands for the given camera and hardware setup.
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.
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.
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.
Co-Designing Assistive Tools to Support Social Interactions by Individuals Living with Deafblindness
(2020)
Deafblindness is a dual sensory impairment that affects many aspects of life, including mobility, access to information, communication, and social interactions. Furthermore, individuals living with deafblindness are under a high risk of social isolation. Therefore, we identified opportunities for applying assistive tools to support social interactions through co-ideation activities with members of the deafblind community. This work presents our co-design approach, lessons learned and directions for designing meaningful assistive tools for dual sensory loss.
Interaction and capturing information from the surrounding is dominated by vision and hearing. Haptics on the other side, widens the bandwidth and could also replace senses (sense switching) for impaired. Haptic technologies are often limited to point-wise actuation. Here, we show that actuation in two-dimensional matrices instead creates a richer input. We describe the construction of a full-body garment for haptic communication with a distributed actuating network. The garment is divided into attachable-detachable panels or add-ons that each can carry a two dimensional matrix of actuating haptic elements. Each panel adds to an enhanced sensoric capability of the human- garment system so that together a 720° system is formed. The spatial separation of the panels on different body locations supports semantic and theme-wise separation of conversations conveyed by haptics. It also achieves directional faithfulness, which is maintaining any directional information about a distal stimulus in the haptic input.
Tactile Navigation with Checkpoints as Progress Indicators?: Only when Walking Longer Straight Paths
(2020)
Persons with both vision and hearing impairments have to rely primarily on tactile feedback, which is frequently used in assistive devices. We explore the use of checkpoints as a way to give them feedback during navigation tasks. Particularly, we investigate how checkpoints can impact performance and user experience. We hypothesized that individuals receiving checkpoint feedback would take less time and perceive the navigation experience as superior to those who did not receive such feedback. Our contribution is two-fold: a detailed report on the implementation of a smart wearable with tactile feedback (1), and a user study analyzing its effects (2). The results show that in contrast to our assumptions, individuals took considerably more time to complete routes with checkpoints. Also, they perceived navigating with checkpoints as inferior to navigating without checkpoints. While the quantitative data leave little room for doubt, the qualitative data open new aspects: when walking straight and not being "overwhelmed" by various forms of feedback in succession, several participants actually appreciated the checkpoint feedback.
Wow, You Are Terrible at This!: An Intercultural Study on Virtual Agents Giving Mixed Feedback
(2020)
While the effects of virtual agents in terms of likeability, uncanniness, etc. are well explored, it is unclear how their appearance and the feedback they give affects people's reactions. Is critical feedback from an agent embodied as a mouse or a robot taken less serious than from a human agent? In an intercultural study with 120 participants from Germany and the US, participants had to find hidden objects in a game and received feedback on their performance by virtual agents with different appearances. As some levels were designed to be unsolvable, critical feedback was unavoidable. We hypothesized that feedback would be taken more serious, the more human the agent looked. Also, we expected the subjects from the US to react more sensitively to criticism. Surprisingly, our results showed that the agents' appearance did not significantly change the participants' perception. Also, while we found highly significant differences in inspirational and motivational effects as well as in perceived task load between the two cultures, the reactions to criticism were contrary to expectations based on established cultural models. This work improves our understanding on how affective virtual agents are to be designed, both with respect to culture and to dialogue strategies.
Deafblindness, also known as dual sensory loss, is the combination of sight and hearing impairments of such extent that it becomes difficult for one sense to compensate for the other. Communication issues are a key concern for the Deafblind community. We present the design and technical implementation of the Tactile Board: a mobile Augmentative and Alternative Communication (AAC) device for individuals with deafblindness. The Tactile Board allows text and speech to be translated into vibrotactile signs that are displayed real-time to the user via a haptic wearable. Our aim is to facilitate communication for the deafblind community, creating opportunities for these individuals to initiate and engage in social interactions with other people without the direct need of an intervener.
Novel manufacturing technologies, such as printed electronics, may enable future applications for the Internet of Everything like large-area sensor devices, disposable security, and identification tags. Printed physically unclonable functions (PUFs) are promising candidates to be embedded as hardware security keys into lightweight identification devices. We investigate hybrid PUFs based on a printed PUF core. The statistics on the intra- and inter-hamming distance distributions indicate a performance suitable for identification purposes. Our evaluations are based on statistical simulations of the PUF core circuit and the thereof generated challenge-response pairs. The analysis shows that hardware-intrinsic security features can be realized with printed lightweight devices.
Neuromorphic computing systems have demonstrated many advantages for popular classification problems with significantly less computational resources. We present in this paper the design, fabrication and training of a programmable neuromorphic circuit, which is based on printed electrolytegated field-effect transistor (EGFET). Based on printable neuron architecture involving several resistors and one transistor, the proposed circuit can realize multiply-add and activation functions. The functionality of the circuit, i.e. the weights of the neural network, can be set during a post-fabrication step in form of printing resistors to the crossbar. Besides the fabrication of a programmable neuron, we also provide a learning algorithm, tailored to the requirements of the technology and the proposed programmable neuron design, which is verified through simulations. The proposed neuromorphic circuit operates at 5V and occupies 385mm 2 of area.
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.
The TriRhenaTech alliance presents a collection of accepted papers of the cancelled tri-national 'Upper-Rhine Artificial Intelligence Symposium' planned for 13th May 2020 in Karlsruhe. The TriRhenaTech alliance is a network of universities in the Upper-Rhine Trinational Metropolitan Region comprising of the German universities of applied sciences in Furtwangen, Kaiserslautern, Karlsruhe, and Offenburg, the Baden-Wuerttemberg Cooperative State University Loerrach, the French university network Alsace Tech (comprised of 14 'grandes écoles' in the fields of engineering, architecture and management) and the University of Applied Sciences and Arts Northwestern Switzerland. The alliance's common goal is to reinforce the transfer of knowledge, research, and technology, as well as the cross-border mobility of students.
Konstrukteure im Maschinenbau stehen häufig vor der Problemstellung, hochfest vorgespannte Schraubenverbindungen und einen durchgehenden Korrosionsschutz zu vereinen. Die Normen und Richtlinien bieten hierzu Stand heute keine ausreichenden Antworten. Die Hochschule Offenburg befasst sich im Rahmen einer industriellen Gemeinschaftsforschung mit der Fragestellung, welchen Einfluss organische Beschichtungen auf die Vorspannkraft insbesondere bei erhöhten Umgebungstemperaturen haben. In dieser Arbeit werden die ersten Ergebnisse zum Einfluss der Einzelschichtstärke des Beschichtungssystems präsentiert.
OVVL (the Open Weakness and Vulnerability Modeller) is a tool and methodology to support threat modeling in the early stages of the secure software development lifecycle. We provide an overview of OVVL (https://ovvl.org), its data model and browser-based UI. We equally provide a discussion of initial experiments on how identified threats in the design phase can be aligned with later activities in the software lifecycle (issue management and security testing).
Threat Modelling is an accepted technique to identify general threats as early as possible in the software development lifecycle. Previous work of ours did present an open-source framework and web-based tool (OVVL) for automating threat analysis on software architectures using STRIDE. However, one open problem is that available threat catalogues are either too general or proprietary with respect to a certain domain (e.g. .Net). Another problem is that a threat analyst should not only be presented (repeatedly) with a list of all possible threats, but already with some automated support for prioritizing these. This paper presents an approach to dynamically generate individual threat catalogues on basis of the established CWE as well as related CVE databases. Roughly 60% of this threat catalogue generation can be done by identifying and matching certain key values. To map the remaining 40% of our data (~50.000 CVE entries) we train a text classification model by using the already mapped 60% of our dataset to perform a supervised machine-learning based text classification. The generated entire dataset allows us to identify possible threats for each individual architectural element and automatically provide an initial prioritization. Our dataset as well as a supporting Jupyter notebook are openly available.
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.
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.
Hochspannungs-Mischstrom-Übertragung (HMÜ) - Eine Ergänzung zu bestehenden Übertragungstechnologien?
(2019)
Bei der Mischstromübertragung wird einem Wechselstrom direkt ein Gleichstrom überlagert. Wechselstrom und Gleichstrom werden also auf dem gleichen Seil geführt.
Dadurch könnten die bereits bestehenden Drehstrom-Übertragungs-Strecken des Übertragungsnetzes genutzt werden.
Durch eine Aufschaltung des Gleichstromes auf vorhandene Freileitungen kann theoretisch bei kurzen Leitungen (<150km) bis zu 50% mehr Wirkleistung und bei großen Übertragungsstrecken (>300km) in etwa eine Verdopplung der übertragbaren Wirkleistung erwartet werden.
Theoretisch betrachtet ist die Mischstrom-Übertragung eine geometrische Addition aller Strom- und Spannungskomponenten, was zu einer Erhöhung der Leiter-Erde-Spannung führt, ohne dabei Einfluss auf die verkettete Spannung zu nehmen.
Außerdem wird die Übertragung von Blindströmen unnötig, da ein natürlicher Betrieb von Leitungen des HDÜ-Netzes empfehlenswert ist.
Die theoretischen Betrachtungen konnten mathematisch bewiesen und die technische Umsetzung mit einem 1:1000-Modellsystem demonstriert und bestätigt werden.
The need for the logistics sector to timely respond to the increasing requirements of a globalised and digitalised world relies greatly on the com- petences and skills of its labour force. It becomes therefore essential to reinforce the cooperation between universities and business partners in the logistics and supply chain management fields across the European region and to build a logistics knowledge cluster supported by a communication and collaboration platform to foster continuous learning, skill acquisition and experience sharing anytime anywhere. In this paper we focus on designing the conceptual and technical framework for a communication and collaboration platform with the aim to establish the communication pipelines between the partner institutions, facilitating user interactions and exchange, leading to the creation of new knowledge and innovation in the logistics field. This framework is based on the requirements of the three main stakeholders: students, lecturers and companies, and consists of four functional areas defined according to the platform opera- tional requirements. A working prototype of the platform was developed using the Moodle learning management system and its core tools to determine its applicability and possible enhancement requirements. In the next stages of the project some additional tools like a knowledge base and the integration of the partners’ learning management systems to form the logistics knowledge cluster will be implemented.
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.
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.
Wireless synchronization of industrial controllers is a challenging task in environments where wired solutions are not practical. The best solutions proposed so far to solve this problem require pretty expensive and highly specialized FPGA-based devices. With this work we counter the trend by introducing a straightforward approach to synchronize a fairly cheap IEEE 802.11 integrated wireless chip (IWC) with external devices. More specifically we demonstrate how we can reprogram the software running in the 802.11 IWC of the Raspberry Pi 3B and transform the receiver input potential of the wireless transceiver into a triggering signal for an external inexpensive FPGA. Experimental results show a mean-square synchronization error of less than 496 ns, while the absolute synchronization error does not exceed 6 μs. The jitter of the output signal that we obtain after synchronizing the clock of the external device did not exceed 5.2 μs throughout the whole measurement campaign. Even though we do not score new records in term of accuracy, we do in terms of complexity, cost, and availability of the required components: all these factors make the proposed technique a very promising of the deployment of large-scale low-cost automation solutions.
Plant oils may be used as a sustainable, nearly CO2neutral fuel for diesel engines. This work investigates experimentally the particulate and gaseous emissions of diesel engines fuelled with different non-esterified, pure plant oils. The data are collected from three engines: a) Common rail 1.7 liter passenger car engine from Opel AG b) 12.8 liter truck engine from VOLVO c) Truck engine from MAN AG.
The emissions of the MAN engine have been used to perform AMES tests to analyze possible health impacts of plant oil operation. Finally, all emission results with plant oils have been compared to traditional gas oils.
Non-esterified plant oils gain ecological and economical importance, particularly in the EU where it is intended to increase the share of renewable energies. Plant oils do not require any chemical treatment so do not cause secondary pollution. The importance of plant oil will increase in Germany for mobile and stationary applications. The generation co-generation of heat and power is subsidized by the German “Erneuerbares Energiegesetz” and the “Kraft-Wärme-Kopplungsgesetz” when renewable fuels are used such as plant oils..
Plant oils have a much higher viscosity than conventional gas oil. It is mandatory to decrease the oil viscosity by heating prior to injection to assure proper injection and to avoid engine damage due to coke formation in the combustion chamber and at the injection nozzle. The German quality standard of Weihenstephan (RK-Qualitätsstandard 05/2000) for rape seed oil should be followed for use as diesel fuel. The chemical composition of plant oils is appreciably different in comparison to diesel fuels derived from mineral oils suggesting also different emission behavior.
Since direct current high energy shock fulguration was initially performed in the mid 1980s, ablation of cardiac arrhythmias has come to widespread use. Today the most frequently used energy source for catheter ablation is radio frequency (RF). It was the German engineer Peter Osypka who made available the HAT 100 as the first simple commercial RF ablator.
Nevertheless, in the first years of ablation, physicians were effectively working in the dark. Until today with an increasing understanding of arrhythmia mechanisms, both at the atrial and ventricular levels, this curative technology has made tremendous progress. Now, due to crucial improvement of RF ablation generators, temperature and contact force sensor catheters in combination with non-flouroscopic electroanatomical mapping technologies, computerized temperature and impedance controlled radiofrequency catheter ablation can be used to cure all types of arrhythmias including atrial and ventricular fibrillation. For the latter, cooled ablation by saline solution irrigated catheters has been developed to a widely used standard method. This procedure resulting in pulmonary vein isolation requires transseptal puncture and is technically demanding. Nevertheless, it has shown to be more effective than antiarrhythmic drug therapy.
While earliest RF ablations were performed with non-steerable catheters, today are used steerable sensor catheters without or with external and internal cooling and tips of 4mm or 8mm length. Further innovations like integration of mapping and cardiac imaging give exact information of the number of pulmonary veins and branching patterns and help to correlate electrical signals with anatomical structures.
The magnetic navigation significantly improved the success rates and safety of catheter ablation. Thus, in most cases RF catheter ablation has developed in the treatment of supraventricular arrhythmias from an alternative approach to drug therapy into the first therapeutic choice providing low complication rates.
In future, robotic navigation will further simplify procedures and reduce radiation exposure of this curative approach.
Introduction: Despite lots of developments in the last years, radiofrequency ablation of rhythm diseases is a safe but still complex procedure that requires special experience and expertise of the physicians and biomedical engineers. Thus, there is a need of special trainings to become familiar with the different equipment and to explain several effects that can be observed during clinical routine.
Methods: The Offenburg University of Applied Sciences offers a biomedical engineering study path specialized in the fields of cardiology, electrophysiology and cardiac electronic implants. It`s Peter Osypka Institute for Pacing and Ablation provides teaching following the slogan “Learning by watching, touching and adjusting”. It conducts lots of trainings for students as well as young physicians interested in electrophysiology and radiofrequency ablation.
Results: In-vitro trainings will be provided using the Osypka HAT 200 and HAT300s, Stockert EPshuttle and SmartAblate system as well as the Boston EPT-1000XP and Maestro 3000 and the Radionics RFG-3E cardiac radio frequency ablation generators. All of them require different handling as well as special accessories like catheter connection cables or boxes and back plates. The participants will be trained in the setup of temperature, power and cut-off impedance dependent on different ablation catheters. Furthermore troubleshooting in hard- and software is part of the program. Performing procedures in pork or animal protein and using physiological saline solution to simulate the blood flow, they can study the influence of contact force and impedance on lesion geometry etc. and to avoid adverse effects like “plops”. Lots of catheter types are available: 4mm tip, 8mm standard and gold tip, open and closed irrigated tip ablation catheters of different companies. The experiments will be completed by measuring the lesion size dependent on the used catheter type and ablation settings.
Conclusion: In-vitro training in radiofrequency ablation is a challenge for biomedical engineering students and young physicians.
Introduction: Patient selection for cardiac resynchronization therapy (CRT) requires quantification of left ventricular conduction delay (LVCD). After implantation of biventricular pacing systems, individual AV delay (AVD) programming is essential to ensure hemodynamic response. To exclude adverse effects, AVD should exceed individual implant-related interatrial conduction times (IACT). As result of a pilot study, we proposed the development of a programmer-based transoesophageal left heart electrogram (LHE) recording to simplify both, LVCD and IACT measurement. This feature was implemented into the Biotronik ICS3000 programmer simultaneously with 3-channel surface ECG.
Methods: A 5F oesophageal electrode was perorally applied in 44 heart failure CRT-D patients (34m, 10f, 65±8 yrs., QRS=162±21ms). In position of maximum left ventricular deflection, oesophageal LVCD was measured between onsets of QRS in surface ECG and oesophageal left ventricular deflection. Then, in position of maximum left atrial deflection (LA), IACT in VDD operation (As-LA) was calculated by difference between programmed AV delay and the measured interval from onset of left atrial deflection to ventricular stimulus in the oesophageal electrogram. IACT in DDD operation (Ap-LA) was measured between atrial stimulus and LA..
Results: LVCD of the CRT patients was characterized by a minimum of 47ms with mean of 69±23ms. As-LA and Ap-LA were found to be 41±23ms and 125±25ms, resp., at mean. In 7 patients (15,9%), IACT measurement in DDD operation uncovered adverse AVD if left in factory settings. In this cases, Ap-LA exceeded the factory AVD. In 6 patients (13,6%), IACT in VDD operation was less than or equal 10ms indicating the need for short AVD.
Conclusion: Response to CRT requires distinct LVCD and AVD optimization. The ICS3000 oesophageal LHE feature can be utilized to measure LVCD in order to justify selection for CRT. IACT measurement simplifies AV delay optimization in patients with CRT systems irrespective of their make and model.
In-vivo and in-vitro comparison of implant-based CRT optimization - What provide new algorithms?
(2011)
Introduction: In cardiac resynchronization therapy (CRT), individual AV delay (AVD) optimization can effectively increase hemodynamics and reduce non-responder rate. Accurate, automatic and easily comprehensible algorithms for the follow-up are desirable. QuickOpt is the first attempt of a semi-automatic intracardiac electrogram (IEGM) based AVD algorithm. We aimed to compare its accuracy and usefulness by in-vitro and in-vivo studies.
Methods: Using the programmable ARSI-4 four-chamber heart rhythm and IEGM simulator (HKP, Germany), the QuickOpt feature of an Epic HF system (St. Jude, USA) was tested in-vitro by simulated atrial IEGM amplitudes between 0.3 and 3.5mV during both, manual and automatic atrial sensing between 0.2 and 1.0mV. Subsequently, in 21 heart failure patients with implanted biventricular defibrillators, QuickOpt was performed in-vivo. Results of the algorithm for VDD and DDD stimulation were compared with echo AV delay optimization.
Results: In-vitro simulations demonstrated a QuickOpt measuring accuracy of ± 8ms. Depending on atrial IEGM amplitude, the algorithm proposed optimal AVD between 90 and 150ms for VDD and between 140 and 200ms for DDD operation, respectively. In-vivo, QuickOpt difference between individual AVD in DDD and VDD mode was either 50ms (20pts) or 40ms (1pt). QuickOpt and echo AVD differed by 41 ± 25ms (7 – 90ms) in VDD and by 18 ± 24ms (17-50ms) in DDD operation. Individual echo AVD difference between both modes was 73 ± 20ms (30-100ms).
Conclusion: The study demonstrates the value of in-vitro studies. It predicted QuickOpt deficiencies regarding IEGM amplitude dependent AVD proposals constrained to fixed individual differences between DDD and VDD mode. Consequently, in-vivo, the algorithm provided AVD of predominantly longer duration than echo in both modes. Accepting echo individualization as gold standard, QuickOpt should not be used alone to optimize AVD in CRT patients.
Introduction: To simplify AV delay (AVD) optimization in cardiac resynchronization therapy (CRT), we reported that the hemodynamically optimal AVD for VDD and DDD mode CRT pacing can be approximated by individually measuring implant-related interatrial conduction intervals (IACT) in oesophageal electrogram (LAE) and adding about 50ms. The programmer-based St Jude QuickOpt algorithm is utilizing this finding. By automatically measuring IACT in VDD operation, it predicts the sensed AVD by adding either 30ms or 60ms. Paced AVD is strictly 50ms longer than sensed AVD. As consequence of those variations, several studies identified distinct inaccuracies of QuickOpt. Therefore, we aimed to seek for better approaches to automate AVD optimization.
Methods: In a study of 35 heart failure patients (27m, 8f, age: 67±8y) with Insync III Marquis CRT-D systems we recorded telemetric electrograms between left ventricular electrode and superior vena cava shock coil (LVtip/SVC = LVCE) simultaneously with LAE. By LVCE we measured intervals As-Pe in VDD and Ap-Pe in DDD operation between right atrial sense-event (As) or atrial stimulus (Ap), resp., and end of the atrial activity (Pe). As-Pe and Ap-Pe were compared with As-LA an Ap-LA in LAE, respectively.
Results: End of the left atrial activity in LVCE could clearly be recognized in 35/35 patients in VDD and 29/35 patients in DDD operation. We found mean intervals As-LA of 40.2±24.5ms and Ap-LA of 124.3±20.6ms. As-Pe was 94.8±24.1ms and Ap-Pe was 181.1±17.8ms. Analyzing the sums of As-LA + 50ms with duration of As-Pe and Ap-LA + 50ms with duration of Ap-Pe, the differences were 4.7±9.2ms and 4.2±8.6ms, resp., only. Thus, hemodynamically optimal timing of the ventricular stimulus can be triggered by automatically detecting Pe in LVCE.
Conclusion: Based on minimal deviations between LAE and LVCE approach, we proposed companies to utilize the LVCE in order to automate individual AVD optimization in CRT pacing.
Vorgestellt wird ein Konzept zur biologischen Methanisierung von Wasserstoff direkt in Biogasreaktoren, mit dem durch Membranbegasung der Methangehalt des Biogases auf > 96 % erhöht werden kann. Essentiell zum Erreichen solch hoher Methanwerte sind die Einhaltung eines optimalen pH-Bereichs und die Vermeidung von H2-Akkumulation. Im Falle einer Limitierung der Methanbildungsrate durch den eigentlichen anaeroben Abbauprozess der Biomasse ist auch eine externe Zufuhr von CO2 zur weiteren Methanbildung denkbar. Das Verfahren soll weiter optimiert und in einem von der Deutschen Bundesstiftung Umwelt geförderten Projekt in der Biogasanlage einer regionalen Käserei in der Praxis getestet werden. Die hier angestrebte Kombination aus dezentraler Abfallverwertung und Eigenenergieerzeugung eines lebensmittelverarbeitenden Betriebs unter Einbindung in ein intelligentes Erneuerbare Energien - Konzept soll einen zusätzlichen Mehrwert liefern.
This work discusses several use cases of post-mortem mobile device tracking in which privacy is required e.g. due to client-confidentiality agreements and sensibility of data from government agencies as well as mobile telecommunication providers. We argue that our proposed Bloomfilter based privacy approach is a valuable technical building block for the arising General Data Protection Regulation (GDPR) requirements in this area. In short, we apply a solution based on the Bloom filters data structure that allows a 3rd party to performsome privacy saving setrelations on a mobiletelco’s access logfile or other mobile access logfile from harvesting parties without revealing any other mobile users in the proximity of a mobile base station but still allowing to track perpetrators.
Printed electronics (PE) is a fast growing technology with promising applications in wearables, smart sensors and smart cards since it provides mechanical flexibility, low-cost, on-demand and customizable fabrication. To secure the operation of these applications, True Random Number Generators (TRNGs) are required to generate unpredictable bits for cryptographic functions and padding. However, since the additive fabrication process of PE circuits results in high intrinsic variation due to the random dispersion of the printed inks on the substrate, constructing a printed TRNG is challenging. In this paper, we exploit the additive customizable fabrication feature of inkjet printing to design a TRNG based on electrolyte-gated field effect transistors (EGFETs). The proposed memory-based TRNG circuit can operate at low voltages (≤ 1 V ), it is hence suitable for low-power applications. We also propose a flow which tunes the printed resistors of the TRNG circuit to mitigate the overall process variation of the TRNG so that the generated bits are mostly based on the random noise in the circuit, providing a true random behaviour. The results show that the overall process variation of the TRNGs is mitigated by 110 times, and the simulated TRNGs pass the National Institute of Standards and Technology Statistical Test Suite.
Printed Electronics is perceived to have a major impact in the fields of smart sensors, Internet of Things and wearables. Especially low power printed technologies such as electrolyte gated field effect transistors (EGFETs) using solution-processed inorganic materials and inkjet printing are very promising in such application domains. In this paper, we discuss a modeling approach to describe the variations of printed devices. Incorporating these models and design flows into our previously developed printed design system allows for robust circuit design. Additionally, we propose a reliability-aware routing solution for printed electronics technology based on the technology constraints in printing crossovers. The proposed methodology was validated on multiple benchmark circuits and can be easily integrated with the design automation tools-set.
Radio frequency identification (RFID) antennas are popular for high frequency (HF) RFID, energy transfer and near field communication (NFC) applications. Particularly for wireless measurement systems the RFID/NFC technology is a good option to implement a wireless communication interface. In this context, the design of corresponding reader and transmitter antennas plays a major role for achieving suitable transmission quality. This work proves the feasibility of the rapid prototyping of a RFID/NFC antenna, which is used for the wireless communication and energy harvesting at the required frequency of 13.56 MHz. A novel and low-cost direct ink writing (DIW) technology utilizing highly viscous silver nanoparticle ink is used for this process. This paper describes the development and analysis of low-cost printed flexible RFID/NFC antennas on cost-effective substrates for a microelectronic vital parameter measurement system. Furthermore, we compare the measured technical parameters with existing copper-based counterparts on a FR4 substrate.
Smart Home or Smart Building applications are a growing market. An increasing challenge is to design energy efficient Smart Home applications to achieve sustainable and green homes. Using the example of the development of an Indoor Smart Gardening system with wireless monitoring and automated watering this paper is discussing in particular the design issue of energy autonomous working sensors and actuators for home automation. Most important part of the presented Smart Gardening system is a 3D printed smart flower pot for single plants. The smart flower pot has integrated a water reservoir for automated plant irrigation and an electronic for monitoring important plant parameters and the water level of the water reservoir. Energy harvesting with solar cells enables energy autonomous working of the flower pot. A low-power wireless interface also integrated in the flowerpot and an external gateway based on a Raspberry Pi 3 enables wireless networking of multiple of those flower pots. The gateway is used for evaluating the plant parameters and as a user interface. Particularly the architecture of the energy autonomous wireless flower pot will be considered, because fully energy autonomous sensors and actuators for home automation could not be implemented without special concepts for the energy supply and the overall electronic.
Process engineering industries are now facing growing economic pressure and societies' demands to improve their production technologies and equipment, making them more efficient and environmentally friendly. However unexpected additional technical and ecological drawbacks may appear as negative side effects of the new environmentally-friendly technologies. Thus, in their efforts to intensify upstream and downstream processes, industrial companies require a systematic aid to avoid compromising of ecological impact. The paper conceptualises a comprehensive approach for eco-innovation and eco- design in process engineering. The approach combines the advantages of Process Intensification as Knowledge-Based Engineering (KBE), inventive tools of Knowledge-Based Innovation (KBI), and main principles and best-practices of Eco-Design and Sustainable Manufacturing. It includes a correlation matrix for identification of eco-engineering contradictions and a process mapping technique for problem definition, database of Process Intensification methods and equipment, as well as a set of strongest inventive operators for eco-ideation.
As engineering graduates and specialists frequently lack the advanced skills and knowledge required to run eco-innovation systematically, the paper proposes a new teaching method and appropriate learning materials 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 appears as negative side effects of eco-friendly solutions.
Based on a literature analysis and own investigations, authors propose to introduce a manageable number of eco-innovation tools into a standard one-semester design course in process engineering with particular focus on the identification of eco-problems in existing technologies, selection of the appropriate new process intensification technologies (knowledge-based engineering), and systematic ideation and problem solving (knowledge-based innovation and invention).
The proposed educational approach equips students with the advanced knowledge, skills and competences in the field of eco-innovation. Analysis of the student’s work allows one to recommend simple-to-use tools for a fast application in process engineering, such as process mapping, database of eco-friendly process intensification technologies, and up to 20 strongest inventive operators for solving of environmental problems. For the majority of students in the survey, even the small workload has strengthened their self-confidence and skills in eco-innovation