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The development of mobile robotic systems is a demanding task regarding its complexity, required resources and skills in multiple fields such as software development, artificial intelligence, mechanical design, electrical engineering, signal processing, sensor technology or control theory. This holds true particularly for soccer playing robots, where additional aspects like high dynamics, cooperation and high physical stress have to be dealt with. In robot competitions such as RoboCup, additional skills in the domains of team, project and knowledge management are of importance.
The European General Data Protection Regulation requires the implementation of Technical and Organizational Measures (TOMs) to reduce the risk of illegitimate processing of personal data. For these measures to be effective, they must be applied correctly by employees who process personal data under the authority of their organization. However, even data processing employees often have limited knowledge of data protection policies and regulations, which increases the likelihood of misconduct and privacy breaches. To lower the likelihood of unintentional privacy breaches, TOMs must be developed with employees’ needs, capabilities, and usability requirements in mind. To reduce implementation costs and help organizations and IT engineers with the implementation, privacy patterns have proven to be effective for this purpose. In this chapter, we introduce the privacy pattern Data Cart, which specifically helps to develop TOMs for data processing employees. Based on a user-centered design approach with employees from two public organizations in Germany, we present a concept that illustrates how Privacy by Design can be effectively implemented. Organizations, IT engineers, and researchers will gain insight on how to improve the usability of privacy-compliant tools for managing personal data.
A Bicycle Simulator Based on a Motion Platform in a Virtual Reality Environment - FIVIS Project
(2007)
In the presented project, a new approach for the prevention of hand movements leading to hazards and for non-contact detection of fingers is intended to permit comprehensive and economical protection on circular saws. The basic principles may also be applied to other machines with manual loading and / or unloading. With an automatic blade guard an improved integration of the protection system can be achieved. In addition a new detection principle is explained. The distinction between skin and wood or other material is achieved by a dedicated spectral analysis in the near infrared region. Using LED and photodiodes it is possible to detect fingers and hands reliably. With a kind of light curtain the intrusion of hands or fingers into the dangerous zone near the blade guard can be prevented.
Domestic Robotics
(2016)
Domestic Robotics
(2008)
In Artificial Intelligence, numerous learning paradigms have been developed over the past decades. In most cases of embodied and situated agents, the learning goal for the artificial agent is to „map“ or classify the environment and the objects therein [1, 2], in order to improve navigation or the execution of some other domain-specific task. Dynamic environments and changing tasks still pose a major challenge for robotic learning in real-world domains. In order to intelligently adapt its task strategies, the agent needs cognitive abilities to more deeply understand its environment and the effects of its actions. In order to approach this challenge within an open-ended learning loop, the XPERO project (http://www.xpero.org) explores the paradigm of Learning by Experimentation to increase the robot's conceptual world knowledge autonomously. In this setting, tasks which are selected by an actionselection mechanism are interrupted by a learning loop in those cases where the robot identifies learning as necessary for solving a task or for explaining observations. It is important to note that our approach targets unsupervised learning, since there is no oracle available to the agent, nor does it have access to a reward function providing direct feedback on the quality of its learned model, as e.g. in reinforcement learning approaches. In the following sections we present our framework for integrating autonomous robotic experimentation into such a learning loop. In section 1 we explain the different modules for stimulation and design of experiments and their interaction. In section 2 we describe our implementation of these modules and how we applied them to a real world scenario to gather target-oriented data for learning conceptual knowledge. There we also indicate how the goaloriented data generation enables machine learning algorithms to revise the failed prediction model.
Gegen unveröffentlichte – nur wenigen Personen bekannte – Sicherheitslücken (Less-than-Zero-Day Vulnerabilities) und diese ausnutzende Angriffsprogramme (Exploits) können IT-Systeme nicht geschützt werden. In der Vergangenheit wurden Sicherheitslücken meist dem Hersteller gemeldet; dieser stellte (allerdings nicht in allen Fällen) eine Fehlerkorrektur zur Verfügung. In jüngerer Zeit werden Sicherheitslücken systematisch (Tool-gestützt) gesucht und an Behörden, Unternehmen und an die organisierte Kriminalität verkauft – und nicht oder nicht sofort dem Hersteller gemeldet. Durch Ausnutzung dieser unveröffentlichten Sicherheitslücken ist Wirtschaftsspionage und Computersabotage (auch der Steuerungsrechner des Internet) unerkannt möglich [GI 2007]. Praktizierte Anwendungen sind – u.a. auch als Titan Rain – dokumentiert [BfDI 2007, Keizer 2006, NSTAC 2007, Pohl 2007, Rath 2007].
With regard to performance well established SW-only design methodologies proceed by making the initial specification run first, then by enhancing its functionality and finally by optimizing it. When designing Embedded Systems (EbS) this approach is not viable since decisive design decisions like e.g. the estimation of required processing power or the identification of those parts of the specification which need to be delegated to dedicated HW depend on the fastness and fairness of the initial specification. We here propose a sequence of optimization steps embedded into the design flow, which enables a structured way to accelerate a given working EbS specification at different layers. This sequence of accelerations comprises algorithm selection, algorithm transformation, data transformation, implementation optimization and finally HW acceleration. It is analyzed how all acceleration steps are influenced by the specific attributes of the underlying EbS. The overall acceleration procedure is explained and quantified at hand of a real-life industrial example.
Der Mutterpass wurde als wichtiges Vorsorgeinstrument für Schwangere Anfang der sechziger Jahre in Papierform eingeführt. Er wird bei 90% aller Schwangerschaften genutzt. Seit seiner Einführung im Jahre 1968 hat jedoch die Komplexität der Vorsorgeuntersuchungen zugenommen, wie auch die Begleitumstände einer Schwangerschaft häufig komplexer geworden sind. Dies war Anlass dafür, die elektronische Abbildung des Papier basierten Mutterpasses zu entwickeln, um den gewachsenen Anforderungen der medizinischen Dokumentation und Evaluation gerecht zu werden. Eine große Herausforderung bei der Konzeption und Entwicklung des elektronischen Mutterpasses war dabei die Definition eines strukturierten und maschinenlesbaren Austauschformates. Darüber hinaus mussten weltweit neue eindeutige Identifier entwickelt werden, um den Mutterpass elektronisch abzubilden. Nach der prototypischen Realisierung einer vollständigen Version wurde im Frühjahr 2008 die Pilotierung in der Metropolregion Rhein-Neckar begonnen.
Die Entwicklung technischer Produkte strebt nach der Akzeptanz durch den Markt. Das abstrakte Gebilde des Marktes wird aber geprägt durch menschliche Entscheidungen. BenutzerInnen arbeiten gerne mit einem technischen System oder sie lehnen es mehr oder weniger ab. Diese Ablehnung durch die BenutzerInnen führt über kurz oder lang auch zur Ablehnung durch die Entscheidungsträger in Firmen und anderen Institutionen, die gemeinsam mit den privaten BenutzerInnen den Markt ausmachen. Somit steht und fällt der wirtschaftliche Erfolg bei der Entwicklung und Vermarktung technischer Produkte mit dem Maß der erreichten Akzeptanz durch die BenutzerInnen.
Improving the Performance of Parallel SpMV Operations on NUMA Systems with Adaptive Load Balancing
(2018)
For a parallel Sparse Matrix Vector Multiply (SpMV) on a multiprocessor, rather simple and efficient work distributions often produce good results. In cases where this is not true, adaptive load balancing can improve the balance and performance. This paper introduces a low overhead framework for adaptive load balancing of parallel SpMV operations. It uses statistical filters to gather relevant runtime performance data and detects an imbalance situation. Three different algorithms were compared that adaptively balance the load with high quality and low overhead. Results show that for sparse matrices, where the adaptive load balancing was enabled, an average speedup of 1.15 (regarding the total execution time) could be achieved with our best algorithm over 4 different matrix formats and two different NUMA systems.
Dieser Beitrag betrachtet den Stand der Entwicklung bei der Vernetzung von Fahrzeugen aus Sicht der IT-Sicherheit. Etablierte Kommunikationssysteme und Verkehrstelematikanwendungen im Automobil werden ebenso vorgestellt und diskutiert wie auch zukünftige Kommunikationstechnologien Car-2-Car und Car-2-X. IT-Sicherheit im Automobil ist ein schwieriges Feld, da es hier um eine Integration von neuen innovativen Anwendungen in eine hochkomplexe bestehende Fahrzeugarchitektur geht, die zu keinen neuen Gefährdungen für die Fahrzeuginsassen führen darf. Zudem bleibt die Funktionsweise dieser Anwendungen mit ihren Auswirkungen auf das informationelle Selbstbestimmungsrecht oft intransparent. Die abschließende Diskussion gibt Handlungsempfehlungen aus Sicht der Verbraucher.
Motivation is a key ingredient for learning: Only if the learner is motivated, successful learning is possible. Educational robotics has proven to be an excellent tool for motivating students at all ages from 8 to 80. Robot competitions for kids, like RoboCupJunior, are instrumental to sustain motivation over a significant period of time. This increases the chances that the learner acquires more in-depth knowledge about the subject area and develops a genuine interest in the field.