Refine
Departments, institutes and facilities
- Fachbereich Wirtschaftswissenschaften (1089)
- Fachbereich Informatik (970)
- Fachbereich Angewandte Naturwissenschaften (577)
- Fachbereich Ingenieurwissenschaften und Kommunikation (512)
- Institut für funktionale Gen-Analytik (IFGA) (512)
- Fachbereich Sozialpolitik und Soziale Sicherung (359)
- Institut für Technik, Ressourcenschonung und Energieeffizienz (TREE) (352)
- Institut für Cyber Security & Privacy (ICSP) (281)
- Institute of Visual Computing (IVC) (272)
- Institut für Verbraucherinformatik (IVI) (217)
Document Type
- Article (1991)
- Conference Object (1646)
- Part of a Book (859)
- Book (monograph, edited volume) (447)
- Report (147)
- Contribution to a Periodical (116)
- Doctoral Thesis (106)
- Preprint (71)
- Lecture (62)
- Working Paper (52)
Year of publication
Has Fulltext
- no (5749) (remove)
Keywords
- Lehrbuch (88)
- Deutschland (30)
- Nachhaltigkeit (26)
- Controlling (25)
- Unternehmen (23)
- Management (20)
- Betriebswirtschaftslehre (17)
- Prozessmanagement (15)
- Sozialversicherung (15)
- Corporate Social Responsibility (14)
This paper presents implementation results of several side channel countermeasures for protecting the scalar multiplication of ECC (Elliptic Curve Cryptography) implemented on an ARM Cortex M3 processor that is used in security sensitive wireless sensor nodes. Our implementation was done for the ECC curves P-256, brainpool256r1, and Ed25519. Investigated countermeasures include Double-And-Add Always, Montgomery Ladder, Scalar Randomization, Randomized Scalar Splitting, Coordinate Randomization, and Randomized Sliding Window. Practical side channel tests for SEMA (Simple Electromagnetic Analysis) and MESD (Multiple Exponent, Single Data) are included. Though more advanced side channel attacks are not evaluated, yet, our results show that an appropriate level of resistance against the most relevant attacks can be reached.
Robot deployment in realistic environments is challenging despite the fact that robots can be quite skilled at a large number of isolated tasks. One reason for this is that robots are rarely equipped with powerful introspection capabilities, which means that they cannot always deal with failures in an acceptable manner; in addition, manual diagnosis is often a tedious task that requires technicians to have a considerable set of robotics skills. In this paper, we discuss our ongoing efforts to address some of these problems. In particular, we (i) present our early efforts at developing a robotic black box and consider some factors that complicate its design, (ii) explain our component and system monitoring concept, and (iii) describe the necessity for remote monitoring and experimentation as well as our initial attempts at performing those. Our preliminary work opens a range of promising directions for making robots more usable and reliable in practice.
Praxisbuch Medizintourismus
(2022)
Das Standardwerk vermittelt einen ausführlichen, fundierten Gesamtüberblick über das Phänomen Medizintourismus, welches besonders in Europa immer mehr an Relevanz gewinnt. Aufgrund der Bedeutung des deutschen Gesundheitssystems mit Behandlungsangeboten für jede Erkrankung finden einerseits zahlreiche Patientenbewegungen in Richtung Deutschland statt, andererseits sind neue Märkte in Europa entstanden, die eine hervorragende medizinische Behandlungsqualität anbieten. (Verlagsangaben)
Psychische Belastungen stehen in ihren negativen Ausprägungen als psychische Fehlbeanspruchungen im Zentrum der Diskussion im Arbeitsschutz. Ihr kontinuierlicher Anstieg sowie die Verbindung mit verschiedenen psychischen und körperlichen Erkrankungen werden durch zahlreiche internationale Studien belegt. Die Herausgeber möchten mit dem Praxishandbuch eine Hilfe für diejenigen bieten, die mit der Aufgabe des Erkennens und der Prävention psychischer Fehlbelastungen konfrontiert sind. Die Anregungen sind für alle Akteure im Bereich der Verhütung arbeitsbedingter Gesundheitgefahren geeignet: Betriebsärzte, Sozialmediziner, Psychologen, Sicherheitsfachkräfte, Führungskräfte, Betriebsräte und sonstige Ansprechpartner, Personalverantwortliche und Sozialpädagogen.
Persons with disabilities have much lower employment rates than the population as a whole and are at a significantly higher risk of living in poverty (OECD, 2011, pp. 50-56 and WHO, 2011, pp. 237-239). However, many of the barriers people with disabilities face, with regards to labor market reintegration, are in fact avoidable. There has for quite some time been evidence that differences in employment and wages, between disabled and non-disabled workers, can only to a limited extent be explained by differences in human capital endowments and productivity (Kidd, Sloane, & Ferko, 2000). Instead, factors such as the absence of access to education and training, and the lack of financial assistance provided are actually significant drivers of labor market exclusion (OECD, 2009, p.15; WHO, 2011, p.239).
This work presents a person independent pointing gesture recognition application. It uses simple but effective features for the robust tracking of the head and the hand of the user in an undefined environment. The application is able to detect if the tracking is lost and can be reinitialized automatically. The pointing gesture recognition accuracy is improved by the proposed fingertip detection algorithm and by the detection of the width of the face. The experimental evaluation with eight different subjects shows that the overall average pointing gesture recognition rate of the system for distances up to 250 cm (head to pointing target) is 86.63% (with a distance between objects of 23 cm). Considering just frontal pointing gestures for distances up to 250 cm the gesture recognition rate is 90.97% and for distances up to 194 cm even 95.31%. The average error angle is 7.28◦.
The Fitness-Fatigue model (Calvert et al. 1976) is widely used for performance analysis. This antagonistic model is based on a fitness-term, a fatigue-term, and an initial basic level of performance. Instead of generic parameter values, individualizing the model needs a fitting of parameters. With fitted parameters, the model adapts to account for individual responses to strain. Even though in most cases fitting of recorded training data shows useful results, without modification the model cannot be simply used for prediction.
The design of future materials for biotechnological applications via deposition of molecules on surfaces will require not only exquisite control of the deposition procedure, but of equal importance will be our ability to predict the shapes and stability of individual molecules on various surfaces. Furthermore, one will need to be able to predict the structure patterns generated during the self-organization of whole layers of (bio)molecules on the surface. In this review, we present an overview over the current state of the art regarding the prediction and clarification of structures of biomolecules on surfaces using theoretical and computational methods.
Surrogate models are used to reduce the burden of expensive-to-evaluate objective functions in optimization. By creating models which map genomes to objective values, these models can estimate the performance of unknown inputs, and so be used in place of expensive objective functions. Evolutionary techniques such as genetic programming or neuroevolution commonly alter the structure of the genome itself. A lack of consistency in the genotype is a fatal blow to data-driven modeling techniques: interpolation between points is impossible without a common input space. However, while the dimensionality of genotypes may differ across individuals, in many domains, such as controllers or classifiers, the dimensionality of the input and output remains constant. In this work we leverage this insight to embed differing neural networks into the same input space. To judge the difference between the behavior of two neural networks, we give them both the same input sequence, and examine the difference in output. This difference, the phenotypic distance, can then be used to situate these networks into a common input space, allowing us to produce surrogate models which can predict the performance of neural networks regardless of topology. In a robotic navigation task, we show that models trained using this phenotypic embedding perform as well or better as those trained on the weight values of a fixed topology neural network. We establish such phenotypic surrogate models as a promising and flexible approach which enables surrogate modeling even for representations that undergo structural changes.