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This contribution presents a data compression scheme for applications in non-volatile flash memories. The objective of the data compression algorithm is to reduce the amount of user data such that the redundancy of the error correction coding can be increased in order to improve the reliability of the data storage system. The data compression is performed on block level considering data blocks of 1 kilobyte. We present an encoder architecture that has low memory requirements and provides a fast data encoding.
A semilinear distributed parameter approach for solenoid valve control including saturation effects
(2015)
In this paper a semilinear parabolic PDE for the control of solenoid valves is presented. The distributed parameter model of the cylinder becomes nonlinear by the inclusion of saturation effects due to the material's B/H-curve. A flatness based solution of the semilinear PDE is shown as well as a convergence proof of its series solution. By numerical simulation results the adaptability of the approach is demonstrated, and differences between the linear and the nonlinear case are discussed. The major contribution of this paper is the inclusion of saturation effects into the magnetic field governing linear diffusion equation, and the development of a flatness based solution for the resulting semilinear PDE as an extension of previous works [1] and [2].
We present a 3d-laser-scan simulation in virtual
reality for creating synthetic scans of CAD models. Consisting of
the virtual reality head-mounted display Oculus Rift and the
motion controller Razer Hydra our system can be used like
common hand-held 3d laser scanners. It supports scanning of
triangular meshes as well as b-spline tensor product surfaces
based on high performance ray-casting algorithms. While point
clouds of known scanning simulations are missing the man-made
structure, our approach overcomes this problem by imitating
real scanning scenarios. Calculation speed, interactivity and the
resulting realistic point clouds are the benefits of this system.
This work proposes an efficient hardware Implementation of sequential stack decoding of binary block codes. The decoder can be applied for soft input decoding for generalized concatenated (GC) codes. The GC codes are constructed from inner nested binary Bose-Chaudhuri-Hocquenghem (BCH) codes and outer Reed-Solomon (RS) codes. In order to enable soft input decoding for the inner BCH block codes, a sequential stack decoding algorithm is used.
Domain-specific modelling is increasingly adopted in the software development industry. While open source metamodels like Ecore have a wide impact, they still have some problems. The independent storage of nodes (classes) and edges (references) is currently only possible with complex, specific solutions. Furthermore the developed models are stored in the extensible markup language (XML) data format, which leads to problems with large models in terms of scaling. In this paper we describe an approach that solves the problem of independent classes and references in metamodels and we store the models in the JavaScript Object Notation (JSON) data format to support high scalability. First results of our tests show that the developed approach works and classes and references can be defined independently. In addition, our approach reduces the amount of characters per model by a factor of approximately two compared to Ecore. The entire project is made available as open source under the name MoDiGen. This paper focuses on the description of the metamodel definition in terms of scaling.
Nowadays, the number of flexible and fast human to application system interactions is dramatically increasing. For instance, citizens interact with the help of the internet to organize surveys or meetings (in real-time) spontaneously. These interactions are supported by technologies and application systems such as free wireless networks, web -or mobile apps. Smart Cities aim at enabling their citizens to use these digital services, e.g., by providing enhanced networks and application infrastructures maintained by the public administration. However, looking beyond technology, there is still a significant lack of interaction and support between "normal" citizens and the public administration. For instance, democratic decision processes (e.g. how to allocate public disposable budgets) are often discussed by the public administration without citizen involvement. This paper introduces an approach, which describes the design of enhanced interactional web applications for Smart Cities based on dialogical logic process patterns. We demonstrate the approach with the help of a budgeting scenario as well as a summary and outlook on further research.
The proposed approach applies current unsupervised clustering approaches in a different dynamic manner. Instead of taking all the data as input and finding clusters among them, the given approach clusters Holter ECG data (long-term electrocardiography data from a holter monitor) on a given interval which enables a dynamic clustering approach (DCA). Therefore advanced clustering techniques based on the well known Dynamic Time Warping algorithm are used. Having clusters e.g. on a daily basis, clusters can be compared by defining cluster shape properties. Doing this gives a measure for variation in unsupervised cluster shapes and may reveal unknown changes in healthiness. Embedding this approach into wearable devices offers advantages over the current techniques. On the one hand users get feedback if their ECG data characteristic changes unforeseeable over time which makes early detection possible. On the other hand cluster properties like biggest or smallest cluster may help a doctor in making diagnoses or observing several patients. Further, on found clusters known processing techniques like stress detection or arrhythmia classification may be applied.
Conducting surveillance impact assessment is the first step to solve the "Who monitors the monitor?" problem. Since the surveillance impacts on different dimensions of privacy and society are always changing, measuring compliance and impact through metrics can ensure the negative consequences are minimized to acceptable levels. To develop metrics systematically for surveillance impact assessment, we follow the top-down process of the Goal/Question/Metric paradigm: 1) establish goals through the social impact model, 2) generate questions through the dimensions of surveillance activities, and 3) develop metrics through the scales of measure. With respect to the three factors of impact magnitude: the strength of sources, the immediacy of sources, and the number of sources, we generate questions concerning surveillance activities: by whom, for whom, why, when, where, of what, and how, and develop metrics with the scales of measure: the nominal scale, the ordinal scale, the interval scale, and the ratio scale. In addition to compliance assessment and impact assessment, the developed metrics have the potential to address the power imbalance problem through sousveillance, which employs surveillance to control and redirect the impact exposures.
Differences in the pitting resistance between cold worked CrNi and CrNiMnN metastable austenites
(2015)
Fachvortrag auf dem Kongress CORROSION 2015, 15-19 March, Dallas, Texas, USA. NACE International
Technology commercialization is described as the most dreadful challenge for technology-based entrepreneurs. The scarcity of resources and limited managerial experience make it a daunting task, putting in danger the whole firm emergence. Prior research has often build upon the resource-based view to propose that the new firms' performance is dependent on their initial resource endowments and configurations. Nevertheless, little is known on how the early-stage decisions of the entrepreneur might influence on the growth of the firm. Scholars have suggested that both technology and market orientation actions could influence the performance and growth of firms in this context; nevertheless, there is limited empirical evidence of the influence of these different orientations in the context of new technology-based firms (NTBFs). In this study we propose to explore the influence of technology and demand creation actions adopting a demand-side view. We use a longitudinal study on a panel dataset (2004-2007) with 249 U.S. new high-technology firms to test our hypothesis. The results point towards a rather limited influence of initial resource configurations, as well as an unexpected influence of market and technology orientation in the growth dimensions of an NTBF. The research holds implications for the management of new technology-based firms and for those interested in supporting the development of technology entrepreneurship.