Numerical characterization and experimental verification of an in-plane MEMS-actuator with thin-film aluminum heater

In this paper, a novel concept of a thermo-mechanical MEMS actuator using aluminum thin-film heaters on a thermal oxide for electrical insulation is presented. The actuator is part of an universal tensile testing platform for thermo-mechanical material characterization of one dimensional materials on a micro- and nano-scopic scale under different environmental conditions, as varying temperatures, pressure, moisture or even vacuum and is realised in BDRIE technology. It is shown, that the actuator concept fulfills the requirements for the use in a tensile loading stage along with heterogeneously integrated nanofunctional elements, following a specimen centered approach in line with bottom-up self-assembly processes. Simulation and experiment agree very well in the thermal and mechanical domain and allow subsequent optimisation of the actuator performance.     

Towards language-to-language transformation

This paper proposes a simplicity-oriented approach and framework for language-to-language transformation of, in particular, graphical languages. Key to simplicity is the decomposition of the transformation specification into sub-rule systems that separately specify purpose-specific aspects. We illustrate this approach by employing a variation of Plotkin’s Structural Operational Semantics (SOS) for pattern-based transformations of typed graphs in order to address the aspect ‘computation’ in a graph rewriting fashion. Key to our approach are two generalizations of Plotkin’s structural rules: the use of graph patterns as the matching concept in the rules, and the introduction of node and edge types. Types do not only allow one to easily distinguish between different kinds of dependencies, like control, data, and priority, but may also be used to define a hierarchical layering structure. The resulting Type-based Structural Operational Semantics (TSOS) supports a well-structured and intuitive specification and realization of semantically involved language-to-language transformations adequate for the generation of purpose-specific views or input formats for certain tools, like, e.g., model checkers. A comparison with the general-purpose transformation frameworks ATL and Groove, illustrates along the educational setting of our graphical WebStory language that TSOS provides quite a flexible format for the definition of a family of purpose-specific transformation languages that are easy to use and come with clear guarantees.

     

The smart car seat: personalized monitoring of vital signs in automotive applications

Embedded wireless sensors are important components of mobile distributed computing networks, and one of the target applications areas is health care. The preservation of mobility for senior citizens is one of the key issues in maintaining an independent lifestyle. Thus health technologies inside a car can contribute both to safety issues (supervision of driver fitness) as well as healthcare issues by monitoring vitals signs imperceptibly. In this paper, three embedded measurement techniques for non-contact monitoring of vital signals have been investigated. Specifically, capacitive electrocardiogram (cECG) monitoring, mechanical movement analysis (ballistocardiogram, BCG) using piezo-foils and inductive impedance monitoring were examined regarding their potential for integration into car seats. All three sensing techniques omit the need for electroconductive contact to the human body, but require defined mechanical boundary conditions (stable distances or, in the case of BCG, frictional connection). The physical principles of operation, the specific boundary conditions regarding automotive integration and the results during wireless operation in a running car are presented. All three sensors were equipped with local intelligence by incorporating a microcontroller. To eliminate the need for additional cabling, a wireless Bluetooth communication module was added and used to transmit data to a measurement PC. Finally, preliminary results obtained during test drives on German city roads and highways are discussed.     

High-Order Numerical Methods for 2D Parabolic Problems in Single and Composite Domains

In this work, we discuss and compare three methods for the numerical approximation of constant- and variable-coefficient diffusion equations in both single and composite domains with possible discontinuity in the solution/flux at interfaces, considering (i) the Cut Finite Element Method; (ii) the Difference Potentials Method; and (iii) the summation-by-parts Finite Difference Method. First we give a brief introduction for each of the three methods. Next, we propose benchmark problems, and consider numerical tests—with respect to accuracy and convergence—for linear parabolic problems on a single domain, and continue with similar tests for linear parabolic problems on a composite domain (with the interface defined either explicitly or implicitly). Lastly, a comparative discussion of the methods and numerical results will be given.     

Toward an execution system for self-healing workflows in cyber-physical systems

Cyber-physical systems (CPS) represent a new class of information system that also takes real-world data and effects into account. Software-controlled sensors, actuators and smart objects enable a close coupling of the cyber and physical worlds. Introducing processes into CPS to automate repetitive tasks promises advantages regarding resource utilization and flexibility of control systems for smart spaces. However, process execution systems face new challenges when being adapted for process execution in CPS: the automated processing of sensor events and data, the dynamic invocation of services, the integration of human interaction, and the synchronization of the cyber and physical worlds. Current workflow engines fulfill these requirements only to a certain degree. In this work, we present PROtEUS—an integrated system for process execution in CPS. PROtEUS integrates components for event processing, data routing, dynamic service selection and human interaction on the modeling and execution level. It is the basis for executing self-healing model-based workflows in CPS. We demonstrate the applicability of PROtEUS within two case studies from the Smart Home domain and discuss its feasibility for introducing workflows into cyber-physical systems.     

The optimal level of CRM IT investments

In light of the growing relevance of customer-oriented business strategies IT investments in the field of Customer Relationship Management have increased considerably. However, firms often could not realize sufficient returns on these IT investments. One major reason for this failure seems to be the lack of appropriate approaches to determine the economic impact of such investments ex ante. Therefore, we develop an economic model to determine the optimal level of Customer Relationship Management IT investments. Using this approach, firms can evaluate, to what extent investments in Customer Relationship Management IT are reasonable. One major result is that in most cases the “all or nothing strategy” pursued by many firms does not lead to the optimal level of investments. To illustrate the practical utility and applicability of the approach, we provide a real world example of a German financial services provider.     

Performance and reliability prediction for evolving service-oriented software systems

During software system evolution, software architects intuitively trade off the different architecture alternatives for their extra-functional properties, such as performance, maintainability, reliability, security, and usability. Researchers have proposed numerous model-driven prediction methods based on queuing networks or Petri nets, which claim to be more cost-effective and less error-prone than current practice. Practitioners are reluctant to apply these methods because of the unknown prediction accuracy and work effort. We have applied a novel model-driven prediction method called Q-ImPrESS on a large-scale process control system from ABB consisting of several million lines of code. This paper reports on the achieved performance prediction accuracy and reliability prediction sensitivity analyses as well as the effort in person hours for achieving these results.     

Integration of beamforming and uncertainty-of-observation techniques for robust ASR in multi-source environments

This paper presents a new approach for increasing the robustness of multi-channel automatic speech recognition in noisy and reverberant multi-source environments. The proposed method uses uncertainty propagation techniques to dynamically compensate the speech features and the acoustic models for the observation uncertainty determined at the beamforming stage. We present and analyze two methods that allow integrating classical multi-channel signal processing approaches like delay and sum beamformers or Zelinski-type Wiener filters, with uncertainty-of-observation techniques like uncertainty decoding or modified imputation. An analysis of the results on the PASCAL-CHiME task shows that this approach consistently outperforms conventional beamformers with a minimal increase in computational complexity. The use of dynamic compensation based on observation uncertainty also outperforms conventional static adaptation with no need of adaptation data.     

Diterpene structure elucidation from 13cnmr spectra with inductive logic programming

We present a novel application ofInductive Logic Programming (ILP) to the problem of diterpene structure elucidation from 13 CNMR spectra. Diterpenes are organic compounds oflow molecular weight with a skeleton of 20 carbon atoms. They are of significant chemical and commercial interest because oftheir use as lead compounds in the search for new pharmaceutical effectors. The interpretation of diterpene 13 CNMR spectra normally requires specialists with detailed spectroscopic knowledge and substantial experience in natural products chemistry, specifically knowledge on peak patterns and chemical structures. Given a database ofpeak patterns for diterpenes with known structure, we apply several ILP approaches to discover correlations between peak patterns and chemical structure. The approaches used include first - order inductive learning, relational instance based learning, induction oflogical decision trees, and inductive constraint logic. Performance close to that of domain experts is achieved, which suffices for practical use.