University of Passau

About: University of Passau is a education organization based out in Passau, Bayern, Germany. It is known for research contribution in the topics: Computer science & Context (language use). The organization has 1543 authors who have published 4763 publications receiving 93338 citations.

Applying crossing reduction strategies to layered compound graphs

Abstract: We present a new method for the application of 2-layer crossing reduction algorithms to layered compound graphs. It is based on an algorithm by Sander [7-9] and improves it with fewer crossings. Our method is optimal in the sense that it does not introduce unnecessary crossings by itself. If used with an optimal 2-layer crossing reduction algorithm, the crossing reduction for 2-layer compound graphs is optimal, too.

Self-Organizing Networked Systems for Technical Applications: A Discussion on Open Issues

Abstract: The concept of self-organization has been examined oftentimes for several domains such as physics, chemistry, mathematics, etc. However, the current technical development opens a new field of self-organizing applications by creating systems of networked and massively distributed hardware with self-organized control. Having this view in mind, this papers reviews the questions: What is a self-organizing system?, What is it not?, Should there be a separate field of science for self-organizing systems? , and What are possible approaches to engineer a self-organizing control system? . The presented ideas have been elaborated at the Lakeside Research Days'08 (University of Klagenfurt, Austria), a workshop that featured guided discussions between invited experts working in the field of self-organizing systems.

Context Induction: a Proof Principle for Behavioural Abstractions

Abstract: An induction principle, called context induction, is presented which is appropriate for the verification of behavioural properties of abstract data types. The usefulness of the proof principle is documented by several applications: the verification of behavioural theorems over a behavioural specification, the verification of behavioural implementations and the verification of "forget-restrict-identify" implementations.

HRV4Training: Large-scale longitudinal training load analysis in unconstrained free-living settings using a smartphone application

Abstract: We describe an approach to support athletes at various fitness levels in their training load analysis using heart rate (HR) and heart rate variability (HRV). A smartphone-based application (HRV4Training) was developed that captures heart activity over one to five minutes using photoplethysmography (PPG) and derives HR and HRV features. HRV4Training integrated a guide for an early morning spot measurement protocol and a questionnaire to capture self-reported training activity. The smartphone application was made publicly available for interested users to quantify training effect. Here we analyze data acquired over a period of 3 weeks to 5 months, including 797 users, breaking down results by gender and age group. Our results suggest a strong relation between HR, HRV and self-reported training load independent of gender and age group. HRV changes due to training were larger than those of HR. We conclude that smartphone-based training monitoring is feasible and a can be used as a practical tool to support large populations outside controlled laboratory environments.