Johannes Kepler University of Linz

About: Johannes Kepler University of Linz is a education organization based out in Linz, Oberösterreich, Austria. It is known for research contribution in the topics: Thin film & Quantum dot. The organization has 6605 authors who have published 19243 publications receiving 385667 citations.

An architecture for context prediction

Abstract: Pervasive Computing is a new area of research with increasing prominence; it is situated at the intersection between human/computer interaction, embedded and distributed systems and networking technology. Its declared aim is a holistic design of computer systems, which is often described as the disappearance of computer technology into the periphery of daily life. One central aspect of this vision is a partial replacement of explicit, obtrusive interfaces for human/computer interaction that demand exclusive user attention with implicit ones embedded into real-world artifacts that allow intuitive and unobtrusive use. This kind of interaction with computer systems suits human users better, but necessitates an adaption of such systems to the respective context in which they are used. Context is, in this regard, understood as any information about the current situation of a person, place or object that is relevant to the user interaction. Context-based interaction, which is pursued by the design and implementation of contextsensitive systems, is therefore one of the building blocks of Pervasive Computing. Within the last five years, a number of seminal publications on the recognition of current context from a combination of different sensors have been written within this field. This dissertation tackles the next logical step after the recognition of the current context: the prediction of future contexts. The general concept is the prediction of abstract contexts to allow computer systems to proactively prepare for future situations. This kind of high-level context prediction allows an integral consideration of all ascertainable aspects of context, in contrast to the autonomous prediction of individual aspects like the geographical position of the user. It allows to consider patterns and interrelations in the user behavior which are not apparent at the lower levels of raw sensor data. The present thesis analyzes prerequisites for user-centered prediction of context and presents an architecture for autonomous, background context recognition and prediction, building upon established methods for data based prediction like the various instances of Markov models. Especial attention is turned to implicit user interaction to prevent disruptions of users during their normal tasks and to continuous adaption of the developed systems to changed conditions. Another considered aspect is the economical use of resources to allow an integration of context prediction into embedded systems. The developed architecture is being implemented in terms of a flexible software framework and evaluated with recorded real-world data from everyday situations. This examination shows that the prediction of abstract contexts is already possible within certain limits, but that there is still room for future improvements of the prediction quality.

What is a feature?: a qualitative study of features in industrial software product lines

Abstract: The notion of features is commonly used to describe the functional and non-functional characteristics of a system. In software product line engineering, features often become the prime entities of software reuse and are used to distinguish the individual products of a product line. Properly decomposing a product line into features, and correctly using features in all engineering phases, is core to the immediate and long-term success of such a system. Yet, although more than ten different definitions of the term feature exist, it is still a very abstract concept. Definitions lack concrete guidelines on how to use the notion of features in practice.To address this gap, we present a qualitative empirical study on actual feature usage in industry. Our study covers three large companies and an in-depth, contextualized analysis of 23 features, perceived by the interviewees as typical, atypical (outlier), good, or bad representatives of features. Using structured interviews, we investigate the rationales that lead to a feature's perception, and identify and analyze core characteristics (facets) of these features. Among others, we find that good features precisely describe customer-relevant functionality, while bad features primarily arise from rashly executed processes. Outlier features, serving unusual purposes, are necessary, but do not require the full engineering process of typical features.

Residue Analysis of the Pharmaceutical Diclofenac in Different Water Types Using ELISA and GC−MS

Abstract: A highly sensitive and specific indirect competitive enzyme-linked immunosorbent assay (ELISA) for the determination of diclofenac in water samples was developed. With pure water, the limit of detection (LOD, S/N = 3) and IC50 were found to be 6 ng/L and 60 ng/L, respectively. The analytical working range was about 20−400 ng/L. Highest cross-reactivity (CR) of 26 tested pharmaceuticals, metabolites, and pesticides was found for 5-hydroxydiclofenac (100%). Other estimated values were well below 4% and, therefore, are negligible. The assay was applied for the determination of diclofenac in tap and surface water samples as well as wastewater collected at 20 sewage treatment plants (STPs) in Austria and Germany. Humic substances were identified as main interference in surface water. Wastewater samples which were only submitted to filtration and dilution yielded about 25% higher diclofenac concentrations using the ELISA compared to GC−MS. However, the ELISA turned out to be a simple, inexpensive, and accurate ...

A light-hole exciton in a quantum dot

Abstract: An electron and a hole trapped in the same quantum dot couple together to form an exciton. Conventionally the hole involved is a heavy hole. Light-hole excitons are now observed by applying elastic stress to initially unstrained gallium arsenide-based dots. The quasiparticles are identified by their optical emission signature, and could be used in future quantum technologies.