Matthias Budde

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Smart phones have become a powerful platform for wearable context recognition. We present a service-based recognition architecture which creates an evolving classification system using feedback from the user community. The approach utilizes classifiers based on fuzzy inference systems which use live annotation to personalize the classifier instance on the(More)
We have used an extension of our slow light technique to provide a method for inducing small density defects in a Bose-Einstein condensate. These sub- resolution, micrometer-sized defects evolve into large-amplitude sound waves. We present an experimental observation and theoretical investigation of the resulting breakdown of superfluidity, and we observe(More)
This paper presents a mobile, low-cost particulate matter sensing approach for the use in Participatory Sensing scenarios. It shows that cheap commercial off-the-shelf (COTS) dust sensors can be used in distributed or mobile personal measurement devices at a cost one to two orders of magnitude lower than that of current hand-held solutions, while reaching(More)
A variety of studies in the past decades have shown that fine particulate matter can be a serious health hazard, contributing to respiratory and cardiovascular disease. Due to this, more and more regulations defining certain permissible concentration limits have been set by governments around the world. However, current standard measurement equipment is(More)
Previous studies have documented advancement in clutch initiation dates (CIDs) in response to climate change, most notably for temperate-breeding passerines. Despite accelerated climate change in the Arctic, few studies have examined nest phenology shifts in arctic breeding species. We investigated whether CIDs have advanced for the most abundant breeding(More)
Participatory Urban Sensing scenarios have increasingly been studied in the past years. At the same time, society’s concern about the effects of pollutants on people’s personal health as well as on the environment grew. This, in conjunction with studies that helped to give a better understanding of those effects, lead to new and stricter regulations and(More)
In mobile and ubiquitous computing, there is a strong need for supporting different users with different interests, needs, and demands. Activity recognition systems for context aware computing applications usually employ highly optimized off-line learning methods. In such systems, a new classifier can only be added if the whole recognition system is(More)
We present <i>MoA</i><sup>2</sup>, a context-aware smartphone app for the ambulatory assessment of mood, tiredness and stress level. In principle, it has two features: (1) mood assessment and (2) mood recognition. The mood assessment system combines benefits of state of the art approaches. The mood recognition is concluded by smartphone-based wearable(More)
The observation and control of particulate matter (PM) pollution in ambient air is increasingly being recognized as an important topic in societies across the globe. Classic measurement approaches provide accurate daily means but are static, expensive and suffer from low spatial resolution and high latency. Distributed measurement grids using real-time(More)
An increasing corpus of research focuses on inferring contexts solely through analysis of changes in surrounding wireless signals without the subject carrying a device (device-free). This paper takes device-free recognition a step further: We present <i>WiDisc</i>, a novel device-free RF system for distinguishing three subject classes (e.g. tall, medium,(More)