802.11-based indoor positioning systems have been under research for quite some time now. However, despite the large attention this topic has gained, most of the research focused on the calculation of position estimates. In this paper, we go a step further and investigate how the position error that is inherent to 802.11-based positioning systems can be estimated. Knowing the position error is crucial for many applications that rely on position information: End users could be informed about the estimated position error to avoid frustration in case the system gives faulty position information. Service providers could adapt their delivered services based on the estimated position error to achieve a higher service quality. Finally, system operators could use the information to inspect whether a location system provides satisfactory positioning accuracy throughout the covered area. For position error estimation, we present four novel algorithms that take different features into account. Advantages of the combination of these four algorithms are explored by using a machine-learning approach. We evaluate our proposed algorithms in two different real-world deployments by using real-world data and emulation. The results show that our algorithms work independently of the environment and the positioning algorithm, and with an average precision for estimating the position error of up to 1.45 meters. The algorithms can-by adjusting parameters-realize different tradeoffs between underestimating and overestimating errors. Furthermore we comment on the algorithms' space and time complexity.
In this paper, we introduce our new visualization service which presents web pages and images on arbitrary devices with differing display resolutions. We analyze the layout of a web page and simplify its structure and formatting rules. The small screen of a mobile device is used much better this way. Our new image adaptation service combines several techniques. In a first step, border regions which do not contain relevant semantic content are identified. Cropping is used to remove these regions. Attention objects are identified in a second step. We use face detection, text detection and contrast based saliency maps to identify these objects and combine them into a region of interest. Optionally, the seam carving technique can be used to remove inner parts of an image. Additionally, we have developed a software tool to validate, add, delete, or modify all automatically extracted data. This tool also simulates different mobile devices, so that the user gets a feeling of how an adapted web page will look like. We have performed user studies to evaluate our web and image adaptation approach. Questions regarding software ergonomics, quality of the adapted content, and perceived benefit of the adaptation were asked.
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Abstract-One of the drawbacks of location fingerprinting systems is the effort that is necessary to set up and update the fingerprint database. In this paper, we propose a novel approach to significantly reduce this effort. We split the area of operation into a grid of quadratic cells and then combine these cells into larger regions of similar signal properties using a clustering algorithm and a novel similarity measure. Thus, less training data is required, and it can be collected in a more efficient way: We move through the area of operation on predefined trajectories and interpolate the approximate position for each measurement. In addition, by storing only one fingerprint for each region, we reduce the computational requirements of the location fingerprinting algorithm considerably. Since the radio measurements are quite similar in such a region, it is hard to estimate the exact location within the region; thus we do not lose much accuracy by clustering. An evaluation of our approach shows that it achieves an accuracy that is sufficient for most locationbased services and at the same time reduces the effort for the collection of the training data to a mere walk of the area of operation.
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