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In today’s information society the vast technical progress and the sinking cost of information and communication technology provide new opportunities for information supply, and new technical support for communication and cooperation over distance. These trends also entail challenges such as supplying information that is adequate for a particular person in a specific situation as well as managing communication among geographically distributed parties efficiently. Context-aware systems that use sensors in order to analyse their environment and to adapt their behaviour. Yet, adequate tools for developing sensor-based infrastructures are missing. We have designed and developed Sens-ation, an open and generic service-oriented platform, which provides powerful, yet easy-to-use, tools to software developers who want to develop context-aware, sensor-based infrastructures. The service-oriented paradigm of Sens-ation enables standardised communication within individual infrastructures, between infrastructures and their sensors, but also among distributed infrastructures. On a whole, Sens-ation facilitates the development allowing developers to concentrate on the semantics of their infrastructures, and to develop innovative concepts and implementations of context-aware systems.
We present a museum guidance system called PhoneGuide that uses widespread camera equipped mobile phones for on-device object recognition in combination with pervasive tracking. It provides additional location- and object-aware multimedia content to museum visitors, and is scalable to cover a large number of museum objects.
We present PhoneGuide – an enhanced museum guidance approach that uses camera-equipped mobile phones and on-device object recognition. Our main technical achievement is a simple and light-weight object recognition approach that is realized with single-layer perceptron neuronal networks. In contrast to related systems which perform computational intensive image processing tasks on remote servers, our intention is to carry out all computations directly on the phone. This ensures little or even no network traffic and consequently decreases cost for online times. Our laboratory experiments and field surveys have shown that photographed museum exhibits can be recognized with a probability of over 90%. We have evaluated different feature sets to optimize the recognition rate and performance. Our experiments revealed that normalized color features are most effective for our method. Choosing such a feature set allows recognizing an object below one second on up-to-date phones. The amount of data that is required for differentiating 50 objects from multiple perspectives is less than 6KBytes.