Titlebook: Smart Sensing and Context; 4th European Confere Payam Barnaghi,Klaus Moessner,Stefan Meissner Conference proceedings 2009 Springer-Verlag B

Evocative

malapropism

不真

Recognizing the Use-Mode of Kitchen Appliances from Their Current Consumption previous work dealt with detecting . device is being used, we go a step further and analyze . the device is being used. We focus on a kitchen scenario where many different devices are relevant to activity recognition. The paper describes a smart, easy to install sensor that we have built to do the

Regurgitation

享乐主义者

Mobile Context Toolboxons. The framework has an extensible layered architecture allowing new sensors and features to be added to the context framework as they become available on mobile phone platforms. The framework provides access to multiple sensors to derive user context, and we present results from experiments with

Felicitous

Statistic-Based Context Recognition in Smart Carctively. Despite many techniques have been developed, we find a lack of complex situation recognition in the smart car environment. This paper presents a novel context recognition approach that is composed of two parts: offline statistic-based situation pattern training and online situation recognit

表脸

Context Cells: Towards Lifelong Learning in Activity Recognition Systemsarried out ideally with no user intervention. In this paper we show by means of an experiment and simulations that we can indeed achieve lifelong learning and automatic labeling by using Context Cells, an architecture capable of sensing, learning, classifying data and exchanging information.

Alveolar-Bone

Using Dempster-Shafer Theory of Evidence for Situation Inference evidential operations for sensor mass functions using context quality and evidence accumulation for continuous situation detection. We demonstrate how our approach enables situation inference with uncertain information using a case study based on a published smart home activity data set.

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