A new application of neurocomputing for data approximation and classification is introduced to process data in a wireless sensor network. For this purpose, a simplified dynamic sliding backpropagation algorithm is implemented on a wireless sensor network for transportation applications. It is able to approximate temperature and humidity in sensor nodes. In addition, two architectures of “radial basis function” (RBF) classifiers are introduced with probabilistic features for data classification in sensor nodes. The applied approximation and classification algorithms could be used in similar applications for data processing in embedded systems.
With the advance of electronics, sound level meters have become more powerful when it comes to analyzing and storing huge amount of measurements. In recent years, these devices have been hooked up to the internet and stream life data. In the IDEA project, the whole concept of a sound observatory is turned upside down by stripping the sensor nodes to their bare essential, and by migrating all logic and data storage to computing centers. This opens new opportunities in particular for long-term environmental sound monitoring and analysis. As unlimited computing power is available, more advanced analysis such as auditory scene analysis can be incorporated. In addition, new analysis methods and indicators can be deployed on the whole network of sound observatories using up-to-date software agent technology. As each observatory is a cheap plug-and-measure device without any buttons or display, participatory sensing becomes easy: citizens plug in their device and data streams to central servers and is displayed on a website of choice for the community. During the presentation, application cases in urban tranquil area, building site noise, wind turbine noise, and train noise monitoring, as well as noise mapping validation will be shown.
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