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Time Dependent Data

  • Dan Chalmers
Part of the Undergraduate Topics in Computer Science book series (UTICS)

Abstract

The world is not a static place—the conditions sensors describe vary over time. In some cases the variation is captured as a change in some state (e.g. light as day becomes night), in other cases the variation describes the reading (e.g. a motion detector) and in yet others the reading is encoded within a varying signal (e.g. sound). In this chapter we shall explore some of the techniques which can be applied to handling these time-varying signals. We review state and event based systems; frequency domain models, including Fourier transforms; and prediction methods.

Keywords

Fast Fourier Transform Discrete Fourier Transform Sine Wave Motion Sensor Meta Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.University of SussexBrightonUnited Kingdom

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