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Advances in Intelligent Signal Processing and Data Mining

Overview of attention for book
Cover of 'Advances in Intelligent Signal Processing and Data Mining'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction to Intelligent Signal Processing and Data Mining
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    Chapter 2 Monte Carlo-Based Bayesian Group Object Tracking and Causal Reasoning
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    Chapter 3 A Sequential Monte Carlo Method for Multi-target Tracking with the Intensity Filter
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    Chapter 4 Sequential Monte Carlo Methods for Localization in Wireless Networks
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    Chapter 5 Advances in Intelligent Signal Processing and Data Mining
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    Chapter 6 Computational Intelligence in Automotive Applications
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    Chapter 7 Detecting Anomalies in Sensor Signals Using Database Technology
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    Chapter 8 Hierarchical Clustering for Large Data Sets
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    Chapter 9 A Novel Framework for Object Recognition under Severe Occlusion
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    Chapter 10 Historical Consistent Neural Networks: New Perspectives on Market Modeling, Forecasting and Risk Analysis
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    Chapter 11 Reinforcement Learning with Neural Networks: Tricks of the Trade
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    Chapter 12 Sliding Empirical Mode Decomposition-Brain Status Data Analysis and Modeling
Attention for Chapter 7: Detecting Anomalies in Sensor Signals Using Database Technology
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Citations

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Chapter title
Detecting Anomalies in Sensor Signals Using Database Technology
Chapter number 7
Book title
Advances in Intelligent Signal Processing and Data Mining
Published by
Springer, Berlin, Heidelberg, January 2013
DOI 10.1007/978-3-642-28696-4_7
Book ISBNs
978-3-64-228695-7, 978-3-64-228696-4
Authors

Gereon Schüller, Andreas Behrend, Wolfgang Koch

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 3 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 67%
Student > Master 1 33%
Readers by discipline Count As %
Computer Science 2 67%
Engineering 1 33%

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