Journal Article10.1109/JSEN.2006.877987
Kernel Density Estimation-Based Data Correlation
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TL;DR: The basis for the authors' calibration procedure is to construct a statistical error model that captures the characteristics of the measurement errors and proposes four alternatives to make the transition from the error model to the calibration model, which is represented by piecewise polynomials.
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Abstract: Calibration is the process of identifying and correcting the most likely error in sensor measurements. The basis for the authors' calibration procedure is to construct a statistical error model that captures the characteristics of the measurement errors. Such an error model can be constructed either offline or online and is derived using the nonparametric kernel-density-estimation techniques. Models constructed using various forms of the kernel smoothing functions are compared using statistical evaluation methods. Based on the selected error model, they propose four alternatives to make the transition from the error model to the calibration model, which is represented by piecewise polynomials. In addition, statistical validation and evaluation methods such as resubstitution, is used in order to establish the interval of confidence for both the error model and the calibration model. Traces of the acoustic signal-based distance measurements recorded by infield deployed sensors are used as their demonstrative example. Finally, they discuss the broad range of applications of the error models and provide an example on how adopting statistical error model as the optimization objective impacts the accuracy of the location discovery problem in wireless sensor networks
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Citations
•Journal Article
A Collaborative Approach to In-Place Sensor Calibration
TL;DR: In this paper, a two-phase post-deployment calibration technique for large-scale, dense sensor deployments is presented, in which the first phase is to use temporal correlation of signals received at neighboring sensors when the signals are highly correlated (i.e. sensors are observing the same phenomenon) to derive the function relating their bias in amplitude.
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Head detection based on 21HT and circle existence model
Min Zhao,Dihua Sun,Tang Yi,Heng-pan He +3 more
- 06 Jul 2012
TL;DR: Wang et al. as discussed by the authors proposed a method for head detection in video sequences captured with fixed vertical mono-camera, which integrated hough transformation, hair-color distribution model and circle existence model.
3
Kernel Density Estimation-Based Data Correlation
TL;DR: The basis for the authors' calibration procedure is to construct a statistical error model that captures the characteristics of the measurement errors and proposes four alternatives to make the transition from the error model to the calibration model, which is represented by piecewise polynomials.
3
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