By Topic

Empirical Mode Decomposition Technique With Conditional Mutual Information for Denoising Operational Sensor Data

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Omitaomu, O.A. ; Comput. Sci. & Eng. Div., Oak Ridge Nat. Lab., Oak Ridge, TN, USA ; Protopopescu, V.A. ; Ganguly, A.R.

This paper presents a new approach for denoising sensor signals using the Empirical Mode Decomposition (EMD) technique and the Information-theoretic method. The EMD technique is applied to decompose a noisy sensor signal into the so-called intrinsic mode functions (IMFs). These functions are of the same length and in the same time domain as the original signal. Therefore, the EMD technique preserves varying frequency in time. Assuming the given signal is corrupted by high-frequency (HF) Gaussian noise implies that most of the noise should be captured by the first few modes. Therefore, our proposition is to separate the modes into HF and low-frequency (LF) groups. We applied an information-theoretic method, namely, mutual information to determine the cutoff for separating the modes. A denoising procedure is applied only to the HF group using a shrinkage approach. Upon denoising, this group is combined with the original LF group to obtain the overall denoised signal. We illustrate our approach with simulated and real-world cargo radiation data sets. The results are compared to two popular denoising techniques in the literature, namely discrete Fourier transform (DFT) and discrete wavelet transform (DWT). We found that our approach performs better than DWT and DFT in most cases, and comparatively to DWT in some cases in terms of: 1) mean square error; 2) recomputed signal-to-noise ratio; and 3) visual quality of the denoised signals.

Published in:

Sensors Journal, IEEE  (Volume:11 ,  Issue: 10 )