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This paper deals with target tracking in a binary wireless sensor network. In this contribution, the target motion is represented by a non linear model based on a jump-Markovian direction. The observation of the trajectory based on the target signal strength is performed by binary sensors. In particular, only one sensor emits one bit by instant what increases the longevity of the wireless sensor network...
This paper introduces a new speech enhancement method, which combines adaptive center weighted average (ACWA) filter with empirical mode decomposition (EMD). Both ACWA and EMD operate in the time domain. The ACWA filter is advantageous as it operates adaptively in the time domain and does not require the stationarity and the whiteness of the signals. Thanks to the data driven decomposition of the...
Distributed estimation is a major feature in wireless sensor networks (WSNs). Recently, hard quantized observations based on sign of innovation (SOI) were used to perform optimal distributed filtering involving thus the SOI Kalman filter (KF)/extended KF (EKF) [1]. In this paper, a SOI-particle filter (SOIPF) is derived to enhance the performance of the distributed estimation procedure. On one hand,...
In this paper, a speech signal noise reduction based on a multiresolution approach referred to as Empirical Mode Decomposition (EMD) [1] is introduced. The proposed speech denoising method is a fully data-driven approach. Noisy signal is decomposed adaptively into oscillatory components called Intrinsic Mode Functions (IMFs), using a temporal decomposition called sifting process. The basic principle...
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