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According to the Slepian-Wolf theorem, by utilizing the correlation knowledge of the sources at the receiver, the joint decoder can achieve the same compression rate as in the case that the sources are encoded by optimum joint encoders.
We propose to address this problem by utilizing the correlation between energy and vibration, i.e., the harvested energy accumulated through time is proportional to the local vibration amplitude and frequency.
By utilizing the correlation inherently exists in phase noise, a Wiener filter preprocessing algorithm based on the statistics of phase noise has been proposed, which performs directly on the received signal without changing the structure of conventional OFDM systems.
Simulation results show that by utilizing the correlation inherently exists in phase noise, the proposed Wiener filter preprocessing algorithm can effectively mitigate the ICI and lower the error floor, and therefore significantly improve the performances of previous researches on phase noise cancelation, especially in the severe phase noise case.
We will show in the next section that by utilizing the correlation inherently exists in phase noise process, the proposed Wiener filter preprocessing algorithm can effectively mitigate the ICI which results from phase noise and lower the error floor, and therefore significantly improve the performances of previous researches phase noise cancelation, especially in severe phase noise case.
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A content adaptive complexity reduction algorithm is proposed in [16] to reduce the 3D-HEVC coding complexity by utilizing the correlations between the base view and the dependent view.
The estimator is further enhanced by utilizing the time correlation information through a forward backward (FB) Kalman filter.
By utilizing the Pearson correlation coefficient and deviation coefficient defined in the Methods section, Figure 8 shows that most transcription factors were aggregated in one cluster.
Then, by utilizing the cross-correlation outputs at two adjacent timing indices, we can obtain the actual delayed timing in the channels.
Next, preamble 2 is used to detect the OFDMA user segment deployment/collision by utilizing the auto-correlation property of the Zadoff-Chu sequence, as introduced in Section 2. This information is passed on to the MAC layer control block.
Figure 2, illustrates the correlation between ranking lists by utilizing the Spearman rank correlation coefficient obtained by employing Equation (23), the Spearman rank correlation coefficient similarity of the rankings in Table 9. {r}_s=1-frac{sum limits_{i=1}^n{D}{nlefteft({n}^2-1right)}, (23 where D is differences between the two ranks and n denotes the sample size.
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CEO of Professional Science Editing for Scientists @ prosciediting.com