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Advances in Data Science and Adaptive Analysis, Volume 8

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Volume 8, Number 1, July 2016

Research Articles

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  - journals/aada/HuangKX16
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  - https://dblp.org/rec/journals/aada/HuangKX16
Dishan Huang, Xianglong Kong, Yibing Xia:
Identification and Cancellation of Pseudo Mode Function in EMD based on Energy Conservation. 1650001:1-1650001:19
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  - journals/aada/KumeN16
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Kenji Kume, Naoko Nose-Togawa:
Filter Characteristics in Image Decomposition with Singular Spectrum Analysis. 1650002:1-1650002:15
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  - journals/aada/KumeN16a
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Kenji Kume, Naoko Nose-Togawa:
Additive Decomposition of Power Spectrum Density in Singular Spectrum Analysis. 1650003:1-1650003:20
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  - journals/aada/LaszukCN16
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Dawid Laszuk, José O. Cadenas, Slawomir J. Nasuto:
On the Phase Coupling of Two Components Mixing in Empirical Mode Decomposition. 1650004:1-1650004:11
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  - journals/aada/ShenCLWS16
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Bo-Wen Shen, Samson Cheung, Jui-Lin F. Li, Yu-Ling Wu, Samuel S. P. Shen:
Multiscale Processes of Hurricane Sandy (2012) as Revealed by the Parallel Ensemble Empirical Mode Decomposition and Advanced Visualization Technology. 1650005:1-1650005:22

Volume 8, Number 2, April 2016

Research Articles

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  - journals/aada/LeungVSZSS16
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Kimberly Leung, Max Velado, Aneesh Subramanian, Guang J. Zhang, Richard C. J. Somerville, Samuel S. P. Shen:
Simulation of High-Resolution Precipitable Water Data by a Stochastic Model with a Random Trigger. 1650006:1-1650006:12
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  - journals/aada/SuterW16
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Bruce W. Suter, Yi Wang:
Sparse Representation with Nonlinear Fourier Atoms. 1650007:1-1650007:19
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Donghoh Kim, Hee-Seok Oh:
Intrinsic Pattern Preserving Boundary Treatment Method for Empirical Mode Decomposition. 1650008:1-1650008:9
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  - journals/aada/PietrafesaBHGYS16
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Leonard J. Pietrafesa, Shaowu Bao, Norden E. Huang, Paul T. Gayes, Tingzhuang Yan, Michael Slattery:
Great Lakes Water Levels: Decomposing Time Series for Attribution. 1650009:1-1650009:29
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Mingshu Zhao, Jianzheng Liu, Yi Wang, Hao Zhu:
On the EMD Sifting Property and Instantaneous Parameters. 1650010:1-1650010:26

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