Sparse Estimation for Structural Variability

AbstractThree salts of the common composition [EuCl2(X-tpy)2][EuCl4(X-tpy)]·nMeCN were obtained from EuCl3·6H2O and the respective organic ligands (X-tpy = 4′-phenyl-2,2′:6′,2″-terpyridine ptpy, 4′-(pyridin-4-yl)-2,2′:6′,2″-terpyridine 4-pytpy, and 4′-(pyridin-3-yl)-2,2′:6′,2″-terpyridine 3-pytpy). These ionic complexes are examples of salts, in which both cation and anion contain Eu3+ with the same organic ligands and chlorine atoms coordinated. As side reaction, acetonitrile transforms into acetamide resulting in the crystallization of the complex [EuCl3(ptpy)(acetamide)] (4). Salts [EuCl2(ptpy)2][EuCl4(ptpy)]·2.34MeCN (1), [EuCl2(4-pytpy)2][EuCl4(4-pytpy)]·0.11MeCN (2), and [EuCl2(3-pytpy)2][EuCl4(3-pytpy)]·MeCN (3) crystallize in different structures (varying in space group and crystal packing) due to variation of the rear atom of the ligand to a coordinative site. Additionally, we show and compare structural variability through the dimeric complexes [Eu2Cl6(ptpy)2(N,N′-spacer)]·N,N′-spacer (5, 6, 7) obtained from [EuCl3(ptpy)(py)] by exchanging the end-on ligand pyridine with several bipyridines (4,4′-bipyridine bipy, 1,2-bis(4-pyridyl)ethane bpa, and 1,2-bis(2-pyridyl)ethylene bpe). In addition, photophysical (photoluminescence) and thermal properties are presented.

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The Influence of Structural Variability on Limit Cycle Oscillation Behaviour

15th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference ◽

10.2514/6.2014-3276 ◽

2014 ◽

Cited By ~ 3

Author(s):

Richard Hayes ◽

Weigang Yao ◽

Simão P. Marques

Keyword(s):

Limit Cycle ◽

Limit Cycle Oscillation ◽

Structural Variability ◽

Cycle Oscillation

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A Novel Detection Scheme with Multiple Observations for Sparse Signal Based on Likelihood Ratio Test with Sparse Estimation

Mathematical Problems in Engineering ◽

10.1155/2016/8535486 ◽

2016 ◽

Vol 2016 ◽

pp. 1-14

Author(s):

Hongbo Zhao ◽

Lei Chen ◽

Wenquan Feng ◽

Chuan Lei

Keyword(s):

Likelihood Ratio ◽

Likelihood Ratio Test ◽

Prior Information ◽

Finite Size ◽

Sparse Signals ◽

Ratio Test ◽

Sparse Estimation ◽

Detection Scheme ◽

Error Exponent ◽

Multiple Observations

Recently, the problem of detecting unknown and arbitrary sparse signals has attracted much attention from researchers in various fields. However, there remains a peck of difficulties and challenges as the key information is only contained in a small fraction of the signal and due to the absence of prior information. In this paper, we consider a more general and practical scenario of multiple observations with no prior information except for the sparsity of the signal. A new detection scheme referred to as the likelihood ratio test with sparse estimation (LRT-SE) is presented. Under the Neyman-Pearson testing framework, LRT-SE estimates the unknown signal by employing thel1-minimization technique from compressive sensing theory. The detection performance of LRT-SE is preliminarily analyzed in terms of error probabilities in finite size and Chernoff consistency in high dimensional condition. The error exponent is introduced to describe the decay rate of the error probability as observations number grows. Finally, these properties of LRT-SE are demonstrated based on the experimental results of synthetic sparse signals and sparse signals from real satellite telemetry data. It could be concluded that the proposed detection scheme performs very close to the optimal detector.

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