Operational parametrization of the &lt;inline-formula&gt;&lt;math display="inline" overflow="scroll"&gt;&lt;mn&gt;1&lt;/mn&gt;&lt;mo&gt;/&lt;/mo&gt;&lt;mi&gt;f&lt;/mi&gt;&lt;/math&gt;&lt;/inline-formula&gt; noise of a sequence of frames by means of the principal component analysis in focal plane arrays

López-Alonso, José Manuel; Alda, Javier

doi:10.1117/1.1579490

Cited by 21 publications

(25 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Moreover, their temporal evolutions are shifted π/2 one with respect to the other. This is the only solution for the eigenvectors to remain orthogonal at the same frequency [10]. Therefore, we may propose the existence of "quasimonochromatic processes" formed by the eigenvectors (and their associated eigenimages and eigenvalues) with the same temporal frequency.…”

Section: Phase Map Computationmentioning

confidence: 99%

“…to finally relate the eigenvalues with sampled values of the Power Spectral Density of a given sequence of frames [10]. In that case, the eigenvectors showed a clear harmonic evolution that allowed a temporal frequency interpretation.…”

Section: Phase Map Computationmentioning

confidence: 99%

“…When describing frames with a given power spectrum density for a temporal-stationary phenomena , PCA can be used to sample the spectrum and characterize it. The involved principal components have quasi-harmonic time dependence [10]. Finally, the eigenvalues, explain in decreasing order the contribution of their associated principal component to the total variance of the original data.…”

Section: Basis Of the Principal Component Analysismentioning

confidence: 99%

See 2 more Smart Citations

Photonic crystal characterization by FDTD and principal component analysis

López-Alonso¹,

Rico-Garcı́a²,

Alda³

2004

Opt. Express

View full text Add to dashboard Cite

Abstract:We demonstrate the capabilities of principal component analysis (PCA) for studying the results of finite-difference time-domain (FDTD) algorithms in simulating photonic crystal microcavities. The spatial-temporal structures provided by PCA are related to the actual electric field vibrating inside the photonic microcavity. A detailed analysis of the results has made it possible to compute the phase maps for each mode of the arrangement at their respective resonant frequencies. The existence of standing wave behavior is revealed by this analysis. In spite of this, some numerical artifacts induced by FDTD algorithms have been clearly detailed through PCA analysis. The data we have analyzed are a given set of maps of the electric field recorded during the simulation.

show abstract

Section: Phase Map Computationmentioning

confidence: 99%

Section: Phase Map Computationmentioning

confidence: 99%

Section: Basis Of the Principal Component Analysismentioning

confidence: 99%

See 1 more Smart Citation

Photonic crystal characterization by FDTD and principal component analysis

López-Alonso¹,

Rico-Garcı́a²,

Alda³

2004

Opt. Express

View full text Add to dashboard Cite

show abstract

“…Besides, when the eigenvalue associated with a principal component cannot be connected with its neighbors, its eigenvector shows a harmonic dependence. We will name these processes as single-value processes, or "quasi-monochromatic" processes 7 . In this case, this harmonic dependence indicates the presence of a very well defined temporal frequency.…”

Section: Fundamentals Of the Principal Component Analysis Applied To mentioning

confidence: 99%

“…In this contribution we show how a multivariate statistical technique may find application in the treatment and interpretation of the FDTD outputs. Principal Component Analysis (PCA) has been successfully applied to the characterization of covariance structures in several fields ranging from the analysis of noise in a sequence of frames to the identification of rare events in the stock market 6,7,8 . In the case treated here, the FDTD results are taken as a collection of frames equally spaced in time.…”

Section: Introductionmentioning

confidence: 99%

Characterization of FDTD artifacts and modes in photonic crystals

López-Alonso

Rico-Garcı́a

Alda

2004

Integrated Optical Devices, Nanostructures, and Displays

View full text Add to dashboard Cite

FDTD algorithms are being used as a numeric tool for the analysis of photonic crystals. The definition of the modes associated with them is of interest for the study of the capabilities of photonic crystal devices. The Principal Component Analysis (PCA) has been applied here to a sequence of images corresponding to the electromagnetic fields obtained from the FDTD simulations. PCA has revealed and quantified the importance of the modes appearing in the photonic crystals. The capability of PCA to produce spatial structures, or maps, associated with temporal evolutions has made possible the calculation of the modulus and phase of the modes existing in the photonic crystal. Some other modes, contributing with an almost negligible amount to the total variance of the original data, are also revealed by the method. Besides, PCA has been used to quantify the contribution of the numerical noise of the algorithm and to identify the effect of artifacts related with the matching of the computational grid and the inner geometry of the photonic crystal.

show abstract

Noninvasive Detection of Filaggrin Molecules by Raman Spectroscopy

González

2014

Filaggrin

View full text Add to dashboard Cite

Operational parametrization of the <inline-formula><math display="inline" overflow="scroll"><mn>1</mn><mo>/</mo><mi>f</mi></math></inline-formula> noise of a sequence of frames by means of the principal component analysis in focal plane arrays

Cited by 21 publications

References 14 publications

Photonic crystal characterization by FDTD and principal component analysis

Photonic crystal characterization by FDTD and principal component analysis

Characterization of FDTD artifacts and modes in photonic crystals

Noninvasive Detection of Filaggrin Molecules by Raman Spectroscopy

Contact Info

Product

Resources

About