A new surface characterization technique: RIMAPS (Rotated Image with Maximum Average Power Spectrum)

2002 ◽  
Vol 206 (1) ◽  
pp. 72-83 ◽  
Author(s):  
N. O. Fuentes ◽  
E. A. Favret
Keyword(s):  
Power Spectrum ◽  
Surface Characterization ◽  
Average Power ◽  
Characterization Technique ◽  
Average Power Spectrum

Description and interpretation of the bracts epidermis of Gramineae (Poaceae) with rotated image with maximum average power spectrum (RIMAPS) technique

Micron ◽  
2008 ◽  
Vol 39 (7) ◽  
pp. 985-991 ◽  
Author(s):  
Eduardo A. Favret ◽  
Néstor O. Fuentes ◽  
Ana M. Molina ◽  
Lorena M. Setten
Keyword(s):  
Power Spectrum ◽  
Average Power ◽  
Average Power Spectrum

Study on Fast Algorithm for SNR and Method for Threshold Confirmation

2013 ◽  
Vol 427-429 ◽  
pp. 1718-1722
Author(s):  
Lin Sun ◽  
Ran Wei ◽  
Fu Ting Bao ◽  
Xian Zhang Tian
Keyword(s):  
Power Spectrum ◽  
Fast Algorithm ◽  
Human Gene ◽  
Signal To Noise Ratio ◽  
Average Power ◽  
Threshold Value ◽  
Computational Experiments ◽  
Rigorous Derivation ◽  
Signal To Noise ◽  
Average Power Spectrum

To reduce the amount of computing resources, a fast algorithm of the average power spectrum and signal-to-noise ratio was presented based on rigorous derivation of the formula. Also, it proved the rule gained from computational experiments. Besides, a method called fitting-optimization to determine the classification threshold value was proposed. It improves the accuracy by about 7% for human gene.

scholarly journals RIMAPS and Varlogram Analysis of Barley Leaf Surfaces

2004 ◽  
Vol 12 (5) ◽  
pp. 24-27 ◽  
Author(s):  
Eduardo A. Favret ◽  
Néstor O. Fuentes
Keyword(s):  
Quantitative Method ◽  
Average Power ◽  
Plant Tissues ◽  
Wild Type ◽  
Abaxial Side ◽  
Leaf Surfaces ◽  
Barley Leaves ◽  
Leaf Mutant ◽  
Barley Leaf ◽  
Average Power Spectrum

It is a common practice to use microscopic images to describe the differences observed between plant tissues. The images illustrate the taxonomic characteristics of the studied species. In this work we introduce a quantitative method for conducting these analyses utilizing digitized images obtained via scanning electron microscopy (SEM) of barley leaf surfaces. The topography of the leaf surfaces of a narrow-leaf mutant and its wild type mother line was characterized, see figure 1, using the Rotated Image with Maximum Average Power Spectrum (RIMAPS) technique and the Variogram method. Spectra resulting from RIMAPS analysis allow us to identify the specimens and to distinguish between the adaxial or the abaxial side of the leaf. These results are complemented by obtaining the typical scale lengths that characterize the abaxial surfaces of both the mutant and the mother line barley leaves.

The Use of Power Spectrum Density for Surface Characterization of Thin Films

2019 ◽  
pp. 379-411 ◽  
Author(s):  
Fredrick Madaraka Mwema ◽  
Esther Titilayo Akinlabi ◽  
Oluseyi Philip Oladijo ◽  
Oluseyi Philip Oladijo
Keyword(s):  
Thin Films ◽  
Power Spectrum ◽  
Surface Characterization ◽  
Power Spectrum Density ◽  
Spectrum Density

Detection for Eggshell Crack Based on Acoustic Feature and Support Vector Machine

2011 ◽  
Vol 58-60 ◽  
pp. 227-232
Author(s):  
Li Rong Xiong
Keyword(s):  
Support Vector Machine ◽  
Power Spectrum ◽  
Crack Detection ◽  
Recognition Rate ◽  
Average Power ◽  
Kernel Functions ◽  
Support Vector ◽  
Acoustic Feature ◽  
Advantages And Disadvantages ◽  
Spectrum Area

The paper has proposed a new method based on acoustic feature and support vector machine. A sound signal acquisition system is designed based on microcontroller, the power spectra is received for good shell eggs and crack eggs. 4 parameters, such as the average power spectrum area (x1), power spectrum area of range value (x2), the first average formant amplitude (x3) and the first formant amplitude range value (x4), are extracted. These 4 parameters are regarded as input vector for support vector machine (SVM). The advantages and disadvantages for classification performance because of different kernel functions and different training sample size are compared, and ultimately the radial basis function (RBF) function is regarded as the best kernel function for the optimal classification results, and then the penalty coefficient C and the normalization coefficient are optimized, the overall recognition rate reached 97.37% or more, running time is about 0. 3s.The results show that SVM has a perfect performance in eggshell crack detection.

Two-Dimensional Fast Fourier Transform and Power Spectrum for Surface Roughness in three Dimensions

1995 ◽  
Vol 209 (5) ◽  
pp. 381-391 ◽  
Author(s):  
W P Dong ◽  
K J Stout
Keyword(s):  
Surface Roughness ◽  
Fourier Transform ◽  
Power Spectrum ◽  
Fast Fourier Transform ◽  
Surface Characterization ◽  
Three Dimensions ◽  
Two Dimensional ◽  
Comprehensive Understanding ◽  
Extract Information

Two-dimensional power spectrums of engineering surfaces contain plenty of information that is important and valuable for surface characterization. However, the characteristics of the two-dimensional spectrums are largely unknown and the algorithm to implement them is not familiar to many engineers or researchers. This paper describes a detailed procedure to implement the two-dimensional fast Fourier transform and power spectrum for surface roughness in three dimensions. Methods used to extract information from the spectrums are introduced. In order to perform two-dimensional spectral analysis and to have a comprehensive understanding of the characteristics of engineering surfaces, an atlas of the two-dimensional spectrums of representative engineering surfaces are presented. The properties of the spectrums are discussed in conjunction with theoretical analysis and visual characterization of the presented spectrums.

Submicron Roughness Determination at the Si-SiO2 Interface and Correlations to Prccessing Steps and Electronic Properties

1987 ◽  
Vol 105 ◽  
Author(s):  
Peter O. Hahn ◽  
I. Lampert ◽  
A. Schnegg
Keyword(s):  
Electronic Properties ◽  
Root Mean Square ◽  
Surface Characterization ◽  
Scattered Light ◽  
Laser Beams ◽  
Root Mean Square Roughness ◽  
Mean Square ◽  
Optical Surface ◽  
Correlation Lengths ◽  
Characterization Technique

AbstractA newly developed optical surface characterization technique using the diffuse scattered light of two laser beams will be presented. The method determines root-mean-square roughness values (RMS) of surfaces down to 1 Å and corresponding correlation lengths in the submicron area.

Surface Characterization in Diamond Turning of Highly Anisotropy Brittle Crystals: A Multi-Spectrum Analysis Approach

2006 ◽  
Vol 532-533 ◽  
pp. 989-992
Author(s):  
Chi Fai Cheung ◽  
Wing Bun Lee ◽  
Suet To
Keyword(s):  
Surface Roughness ◽  
Power Spectrum ◽  
Spectrum Analysis ◽  
Surface Characterization ◽  
Single Point ◽  
Diamond Turning ◽  
Surface Generation ◽  
Factors Affecting ◽  
Important Means ◽  
Brittle Crystals

This paper presents a multi-spectrum analysis method for the characterization of the surface generation in single-point turning of brittle single crystals. The features on the diamond turned surfaces were extracted and analysed by the power spectrum analysis of the surface roughness profiles measured at a number radial sections of the workpiece. By the analysis of the variation of the spectral patterns in the multi-spectrum plots, the surface roughness and materials effect on surface generation are found to be strongly related to the power spectrum. This provides an important means to explain quantitatively the effect of factors affecting the surface generation in diamond turning brittle crystals.

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