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.

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.

scholarly journals Analysis on the Correlation Degree between the Driver’s Reaction Ability and Physiological Parameters

2017 ◽  
Vol 2017 ◽  
pp. 1-8
Author(s):  
Shiwu Li ◽  
Mengzhu Guo ◽  
Linhong Wang ◽  
Meng Chai ◽  
Facheng Chen ◽  
...  
Keyword(s):  
Reaction Time ◽  
Power Spectrum ◽  
Mathematical Models ◽  
Gaussian Function ◽  
Average Power ◽  
Functional Relation ◽  
Impact Factors ◽  
Driving Time ◽  
Correlation Degree ◽  
The Impact

In this paper, the correlation degree between driver’s reaction time and the physiological signal is analyzed. For this purpose, a large number of road experiments are performed using the biopac and the reaction time test systems to collect data. First, the electroencephalograph (EEG) signal is processed by using the fast Fourier and the inverse Fourier transforms. Then, the power spectrum densities (PSD) ofα,β,δ, and EEG wave are calculated by Welch procedure. The average power of the power spectrum ofα,β, andθis calculated by the biopac software and two ratio formulas,(α+θ)/βandα/β, are selected to be the impact factors. After that the heart rate and the standard deviation of RR interval are calculated from the electrocardiograph (ECG) signal. Lastly, the correlation degree between the eight impact factors and the reaction time are analyzed based on the grey correlation analysis. The results demonstrate thatα/βhas the greatest correlation to the reaction time except EEG-PSD. Furthermore, two mathematical models for the reaction time-driving time and theα/β-driving time are developed based on the Gaussian function. These mathematical models are then finally used to establish the functional relation ofα/β-the reaction time.

scholarly journals A Method to Estimate Fried's Seeing Parameter from a Time Series of Arbitrary Resolved Structures Imaged Through the Atmosphere

1984 ◽  
Vol 79 ◽  
pp. 203-220
Author(s):  
O. von der Lühe
Keyword(s):  
Signal To Noise Ratio ◽  
Imaging Techniques ◽  
Average Power ◽  
Speckle Interferometry ◽  
Reference Object ◽  
Ratio Method ◽  
Modulation Transfer ◽  
Fourier Plane ◽  
First Results ◽  
Average Power Spectrum

AbstractOne of the major fields of application of speckle-interferometric and speckle-imaging techniques is the photometry of astronomical objects exhibiting structure smaller than the seeing limit. The accuracy of the photometry depends critically on the accuracy to which the modulation transfer function (MTF), that describes the atmospheric-telescopic attenuation of the Fourier amplitudes of the object under consideration, is known. The estimation of the effective MTF is especially difficult when no known reference object is within the field of view.A method is presented that allows to estimate the effective MTF from the observation of arbitrary structure with the use of FRIED-KORFF theory. The ratio of the squared modulus of the average Fourier transform and the average power spectrum serves as an estimator for the FRIED parameter r0. To a first approximation, this ratio is independent from the observed object. Additionally, the behaviour of the ratio in regions beyond the seeing limit in the Fourier plane may be analyzed to obtain an estimate of the speckle interferometry signal-to-noise ratio. The basic concept of the ratio method will be described, its accuracy will be discussed. First results on the application of the ratio method to observations of solar granulation will be presented.

scholarly journals Feature Extraction of Electroencephalography Signals Using Fast Fourier Transform

2016 ◽  
Vol 10 (2) ◽  
pp. 49 ◽  
Author(s):  
Hindarto Hindarto ◽  
Sumarno Sumarno
Keyword(s):  
Brain Computer Interface ◽  
Average Power ◽  
Empirical Evaluation ◽  
Computation Time ◽  
Current Approach ◽  
Computer Interface ◽  
Signal Features ◽  
Hidden Layer ◽  
The Brain ◽  
Average Power Spectrum

This article discusses a method within the area of brain-computer interface. The proposed method is to use the features extracted from the Electroencephalograph signal and a three hidden-layer artificial neural network to map the brain signal features to the computer cursor movement. The evaluated features are the root mean square and the average power spectrum. The empirical evaluation using 200 records taken from 2003 BCI Competition dataset shows that the current approach can accurately classify a simple cursor movement within 92.5% accuracy in a short computation time.

The on-line use of histogram data for high-speed correction and alignment of electron microscope images

1984 ◽  
Vol 42 ◽  
pp. 556-557
Author(s):  
William Krakow
Keyword(s):  
Power Spectrum ◽  
High Speed ◽  
Direct Memory Access ◽  
Digital Television ◽  
Contrast Transfer Function ◽  
The Past ◽  
High Resolution Tem ◽  
On Line ◽  
Correction Of Astigmatism ◽  
The Fourier Transform

In the past few years on-line digital television frame store devices coupled to computers have been employed to attempt to measure the microscope parameters of defocus and astigmatism. The ultimate goal of such tasks is to fully adjust the operating parameters of the microscope and obtain an optimum image for viewing in terms of its information content. The initial approach to this problem, for high resolution TEM imaging, was to obtain the power spectrum from the Fourier transform of an image, find the contrast transfer function oscillation maxima, and subsequently correct the image. This technique requires a fast computer, a direct memory access device and even an array processor to accomplish these tasks on limited size arrays in a few seconds per image. It is not clear that the power spectrum could be used for more than defocus correction since the correction of astigmatism is a formidable problem of pattern recognition.

Log-log scale roughness spectroscopy of surfaces

1993 ◽  
Vol 51 ◽  
pp. 224-225
Author(s):  
P. Fraundorf ◽  
B. Armbruster
Keyword(s):  
Power Spectrum ◽  
Autocorrelation Function ◽  
Power Spectra ◽  
Scanning Force Microscopy ◽  
Scanning Tunneling ◽  
Tunneling Microscopy ◽  
Force Microscopy ◽  
Scanning Force ◽  
Lateral Structure ◽  
Scale Roughness

Optical interferometry, confocal light microscopy, stereopair scanning electron microscopy, scanning tunneling microscopy, and scanning force microscopy, can produce topographic images of surfaces on size scales reaching from centimeters to Angstroms. Second moment (height variance) statistics of surface topography can be very helpful in quantifying “visually suggested” differences from one surface to the next. The two most common methods for displaying this information are the Fourier power spectrum and its direct space transform, the autocorrelation function or interferogram. Unfortunately, for a surface exhibiting lateral structure over several orders of magnitude in size, both the power spectrum and the autocorrelation function will find most of the information they contain pressed into the plot’s origin. This suggests that we plot power in units of LOG(frequency)≡-LOG(period), but rather than add this logarithmic constraint as another element of abstraction to the analysis of power spectra, we further recommend a shift in paradigm.

Sign in / Sign up

Export Citation Format

Share Document