Pattern recognition-based optical technique for non-destructive detection of Ectomyelois ceratoniae infestation in pomegranates during hidden activity of the larvae

2019 ◽  
Vol 206 ◽  
pp. 552-557 ◽  
Author(s):  
Bahareh Jamshidi ◽  
Ezeddin Mohajerani ◽  
Hossein Farazmand ◽  
Asghar Mahmoudi ◽  
Abolfazl Hemmati
Keyword(s):  
Pattern Recognition ◽  
Optical Technique ◽  
Ectomyelois Ceratoniae ◽  
Non Destructive

X-Rays for Fluid Flow Inspection

2004 ◽  
Author(s):  
D. Chambellan ◽  
O. Gal ◽  
S. Legoupil ◽  
A. Vabre
Keyword(s):  
Heat Transfer ◽  
Fluid Flow ◽  
Specific Gravity ◽  
Void Fraction ◽  
X Rays ◽  
Optical Technique ◽  
X Ray ◽  
Non Destructive Evaluation ◽  
Intensive Use ◽  
Non Destructive

X-rays techniques are widely used in the non-destructive evaluation field for mechanical inspection. However, development of new x-ray detectors and sources over the last decade has let to an intensive use of this technique in other fields. In this paper, we describe the use of X-rays techniques in the field of fluid flow engineering (fluidics and heat transfer). This technique is very attractive since measurements can be performed even if pressure, temperature require the use of opaque walls. In addition the X-ray technique is well suited to multiphase flows where optical technique can not be used if void fraction is larger than few percents. Specific gravity, mass or void fraction are the main accessible parameters.

Optical Technique for Characterization of High Energy Ion Implanted Materials

1992 ◽  
Vol 268 ◽  
Author(s):  
Gustavo E Aizenberg ◽  
Pieter L Swart ◽  
Beatrys M Lacquet
Keyword(s):  
Refractive Index ◽  
Digital Signal ◽  
Amorphous Layer ◽  
High Energy ◽  
Optical Technique ◽  
Concentration Peak ◽  
Non Destructive ◽  
Reflectance Data ◽  
Ion Implanted

ABSTRACTA new method for the characterization of high energy ion-implanted materials has been developed. The refractive index and thickness of the amorphous layer produced by ion-implantation as well as the recrystallized layer formed by annealing of the ionimplanted samples can be determined by means of this non-destructive optical technique.For frequencies where the carriers do not respond, the measured reflectance is bilinear transformed, and further digital signal processing yields information about thickness and refractive index of the abovementioned layers. When working at optical frequencies where the carriers can respond to the electromagnetic field the physical position of the peak concentration follows directly from the processed reflectance data. Simulated and experimental data have been analyzed. The position of the boundaries between the amorphous, recrystallized and substrate zones, as well as the position of the carrier concentration peak can be determined for various steps of annealing. The algorithm has the advantage of being simple and time efficient.

Non Destructive Profile Measurements of Annealed Shallow Implants

1999 ◽  
Vol 568 ◽  
Author(s):  
Peter Borden ◽  
Regina Niimeiier ◽  
Karen Lingel
Keyword(s):  
Edge Effects ◽  
Spot Size ◽  
Optical Technique ◽  
Fine Scale ◽  
Exclusion Zone ◽  
Spreading Resistance ◽  
Spatial Uniformity ◽  
Difficult Challenge ◽  
Edge Exclusion ◽  
Non Destructive

ABSTRACTControlling the profile of shallow doped layers is critical for obtaining desired performance from deep sub-micron devices. An especially difficult challenge is measuring depth uniformity of the activated implant over the full area of a wafer.This paper demonstrates the capabilities of a new rapid, non-contact optical technique for measuring active doping depth of shallow implants. Employing a 2 Pim spot size, it provides a measurement of fine-scale spatial uniformity in dimensions approaching those of individual devices. By measuring multiple sites, it can rapidly characterize uniformity over large areas and edge-to-edge, without an edge exclusion zone.Data will be presented showing performance on layers varying in depth from 200 to 1200 Å. Edge-to-edge diameter scans on 8” wafers with le15, B11 implants at energies in the range of 200 to 2,000 eV and various RTA temperatures show radially symmetric uniformity patterns of depth and edge effects in the outermost centimeter. Measurements are validated based on correlation to spreading resistance profiles for a number of samples.

scholarly journals Near infrared hyperspectral images and pattern recognition techniques used to identify etiological agents of cotton anthracnose and ramulosis

2018 ◽  
Author(s):  
Priscila Aires ◽  
Francisco Gambarra-Neto ◽  
Wirton Coutinho ◽  
Alderi Araujo ◽  
Gilvan da Silva ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Near Infrared ◽  
Intergenic Spacer ◽  
Linear Discriminant ◽  
Intergenic Spacer Region ◽  
Standard Normal Variate ◽  
Standard Normal ◽  
Etiological Agents ◽  
Speed Accuracy ◽  
Non Destructive

Hyperspectral imaging near infrared (HSI-NIR) has the potential to be used as a non-destructive approach for the analysis of new microbiological matrices of agriculture interest. This article describes a new method for accurately and rapidly classifying the etiological agents Colletotrichum gossypii (CG) and C. gossypii var. cephalosporioides (CGC) grown in a culture medium, using scattering reflectance HSI-NIR and multivariate pattern recognition analysis. Five strains of CG and 46 strains of CGC were used. CG and CGC strains were grown on Czapek-agar medium at 25 °C under a 12-hour photoperiod for 15 days. Molecular identification was performed as a reference for the CG and CGC classes by polymerase chain reaction of the intergenic spacer region of rDNA. The scattering coefficient µs and the absorption coefficient µa were obtained, which resulted in a µs value for CG of 1.37 × 1019 and for CGC of 5.83 × 10–11. These results showed that the use of the standard normal variate was no longer essential and reduced the spectral range from 1000–2500 nm to 1000–1381 nm. The results evidenced two type II errors for the CG 457-2 and CGC 39 samples in the soft independent modelling model of the analogy model. There were no classification errors using the algorithm of the successive projections for variable selection in linear discriminant analysis (SPA-LDA). A parallel validation of the results obtained with SPA-LDA was performed using a box plot analysis with the 11 variables selected by SPA, in which there were no outliers for the HSI-NIR models. The new HSI-NIR and SPA-LDA procedures for the classification of CG and CGC etiological agents are noted for their greater analytical speed, accuracy, simplicity, lower cost and non-destructive nature.

scholarly journals Pattern Classification of Decomposed Wavelet Information using ART2 Networks for echoes Analysis

2008 ◽  
Vol 6 (01) ◽  
Author(s):  
M. Solí­s ◽  
H. Bení­tez-Pérez ◽  
E. Rubio ◽  
L. Medina-Gómez ◽  
E. Moreno ◽  
...  
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Ultrasonic Pulse ◽  
Destructive Testing ◽  
Resonance Theory ◽  
Adaptive Resonance ◽  
Network Training ◽  
The Neural Network ◽  
Detection And Localization ◽  
Non Destructive

The Ultrasonic Pulse-Echo technique has been successfully used in a non-destructive testing of materials. To perform Ultrasonic Non-destructive Evaluation (NDE), an ultrasonic pulsed wave is transmitted into the materials using a transmitting/receiving transducer or arrays of transducers,that produces an image of ultrasonic reflectivity. The information inherent in ultrasonic signals or image are the echoes coming from flaws, grains, and boundaries of the tested material. The main goal of this evaluation is to determine the existence of defect, its size and its position; for that matter, an innovative methodology is proposed based on pattern recognition and wavelet analysis for flaws detection and localization. The pattern recognition technique used in this work is the neural network named ART2 (Adaptive Resonance Theory) trained by the information given by the time-scale information of the signals via the wavelet transform. A thorough analysis between the neural network training and the type wavelets used for the training has been developed, showing that the Symlet 6 wavelet is the optimum for our problem.

Sign in / Sign up

Export Citation Format

Share Document