An auxiliary intelligent identification system for contraband of x-ray machine

2020 ◽  
Author(s):  
Demao Ye ◽  
Zhiyuan Li ◽  
Yuhui Wang ◽  
Shiheng Yan
Keyword(s):  
Identification System ◽  
X Ray

Qualitative phase analysis system for crystalline mixtures based on X-ray powder diffraction file

2004 ◽  
Vol 19 (4) ◽  
pp. 340-346
Author(s):  
YuanYuan Qiao ◽  
YunFei Xi ◽  
DongTao Zhuo ◽  
Ji Jun Wang ◽  
ShaoFan Lin
Keyword(s):  
Phase Analysis ◽  
Powder Diffraction ◽  
Identification System ◽  
Phase Identification ◽  
Powder Diffraction File ◽  
X Ray ◽  
Analysis System ◽  
Qualitative Phase ◽  
Full Peak

A qualitative phase identification system for crystalline mixtures is presented. The system provides up to five-phase qualitative identification using up to nine-peak filtration, and additive full peak matching based on the powder diffraction file of ICDD. It was implemented using Microsoft Visual C++, and runs under most common Windows systems. Screenshots and examples are included.

Newly Automated EPMA with Phase Identification System

2001 ◽  
Vol 7 (S2) ◽  
pp. 980-981
Author(s):  
S. Notoya ◽  
H. Takahashi ◽  
T. Okumura ◽  
C.H. Nielsen
Keyword(s):  
High Resolution ◽  
Optical Microscope ◽  
Image Display ◽  
Identification System ◽  
Phase Identification ◽  
Analysis Software ◽  
X Ray ◽  
Electron Probe Microanalyzer ◽  
Mouse Pointer ◽  
Live Image

We have developed a new Electron Probe Microanalyzer (EPMA), JXA-8100/8200, with improved basic capabilities such as X-ray intensities of wavelength dispersive spectrometers (WDS), imaging functions, automated functions and analysis software. Fig. 1 shows the appearance of JXA-8200, WD/ED combined microanalyzer. in this session, we report mainly on the improved imaging functions, automated functions and analysis software.The JXA-8100/8200 is the first EPMA in the world to feature 1280 x 1024 pixels high resolution live scanning image display. Regarding scanning image, two or four different signal live images, of course including X-ray images, can be displayed simultaneously. Further, image mixing is also possible to display. On the high resolution image, an operator can choose the probe position or the stage position by mouse clicking. The stage position can also be chosen on the optical microscope (OM) live image. Another new “Swing Mouse” function, which is the seamless movement of mouse pointer between the scanning image display and the computer display, has been developed.Advanced automated functions, such as autofocus, auto astigmatism and auto contrast brightness, are effective to optimize the scanning image.

Automating Human Identification Using Dental X-Ray Radiographs

2011 ◽  
pp. 280-314
Author(s):  
Omaima Nomir ◽  
Mohamed Abdel Mottaleb
Keyword(s):  
Field Energy ◽  
Identification System ◽  
Fourier Descriptors ◽  
X Ray ◽  
Advantages And Disadvantages ◽  
The Third ◽  
Salient Points ◽  
Chamfer Distance ◽  
Overall Performance ◽  
Matching Techniques

The goal of forensic dentistry is to identify individuals based on their dental characteristics. This chapter presents a system for automating that process by identifying people from dental X-ray images. Given a dental image of a postmortem (PM), the proposed system retrieves the best matches from an antemortem (AM) database. The system automatically segments dental X-ray images into individual teeth and extracts representative feature vectors for each tooth, which are later used for retrieval. This chapter details a new method for teeth segmentation, and three different methods for representing and matching teeth. Each method has a different technique for representing the tooth shape and has its advantages and disadvantages compared with the other methods. The first method represents each tooth contour by signature vectors obtained at salient points on the contour of the tooth. The second method uses Hierarchical Chamfer distance for matching AM and PM teeth. In the third method, each tooth is described using a feature vector extracted using the force field energy function and Fourier descriptors. During retrieval, according to a matching distance between the AM and PM teeth, AM radiographs that are most similar to a given PM image, are found and presented to the user. To increase the accuracy of the identification process, the three matching techniques are fused together. The fusion of information is an integral part of any identification system to improve the overall performance. This chapter introduces some scenarios for fusing the three matchers at the score level as well as at the fusion level.

Research Based on Optical Non-Destructive Testing of Pigment Identification

2016 ◽  
Vol 16 (4) ◽  
pp. 3583-3586 ◽  
Author(s):  
Jigang Wang ◽  
Shengcai Hao ◽  
Wenhua Zhou ◽  
Xiaokun Qi ◽  
Jilong Shi
Keyword(s):  
Three Dimensional ◽  
Potential Method ◽  
Identification System ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
X Ray ◽  
Ultramarine Blue ◽  
Bright Yellow ◽  
Cultural Relic ◽  
Non Destructive

Optical Non-Destructive Testing (ONDT) can be applied as penetrating elemental and structure analysis technology in the pigments identification field. Three-dimensional video microscopy, Raman microscopy and energy dispersive X-ray fluorescence spectroscopy are employed to measure the materials based on a Qing Dynasty meticulous painting. The results revealed that the dark yellow area within the decorative patterns was presented due to the interaction of Emerald green and hematite, and the bright yellow edge area was delineated by Cu–Zn–Pb composition. The interesting thing is that an artificial synthetic ultramarine blue was checked in the painting. According to the first synthesized time of ultramarine blue and Paris green, the time limit of the painting completion can be identified. The principle of Pigment subtractive colorant and nitikaset method were employed to interpreting the results. Optical testing combined with the area of cultural relic identification can be a potential method to build an expert identification system successfully. This work also help lay the optical method groundwork for further cultural relic identification, sterilization, and preservation.

Predict pneumonia with chest X-ray images based on convolutional deep neural learning networks

2020 ◽  
Vol 39 (3) ◽  
pp. 2893-2907 ◽  
Author(s):  
Huaiguang Wu ◽  
Pengjie Xie ◽  
Huiyi Zhang ◽  
Daiyi Li ◽  
Ming Cheng
Keyword(s):  
Median Filter ◽  
Recognition Rate ◽  
Identification System ◽  
X Rays ◽  
Learning Networks ◽  
Low Contrast ◽  
X Ray ◽  
Detection Model ◽  
Chest X Ray ◽  
Fitting In

The chest X-ray examination is one of the most important methods for screening and diagnosing of many lung diseases. Diagnosis of pneumonia by chest X-ray is one of the common methods used by medical experts. However, the image quality of chest X-Ray has some defects, such as low contrast, overlapping organs and blurred boundary, which seriously affects detecting pneumonia in chest X-rays. Therefore, it has important medical value and application significance to construct a stable and accurate automatic detection model of pneumonia through a large number of chest X-ray images. In this paper, we propose a novel hybrid system for detecting pneumonia from chest X-Ray image: ACNN-RF, which is an adaptive median filter Convolutional Neural Network (CNN) recognition model based on Random forest (RF). Firstly, the improved adaptive median filtering is employed to remove noise in the chest X-ray image, which makes the image more easily recognized. Secondly, we establish the CNN architecture based on Dropout to extract deep activation features from each chest X-ray image. Finally, we employ the RF classifier based on GridSearchCV class as a classifier for deep activation features in CNN model. It not only avoids the phenomenon of over-fitting in data training, but also improves the accuracy of image classification. During our experiment, the public chest X-ray image dataset used in the experiment contains 5863 images, which comprises 4265 frontal-view X-ray images of 1574 unique patients. The average recognition rate of pneumonia is up to 97% by the proposed ACNN-RF. The experimental results show that the ACNN-RF identification system is more effective than the previous traditional image identification system.

Searching and Matching of X-Ray Powder Diffraction Patterns Using a Programmable Calculator

1986 ◽  
Vol 1 (3) ◽  
pp. 235-239
Author(s):  
Milan Škrobian ◽  
Tomas Havlik ◽  
Milan Havlik
Keyword(s):  
Phase Analysis ◽  
Powder Diffraction ◽  
Identification System ◽  
Published Data ◽  
Standard Pattern ◽  
Programmable Calculator ◽  
X Ray ◽  
Mineral Sample ◽  
Tape Cassette ◽  
Diffraction Patterns

AbstractA simple, practical search/match program, RIFRAN 85, has been written and implemented for the EMG 666B programmable calculator. The computer programs are written in EMG Assembler, which is identical to the assembler language for the Hewlett-Packard 9821 calculator. The EMG 666B is made in Hungary and has 8 kbytes of operational memory. The programs interactively provide qualitative phase analysis of X-ray powder diffraction patterns using standard files collected from published data and stored on a compact magnetic tape cassette. Each standard pattern can comprise up to 35 two-theta — intensity pairs. The identification procedure is based on the comparison of the diffraction data of the standard and of the unknown within limits imposed by user-established match and chemical criteria. This paper describes the algorithm used and the performance of the RIFRAN 85 identification system. The system's operation is illustrated using an example of phase analysis of a mineral sample.

Misidentification of Major Constituents by Automatic Qualitative Energy Dispersive X-ray Microanalysis: A Problem that Threatens the Credibility of the Analytical Community

2005 ◽  
Vol 11 (6) ◽  
pp. 545-561 ◽  
Author(s):  
Dale E. Newbury*
Keyword(s):  
Incident Beam ◽  
Electron Excitation ◽  
Niobium Alloy ◽  
Energy Dispersive ◽  
Identification System ◽  
Chromium Alloy ◽  
X Rays ◽  
X Ray ◽  
Analytical Technique ◽  
Peak Identification

Automatic qualitative analysis for peak identification is a standard feature of virtually all modern computer-aided analysis software for energy dispersive X-ray spectrometry with electron excitation. Testing of recently installed systems from four different manufacturers has revealed the occasional occurrence of misidentification of peaks of major constituents whose concentrations exceeded 0.1 mass fraction (10 wt%). Test materials where peak identification failures were observed included ZnS, KBr, FeS2, tantalum-niobium alloy, NIST Standard Reference Material 482 (copper–gold alloy), Bi2Te3, uranium–rhodium alloys, platinum–chromium alloy, GaAs, and GaP. These misidentifications of major constituents were exacerbated when the incident beam energy was 10 keV or lower, which restricted or excluded the excitation of the high photon energy K- and L-shell X-rays where multiple peaks, for example, Kα (K-L2,3)–Kβ (K-M2,3); Lα (L3-M4,5)–Lβ (L2-M4)–Lγ (L2-N4), are well resolved and amenable to identification with high confidence. These misidentifications are so severe as to properly qualify as blunders that present a serious challenge to the credibility of this critical analytical technique. Systematic testing of a peak identification system with a suite of diverse materials can reveal the specific elements and X-ray peaks where failures are likely to occur.

Computerized Multi Phase X-Ray Powder-Diffraction Identification System

1967 ◽  
Vol 11 ◽  
pp. 376-384 ◽  
Author(s):  
G. G. Johnson ◽  
V. Vand
Keyword(s):  
Powder Diffraction ◽  
Relative Concentration ◽  
Identification System ◽  
Chemical Information ◽  
Computer Search ◽  
Search System ◽  
Powder Diffraction File ◽  
X Ray ◽  
Materials Research ◽  
Multi Phase

AbstractA computer search system utilising the Powder Diffraction File compiled by the Joint Committee on Powder Diffraction Standards, originally developed for an IBM 7074 in FORTRAN II and reported at the Pittsburgh (1966) conference, has beer. revised and extended to run on IBM 360/50. The system is now written in FORTRAN IV. This search system, which uses all the lines of the reference patterns, has successfully identified up to six standard reference patterns from a multiphase unknown X-ray diffraction pattern in less than 2 min running time. No chemical information is necessary for the system to run. In the revised program, the chemical composition of the patterns is now available from the magnetic tapes in immediate conjunction with the printout of the “most likely” components of the mixture. However, this chemical information is not used by the program itself in the search procedure since, if the unknown pattern is absent from the file, it is helpful to know those compounds which are isostructural with the unknown pattern. With the immediate use of chemical information, these patterns would be eliminated. An estimation of the relative concentration of each of the components, based on absolute intensities, is also calculated by the program. This identification system has been run on experimental data both of the Guinier type and of a less reliable type, with the present Powder Diffraction File on the search tape. Although the number of false matches was increased with the poorer quality of input data, the programs yielded excellent results for both single- and multiple-phase patterns even with poor data and the absence of any chemical information. A series of results from the Materials Research Laboratory of The Pennsylvania State University, illustrating the system in operation with increasingly difficult mixtures, will be given. With such a system in operation at such a small cost, the diffractionist can concentrate on the results and meaning of the identification rather than on the method of identification itself.

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