scholarly journals An Innovative Security Screening Architecture for Detecting Illicit Goods and Threats

2021 ◽  
Vol 15 ◽  
pp. 153-160
Author(s):  
Athanasios Skraparlis ◽  
Klimis Ntalianis ◽  
Dimitris Kouremenos ◽  
Nikolaos Mastorakis
Keyword(s):  
Pressure Sensors ◽  
Sensory Information ◽  
Image Data ◽  
Security Screening ◽  
X Ray ◽  
Spectral Signatures ◽  
Knowledge Infrastructure ◽  
Available Information ◽  
Screening Equipment ◽  
Intelligent Screening

Every year, millions of letters/parcels containing illicit goods are detected by customs authorities, which use traditional security screening equipment. However this equipment cannot detect all kinds of illicit goods and the detection procedure heavily depends on the attention of the customs officer. In order to achieve sufficiently fast intelligent screening of the large volumes of letters/parcels and detect all common kinds of threats, this paper proposes a highly innovative architecture well-beyond the state-of–art. In particular the proposed architecture monitors every letter/parcel by incorporating: (a) terahertz/X-ray sensors, (b) chemical, biological, radiological and nuclear (CBNR) sensors, (c) artificial robot-noses for narcotics, explosives etc., (d) magnetometers for weapons, firearms, banknotes etc., (e) acoustic sensors for liquids/gases/solids, (f) weight/pressure sensors to measure weight distribution, size and shape. Sensory information can be: (a) used to create a “Spectral Signatures Dictionary of Illicit Goods and Threats”, (b) fused to segment/isolate illicit goods and (c) visualized in the form of annotated high-resolution tensor-structured (3D/4D) multisensory image data. The proposed solution also gathers available information for the sender/recipient from various resources, while it also analyzes data from the dark web. All information is forwarded to an AI-based knowledge infrastructure.

Eds Of Radioactive Particles By Self-Induced Fluorescence

1990 ◽  
Vol 48 (2) ◽  
pp. 238-239
Author(s):  
Gregory L. Finch ◽  
Richard G. Cuddihy
Keyword(s):  
Electron Beam Irradiation ◽  
Nuclear Reactor ◽  
X Rays ◽  
Reactor Accident ◽  
Internal Radiation ◽  
X Ray ◽  
External Sources ◽  
X Irradiation ◽  
Available Information ◽  
Individual Particles

The elemental composition of individual particles is commonly measured by using energydispersive spectroscopic microanalysis (EDS) of samples excited with electron beam irradiation. Similarly, several investigators have characterized particles by using external monochromatic X-irradiation rather than electrons. However, there is little available information describing measurements of particulate characteristic X rays produced not from external sources of radiation, but rather from internal radiation contained within the particle itself. Here, we describe the low-energy (< 20 KeV) characteristic X-ray spectra produced by internal radiation self-excitation of two general types of particulate samples; individual radioactive particles produced during the Chernobyl nuclear reactor accident and radioactive fused aluminosilicate particles (FAP). In addition, we compare these spectra with those generated by conventional EDS.Approximately thirty radioactive particle samples from the Chernobyl accident were on a sample of wood that was near the reactor when the accident occurred. Individual particles still on the wood were microdissected from the bulk matrix after bulk autoradiography.

scholarly journals COVID-19 diagnosis from chest X-ray images using transfer learning: Enhanced performance by debiasing dataloader

2021 ◽  
Vol 29 (1) ◽  
pp. 19-36
Author(s):  
Çağín Polat ◽  
Onur Karaman ◽  
Ceren Karaman ◽  
Güney Korkmaz ◽  
Mehmet Can Balcı ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Critical Time ◽  
Image Data ◽  
Mapping Method ◽  
Fine Tuning ◽  
Learning Stage ◽  
X Ray ◽  
Testing Dataset ◽  
X Ray Imaging ◽  
Chest X Ray

BACKGROUND: Chest X-ray imaging has been proved as a powerful diagnostic method to detect and diagnose COVID-19 cases due to its easy accessibility, lower cost and rapid imaging time. OBJECTIVE: This study aims to improve efficacy of screening COVID-19 infected patients using chest X-ray images with the help of a developed deep convolutional neural network model (CNN) entitled nCoV-NET. METHODS: To train and to evaluate the performance of the developed model, three datasets were collected from resources of “ChestX-ray14”, “COVID-19 image data collection”, and “Chest X-ray collection from Indiana University,” respectively. Overall, 299 COVID-19 pneumonia cases and 1,522 non-COVID 19 cases are involved in this study. To overcome the probable bias due to the unbalanced cases in two classes of the datasets, ResNet, DenseNet, and VGG architectures were re-trained in the fine-tuning stage of the process to distinguish COVID-19 classes using a transfer learning method. Lastly, the optimized final nCoV-NET model was applied to the testing dataset to verify the performance of the proposed model. RESULTS: Although the performance parameters of all re-trained architectures were determined close to each other, the final nCOV-NET model optimized by using DenseNet-161 architecture in the transfer learning stage exhibits the highest performance for classification of COVID-19 cases with the accuracy of 97.1 %. The Activation Mapping method was used to create activation maps that highlights the crucial areas of the radiograph to improve causality and intelligibility. CONCLUSION: This study demonstrated that the proposed CNN model called nCoV-NET can be utilized for reliably detecting COVID-19 cases using chest X-ray images to accelerate the triaging and save critical time for disease control as well as assisting the radiologist to validate their initial diagnosis.

Structure and activity of phosphoglycerate mutase

1981 ◽  
Vol 293 (1063) ◽  
pp. 121-130 ◽  
Keyword(s):  
Active Site ◽  
Transfer Reaction ◽  
Chemical Kinetic ◽  
Phosphoglycerate Mutase ◽  
Phosphoryl Transfer ◽  
X Ray ◽  
Histidine Residues ◽  
Ping Pong ◽  
Available Information ◽  
Structure And Activity

The structure of yeast phosphoglycerate mutase determined by X-ray crystallographic and amino acid sequence studies has been interpreted in terms of the chemical, kinetic and mechanistic observations made on this enzyme. There are two histidine residues at the active site, with imidazole groups almost parallel to each other and approximately 0.4 nm apart, positioned close to the 2 and 3 positions of the substrate. The simplest interpretation of the available information suggests that a ping-pong type mechanism operates in which at least one of these histidine residues participates in the phosphoryl transfer reaction. The flexible C-terminal region also plays an important role in the enzymic reaction.

Chest Imaging with Dual-Energy Subtraction Digital Tomosynthesis

1993 ◽  
Vol 34 (4) ◽  
pp. 346-350 ◽  
Author(s):  
S. Sone ◽  
T. Kasuga ◽  
F. Sakai ◽  
H. Hirano ◽  
K. Kubo ◽  
...  
Keyword(s):  
Low Voltage ◽  
Image Data ◽  
Dual Energy ◽  
Processing Unit ◽  
Digital Tomosynthesis ◽  
Chest Imaging ◽  
Layer Height ◽  
Pulmonary Lesions ◽  
X Ray ◽  
Energy Subtraction

Dual-energy subtraction digital tomosynthesis with pulsed X-ray and rapid kV switching was used to examine calcifications in pulmonary lesions. The digital tomosynthesis system used included a conventional fluororadiographic TV unit with linear tomographic capabilities, a high resolution videocamera, and an image processing unit. Low-voltage, high-voltage, and soft tissue subtracted or bone subtracted tomograms of any desired layer height were reconstructed from the image data acquired during a single tomographic swing. Calcifications, as well as their characteristics and distribution in pulmonary lesions, were clearly shown. The images also permitted discrimination of calcifications from dense fibrotic lesions. This technique was effective in demonstrating calcifications together with a solitary mass or disseminated nodules.

Development of the X-ray Security Screening Systems at ADANI

2019 ◽  
pp. 249-259
Author(s):  
D. A. Bairashewski ◽  
G. Yu. Drobychev ◽  
V. A. Karas ◽  
V. V. Komarov ◽  
M. V. Protsko
Keyword(s):  
Security Screening ◽  
X Ray ◽  
Screening Systems

scholarly journals Structural Modeling of Glutathiones Containing Selenium and Tellurium

2016 ◽  
Vol 8 (1) ◽  
pp. 102
Author(s):  
Valter A. Nascimento ◽  
Petr Melnikov ◽  
André V. D. Lanoa ◽  
Anderson F. Silva ◽  
Lourdes Z. Z. Consolo
Keyword(s):  
Molecular Mechanics ◽  
Structural Characteristics ◽  
Structural Modeling ◽  
Structural Description ◽  
Ionic Radii ◽  
X Ray ◽  
Bond Angles ◽  
Bond Lengths ◽  
Available Information ◽  
Related Compounds

<p>The comparative structural modeling of reduced and oxidized glutathiones, as well as their derivatives containing selenium and tellurium in chalcogen sites (Ch = Se, Te) has provided detailed information about the bond lengths and bond angles, filling the gap in the structural characteristics of these tri-peptides. The investigation using the molecular mechanics technique with good approximation confirmed the available information on X-ray refinements for the related compounds. It was shown that Ch-H and Ch-C bond lengths grow in parallel with the increasing chalcogen ionic radii. Although the distances C-C, C-O, and C-N are very similar, the geometry of GChChG glutathiones is rich in conformers owing to the possibility of rotation about the bridge Ch-Ch. It is confirmed that the distances Ch-Ch are essentially independent of substituents in most of chalcogen compounds from elemental chalcogens to oxydized glutathiones. The standard program Hyperchem 7.5 has proved to be an appropriate tool for the structural description of less-common bioactive compositions when direct X-ray data are missing.</p>

Automated Analysis of SEM X-Ray Spectral Images: A Powerful New Microanalysis Tool

2003 ◽  
Vol 9 (1) ◽  
pp. 1-17 ◽  
Author(s):  
Paul G. Kotula ◽  
Michael R. Keenan ◽  
Joseph R. Michael
Keyword(s):  
Image Data ◽  
Large Data ◽  
Automated Analysis ◽  
Phase Identification ◽  
Data Sets ◽  
Multivariate Statistical ◽  
X Ray ◽  
Analysis Technique ◽  
Statistical Analysis Technique ◽  
Multi Phase

Spectral imaging in the scanning electron microscope (SEM) equipped with an energy-dispersive X-ray (EDX) analyzer has the potential to be a powerful tool for chemical phase identification, but the large data sets have, in the past, proved too large to efficiently analyze. In the present work, we describe the application of a new automated, unbiased, multivariate statistical analysis technique to very large X-ray spectral image data sets. The method, based in part on principal components analysis, returns physically accurate (all positive) component spectra and images in a few minutes on a standard personal computer. The efficacy of the technique for microanalysis is illustrated by the analysis of complex multi-phase materials, particulates, a diffusion couple, and a single-pixel-detection problem.

scholarly journals Background X-Ray Emission from Hot Gas in CDM and CDM+ Lambda Universes: Spectral Signatures

1995 ◽  
Vol 451 ◽  
pp. 436 ◽  
Author(s):  
Renyue Cen ◽  
Hyesung Kang ◽  
Jeremiah P. Ostriker ◽  
Dongsu Ryu
Keyword(s):  
X Ray ◽  
Spectral Signatures ◽  
Hot Gas
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