Optical detection of alpha emitting radionuclides in the environment

2021 ◽  
Author(s):  
Faton Krasniqi
Keyword(s):  
Short Range ◽  
Large Scale ◽  
Detection System ◽  
Optical Detection ◽  
Alpha Particles ◽  
Remote Detection ◽  
Incident Management ◽  
Detection Approach ◽  
Significant Damage

<p>Radiological emergencies involving accidental or deliberate dispersion of alpha emitting radionuclides in the environment can cause significant damage to humans and societies in general. A detection system to measure large-scale contamination of these radionuclides is currently not available. In case of a contamination, the only option is to evacuate the population from the affected areas and then run diagnostics by hand due to the short range of alpha particles in air, exposing thus the emergency teams to considerable risk. Even then, the results of emergency field applications are notoriously ambiguous, time consuming and tedious due to the centimetre range of the alpha particles in air. A novel detection approach which is capable of remote detection of alpha-emitting radionuclides in the environment will be reported. This approach will assist the on-site incident management and will enable detection of contamination threats without contact—from safe distances—avoiding thus contamination of operators and equipment.</p>

scholarly journals An Efficient and Packing-Resilient Two-Phase Android Cloned Application Detection Approach

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Fang Lyu ◽  
Yaping Lin ◽  
Junfeng Yang
Keyword(s):  
Real World ◽  
Large Scale ◽  
Detection System ◽  
Malicious Code ◽  
Security And Privacy ◽  
Two Phase ◽  
Android Malware ◽  
Detection Approach ◽  
Application Industry ◽  
Selection Phase

The huge benefit of mobile application industry has attracted a large number of developers and attendant attackers. Application repackaging provides help for the distribution of most Android malware. It is a serious threat to the entire Android ecosystem, as it not only compromises the security and privacy of the app users but also plunders app developers’ income. Although massive approaches have been proposed to address this issue, plagiarists try to fight back through packing their malicious code with the help of commercial packers. Previous works either do not consider the packing issue or rely on time-consuming computations, which are not scalable for large-scale real-world scenario. In this paper, we propose FUIDroid, a novel two-phase app clones detection system that can detect the packed cloned app. FUIDroid includes a function-based fast selection phase to quickly select suspicious apps by analyzing apps’ description and a further UI-based accurate detection phase to refine the detection result. We evaluate our system on two sets of apps. The result from experiment on 320 packed samples demonstrates that FUIDroid is resilient to packed apps. The evaluation on more than 150,000 real-world apps shows the efficiency of FUIDroid in large-scale scenario.

Short-range biological standoff detection system (SR-BSDS)

2000 ◽  
Author(s):  
V. James Cannaliato ◽  
Bruce W. Jezek ◽  
Larry Hyttinen ◽  
John B. Strawbridge ◽  
William J. Ginley
Keyword(s):  
Short Range ◽  
Detection System ◽  
Standoff Detection

Erratum: Short Range Alpha-Particles from Fluorine and Lithium Bombarded by Protons

1950 ◽  
Vol 77 (2) ◽  
pp. 287-287 ◽  
Author(s):  
W. E. Burcham ◽  
Joan M. Freeman
Keyword(s):  
Short Range ◽  
Alpha Particles

scholarly journals An Industrial Micro-Defect Diagnosis System via Intelligent Segmentation Region

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2636 ◽  
Author(s):  
Xia Fang ◽  
Wang Jie ◽  
Tao Feng
Keyword(s):  
Neural Network ◽  
Defect Detection ◽  
Large Scale ◽  
Detection System ◽  
Scale Production ◽  
System Matrix ◽  
Rotating Platform ◽  
Limited Region ◽  
The Neural Network ◽  
Large Scale Production

In the field of machine vision defect detection for a micro workpiece, it is very important to make the neural network realize the integrity of the mask in analyte segmentation regions. In the process of the recognition of small workpieces, fatal defects are always contained in borderline areas that are difficult to demarcate. The non-maximum suppression (NMS) of intersection over union (IOU) will lose crucial texture information especially in the clutter and occlusion detection areas. In this paper, simple linear iterative clustering (SLIC) is used to augment the mask as well as calibrate the score of the mask. We propose an SLIC head of object instance segmentation in proposal regions (Mask R-CNN) containing a network block to learn the quality of the predict masks. It is found that parallel K-means in the limited region mechanism in the SLIC head improved the confidence of the mask score, in the context of our workpiece. A continuous fine-tune mechanism was utilized to continuously improve the model robustness in a large-scale production line. We established a detection system, which included an optical fiber locator, telecentric lens system, matrix stereoscopic light, a rotating platform, and a neural network with an SLIC head. The accuracy of defect detection is effectively improved for micro workpieces with clutter and borderline areas.

scholarly journals Supercomputer simulations of structure formation in the Universe

2016 ◽  
Vol 12 (S325) ◽  
pp. 10-16
Author(s):  
Tomoaki Ishiyama
Keyword(s):  
Domain Decomposition ◽  
Structure Formation ◽  
Short Range ◽  
Large Scale ◽  
Massively Parallel ◽  
Average Performance ◽  
And Performance ◽  
Performance Results ◽  
The Universe ◽  
Peak Speed

AbstractWe describe the implementation and performance results of our massively parallel MPI†/OpenMP‡ hybrid TreePM code for large-scale cosmological N-body simulations. For domain decomposition, a recursive multi-section algorithm is used and the size of domains are automatically set so that the total calculation time is the same for all processes. We developed a highly-tuned gravity kernel for short-range forces, and a novel communication algorithm for long-range forces. For two trillion particles benchmark simulation, the average performance on the fullsystem of K computer (82,944 nodes, the total number of core is 663,552) is 5.8 Pflops, which corresponds to 55% of the peak speed.

scholarly journals ECE laboratory in the Vinca institute: Its basic characteristics and fundamentals of electrochemic etching on polycarbonate

2003 ◽  
Vol 18 (2) ◽  
pp. 57-60 ◽  
Author(s):  
Zora Zunic ◽  
Predrag Ujic ◽  
Igor Celikovic ◽  
Kenzo Fujimoto
Keyword(s):  
Large Scale ◽  
Electrochemical Etching ◽  
Initial Step ◽  
Field Application ◽  
Alpha Particles ◽  
Experimental Phase ◽  
Laboratory Equipment ◽  
Etch Pit ◽  
Large Scale Survey ◽  
Basic Characteristics

This paper deals with the introductory aspects of the Electrochemical Etching Laboratory installed at the VINCA Institute in the year 2003. The main purpose of the laboratory is its field application for radon and thoron large-scale survey using passive radon/thoron UFO type detectors. Since the etching techniques together with the laboratory equipment were transferred from the National Institute of Radiological Sciences, Chiba, Japan, it was necessary for both etching conditions to be confirmed and to be checked up^ i. e., bulk etching speeds of chemical etching and electrochemical etching in the VINCA Electrochemical Etching Laboratory itself. Beside this initial step, other concerns were taken into consideration in this preliminary experimental phase such as the following: the measurable energy range of the polycarbonate film, background etch pit density of the film and its standard deviation and reproducibility of the response to alpha particles for different sets of etchings.

scholarly journals Applying Machine Learning for Improving Performance Classification on Driving Behavior

2021 ◽  
Vol 4 (1) ◽  
pp. 8
Author(s):  
Ahmad Iwan Fadli ◽  
Selo Sulistyo ◽  
Sigit Wibowo
Keyword(s):  
Machine Learning ◽  
Traffic Accident ◽  
Large Scale ◽  
Detection System ◽  
Difficult Problem ◽  
Sensor Data ◽  
Driving Safety ◽  
Support Vector ◽  
Classification Methods ◽  
Machine Learning Classification

Traffic accident is a very difficult problem to handle on a large scale in a country. Indonesia is one of the most populated, developing countries that use vehicles for daily activities as its main transportation.  It is also the country with the largest number of car users in Southeast Asia, so driving safety needs to be considered. Using machine learning classification method to determine whether a driver is driving safely or not can help reduce the risk of driving accidents. We created a detection system to classify whether the driver is driving safely or unsafely using trip sensor data, which include Gyroscope, Acceleration, and GPS. The classification methods used in this study are Random Forest (RF) classification algorithm, Support Vector Machine (SVM), and Multilayer Perceptron (MLP) by improving data preprocessing using feature extraction and oversampling methods. This study shows that RF has the best performance with 98% accuracy, 98% precision, and 97% sensitivity using the proposed preprocessing stages compared to SVM or MLP.

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