Results of a UV TEPS-Raman energetic detection system (TREDS-2) for standoff detection

2009 ◽  
Author(s):  
Robert D. Waterbury ◽  
Alan R. Ford ◽  
Jeremy B. Rose ◽  
Edwin L. Dottery
Keyword(s):  
Detection System ◽  
Standoff Detection

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

scholarly journals Multispectral LIF-Based Standoff Detection System for the Classification of CBE Hazards by Spectral and Temporal Features

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2524 ◽  
Author(s):  
Lea Fellner ◽  
Marian Kraus ◽  
Florian Gebert ◽  
Arne Walter ◽  
Frank Duschek
Keyword(s):  
Spectral Data ◽  
Classification Accuracy ◽  
Detection System ◽  
Laser Source ◽  
Nanosecond Laser ◽  
Time Resolved Fluorescence ◽  
Data Set ◽  
Time Resolved ◽  
Standoff Detection

Laser-induced fluorescence (LIF) is a well-established technique for monitoring chemical processes and for the standoff detection of biological substances because of its simple technical implementation and high sensitivity. Frequently, standoff LIF spectra from large molecules and bio-agents are only slightly structured and a gain of deeper information, such as classification, let alone identification, might become challenging. Improving the LIF technology by recording spectral and additionally time-resolved fluorescence emission, a significant gain of information can be achieved. This work presents results from a LIF based detection system and an analysis of the influence of time-resolved data on the classification accuracy. A multi-wavelength sub-nanosecond laser source is used to acquire spectral and time-resolved data from a standoff distance of 3.5 m. The data set contains data from seven different bacterial species and six types of oil. Classification is performed with a decision tree algorithm separately for spectral data, time-resolved data and the combination of both. The first findings show a valuable contribution of time-resolved fluorescence data to the classification of the investigated chemical and biological agents to their species level. Temporal and spectral data have been proven as partly complementary. The classification accuracy is increased from 86% for spectral data only to more than 92%.

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

1999 ◽  
Author(s):  
William Suliga ◽  
Ralph L. Burnham ◽  
Timothy Deely ◽  
William Gavert ◽  
Mark S. Pronko ◽  
...  
Keyword(s):  
Short Range ◽  
Detection System ◽  
Standoff Detection

FT-IR Standoff Detection of Thermally Excited Emissions of Trinitrotoluene (TNT) Deposited on Aluminum Substrates

2013 ◽  
Vol 67 (2) ◽  
pp. 181-186 ◽  
Author(s):  
John R. Castro-Suarez ◽  
Leonardo C. Pacheco-Londoño ◽  
Miguel Vélez-Reyes ◽  
Max Diem ◽  
Thomas J. Tague ◽  
...  
Keyword(s):  
Energetic Materials ◽  
Solid Phase ◽  
Detection System ◽  
Target Surface ◽  
Least Squares Regression ◽  
Passive Mode ◽  
Standoff Detection ◽  
Highly Energetic Materials ◽  
Aluminum Plates ◽  
Aluminum Substrates

A standoff detection system was assembled by coupling a reflecting telescope to a Fourier transform infrared spectrometer equipped with a cryo-cooled mercury cadmium telluride detector and used for detection of solid-phase samples deposited on substrates. Samples of highly energetic materials were deposited on aluminum substrates and detected at several collector-target distances by performing passive-mode, remote, infrared detection measurements on the heated analytes. Aluminum plates were used as support material, and 2,4,6-Trinitrotoluene (TNT) was used as the target. For standoff detection experiments, the samples were placed at different distances (4 to 55 m). Several target surface temperatures were investigated. Partial least squares regression analysis was applied to the analysis of the intensities of the spectra obtained. Overall, standoff detection in passive mode was useful for quantifying TNT deposited on the aluminum plates with high confidence up to target–collector distances of 55 m.

U.S. Army Soldier and Biological Chemical Command counterproliferation long-range biological standoff detection system (CP LR-BSDS)

1999 ◽  
Author(s):  
Lawrence A. Condatore, Jr. ◽  
Richard B. Guthrie ◽  
Bruce J. Bradshaw ◽  
Kenyon E. Logan ◽  
Larry S. Lingvay ◽  
...  
Keyword(s):  
Long Range ◽  
Detection System ◽  
Standoff Detection

A Bioaerosol Detection IRLidar

2014 ◽  
Vol 941-944 ◽  
pp. 1178-1183
Author(s):  
Hui Yang ◽  
Xue Song Zhao ◽  
Jun Jun Zong
Keyword(s):  
Extinction Coefficient ◽  
Detection System ◽  
Extinction Coefficients ◽  
Standoff Detection ◽  
Time Space ◽  
Slant Path ◽  
Bioaerosol Detection ◽  
Overlap Coefficient

Lidar is the main standoff detection system against bioagent/bioaerosol, An infrared lidar has been developed for bioagent/bioaerosol monitoring. The overall architecture and specifications of the lidar were described. The results of overlap coefficient, horizontal and slant measurements at Hefei, such as comparison of observed horizontal extinction coefficients between commercial MPL Lidar and the IRLidar, the slant path time-space extinction coefficient distributions have been revealed and discussed. The results show that the IRLidar is a reliable lidar with advanced performances.

Analysis of electron-diffraction intensity profiles using a photodiode-array detection system

1983 ◽  
Vol 41 ◽  
pp. 322-323
Author(s):  
J. B. Warren
Keyword(s):  
Electron Diffraction ◽  
Diffraction Pattern ◽  
Detection System ◽  
High Energy ◽  
Photographic Emulsion ◽  
Diffraction Intensity ◽  
Silicon Photodiode ◽  
Photodiode Array Detection ◽  
Analog To Digital ◽  
Photodiode Array

Electron diffraction intensity profiles have been used extensively in studies of polycrystalline and amorphous thin films. In previous work, diffraction intensity profiles were quantitized either by mechanically scanning the photographic emulsion with a densitometer or by using deflection coils to scan the diffraction pattern over a stationary detector. Such methods tend to be slow, and the intensities must still be converted from analog to digital form for quantitative analysis. The Instrumentation Division at Brookhaven has designed and constructed a electron diffractometer, based on a silicon photodiode array, that overcomes these disadvantages. The instrument is compact (Fig. 1), can be used with any unmodified electron microscope, and acquires the data in a form immediately accessible by microcomputer.Major components include a RETICON 1024 element photodiode array for the de tector, an Analog Devices MAS-1202 analog digital converter and a Digital Equipment LSI 11/2 microcomputer. The photodiode array cannot detect high energy electrons without damage so an f/1.4 lens is used to focus the phosphor screen image of the diffraction pattern on to the photodiode array.

Data Acquisition and Handling Unit for a Quantitative Use of Electron Energy Loss Spectroscopy

1977 ◽  
Vol 35 ◽  
pp. 232-233 ◽  
Author(s):  
P. Trebbia ◽  
P. Ballongue ◽  
C. Colliex
Keyword(s):  
Electron Microscope ◽  
Energy Loss ◽  
Electron Energy ◽  
Electron Energy Loss Spectroscopy ◽  
Single Channel ◽  
Detection System ◽  
Surface Barrier ◽  
Solid State Detector ◽  
Electrostatic Mirror ◽  
Electron Energy Loss

An effective use of electron energy loss spectroscopy for chemical characterization of selected areas in the electron microscope can only be achieved with the development of quantitative measurements capabilities.The experimental assembly, which is sketched in Fig.l, has therefore been carried out. It comprises four main elements.The analytical transmission electron microscope is a conventional microscope fitted with a Castaing and Henry dispersive unit (magnetic prism and electrostatic mirror). Recent modifications include the improvement of the vacuum in the specimen chamber (below 10-6 torr) and the adaptation of a new electrostatic mirror.The detection system, similar to the one described by Hermann et al (1), is located in a separate chamber below the fluorescent screen which visualizes the energy loss spectrum. Variable apertures select the electrons, which have lost an energy AE within an energy window smaller than 1 eV, in front of a surface barrier solid state detector RTC BPY 52 100 S.Q. The saw tooth signal delivered by a charge sensitive preamplifier (decay time of 5.10-5 S) is amplified, shaped into a gaussian profile through an active filter and counted by a single channel analyser.

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