Detection and identification of mines from natural magnetic and electromagnetic resonances

1995 ◽  
Author(s):  
Gary D. Sower ◽  
Steven P. Cave
Keyword(s):  
Detection And Identification ◽  
Electromagnetic Resonances

Identification of hepatitis a virus in cell cultures by solid phase immune electron microscopy

1986 ◽  
Vol 44 ◽  
pp. 238-239
Author(s):  
C.D. Humphrey ◽  
T.L. Cromeans ◽  
E.H. Cook ◽  
D.W. Bradley
Keyword(s):  
Electron Microscopy ◽  
Liquid Phase ◽  
Cell Cultures ◽  
Rapid Detection ◽  
Hepatitis A ◽  
Hepatitis A Virus ◽  
Solid Phase ◽  
Immune Electron Microscopy ◽  
Detection And Identification

There is a variety of methods available for the rapid detection and identification of viruses by electron microscopy as described in several reviews. The predominant techniques are classified as direct electron microscopy (DEM), immune electron microscopy (IEM), liquid phase immune electron microscopy (LPIEM) and solid phase immune electron microscopy (SPIEM). Each technique has inherent strengths and weaknesses. However, in recent years, the most progress for identifying viruses has been realized by the utilization of SPIEM.

115: Detection and Identification of Bacterial DNA and HIV Gag in the Semen of HIV Infected Men

2004 ◽  
Vol 171 (4S) ◽  
pp. 30-30
Author(s):  
Robert C. Eyre ◽  
Ann A. Kiessling ◽  
Thomas E. Mullen ◽  
Rachel L. Kiessling
Keyword(s):  
Bacterial Dna ◽  
Detection And Identification

Modern methods of bacteria detection and identification

2013 ◽  
Vol 4 (1) ◽  
pp. 65-72
Author(s):  
Dorota Żabicka ◽  
Elżbieta Literacka
Keyword(s):  
Bacteria Detection ◽  
Detection And Identification ◽  
Modern Methods

Detection and Identification of Voltage Variation Events Based on Artificial Neural Network

2020 ◽  
Vol 13 (5) ◽  
pp. 224
Author(s):  
Dimas Okky Anggriawan ◽  
Rauf Hanrif Mubarok ◽  
Eka Prasetyono ◽  
Endro Wahjono ◽  
M. Iqbal Fitrianto ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Detection And Identification ◽  
Voltage Variation ◽  
Artificial Neural

Object Detection Using an Ideal Observer Model

2020 ◽  
Vol 2020 (16) ◽  
pp. 41-1-41-7
Author(s):  
Orit Skorka ◽  
Paul J. Kane
Keyword(s):  
Object Detection ◽  
Matched Filter ◽  
Image Sensors ◽  
Ideal Observer ◽  
Noise Power ◽  
Relative Evaluation ◽  
Detection And Identification ◽  
Cross Correlation Analysis ◽  
Observer Model ◽  
The Ideal

Many of the metrics developed for informational imaging are useful in automotive imaging, since many of the tasks – for example, object detection and identification – are similar. This work discusses sensor characterization parameters for the Ideal Observer SNR model, and elaborates on the noise power spectrum. It presents cross-correlation analysis results for matched-filter detection of a tribar pattern in sets of resolution target images that were captured with three image sensors over a range of illumination levels. Lastly, the work compares the crosscorrelation data to predictions made by the Ideal Observer Model and demonstrates good agreement between the two methods on relative evaluation of detection capabilities.

Sign in / Sign up

Export Citation Format

Share Document