Ultimate sensitivity and information capacity of thermal imagers and other electronic-vision systems

1997 ◽  
Author(s):  
Igor I. Taubkin ◽  
Mikhail A. Trishenkov
Keyword(s):  
Information Capacity ◽  
Vision Systems ◽  
Ultimate Sensitivity ◽  
Thermal Imagers

Information capacity of electronic vision systems

1996 ◽  
Vol 37 (6) ◽  
pp. 675-693 ◽  
Author(s):  
Igor I. Taubkin ◽  
Mikhail A. Trishenkov
Keyword(s):  
Information Capacity ◽  
Vision Systems

Information capacity and bandwidth limitations in the SEM

1989 ◽  
Vol 47 ◽  
pp. 84-85
Author(s):  
D. C. Joy ◽  
R. D. Bunn
Keyword(s):  
Signal To Noise Ratio ◽  
Critical Dimension ◽  
Information Capacity ◽  
Acquisition Time ◽  
Signal To Noise ◽  
Sem Image ◽  
Probe Size ◽  
Minimum Number ◽  
Noise Ratio ◽  
Signal Channel

The information available from an SEM image is limited both by the inherent signal to noise ratio that characterizes the image and as a result of the transformations that it may undergo as it is passed through the amplifying circuits of the instrument. In applications such as Critical Dimension Metrology it is necessary to be able to quantify these limitations in order to be able to assess the likely precision of any measurement made with the microscope.The information capacity of an SEM signal, defined as the minimum number of bits needed to encode the output signal, depends on the signal to noise ratio of the image - which in turn depends on the probe size and source brightness and acquisition time per pixel - and on the efficiency of the specimen in producing the signal that is being observed. A detailed analysis of the secondary electron case shows that the information capacity C (bits/pixel) of the SEM signal channel could be written as :

Increasing the information capacity of a fiber-optic multi-sensor converter of binary mechanical signals into electrical signals

2020 ◽  
pp. 15-23
Author(s):  
V. M. Grechishnikov ◽  
E. G. Komarov
Keyword(s):  
Fiber Optic ◽  
Information Capacity ◽  
Parallel Structure ◽  
Optical Scheme ◽  
Digital To Analog Converter ◽  
Electrical Signals ◽  
Mechanical Signals ◽  
Analog Converter ◽  
Output Code ◽  
Digital To Analog

The design and operation principle of a multi-sensor Converter of binary mechanical signals into electrical signals based on a partitioned fiber-optic digital-to-analog Converter with a parallel structure is considered. The digital-to-analog Converter is made from a set of simple and technological (three to five digit) fiber-optic digital-to-analog sections. The advantages of the optical scheme of the proposed. Converter in terms of metrological and energy characteristics in comparison with single multi-bit converters are justified. It is shown that by increasing the number of digital-analog sections, it is possible to repeatedly increase the information capacity of a multi-sensor Converter without tightening the requirements for its manufacturing technology and element base. A mathematical model of the proposed Converter is developed that reflects the features of its operation in the mode of sequential time conversion of the input code vectors of individual fiber-optic sections into electrical analogues and the formation of the resulting output code vector.

The machine training in problems of satellite images’s processing

Metrologiya ◽  
2020 ◽  
pp. 15-37
Author(s):  
L. P. Bass ◽  
Yu. A. Plastinin ◽  
I. Yu. Skryabysheva
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Computer Vision ◽  
Satellite Images ◽  
Vision Systems ◽  
Earth Remote Sensing ◽  
Practical Applications ◽  
Convolution Neural Networks ◽  
Computer Vision Systems ◽  
Trained Neural Network

Use of the technical (computer) vision systems for Earth remote sensing is considered. An overview of software and hardware used in computer vision systems for processing satellite images is submitted. Algorithmic methods of the data processing with use of the trained neural network are described. Examples of the algorithmic processing of satellite images by means of artificial convolution neural networks are given. Ways of accuracy increase of satellite images recognition are defined. Practical applications of convolution neural networks onboard microsatellites for Earth remote sensing are presented.

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