Adaptive computing technology: an enabling processing technology for advanced sensor systems

2002 ◽  
Author(s):  
J.E. Hilger
Keyword(s):  
Processing Technology ◽  
Adaptive Computing ◽  
Sensor Systems ◽  
Computing Technology

DEFACTO: A design environment for adaptive computing technology

1999 ◽  
pp. 570-578 ◽  
Author(s):  
Kiran Bondalapati ◽  
Pedro Diniz ◽  
Phillip Duncan ◽  
John Granacki ◽  
Mary Hall ◽  
...  
Keyword(s):  
Adaptive Computing ◽  
Computing Technology ◽  
Design Environment

Implementing advanced image processing technology in sensor systems for security and surveillance

2007 ◽  
Author(s):  
Tom Riley ◽  
Mark Bernhardt ◽  
Catherine Cowell ◽  
Duncan Hickman ◽  
Moira Smith
Keyword(s):  
Image Processing ◽  
Processing Technology ◽  
Sensor Systems ◽  
Image Processing Technology

Digital differential hysteresis iimage processing displays what the microscope acquires but the eye can't see

1995 ◽  
Vol 53 ◽  
pp. 642-643
Author(s):  
Klaus-Ruediger Peters
Keyword(s):  
Image Processing ◽  
Visual System ◽  
High Precision ◽  
Image Data ◽  
Processing Technology ◽  
Output Data ◽  
Contrast Resolution ◽  
Pixel Intensity ◽  
Data Reading ◽  
Hysteresis Properties

Differential hysteresis processing is a new image processing technology that provides a tool for the display of image data information at any level of differential contrast resolution. This includes the maximum contrast resolution of the acquisition system which may be 1,000-times higher than that of the visual system (16 bit versus 6 bit). All microscopes acquire high precision contrasts at a level of <0.01-25% of the acquisition range in 16-bit - 8-bit data, but these contrasts are mostly invisible or only partially visible even in conventionally enhanced images. The processing principle of the differential hysteresis tool is based on hysteresis properties of intensity variations within an image.Differential hysteresis image processing moves a cursor of selected intensity range (hysteresis range) along lines through the image data reading each successive pixel intensity. The midpoint of the cursor provides the output data. If the intensity value of the following pixel falls outside of the actual cursor endpoint values, then the cursor follows the data either with its top or with its bottom, but if the pixels' intensity value falls within the cursor range, then the cursor maintains its intensity value.

Heterodyne processing scheme for low coherence interferometric sensor systems

1991 ◽  
Vol 138 (6) ◽  
pp. 393
Author(s):  
B.T. Meggitt ◽  
W.J.O. Boyle ◽  
K.T.V. Grattan ◽  
A.E. Baruch ◽  
A.W. Palmer
Keyword(s):  
Sensor Systems ◽  
Processing Scheme ◽  
Low Coherence ◽  
Interferometric Sensor

MACHINE LEARNING FOR SOFTWARE

2019 ◽  
pp. 251-255
Author(s):  
Shafagat Mahmudova
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Soft Computing ◽  
Computing Technology

The study machine learning for software based on Soft Computing technology. It analyzes Soft Computing components. Their use in software, their advantages and challenges are studied. Machine learning and its features are highlighted. The functions and features of neural networks are clarified, and recommendations were given.

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