Noise, grain

Perception and Entropy Inspired Ultrasonic Grain Noise Suppression, Using Noncoherent Detector Statistics. 

A novel approach for suppression of grain noise in ultrasonic signals, based on noncoherent detector statistics and signal entropy, is presented. The performance of the technique is demonstrated using ultrasonic B-scans from samples with coarse material structure. 

The main goal of ultrasonic grain noise suppression in material flaw detection is to improve the perceptual possibilities of the operator to observe defect echoes. The suppression is defined as perceptually ideal when a received signal (or image) which contains echoes buried in noise is filtered to yield nonzero values only at the positions of the defect echoes. 

Filters that output noncoherent detector statistics have, in our recent work [1], shown to be very powerful for grain noise suppression in ultrasonics. However, such filters require the operator to carefully specify a transient prototype as a model of the defect echoes which should be detected. Here a new approach is presented, based on the above ideas about perception, which eliminates the need for the operator to manually specify a defect prototype. 

The second example shows results obtained with an angle beam probe for transverse waves in coarse grained grey cast iron. Two commercially available probes are compared the composite design SWK 60-2 and the standard design SWB 60-2. The reflector in this example is a side-drilled hole of 5 mm diameter. The A-scans displayed below in Fig. 5 and 6 show that the composite probe has a higher sensitivity by 12 dB and that the signal to noise ratio is improved by more than 6 dB. 

As there is a difference between the measurements and the values of the calculated function, we can safely assume that the fitted parameters are not perfect. They are our best estimates for the true parameters and an obvious question is, how reliable are these fitted parameters Are they tightly or they are loosely defined As long as the assumption of random white noise applies, there are formulas that allow the computation of the standard deviation of the fitted parameters. While these answers should always be taken with a grain of salt, they do give an indication of how well defined the parameters are. 

On development, the grains of the L4 emulsion swell to about 0.25 /un. However, the granularity observed on the topograph when viewed under the microscope is not a result of the grain of the film but is statistical shot noise , arising from the statistical variation in the number of developed grains per unit area. This... 

This technology cannot be located near noise-sensitive areas. The operating system does not work well in temperature extremes, such as below 30 or above 100°F. The technology is ex situ, requiring soil excavation. The technology changes the physical characteristics of fine-grained soils such as clay and topsoil. 

Frieden (1971,1972) attacked this problem by seeking the solution that has the greatest probability of occurrence. The lower-bound condition [d(x) positive] was a natural consequence of the physical random-grain model that he used. This work was unique in that separate solutions were found for o(x) and the noise. In its simplest form, the original method required the data to be all positive. Even though o(x) satisfies this requirement, i(x) often does not, because of noise excursions that, in spectra, may be found in the base-line region. A bias term was added to the data to ensure positivity. 

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