Signal to noise

Cosmic rays passing through the photosensitive region can produce thousands of photoelectrons. This effect results in a very strong sharp signal in the Raman spectrum. Quantification of spike noise is difficult due to the random nature of its occurrence. However, it is usually quite obvious to the observer when a spectrum of biological tissue contains a contribution from spike noise. These spikes can be erased from the spectrum or the whole spectrum can be discarded. They can also be circumvented by averaging several scans. 

The effect of ambient lighting, another source of fixed pattern noise, on tissue Raman spectra should also be considered. 

Source noise is caused by fluctuations in the irradiance of the incident light, which inherently causes fluctuations in the Raman scattering. Simultaneous 

An often neglected source of fixed pattern noise is that caused by instrument alignment and calibration errors. Unwanted information about the performance of the Raman instrument is added to the Raman spectrum. Calibration drift errors should therefore also be considered. 

Cardona, X. Zhou and T. Strach, in Proc. 10th Anniv. HTS Workshop Phys. Mater AppL, ed. B. Battlog, World Scientific Singapore, 1996, p. 72-75. 

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For the case of noise presence (4, 0) the signal to noise ratio [Pg.125]

The detectability of critical defects with CT depends on the final image quality and the skill of the operator, see figure 2. The basic concepts of image quality are resolution, contrast, and noise. Image quality are generally described by the signal-to-noise ratio SNR), the modulation transfer function (MTF) and the noise power spectrum (NFS). SNR is the quotient of a signal and its variance, MTF describes the contrast as a function of spatial frequency and NFS in turn describes the noise power at various spatial frequencies [1, 3]. 

Sandborg, M. and G. Alm-Carlsson, Influence of x-ray energy spectrum, contrasting detail and detector on the signal-to-noise ratio (SNR) and detective quantum efficiency (DQE) in projection radiography. Phys. Med. Biol., 1992. 37(6) p. 1245-1263. 

In order to maximize the excitation, precautions have to be taken to avoid cross-talk between excitation and signal. Therefore differential probes are commonly used with a SQUID system Nevertheless, for the discussed defects the SQUID system has a lower excitation field by a factor of about 100 compared with the commereial system This we must keep in mind, when we compare measured signal to noise ratios. There is a potential to improve for small defeets, when eross-talk is managed very well. 

So, a comparison of different types of magnetic field sensors is possible by using the impulse response function. High amplitude and small width of this bell-formed function represent a high local resolution and a high signal-to-noise-characteristic of a sensor system. On the other hand the impulse response can be used for calculation of an unknown output. In a next step it will be shown a solution of an inverse eddy-current testing problem. 

The combination of contrast and granularity produces a signal to noise ratio which allows for direct comparison of various films. The classes have minimum values for eontrast and maximum values for graininess. The ASTM classification system employs the same parameters as the European Standard EN584-1 and ISO CD (see Table 1). 

The obtained image has decreased signal to noise ratio and a very good quality which helps for interpretation. 

In fig. 2 an ideal profile across a pipe is simulated. The unsharpness of the exposure rounds the edges. To detect these edges normally a differentiation is used. Edges are extrema in the second derivative. But a twofold numerical differentiation reduces the signal to noise ratio (SNR) of experimental data considerably. To avoid this a special filter procedure is used as known from Computerised Tomography (CT) /4/. This filter based on Fast Fourier transforms (1 dimensional FFT s) calculates a function like a second derivative based on the first derivative of the profile P (r) ... 

The base of this estimation is the signal to noise ratio. The lowest signal to noise ratio S/N which is necessary as a minimum to discern a signal from noise is S/N = 2 1 (4). Referring to the limiting values for the granularity Oj, of the film system classes the smallest density difference AD of an defect which would just be visible should be at least two times greater than On. 

Flaws under this dimension will be under the critical signal-to-noise ratio of 2 1 for a given film system class and for instance for the borderline film of class C5 a flaw must be already -21 % deeper for perception than for the film of class C4. 

A corresponding composite probe with the same frequency and crystal size, however, detects the test flaw much better the echo has a 12 dB higher amplitude (see Fig. 4) and in addition, the noise level is much lower, resulting in an improved signal to noise ratio. This effect is especially observed at high sound attenuation. However, in materials with low attenuation or in case of shorter sound paths the standard probe yields a comparable good signal to noise ratio. 

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. 

Composite transducers will replace conventional transducers in applications where the improvement of test sensitivity, signal to noise ratio and axial resolution are mandatory. It must nevertheless also be noted in connection with the broadband feature that though composite probes have a specified nominal frequency, the echo signals allow no echo amplitude... 

As any conventional probe, acoustic beam pattern of ultrasound array probes can be characterized either in water tank with reflector tip, hydrophone receiver, or using steel blocks with side-drilled holes or spherical holes, etc. Nevertheless, in case of longitudinal waves probes, we prefer acoustic beam evaluation in water tank because of the great versatility of equipment. Also, the use of an hydrophone receiver, when it is possible, yields a great sensitivity and a large signal to noise ratio. 

Signal processing in mechanical impedance analysis (MIA) pulse flaw detectors by means of cross correlation function (CCF) is described. Calculations are carried out for two types of signals, used in operation with single contact and twin contact probes. It is shown that thi.s processing can increase the sensitivity and signal to noise ratio. 

Fig.5 shows the noise influence on CCF for both types of pulses and different b values. This influence is weak, especially for q(t) type of signals. For s(t) signals growth of 2-factor critically increases the signal to noise ratio. For q(t) signals this effect is much weaker and depends on quantity of periods in pulses. 

Correlative signal processing in MIA pulse flaw detectors is an effective way to increase the sensitivity and signal to noise ratio. Instruments with such processing system should be provided with a device for adjusting and sustaining initial phases of both current and reference pulses. 

Based upon a piezoelectric 1-3-composite material, air-bome ultrasonic probes for frequencies up to 2 MHz were developped. These probes are characterized by a bandwidth larger than 50 % as well as a signal-to-noise ratio higher than 100 dB. Applications are the thickness measurement of thin powder layers, the inspection of sandwich structures, the detection of surface near cracks in metals or ceramics by generation/reception of Rayleigh waves and the inspection of plates by Lamb waves. 

In order to obtain a high signal-to-noise ratio sufficient acoustical power is necessary. For special applications a programmable pulser (transmitter) is required in order to optimize the frequency spectrum. 

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