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The fringes can be arti cially sharpened through electronic control of the contrast The precision by simple visual observation can certainly exceed l=50, and an on-line computer analysis may give ultrahigh precision Fringe sharpening can also be achieved through nonlinear photography Precision densitometric traces of a regular photographic record of two-beam fringes or a direct photoelectric scanning of the fringe eld, when suitably analyzed, can also give very high precision (Rosier, 1962; Roesler and Traub, 1966; Dew 1964, 1966) A more recent technique, oscillating mirror interferometry (see 13) with a simple two-beam instrument but with an electronic analyzing system, can also achieve a precision of l=1000 (Raymond, 1970; Bruce and Sharpless, 1975) In fact, Moss et al (1971) and Logan (1973) stated that their two-beam heterodyne (Michelson) interferometry, used in a gravitational wave detection system, can detect a displacement of a mirror with a precision of 10 6 A.

To add a layer, use the Add method of the Layers collection of objects:

A somewhat different technique of detecting an optical path variation down to 10 5 A through the measurement of the beat signal from a three-longitudinal-mode three-mirror laser cavity has also been reported in the literature (Boersch et al, 1974) Here one should remember that the overall thermal and mechanical stability of the interferometer assembly must be higher by an order of magnitude than the precision expected from the system, unless the very purpose of the testing is to measure the relative instability (Dyson, 1968) The ultimate precision in interferometry is limited by the noise inherent in photoelectric detection (Hanes, 1959, 1963; Hill and Bruce, 1962; Raymond, 1970) and also by the diffraction phenomena that lead to the optical uncertainty principle, analogous to Heisenberg s uncertainty principle (Heisenberg, 1949), discussed by Tolan-sky and Emara (1955), Thornton (1957), Koppelmann (1966), and Lang and Scott (1968).

(4.112)

The very high resolution, on the order of an angstrom ora fraction thereof, that is obtained through multiple-beam interferometry is in the longitudinal, not in the lateral direction The resolution in the lateral direction is determined by the wavelength of the radiation due to diffraction The choice of multiple-beam interferometry should be guided by the following considerations: (a) the time available for the test, (b) the maximum precision of the test that is really necessary for the particular job the test surface is designed for, and (c) the equipment available in the laboratory..

Font Face command, including the opening bracket elsewhere such as in links. Otherwise, use Replace & Find to review each replacement. If you have multiple pages, you ll need to accommodate them too.

Figure 6-7: Select Dissolve from the Mode pop-up menu on the Options bar to achieve a speckled frame effect.

following analogy: You and several other people are in a room together (your LAN), and you have only one door in the room. That door is your gateway to the rest of the world. You can communicate within the room, but any messages or communications that you or anyone else in the room sends out or receives must go through that gateway. The type of router or gateway you have on your network is really not that important; you need to know only the IP address of its LAN interface. The IP address of each device on the network is unique but must be related to the IP address of the LAN interface, as explained in the following section.

distributions are unknown and even the category membership of training patterns is unknown We begin in Chap (Bayes decision theory) by considering the ideal case in which the probability structure underlying the categories is known perfectly While this sort of situation rarely occurs in practice, it permits us to determine the optimal (Bayes) classi er against which we can compare all other methods Moreover in some problems it enables us to predict the error we will get when we generalize to novel patterns In Chap (Maximum Likelihood and Bayesian Parameter Estimation) we address the case when the full probability structure underlying the categories is not known, but the general forms of their distributions are ie, the models Thus the uncertainty about a probability distribution is represented by the values of some unknown parameters, and we seek to determine these parameters to attain the best categorization In Chap.

a(s)x(s)

(Nonparametric techniques) we move yet further from the Bayesian ideal, and assume that we have no prior parameterized knowledge about the underlying probability structure; in essence our classi cation will be based on information provided by training samples alone Classic techniques such as the nearest-neighbor algorithm and potential functions play an important role here We then in Chap (Linear Discriminant Functions) return somewhat toward the general approach of parameter estimation We shall assume that the so-called discriminant functions are of a very particular form viz, linear in order to derive a class of incremental training rules Next, in Chap.