Feature identification

If the measurements are of an exploratory nature, the initial goal is the identification of the atmospheric gases that produce observable spectral features. An example of this task is the analysis of the Voyager infrared spectra of Saturn s satellite Titan (Kunde et al., 1981 Maguire et al, 1981 Samuelson et al, 1981). A Titan spectrum is shown in Fig. 6.4.2 along with laboratory spectra of HC3N and C2N2. 

3 cm resolution of the Voyager infrared spectrometer does not permit the detection of individual spectral lines, but the Q-branches of numerous absorption bands stand out as sharp features, and for some bands the P- and R-branch contours are also evident. The features appear in emission because the bands are opaque, and all contributions to the radiances originate in the stratosphere where the temperature is increasing with height. In most cases atmospheric constituents can be identified by comparison of the locations of the observed spectral features with those of candidate gases obtained from laboratory measurements as shown in Fig. 6.4.2. However, for weak features more sophisticated approaches must sometimes be used. 

Statistical techniques are powerful tools, which can be used in the search for minor constituents in planetary spectra. Correlation analysis is particularly applicable to the detection of gases with many, generally weak spectral features. Correlation analysis is best applied when the signatures of a suspected constituent are of the same magnitude or possibly even less than the noise level of the instrument. Under such conditions visual inspection of a spectral region where known lines of a particular gas should appear may not be conclusive. The advantage of correlation analysis is that many spectral positions can be searched simultaneously. 

Correlation expresses the degree of linear coherence between two functions, F and G. We define the covariance function, C(S), by 

If F equals G we obtain the autocovariance, also called the autocorrelation function, although the latter name is sometimes reserved for the normalized version, C(S)/C(0). Correlation analyses play important roles in communication theory, where F and G normally are functions of time (y = t) and the lag is a time delay in our case y represents the wavenumber and S a wavenumber shift. The mathematical formulation of correlation theory can be found in books on information and communication theory, such as Goldman (1953). 

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Analytical investigations may be undertaken to identify the presence of an ABS polymer, characterize the polymer, or identify nonpolymeric ingredients. Fourier transform infrared (ftir) spectroscopy is the method of choice to identify the presence of an ABS polymer and determine the acrylonitrile—butadiene—styrene ratio of the composite polymer (89,90). Confirmation of the presence of mbber domains is achieved by electron microscopy. Comparison with available physical property data serves to increase confidence in the identification or indicate the presence of unexpected stmctural features. Identification of ABS via pyrolysis gas chromatography (91) and dsc ((92) has also been reported. 

Numeric-to-symbohc transformations are used in pattern-recognition problems where the network is used to classify input data vectors into specific labeled classes. Pattern recognition problems include data interpretation, feature identification, and diagnosis. 

COMPUTER CALCULATED OPTIMA WITHIN THE REGION OF THE INDICATED SPECTRAL FEATURE. SPECTRAL FEATURE IDENTIFICATIONS ACCORDING TO CARIATI (1983). 

CSSC-08/09 is to be used as follows. It is to be used for Section 2, Questions 002013 through 002023, and all following sections. The features to be coded are decided on first. Enter these vertically from left to right on lines 1-6 on as many copies of CSSC-08/09 as are required to hold them. (There is space for 60 features per sheet. However, columns may be left blank to improve readability for later proof-reading.) Enter one feature identification number over each column (Fig. 2). 

The feature identification number as entered is organized into a 6 digit vertical column digit 1 is a leading "0" digits 2 and 3 convey the Section digits 4, 5, and 6 represent the feature number within the Section. 

Component Vision Feature identification on device Package outline 1... 

Modem methods of peptide sequencing follow a strategy similar to that used to sequence insulin but are automated and can be carried out on a small scale A key feature is repetitive N terminal identification using the Edman degradation... 

The spectrum shown in Fig. 7.5 shows the appropriate portion of the spectrum for a copolymer prepared from a feedstock for which fj = 0.153 It turns out that each polyene produces a set of three bands The dyad is identified with the peaks at X = 298, 312, and 327 nm the triad, with X = 347 367, and 388 nm and the tetrad with X = 412 and 437 nm. Apparently one of the tetrad bands overlaps that of the triad and is not resolved. Likewise only one band (at 473 nm) is observed for the pentad. The identification ol these features can be confirmed with model compounds and the location and relative intensities of the peaks has been shown to be independent of copolymer composition. 

Microscopical Examination. All fibers have distinguishing features which either allow outright identification or classification iato narrower grouping for specialized analysis. Fiber cross sections are particularly usehil for identification. 

The physical techniques used in IC analysis all employ some type of primary analytical beam to irradiate a substrate and interact with the substrate s physical or chemical properties, producing a secondary effect that is measured and interpreted. The three most commonly used analytical beams are electron, ion, and photon x-ray beams. Each combination of primary irradiation and secondary effect defines a specific analytical technique. The IC substrate properties that are most frequendy analyzed include size, elemental and compositional identification, topology, morphology, lateral and depth resolution of surface features or implantation profiles, and film thickness and conformance. A summary of commonly used analytical techniques for VLSI technology can be found in Table 3. 

Most defects can be detected using one or more appropriate nondestructive testing techniques. However, in the absence of routine nondestructive testing inspections, identification of defects in installed equipment is generally limited to those that can be observed visually. Defects such as high residual stresses, microstructural defects such as sensitized welds in stainless steel, and laminations will normally remain undetected. Defects that can be detected visually have the following features ... 

Identification. The primary identif3dng feature is confinement of metal loss to the weld bead (Fig. 15.3), although in advanced stages base metal immediately adjacent to the weld bead may also be affected. Note that this feature seems to distinguish galvanic corrosion of welds from other weld-related corrosion, such as weld decay, which preferentially attacks the immediately adjacent base metal (Fig. 15.4). 

A hazard identification technique in which all known failure modes of components or features of a system are considered in turn, and undesired outcomes are noted... 

From a map at low resolution (5 A or higher) one can obtain the shape of the molecule and sometimes identify a-helical regions as rods of electron density. At medium resolution (around 3 A) it is usually possible to trace the path of the polypeptide chain and to fit a known amino acid sequence into the map. At this resolution it should be possible to distinguish the density of an alanine side chain from that of a leucine, whereas at 4 A resolution there is little side chain detail. Gross features of functionally important aspects of a structure usually can be deduced at 3 A resolution, including the identification of active-site residues. At 2 A resolution details are sufficiently well resolved in the map to decide between a leucine and an isoleucine side chain, and at 1 A resolution one sees atoms as discrete balls of density. However, the structures of only a few small proteins have been determined to such high resolution. 

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