941 resultados para Automatic classification


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We have applied a number of objective statistical techniques to define homogeneous climatic regions for the Pacific Ocean, using COADS (Woodruff et al 1987) monthly sea surface temperature (SST) for 1950-1989 as the key variable. The basic data comprised all global 4°x4° latitude/longitude boxes with enough data available to yield reliable long-term means of monthly mean SST. An R-mode principal components analysis of these data, following a technique first used by Stidd (1967), yields information about harmonics of the annual cycles of SST. We used the spatial coefficients (one for each 4-degree box and eigenvector) as input to a K-means cluster analysis to classify the gridbox SST data into 34 global regions, in which 20 comprise the Pacific and Indian oceans. Seasonal time series were then produced for each of these regions. For comparison purposes, the variance spectrum of each regional anomaly time series was calculated. Most of the significant spectral peaks occur near the biennial (2.1-2.2 years) and ENSO (~3-6 years) time scales in the tropical regions. Decadal scale fluctuations are important in the mid-latitude ocean regions.

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A digital minicomputer has been interfaced with a scanning electron microscope, and programmed to control the excitations of the objective lens and the stigmator of the microscope. The electron beam is scanned by a digital scan generator and the digitised video signal is used for computations. To focus the microscope, a parameter related to the 'sharpness' of the image is maximised, and to set the stigmator, the directional information in the above- and below-focus images is used. | A digital minicomputer has been interfaced with a scanning electron microscope, and programmed to control the excitations of the objective lens and the stigmator of the microscope. The electron beam is scanned by a digital scan generator and the digitized video signal is used for computations. To focus the microscope, a parameter related to the 'sharpness' of the image is maximized, and to set the stigmator, the directional information in the above and below-focus images is used.

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A novel technique for automated topographical analysis in the SEM has been investigated. It utilizes a 16-bit minicomputer arranged to act as an automatic focusing unit. The computer is coupled to the objective lens of the microscope, by means of a digital to analogue converter, and may regulate the excitation of the lens under program control. Further digital-to-analogue converters allow the computer to act as a programmable scan generator by applying ramp waveforms to the scan amplifiers, permitting the beam to be swept over a small sub-region of the field of interest. The video signal is sampled and applied to an analogue-to-digital converter; the resultant binary numbers are stored in computer memory as an array of values representing relative image intensities within a subregion. A differencing algorithm applied to the collected data allows the level of objective lens excitation to be found at which the sharpness of the image is optimized, and the excitation may be related to the working distance for that subregion through a previous calibration experiment. The sensitivity of the method for detecting small height changes is theoretically of the order of 1 μm.