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Figure 13-10 Vertical resolution in video. For a test pattern of 3 horizontal black bars whose width is equal to that of a raster line and whose spacing is equal to 2 lines, the resolution and contrast of the lines on the monitor depend on the position of the bars with respect to the raster scan lines of the camera. The raster lines are the white horizontal stripes, and their width is determined by the diameter of the scanning electron beam. (a) With bars perfectly aligned with the raster, maximum resolution and contrast are obtained. (b) With bars straddling two raster lines, there is no resolution, and the region occupied by the bars appears a medium gray. (c) With the bars at an intermediate location (the usual case), the lines are resolved, but the contrast is reduced, the bars appearing medium gray. For this reason, the average vertical resolution is usually calculated as 0.7 of the maximum number of display lines of the camera, or 486 0.7 340 lines.



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A nanowire is simply a solid, cylindrical wire with a diameter on the scale of a few nanometers These can be fabricated from a variety of materials (silicon, germanium, gallium nitride, metals, oxides, etc) to a length of several micrometers Semiconductor nanowires are 1D structures with unique electrical and optical properties that are used as building blocks in nanoscale circuit design Their low dimensionality means that they exhibit quantum con nement effects One of the challenges lies in understanding the electron conduction and transport properties of these nanowires and how these can be used as interconnections for integrating various nanoscale devices such as single electron transistors and quantum cellular automata These issues are relevant to the ultimate design of a nanoscale IC regardless of the nature of the active element As such these issues represent a fundamental element of the road map leading to nanoscale integration According to C.





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FIGURE 4.8 Censored data histogram of T1 , the waiting time from state 1 to state 2, for Example 4.4.

Yeager, 1949 Bell and Johnson, 1974 Kennedy and Krinard, 1974 Hall and Smith, 1955 All died in one year. Harris, 1975 47% survival after 73 days of ooding. Denyer and Riley, 1964 Survived 6 years of ooding. Broadfoot and Williston, 1973 Kennedy and Krinard, 11-year-old plantation. 1974 Yeager, 1949 Keeland and Sharitz, Maximum growth in deep, periodically 1995 ooded riverine sites. Keeland and Sharitz, Maximum growth in deep, periodically 1995 ooded riverine sites. Williston, 1962 Ahlgren and Hansen, 90% survival after 48 days of ooding. 1957

Financial Modelling in Python expected libors = numpy.zeros(10) expected libors.fill(expected libor) expected swaps = numpy.zeros(10) expected swaps.fill(expected swap) actual libors = ret[:, 0] actual swaps = ret[:, 1] assert seq close(actual libors, expected libors) assert seq close(actual swaps, expected swaps)

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Consumer Price Index (CPI): National Statistics market basket used to measure changes in the prices of goods and services bought by a typical household; complements and in some cases replaces the Retail Price Index (RPI) consumer surplus: The benefit consumers get when they can buy something for less than the maximum amount that they are willing to pay for it deadweight loss: The amount by which total surplus is reduced whenever output is less than the socially optimal output level deflation: When the overall level of prices in the economy is falling demand: The whole range of quantities that a person or group of people with a given income and preferences demands at various possible prices demand curve: A line on a graph that represents how much of a good or service buyers are going to consume at various prices.

P H v p ; HP HT R respectively. This only works if the dimension of the measurement equals 1 (i.e., the rank of H is 1), which is the reason that square-root ltering must process measurement vector components one at a time. The corresponding UD predictor algorithm was discovered by Catherine Thornton, and was the subject of her Ph.D. dissertation [116]. It is based on a relatively robust orthogonalization method developed by Ake Bjorck [11] and called ``modi ed Gram Schmidt.'' Bierman [10] refers to it as ``modi ed weighted Gram Schmidt'' (MWGS), which is much longer than its appropriate name, ``Thornton's method.'' A listing of its implementation in Matlab (from thornton.m on the accompanying diskette) is presented in Table 8.3. The corresponding Matlab listing of the Bierman corrector algorithm (from bierman.m on the accompanying diskette) is given in Table 8.4.

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