Spectral power

The integral of the temperature gradient of the spectral power density from wavelength Xl to X2, is readily calculable using the Planck radiation law (5). Constant emissivity is assumed for equation 3. 

Fig. 6. The stimulus perceived as color is made up of the spectral power (or, as here, energy) curve of a source times the spectral reflectance (or transmittance) curve of an object times the appropriate spectral response curves (one shown here) of the eye (3).

The conversion of radiated power (P in watts) to luminous flux (F in lumens) is achieved by considering the variation with wavelength of the human eye s photopie response. Then the spectral power from the source (PA in, lor example, W/nnt) is convoluted with the relative spectral response of the eye (V tabulated by the CIE) according to ... 

Uniform and pitting-type corrosion of various materials (carbon steels, stainless steels, aluminum, etc.) could be characterized in terms of noise properties of the systems fluctuation amplitudes in the time domain and spectral power (frequency dependence of power) of the fluctuations. Under-film corrosion of metals having protective nonmetallic coatings could also be characterized. Thus, corrosion research was enriched by a new and sufficiently correct method of looking at various aspects of the action of corrosive media on metals. 

Autocorrelation function Power spectrum (Spectral power density)... 

In Eqs. (7)—(10), 5(A) is the spectral power distribution of the illuminant, and R A) is the spectral reflectance factor of the object. Jc(A), y(A), and 5(A) are the color-matching functions of the observer. In the usual practice, k is defined so that the tristimulus value, Y, for a perfect reflecting diffusor (the reference for R A)) equals 100. Using the functions proposed by the CIE in 1931, y(A) was made identical to the spectral photopic luminous efficiency function, and consequently its tristimulus value, Y, is a measure of the brightness of objects. The X and Z values describe aspects of color that permit identification with various spectral regions. 

Spectral power distribution, standard illuminant, 7 313 Spectral properties... 

Figure 19.2 Spectral power distribution of UV radiation from a solar simulator (ORIEL) (SSUV) simulated solar zenithal UV (intense solar UVB domain) (DUV) simulated daily UV (attenuated solar UVB domain) (UVA) solar UVA (no UVB).

Figure 1. The brilliant orange-red emission of Eu (A), contrasted to the bluish-white spectral power distribution of average daylight (B)...

Figure 6. The spectral power distribution of a fluorescent lamp containing two rare earth phosphors, those of Figures 1 (Curve A) and 5 (Curve A), and green-emitting zinc silicate Mn. A closer approximation to Figure 3 is desirable.

The spectral power distribution of Figure 6 is as close as we can presently get to the ideal prime-color mixture. 

Spectral distribution describes the relative radiant energy as a function of fhe wavelength emitted by a bulb or the wavelength distribution of energy arriving at a surface and is offen expressed in relafive (normalized) terms. It can be displayed as a plot or a table, and in this form if allows comparison of various bulbs and can be applied fo calculafions of spectral power and energy. ... 

Figure F5.1.2 The spectral power distribution curves of standard CIE illuminants. Figure courtesy of GretagMacbeth. This black and white facsimile of the figure is intended only as a placeholder for full-color version of figure go to httptfwww.currentprotocols.com/colorfigures...

J3 = spectral power density in watts/cm2cmK emitted by an element of the scene. 

Of course, there is a major drawback of short excitation pulses. Since, due to sample heating, the laser intensity cannot be increased in the same way as the pulses are shortened, the product I Tp decreases, and, as a consequence, the signal vanishes in the baseline noise. When viewed from the frequency domain, this is a direct consequence of the low spectral power density of short excitation pulses. The decrease of the concentration signal is already obvious for the short excitation times in Fig. 13. 

In this section, more general excitation patterns will be discussed, which allow for tailored deconvolutable excitations with high spectral power densities. Periodic amplitude modulation with a single frequency has been proposed in the literature [73]. It is well suited for noise suppression by lock-in detection, but in practice it suffers from stability problems during the slow frequency sweep. 

Pseudostochastic random binary sequences in combination with fast Fourier transform and correlation techniques avoid these problems and allow for a direct measurement of g(t) with high spectral power density and frequency multiplexing (stochastic TDFRS). Tailoring of the pseudostochastic sequences even allows for a selective enhancement and suppression of certain frequencies and, hence, of certain molecular species [74]. 

From Eq. (68), the amplification Uk of the spectral power density of the noise is... 

A color correction may also be achieved by using filters. Table 3.1 shows the type of filter used by professional photographers to achieve accurate color reproduction. The required filter depends on the type of illuminant and also on the type of film. The type of light source can be described using the temperature of a black-body radiator. A black-body radiator is a light source whose spectral power distribution depends only on its temperature (Jacobsen et al. 2000). The color temperature of a fight source is the temperature of a black-body radiator, which essentially has the same spectral distribution in the visible region. The concept of a black-body radiator is formally introduced in Section 3.5. 

Funt et al. (1991, 1992) use a finite dimensional linear model to recover ambient illumination and the surface reflectance by examining mutual reflection between surfaces. Ho et al. (1992) show how a color signal spectrum can be separated into reflectance and illumination components. They compute the coefficients of the basis functions by finding a least squares solution, which best fits the given color signal. However, in order to do this, they require that the entire color spectrum and not only the measurements from the sensors is available. Ho et al. suggest to obtain the measured color spectrum from chromatic aberration. Novak and Shafer (1992) suggest to introduce a color chart with known spectral characteristics to estimate the spectral power distribution of an unknown illuminant. 

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