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The hydrolytic resistance test described in this chapter is the powdered glass test and not the water attack test as described above; see Appendix <661>, USP 27 [1].
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To create a single EJB, it is necessary to create and manage many source files (such as the home interface, the implementation, the local interface, the descriptor, and so on ) even before counting the support classes that need to be created and managed. Best practices in Java EE development recommend creating applications out of fine-grained reusable components, and this exacerbates the proliferation of source files even for the smallest Java EE project. Ironically, most of these required files are essentially boilerplate code that is very tedious to create and maintain. Extremely sophisticated tools can be used to generate and maintain such boilerplate code, but for most J2EE 1.4 developers these tools are often inaccessible because of cost or resource constraints. (There are exceptions, such as the open source XDoclet tool.) Any practicing J2EE 1.4 developer can attest to the project complexity originating from the complex nature of the EJB 2.x model.
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In this case, however, the phasors of the nonlinear current Ig,n are analytically computed from the powers of the voltage Fourier series (see the probing method for the Volterra series), without any Fourier transform. In fact, the current phasor computation requires a good deal of formalism, but it can be easily handled by suitable numerical arrangements. The rest of the procedure is similar to the standard harmonic balance method, and the nonlinear equation system is formally identical to eq. (1.77) or eq. (1.90). The method is called spectral balance because only manipulations of spectra are involved, and no time-domain waveforms are computed [66 70]. The method is very ef cient especially for multi-tone input signal, up to several tens of input tones. By way of illustration, the spectral regrowth of a pseudorandom modulated signal is shown in Figure 1.37. Both the input and the output to the nonlinear system are shown, as computed by a multi-tone spectral balance algorithm.
The integrand is an odd function of z, and the integration limits are symmetric about the point z = 0. Immediately we conclude that ES(2) = 0 in this particular case. Thus, we conclude that Simpson s rule gives the exact result when f (x) is a cubic polynomial. Hermite interpolation (considered in a general way in Section 6.4) is polynomial interpolation where not only does the interpolating polynomial match f (x) at the sample points xk but the rst derivative of f (x) is matched as well. It is useful to interpolate f (x) with a cubic polynomial that we will denote by r(x) at the points (a, f (a)), (b, f (b)), and (c, f (c)), and also such that r (1) (c) = f (1) (c). A cubic polynomial is speci ed by four coef cients, so these constraints uniquely determine r(x). In fact r(x) = p(x) + (x a)(x b)(x c) for which = 4[p(1) (c) f (1) (c)] . (b a)2 (9.48a)
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