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Fortunately, the commonly used activation functions, such as the cubic function and the hyperbolic tangent function, satisfy these conditions. In this chapter, we have discussed estimating functions and semiparametric approach to blind separation/deconvolution, and discussed the convergence and e ciency of the batch estimator and natural gradient learning. First blind separation and multichannel deconvolution is formulated in the framework of the semiparametric model and a family of estimating functions and standardized estimating functions are derived by using e cient score functions. The advantage of using the semiparametric approach is that we do not need to estimate the nuisance parameters- the probability density functions of source signals in blind deconvolution. It is inferred from the theory of estimating functions that the batch estimator of the estimating equation converges to the true solution as the number of observed data tends to in nity. If stability conditions are satis ed, the natural gradient learning also converges to the true solution whatever the probability density function of the source signals. The supere ciency of both the batch estimator and natural gradient learning is proven when certain local conditions are satis ed. Appendix A. Representation of Operator K(z) In this appendix, we present the explicit form of operator K(z) and its inverse K 1 (z) and give a de nition of the transpose KT (z) of K(z) [1358]. Assume that the recovered signal y(k) is spatially mutually independent and temporally i.i.d. Lemma A.6 For any p = q, E Fp Xq = 0. (A.1)



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Preparation of this chapter was supported by National Institute of Mental Health grant MH63723-01A1. Correspondence should be sent to Robert F. Bornstein, PhD, Department of Psychology, Box 407, Gettysburg College, Gettysburg, PA 17325 (phone 717-337-6175; Fax 717-337-6172; e-mail bbornste@gettysburg.edu). 164

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The bottom section of the Materials palette allows you to add three types of 2D image files that map an image onto a 3D model to add interest to your materials. You choose these from the drop-down lists: n Diffuse map. There are nine kinds of diffuse maps. Texture maps allow you to add any type of pattern to a plain color. For example, you can add a bitmap image of a plaid to a fabric material. The other seven types of maps are procedural maps, which allow you to specify a variety of settings to define the map. Procedural maps use mathematical algorithmsinstead of bitmaps to define a material, thus giving you many more options. You can choose from Checker, Gradient Ramp, Marble, Noise, Speckle, Tiles, Waves, and Wood. n Reflection map. This is available if you choose the Advanced or Advanced Metal material type. You have the same options as for diffuse maps. Reflection maps simulate outside objects reflected on the surface of a shiny object, such as a room seen on a shiny doorknob. n Opacity map. Mimics areas of opacity and transparency when a material needs to vary in opacity. Anywhere that the opacity map is applied is transparent, while other areas are opaque. You can choose from the same types listed for diffuse maps. n Bump map. Creates the effect of varying heights, that is, bumps. An image used for a bump map is usually black and white, and meshes with a texture map to simulate changes in height. You have the same types as for diffuse maps.

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