By Peter W. Hawkes
The 4 surveys contained during this quantity illustrate broadly assorted elements of imaging and electron physics. the 1st chapters tackle functions of electron microscopy, together with the position of this method in mineralogy and a research of the excessive- answer electron microscopy of quasicrystals. The latter is de facto a brief monograph providing the underlying crystallography and explaining intimately how the corresponding photos and diffraction styles will be interpreted. The 3rd contribution is an account of a brand new method of deconvolution in photo processing within which the writer, via associating a polynomial with the gray-level values of (discrete) pictures, indicates that it really is attainable to invert the convolutional relation that describes many forms of picture formation. the quantity ends with an in depth dialogue of the twin de Broglie wave, together with a serious account of latest advancements within the debate over the life and position of one of these wave.
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Additional info for Advances in Imaging and Electron Physics, Vol. 124
The prediction gain is very good; however, the inversion problem— namely, the reconstruction of the input signal by using a quantized residual signal—is very critical, because even a soft quantization of the residual signal leads to an unstable inverted Volterra ﬁlter. Therefore, the block-based conﬁguration is unsuitable for coding applications. Moreover, the numerical complexity of such block-based approaches is very high. Adaptive identiﬁcation of the Volterra ﬁlter coefﬁcients yields the possibility of reducing the computational burden.
Appendix II explains how the algorithm of Table 1 can be derived. Regarding the initialization of the algorithm, we can choose ⎧ d1,0 = 0 ⎪ ⎪ ⎪ ⎨z0,i = 0 h0 = 0 ⎪ ⎪ ⎪ ⎩γ1,0 = 1 with δ≪1 α0,i = δ This choice leads to a limited memory of initial conditions during the transitory convergence period, but even to sharply varying parameters in the same period, which can overﬂow the computational precision of processors, especially when there is limited word length. This problem can be avoided by taking α0,i = with ≫1 This initialization gives slowly varying parameters during the transitory convergence period, which is extended proportionally to .
Although the LMS approach is a simple stochasticgradient adaptive technique, it only approximately solves the problem depicted in Eq. (44) of Section V, and its convergence to the ﬁnal coefﬁcient values is very slow. A much faster convergence is obtained by using RLS algorithms, which are recursive solutions of Eq. (44) of Section V. In these algorithms, care must be taken to ensure that the autocorrelation matrix does not lose its symmetry and positiveness during the adaptation, so that numerical instabilities can be avoided in limited-precision environments.
Advances in Imaging and Electron Physics, Vol. 124 by Peter W. Hawkes