Quantum-Mechanical Signal Processing and Spectral Analysis by Dzevad Belkic

By Dzevad Belkic

Quantum-Mechanical sign Processing and Spectral research describes the unconventional program of quantum mechanical how to sign processing throughout more than a few interdisciplinary study fields. Conventionally, sign processing is considered as an engineering self-discipline with its personal particular scope, equipment, matters and priorities, now not often encompassing quantum mechanics. besides the fact that, the dynamics of structures that generate time indications may be effectively defined by means of the overall ideas and techniques of quantum physics, specially in the Schr?dinger framework. so much time signs which are measured experimentally are mathematically comparable to quantum-mechanical auto-correlation features outfitted from the evolution operator and wavefunctions. This truth permits us to use the wealthy conceptual thoughts and mathematical equipment of quantum mechanics to sign processing.Among the major quantum-mechanical sign processing tools, this publication emphasizes the position of Pade approximant and the Lanczos set of rules, highlighting the foremost advantages in their blend. those tools are conscientiously integrated inside of a unified framework of scattering and spectroscopy, constructing an algorithmic strength that may be exported to different disciplines. the newness of the author's method of key sign processing difficulties, the harmonic inversion and the instant challenge, is in developing the Pade approximant and Lanczos set of rules as fullyyt algerbraic spectral estimators. this is often of paramount theoretical and sensible value, as now spectral research might be performed from closed analytical expressions. This overrides the infamous mathematical ill-conditioning issues of round-off blunders that plague inverse reconstructions in these fields that depend on sign processing.Quantum-Mechanical sign Processing and Spectral research can be a useful source for researchers fascinated with sign processing throughout quite a lot of disciplines.

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Additional info for Quantum-Mechanical Signal Processing and Spectral Analysis (Series in Atomic and Molecular Physics) (Series in Atomic Molecular Physics)

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The matrix Hn (cs ) is called the Hankel matrix [222]. 14) is named the data matrix. 15), which is associated with matrix Hn (cs ) is called the Hankel determinant [83, 87, 223]. For s = 0 and s = 1 the Hankel (0) matrices Hn (c0 ) and Hn (c1 ) become the overlap matrix Sn = Un and the (1) evolution/relaxation matrix Un = Un in the Schr¨odinger basis set {| n )}   c0 c1 c2 · · · cn−1  c1 c2 c3 ··· cn     c2 c3 c4 · · · cn+1  Sn = det Sn Sn = Hn (c0 ) =    .. ..  .  . . .  cn cn−1     Un = Hn (c1 ) =    cn+1 ··· c2n−2 ··· ··· ··· ..

Cn cn−1     Un = Hn (c1 ) =    cn+1 ··· c2n−2 ··· ··· ··· .. cn c1 c2 c3 .. c2 c3 c4 .. c3 c4 c5 .. cn cn+1 cn+2 ··· cn+1 cn+2 .. 16) Un = det Un . 17) The results of the first four determinants Hn (c0 ) and Hn (c1 ) with 1 ≤ n ≤ 4 are given by det S1 = H1 (c0 ) = c0 c0 det S2 = H2 (c0 ) = c1 c1 c2 = c0 c2 − c12 det S3 = H3 (c0 ) = c0 c1 c2 c1 c2 c3 c2 c3 c4 = 2c1 c2 c3 + c0 c2 c4 − c0 c32 − c12 c4 − c23 det S4 = H4 (c0 ) = c0 c1 c2 c3 c1 c2 c3 c4 c2 c3 c4 c5 c3 c4 c5 c6 Copyright © 2005 IOP Publishing Ltd.

Therefore, it follows that g(t) = 0 for t = 0, which shows that the set {t n }(n = M, M +1, M +2, . ) with the first M terms discarded is still closed2 in the interval (0, 1). 23) [227]. 19) is not orthogonal, so that the dimension of the Hilbert space associated with {t n }(n = 0, 1, 2, . ) is not necessarily reduced by removal of a M−1 subset {t n }n=0 [227]. This feature of a set remaining to be closed after dropping its first M terms is relevant to signal processing. Namely, using the Cayley– Hamilton theorem [228], we can formally replace the scalar power function t n by the nth power of the evolution operator applied to the initial state | 0 ) leading ˆ n (τ )| 0 )}.

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