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|  | | 为了更好的帮助您理解掌握查询词或其译词在地道英语中的实际用法,我们为您准备了出自英文原文的大量英语例句,供您参考。 | |
In the process we study the properties of different homogeneous models for ${\mathbb H}H(n).$
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We exploit an analogy between the trigonometric moment problem and prediction theory for a stationary stochastic process.
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We exploit an analogy between the trigonometric moment problem and prediction theory for a stationary stochastic process.
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Using this characterization we recapture Daubechies' orthonormal wavelets bypassing the spectral factorization process.
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Consider the fractional Brownian motion process $B_H(t), t\in [0,T]$,
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NEW TECHNOLOGY FOR DRUG DISCOVERY BASED UPON INSERTION OF LIGANDS INTO GENE SEQUENCES BY NUCLEAR RECEPTOR PROTEINS
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Two scheduling problems in group technology with deteriorating jobs
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This paper considers single-machine scheduling problems in group technology with the jobs' processing times being simple linear functions of their start times.
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Impact of operating costs on investment strategies in new technology adoption with a further new technology anticipated
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This paper studies a dynamic duopoly model in which two firms compete in adoption of current technology with a further new technology anticipated.
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A number of q,t-analogues of this fact were conjectured in [10]; the present paper proves most of those conjectures, as well as some new identities suggested by the proof technique.
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In this paper we present a technique for proving bounds of the Boas-Kac-Lukosz type for unsharply restricted functions with nonnegative Fourier transforms.
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This technique gives rise to several "epsilonized" versions of the Boas-Kac-Lukosz bound (which deals with the case f(u) = 0, |u| ≥ 1).
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The basic technique uses factorization of group elements and Gel'fand-Tsetlin bases to simplify the computations, and may be extended to treat the computation of Fourier transforms of finitely supported distributions on the group.
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Our technique applies, in particular, to the Shannon and Journe wavelet sets.
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Prediction for Two Processes and the Nehari Problem
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Extending this theory, we show how to use correlations between two processes to predict one from the other.
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Prediction for two processes and the nehari problem
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Extending this theory, we show how to use correlations between two processes to predict one from the other.
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The results are interpreted in the context of spectral estimation of ARMA processes.
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