نتایج جستجو برای: bene kernel oil

تعداد نتایج: 190765  

2007
Louis Dorard David R. Hardoon John Shawe-Taylor

In this paper a novel method for performing music in the style of famous pianists is presented. We use Kernel Canonical Correlation Analysis (KCCA), a method which looks for a common semantic representation between two views, to learn the correlation between a representation of a musical score and a representation of an artist’s performance of that score. We use the performance representation b...

2017
Jaime Cuevas José Crossa Osval A Montesinos-López Juan Burgueño Paulino Pérez-Rodríguez Gustavo de Los Campos

The phenomenon of genotype × environment (G × E) interaction in plant breeding decreases selection accuracy, thereby negatively affecting genetic gains. Several genomic prediction models incorporating G × E have been recently developed and used in genomic selection of plant breeding programs. Genomic prediction models for assessing multi-environment G × E interaction are extensions of a single-...

2011
Tzvetan Ivanov Brian D.O. Anderson P.-A. Absil Michel Gevers

In this paper we derive a canonical lower bound for the autocovariance function of any unbiased transfer-function estimator. As a generalization of the Cramér-Rao bound, the Cramér-Rao kernel that we define can be derived without parametrizing the model set. The Cramér-Rao kernel is thus one of the cornerstones for experiment design formulations that do not depend on the choice of coordinates.

2006
MICHAEL T. JURY

SPACES, AND NORMS OF WEIGHTED COMPOSITION OPERATORS MICHAEL T. JURY Abstract. We prove that the norm of a weighted composition operator on the Hardy space H2 of the disk is controlled by the norm of the weight function in the de Branges-Rovnyak space associated to the symbol of the composition operator. As a corollary we obtain a new proof of the boundedness of composition operators on H2, and ...

2016
Maoqi Chen Ales Holobar Xu Zhang Ping Zhou

Decomposition of electromyograms (EMG) is a key approach to investigating motor unit plasticity. Various signal processing techniques have been developed for high density surface EMG decomposition, among which the convolution kernel compensation (CKC) has achieved high decomposition yield with extensive validation. Very recently, a progressive FastICA peel-off (PFP) framework has also been deve...

2013
Changqing Yang Jianhua Hou

This paper presents a numerical method for solving the Volterra integral equation with a convolution kernel. The integral equation was first converted to an algebraic equation using the Laplace transform, after which its numerical inversion was determined by power series. The Padé approximants were effectively used to improve the convergence rate and accuracy of the computed series. The method ...

2016
ZAFAR IQBAL AFTAB AHMED SOOMRO NAZEER AHMAD SHAH ABDUL SATTAR

Aim: To study the effect of palm oil and Nigella sativa on the aorta and coronary arteries of albino rats. Methods: Sixty albino rats including equal number of males and females were obtained from PCSIR Laboratories, Lahore. After overnight fasting of 12-14 hour all the animals were weighed and zero and 24 weeks. The rats were divided into five groups of twelve animals each. Each group had six ...

2017
Charles R. Hurburgh Yanfang Wu Juraj Siska C. R. Hurburgh J. Siska

Soybean physical and chemical properties changed by size (from 4.8 to 8.8 mm diameter), but soybean size and seed density did not affect the protein and oil determination accuracy of three near-infrared transmission analyzers. Corn samples were also separated by size and kernel density. Changes in corn kernel density and size introduced small errors in near-infrared transmission protein, oil, a...

2007
Thomas Dreibholz

3 Installation 3 3.1 Installation of the SCTP Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 3.1.1 Installation of Kernel SCTP for Linux . . . . . . . . . . . . . . . . . . . . . 3 3.1.2 Installation of Userland SCTP SCTPLIB/SOCKETAPI . . . . . . . . . . . . . 4 3.2 Installation of the RSPLIB Package . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 3.2.1 Preparation W...

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