Results 131 to 140 of about 1,691 (180)

Characterisation of Nanocellulose Types Using Complementary Techniques and Its Application to Detecting Bacterial Nanocellulose in Food Products. [PDF]

Nanomaterials (Basel)
Geiss O, Bianchi I, Blazevic I, Bucher G, El-Hadri H, Fumagalli F, Ponti J, Verra C, Barrero-Moreno J.   +8 more
europepmc   +1 more source

Preparation and Characterization of Polydatin-Chitosan Nanocapsules for Enhanced Drug Delivery Efficacy. [PDF]

Molecules
Nichil D, Migani S, Colone M, Severini L, Sennato S, Bozzuto G, Patrizi A, Bombelli C, Ravagnan G, Stringaro A, Mattiello L.   +10 more
europepmc   +1 more source

Mimicking classical noise in ion channels by quantum decoherence. [PDF]

Sci Rep
Seifi M, Soltanmanesh A, Shafiee A.
europepmc   +1 more source

A modulation format recognition and optical signal-to-noise ratio monitoring scheme based on residual network and Taylor score pruning. [PDF]

PLoS One
Liang J, Bao Y.
europepmc   +1 more source

Diffusing Wave Microrheology in Polymeric Fluids. [PDF]

Polymers (Basel)
Phillies GDJ.
europepmc   +1 more source

Stochastic analysis of Ebola infection in small zoonotic niches. [PDF]

R Soc Open Sci
Mursel S, Bocchini P, Buceta J.
europepmc   +1 more source

MA parameter estimation using higher-order cumulant statistics

, 1996
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Higher-order Multi-cumulant Factor Analysis

SSRN Electronic Journal, 2021
We estimate the latent factors driving the non-normal dependence in high dimensional panel data using Higher-order multi-cumulant Factor Analysis (HFA). This new approach consists of an eigenvalue ratio test to select the number of non-Gaussian factors, and uses alternating regressions to estimate both the Gaussian and non-Gaussian factors.
Wanbo Lu, Guanglin Huang, Kris Boudt
openaire   +1 more source

Blind estimation using higher-order cumulants

Proceedings of ISCAS'95 - International Symposium on Circuits and Systems, 2002
This paper presents a higher-order cumulant based blind estimation algorithm for estimating the channel matrix in a n-sensor-m-source system under a noisy environment. The output from an array of n spatially separated sensors is the only available information that can be used to derive the channel parameters. The algorithm requires neither the additive
W.K. Lai, P.C. Ching
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Non-gaussian ARMA identification via higher order cumulant

Signal Processing, 1993
Abstract Combining the conceptual breakthrough of minimum entropy deconvolution (MED) with higher order statistics (HOS), we present an extension algorithm of a two-step procedure to identify nonminimum phase system (NMPS). The fourth order cumulants of the observed data, rather than the innovation sequence, are used to resolve the true zero location
Lin, Pinxing, Mao, Shiyi
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