The paper focuses on the analysis of myoelectric evoked potentials and their progressive scaling, as well as morphological changes using orthogonal basis functions with essentially finite time support; and the compression of the information content using principal component analysis. The application of the Hermite-Rodriguez and the associated Hermite functions is discussed as a means to provide compact information about the shape of the M-wave or of the power spectral density function of either voluntary or electrically elicited myoelectric signals; a means to estimate scaling factors; and a means to describe and classify nonstationarities. The principal component analysis shows the possibility of a compression ratio of at least 10: 1 in the storage of M-wave sequences. The paper also describes three methods for the estimation of delay between similar signals, and therefore for estimation of conduction velocity. They are based on normalised integrals, Fourier transform matching and matching in the time domain. In particular conditions they provide different results for the same pair of signals. The concept of delay and the performance of these methods are reviewed and discussed. The paper is not exhaustive. It has the main objective of making the reader aware of the wealth of methods available for nonstationary myoelectric signal analysis and conduction velocity estimation, and of the need to use them with knowledge of their respective advantages, disadvantages, peculiarties and limitations.

Advances in processing of surface myoelectric signals: Part 2.

A method for obtaining the correlation of two Hermite neural networks is developed. The method is based on the fact that a Hermite function is unchanged by the Fourier transform, which allows an expression for the correlation to be obtained directly from the network weights without the need for the Fourier transform. Comparative results with other neural network correlation methods are presented on simulated radar signals.

/pdf/hermite-neural-network-correlation-and-application-1u6mlcko3y.pdf

Hermite neural network correlation and application

Acute pulmonary edema after diazepam–ketamine in a dog

Bottom-up visual image processing probed with weighted Hermite polynomials

Effects of numerical anti-diffusion in closed unsteady flows governed by two-dimensional Navier-Stokes equation

It is known that the diffusion of a density profile is obtained by convolving a Gaussian with the given density profile. This suggests that the time reversed process (antidiffusion) can be defined by an appropriate convolution inverse. We show that this is indeed the case and we develop a general method for calculating convolution inverses.

Deconvolution of Gaussian filters and antidiffusion

We prove that if we extend the algebra of Schwartz distributions in a suitable fashion, the Schrodinger free-particle flow group can be analytically continued for imaginary time to a full group for the diffusion equation.

Schrödinger free-particle flow group and the classical diffusion semi-group

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Deconvolution of Gaussian filters and antidiffusion

Schrödinger free-particle flow group and the classical diffusion semi-group