The most sensitive haloscopes that search for axion dark matter through the two photon electromagnetic anomaly, convert axions into photons mixing of with a large DC magnetic field. In this work we apply Poynting theorem to resulting modified electrodynamics and identify possible vectors, one similar Abraham vector other Minkowski in electrodynamics. latter picks up extra non-conservative terms...

The analysis and equivalent circuit modeling of square patch FSS adopting an efficient vector-fitting is proposed. The simulations of microstructure are performed with CST Microwave Studio on single-substrate for different physical parameters, angle of incidences and polarizations. Then circuit model is extracted and developed using vector-fitting tool and implemented in SPICE (Simulation Progr...

Introducing a statistical model of noise in terms of the covariance matrix of the N-vector, we point out that the least-squares conic fitting is statistically biased. We present a new fitting scheme called renormalization for computing an unbiased estimate by automatically adjusting to noise. Relationships to existing methods are discussed, and our method is tested using real and synthetic data.

Many different techniques to reduce the dimensions of a model have been proposed in the near past. Krylov subspace methods are relatively cheap, but generate non-optimal models. In this paper a combination of Krylov subspace methods and Orthonormal Vector Fitting is proposed. In that way an optimal model for a large model can be generated. In the first step, a Krylov subspace method reduces the...

An algebraic curve is defined as the zero set of a multivariate polynomial. We consider the problem of fitting an algebraic curve to a set of vectors given an additional set of vectors labelled as interior or exterior to the curve. The problem of fitting a linear curve in this way is shown to lend itself to a support vector representation, allowing non-linear curves and high dimensional surface...

For brevity, we adopt array notation. The function takes four arguments (cf. line 1): X and y correspond to all training data and labels respectively, τ is the tailsize, and ς is the coverage threshold. The training function then iterates through every class Cl ∈ C (line 2), fitting a vector of Ψ-models (Ψl) for that class (line 3). The fitting function is presented in Alg. 2 and takes four arg...

brain-computer interface systems are a new mode of communication which provides a new path between brain and its surrounding by processing eeg signals measured in different mental states.  therefore, choosing suitable features is demanded for a good bci communication. in this regard, one of the points to be considered is feature vector dimensionality. we present a method of feature reduction us...

Recently developed Monte Carlo methods of estimating the dominance ratio (DR) rely on autoregressive (AR) fittings of a computed time series. This time series is obtained by applying a projection vector to the fission source distribution of the problem. The AR fitting order necessary to accurately extract the mode corresponding to DR is dependent on the number of fission source bins used. This ...

We consider estimation of the covariance matrix of a random vector under the constraint that certain elements in the covariance matrix are zero. Assuming that the random vector follows a multivariate normal distribution, in which case the model is also known as covariance graph model, we present a new algorithm for maximum likelihood estimation of the covariance matrix with zero pattern. We giv...