application of interpolation is in semi-Lagrangian methods. In semi-Lagrangian methods, the departure points of the particles arriving at the regular grid are calculated and the scalar quantity is interpolated at the departure points. A common problem is the creation of spurious fluctuations in interpolation values in areas with strong gradients if polynomial interpolation of higher than first degree is used. In order to remove spurious fluctuation, it is necessary to use monotone interpolation. The concept of monotone interpolation requires that the value of the interpolation of the function at the interval between two neighboring grid points should not be greater or less than the maximum and minimum of the function at that interval, respectively. That is, no relative extrema should be produced in the interval. The most famous method for monotone interpolation results from changing the derivatives used in cubic Hermit polynomial with respect to the slop of the function within the interpolation interval. By changing the derivatives, one can adjust the curvature of the interpolator between the beginning and the end of the interpolated interval. The main idea of the change in derivatives is that the derivatives in each interval should not exceed by roughly three times the slop of the function on that interval. In this paper, the curvature of the cubic Hermit polynomial is adjusted between the maximum value still giving monotonicity and the minimum value, corresponding to linear interpolation between the two points. The Hermit functions need derivatives of the function on the grid points for interpolation. For this reason, the cubic Hermit was chosen for implementation of the monotonicity procedure. The monotone piecewise cubic interpolants are easy to use, of sufficient accuracy and have been widely used. This interpolation is of second order accuracy near strict local extrema due to the application of the monotonicity constraint. It is generally the case that most monotonicity-preserving methods sacrifice accuracy in order to obtain monotonicity. Numerical experiments with this method show that the maximum curvature still giving monotonicity may bring the cubic Hermit function close to the relative maximum and minimum in the interval. The (weakly monotone) interpolant with minimum curvature between the two neighboring points may decrease the accuracy of the interpolation as the curve connecting the two points tends to a line. The best choice for curvature in terms of both accuracy and monotonicity is intermediate between the discussed maximum and minimum values. For an important example of practical application in meteorology, the presented method is used in transforming meteorological data in horizontal and vertical directions. An error analysis suggests that interpolation in the vertical direction for transferring data from pressure to the hybrid σ-θ , exhibits a large sensitivity to the amount of curvature used in the implementation. Maintaining maximum curvature in the interpolation function reduces the distance between vertical surfaces such that the crossing of vertical surfaces will result in error.

This paper presents an efficient numerical analysis method called the Newton-Cotes-Hermite-Four-Point (NCH-4P) method for the time history analysis of structural systems with one and multiple degrees of freedom under earthquake effect. The method combines the numerical integration formula of the Newton-Cotes four-point method with the Hermite interpolation formulas to form a new algorithm for solving the vibration equation. The method can handle both linear and nonlinear systems, as well as different types of loading, such as external forces and earthquake excitation. The method is developed for the first time for the analysis of linear and nonlinear damped and undamped structural systems with one and multiple degrees of freedom. The method shows remarkable performance superiority in terms of accuracy, speed, convergence and simplicity compared to the pseudo-analytical Newmark-beta method and the semi-analytical Duhamel integral method. The method modifies the differential equation of motion to have a suitable form for numerical integration. Unlike the nonlinear Newmark-beta method, the method does not require an independent process such as Newton’s iteration to account for nonlinear effects; instead, a series of simple repeated calculations leads to the convergence of the response in nonlinear behavior. The numerical results demonstrate the efficiency of the method in estimating the response of systems under the famous EL-Centro record.

Classification of heart arrhythmia is an important step in developing devices for monitoring the health of individuals. This paper proposes a three module system for classification of electrocardiogram (ECG) beats. These modules are: denoising module, feature extraction module and a classification module. In the first module the stationary wavelet transform (SWF) is used for noise reduction of the ECG signals. The feature extraction module extracts a balanced combination of the Hermit features and three timing interval feature. Then a number of multi-layer perceptron (MLP) neural networks with different number of layers and eight training algorithms are designed. Seven files from the MIT/BIH arrhythmia database are selected as test data and the performances of the networks, for speed of convergence and accuracy classifications, are evaluated. Generally all of the proposed algorisms have good training time, however, the resilient back propagation (RP) algorithm illustrated the best overall training time among the different training algorithms. The Conjugate gradient back propagation (CGP) algorithm shows the best recognition accuracy about 98.02% using a little amount of features.

In this paper, a new numerical method for solving the fractional optimal control problem of the time delay is presented. The fractional integral and the fractional derivative are the Riemann-Liouville type and the Caputo type, respectively. In this method, the cardinal Hermite functions are used as a basis to approximate functions. Moreover, we obtain the fractional and delay integral operational matrices and use them to solve this optimal control problem. Using the collocation method, the problem leads to a system of algebraic equations, that is solved by Newton's iterative method. Finally, numerical examples are presented to investigate the efficiency of this method.

In this paper, we decide to select the best center nodes of radial basis functions by applying the Multiple Criteria Decision Making (MCDM) techniques. Two methods based on radial basis functions to approximate the solution of partial differential equation by using collocation method are applied. The first is based on the Kansa’ s approach, and the second is based on the Hermite interpolation. In addition, by choosing five sets of center nodes: Uniform grid, Cartesian, Chebyshev, Legendre and Legendre-Gauss-Lobato (LGL) as alternatives and achieving the error, condition number of interpolation matrix and memory time as criteria, rating of cases with the help of PROMETHEE technique is obtained. In the end, the best center nodes and method is selected according to the rankings. This ranking shows that Hermite interpolation by using non-uniform nodes as center nodes is more suitable than Kansa’ s approach with each center nodes.