Discrete Convolution Associated with Fractional Cosine and Sine Series

Fractional sine series(FRSS)and fractional cosine series(FRCS)are the discrete form of the fractional cosine transform(FRCT)and fractional sine transform(FRST).The recent stud-ies have shown that discrete convolution is widely used in optics,signal processing and applied mathematics.In this paper,firstly,the definitions of fractional sine series(FRSS)and fractional co-sine series(FRCS)are presented.Secondly,the discrete convolution operations and convolution theorems for fractional sine and cosine series are given.The relationship of two convolution opera-tions is presented.Lastly,the discrete Young’s type inequality is established.The proposed theory plays an important role in digital filtering and the solution of differential and integral equations.

The Fractional Investigation of Some Nonlinear Partial Differential Equations by Using an Efficient Procedure

The nonlinearity inmany problems occurs because of the complexity of the given physical phenomena.The present paper investigates the non-linear fractional partial differential equations’solutions using the Caputo operator with Laplace residual power seriesmethod.It is found that the present technique has a direct and simple implementation to solve the targeted problems.The comparison of the obtained solutions has been done with actual solutions to the problems.The fractional-order solutions are presented and considered to be the focal point of this research article.The results of the proposed technique are highly accurate and provide useful information about the actual dynamics of each problem.Because of the simple implementation,the present technique can be extended to solve other important fractional order problems.

Research progress on discretization of fractional Fourier transform

As the fractional Fourier transform has attracted a considerable amount of attention in the area of optics and signal processing, the discretization of the fractional Fourier transform becomes vital for the application of the fractional Fourier transform. Since the discretization of the fractional Fourier transform cannot be obtained by directly sampling in time domain and the fractional Fourier domain, the discretization of the fractional Fourier transform has been investigated recently. A summary of discretizations of the fractional Fourier transform developed in the last nearly two decades is presented in this paper. The discretizations include sampling in the fractional Fourier domain, discrete-time fractional Fourier transform, fractional Fourier series, discrete fractional Fourier transform （including 3 main types： linear combination-type; sampling-type; and eigen decomposition-type）, and other discrete fractional signal transform. It is hoped to offer a doorstep for the readers who are interested in the fractional Fourier transform.

An Approach to Differential Geometry of Fractional Order via Modified Riemann-Liouville Derivative

In order to cope with some difficulties due to the fact that the derivative of a constant is not zero with the commonly accepted Riemann-Liouville definition of fractional derivative, one （Jumarie） has proposed recently an alternative referred to as （local） modified Riemann-Liouville definition, which directly, provides a Taylor＇s series of fractional order for non differentiable functions. We examine here in which way this calculus can be used as a framework for a differential geometry of fractional or- der. One will examine successively implicit function, manifold, length of curves, radius of curvature, Christoffel coefficients, velocity, acceleration. One outlines the application of this framework to La- grange optimization in mechanics, and one concludes with some considerations on a possible fractional extension of the pseudo-geodesic of thespecial relativity and of the Lorentz transformation.

Traffic Prediction in 3G Mobile Networks Based on Multifractal Exploration

Traffic prediction plays an integral role in telecommunication network planning and network optimization. In this paper, we investigate the traffic forecasting for data services in 3G mobile networks. Although the Box-Jenkins model has been proven to be appropriate for voice traffic （since the arrival of calls follows a Poisson distribution）, it has been demonstrated that the Internet traffic exhibits statistical self-similarity and has to be modeled using the Fractional AutoRegressive Integrated Moving Average （FARIMA） process. However, a few studies have concluded that the FARIMA process may fail in modeling the Internet traffic. To this end, we conducted experiments on the modeling of benchmark Internet traffic and found that the FARIMA process fails because of the significant multifractal characteristic inherent in the traffic series. Thereafter, we investigate the traffic series of data services in a 3G mobile network from a province in China. Rich multifractal spectra are found in this series. Based on this observation, an integrated method combining the AutoRegressive Moving Average （ARMA） and FARIMA processes is applied. The obtained experimental results verify the effectiveness of the integrated prediction method.