A high-Q TIME-VARIANT (TV) band-pass filter (BPF) is analyzed in this paper. N parallel identical low-pass RC filters make this BPF, in which each of these N filters is sequentially switched on for an equal TIME interval, periodically. The resulting BPF provides a stable frequency response and is insensitive to element variations. Through mathematical analysis, equations are derived for the frequency response and the bandwidth of the BPF. Moreover, the validity of these equations is confirmed by experimental results. These equations make the design of the above high-Q BPF an easy task.

In this paper, we study linear fuzzy TIME-VARIANT optimal control SYSTEMs using the generalized differentiability concept and we present the general form of optimal controls and states. Some examples are provided to illustrate our results.

Fuzzy TIME series have been developed during the last decade to improve the forecast accuracy. Many algorithms have been applied in this approach of forecasting such as high order TIME inVARIANT fuzzy TIME series. In this paper, we present a hybrid algorithm to deal with the forecasting problem based on TIME VARIANT fuzzy TIME series and particle swarm optimization algorithm, as a highly efficient and a new evolutionary computation technique inspired by birds’ flight and communication behaviors. The proposed algorithm determines the length of each interval in the universe of discourse and degree of membership values, simultaneously. Two numerical data sets are selected to illustrate the proposed method and compare the forecasting accuracy with four fuzzy TIME series methods. The results indicate that the proposed algorithm satisfactorily competes well with similar approaches.

In this paper, the problem of delay dependent robust asymptotically stable for uncertain linear TIME-VARIANT SYSTEM with multiple delays is investigated. A new delay-dependent stability sufficient condition is given by using the Lyapunov method, linear matrix inequality (LMI), parameterized first-order model transformation technique and transformation of the interval uncertainty in to the norm bounded uncertainty. A numerical example is presented to illustrate our present stability criterion allows an upper bound which is bigger on the size of the delay in comparison with those in the literature.

In this paper, optimal production and maintenance planning of a flexible manufacturing SYSTEM under a TIME VARIANT demand is considered. There is a preventive maintenance plan to reduce the failure rate of the machine. It is assumed that the failure rate of the machine is a function of its age and its maintenance rate. It is, also, assumed that the demand of the manufacturing product is TIME dependent and its rate depends on the level of the advertisement on that product. The objective is to maximize the expected discounted total profit of the firm over an infinite TIME horizon. To solve this optimization problem, first, an optimal control is characterized by a set of Hamilton-Jacobi-Bellman partial differential equations. Then, since this set of equations cannot be solved analytically, this stochastic optimal control model is approximated by a deterministic optimal control problem. By solving this new deterministic problem under practical assumptions, a set of suboptimal controls can be found.

Due to the spherical divergence and specifically absorption in the earth, amplitude of a propagating seism wave varies as a function of TIME. This stretches the wavelet in TIME and reduces the TIME (or vertical) resolution of the seismic sections. To overcome the problem one has to apply so called spatial migration or deconvolution on data. Usually the least square Wiener deconvolution is used to boost up the attenuated frequency components. Unfortunately, the Wiener based deconvolution methods assume that the source generated seismic wavelet is stationary (i.e. its frequency content remains unchanged within the record). A method of deconvolution in the Gabor domain is applied in this paper that considers the seismic data as a non-stationary phenomenon.The Gabor transform (Equation 1) is a windowed or short TIME Fourier transform, where the window used to isolate the frequency content of input record in TIME, is a Gaussian type. According to the uncertainty principle, the Gabor transform has the least uncertainty among other windowed Fourier transforms.Please clik on PDF icon to viwe the complet abstract.

This paper presents a novel three-dimensional (3D) TIME-VARIANT hybrid channel model for massive multiple input multiple output (M-MIMO) wireless SYSTEMs. The main aim of the proposed model is to find channel characteristics in a simple and precise manner. To achieve this purpose, this channel model consists of two deterministic and stochastic modes. By using the idea of equivalent planes and ray tracing method, the channel multipath components (MPCs) are calculated in the deterministic mode. In the stochastic mode, those parts of the propagation environment that are too complex to be modeled in the deterministic mode, are modeled based on the cluster concept. Then, the MPCs characteristics are calculated by utilizing appropriate random distributions. The doppler effect is also taken into account due to the probability of existence relative velocities among channel components. This model is validated by comparing simulation results with those of the previously developed channel models. Finally, the channel model is applied to a real scenario to extract some of the important characteristics of the propagation environment.