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.

Runoff production is due to watershed response to rainfall events. Various research has been performed to accurately determine the watershed response. In most response models, as in kinematic wave-based models, require detailed input data such as cover characteristics, slope, initial moisture, and soil infiltration properties. In this study, a TIME-area histogram extraction technique was presented via genetic algorithm optimization and DECONVOLUTION methods and results were evaluated in theoretical and real watersheds. In the model presented, a set of rainfall-runoff events in matrix form were called as inputs while the corresponding TIME-area diagrams were extracted. The results showed that the accuracy of the model in estimating the response of a theoretical watershed was 99%, while similar accuracy in the direct approach was 74%. The accuracy of the model in estimating the response of the V-shaped geometric watershed and the real Walnut Gulch watershed reached an average of 99%. Therefore, the model introduced in this research is effective in determining the TIME-area diagram of the V-shaped watershed and may be used in other basins.

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 order to increase the signal/noise ratio, one of the important challenges in seismic data processing is suppression of random noises. In this paper f-x DECONVOLUTION or DECONVOLUTION in frequency-space domain is employed for reduction of random noises from seismic section. This is based on data transformation from TIME-space domain to frequency-space domain. Seismic events are correlatable along x-direction from trace to trace but random noises are not. In fact f-x DECONVOLUTION is able to predict coherent events from trace to trace in space direction. In this paper a computer code for f-x DECONVOLUTION has been written. This program applied on shot records and zero offset section with different levels of random noises. Consequently, the ability of f-x DECONVOLUTION in reduction of random noises has been proved.

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.

DECONVOLUTION is considered as a successful tool in seismic exploration for increasing the temporal resolution of the data. Gabor DECONVOLUTION is proposed to treat the non-stationarity issue of the problem by breaking it into several stationary sub-problems via a Gaussian window, solving them independently, and then, recombining/projecting the sub-solutions into an approximate solution to the original nonstationary problem. The projected Gabor DECONVOLUTION has recently been proposed by the second author as an improvement over Gabor DECONVOLUTION. In the projected Gabor DECONVOLUTION, the sub-problems are projected to a unified problem in TIME domain, and then, the resulting problem is solved. This modification brings useful advantages over the Gabor DECONVOLUTION including an improved convergence property, more efficiency for sparse DECONVOLUTION, more flexibility for incorporating prior information in the presence of noise, and more reflectivity structure via a least-squares method. In this paper, we propose a method for sparse and non-sparse DECONVOLUTION of non-stationary seismic signals in the presence of Gaussian and spike-like random noises. Numerical tests using simulated and field data are presented to show high performance of the proposed method for generating accurate and stable reflectivity models from nonstationary seismograms....

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.

Background and the purpose of the study: Affinity-based target DECONVOLUTION is an emerging method for the identification of interactions between drugs/drug candidates and cellular proteins, and helps to predict potential activities and side effects of a given compound. In the present study, we hypothesized that a part of safranal pharmacological effects, one of the major constituent of Crocus sativus L., relies on its physical interaction with target proteins.Methods: Affinity chromatography solid support was prepared by covalent attachment of safranal to agarose beads.After passing tissue lysate through the column, safranal-bound proteins were isolated and separated on SDS-PAGE or two-dimensional gel electrophoresis. Proteins were identified using MALDI-TOF/TOF mass spectrometry and Mascot software.Results and major conclusion: Data showed that safranal physically binds to beta actin, cytochrome b-c1 complex sub-unit 1, trifunctional enzyme sub-unit beta and ATP synthase sub-unit alpha and beta. These interactions may explain part of safranal’s pharmacological effects. However, phenotypic and/or biological relevance of these interactions remains to be elucidated by future pharmacological studies.