Introducing an Efficient Statistical Model for Automatic Modulation Classification

Journal of Signal Processing Systems - Nowadays, Automatic Modulation Classification (AMC) plays an important role in many applications of cooperative and non-cooperative communication such as...     

Two-dimensional strip spectral correlation algorithm to fast estimation of 2D-cyclic spectral function for texture analysis

This paper presents a new affective scheme to estimate the two-dimensional cyclic spectral function of a texture as a two-dimensional signal. Recently, considering textures as cyclostationary signals, several algorithms have been introduced to utilize the more discriminant features of hidden periodicity for texture analysis, such as one-dimensional strip spectral correlation analysis (1D-SSCA), one-dimensional FFT-accumulated method, and direct frequency smoothing method. Although the reported results of these algorithms are proper, all of them suffer a drawback: they sweep texture images row by row and column by column and analyze them as one-dimensional signals, and hence lose the relationships between neighboring pixels. In this paper a new efficient extended algorithm namely two-dimensional SSCA is proposed to estimate the two-dimensional cyclic spectral function for two-dimensional signals. This algorithm is fast respect to other cyclic spectral function estimators and is based on 1D-SSCA algorithm. The effectiveness of the proposed algorithm is evaluated on three well-known databases. The experimental results illustrate that the proposed scheme is computationally efficient, generates flexible features and improves correct classification rate, in comparison with other studies in this field.     

Study of removal of Direct Yellow 12 by cadmium oxide nanowires loaded on activated carbon

In this research, cadmium oxide nanowires loaded on activated carbon (CdO-NW-AC) has been synthesized by a simple procedure and characterized by different techniques such as XRD, SEM and UV–vis spectrometry. This new adsorbent has been efficiently utilized for the removal of the Direct Yellow 12 (DY-12) from wastewater. To obtain maximum DY-12 removal efficiency, the influences of variables such as pH, DY-12 concentration, amount of CdO-NW-AC, contact time, and temperature have been examined and optimized in a batch method. Following the variable optimization, the experimental equilibrium data (at different concentration of DY-12) was fitted to conventional isotherm models such as Langmuir, Freundlich and Tempkin. The applicability of each method is based on the R² and error analysis for each model. It was found that the experimental equilibrium data well fitted to the Langmuir isotherm model. The dependency of removal process to time and the experimental data follow second order kinetic model with involvement of intraparticle diffusion model. The negative value of Gibbs's free energy and positive value of adsorption enthalpy show the spontaneous and endothermic nature of adsorption process.