This paper proposes an application of ant colony optimization for harmonic elimination in single-phase seven level grid tied inverter for residential applications. Also the derived equation for total harmonic distortion (THD) of the output voltage of inverter is used as the objective function in the ACO algorithm. This proposed scheme consists of grid-tied H- bridge inverter along with Solar PV Cell and DC-DC boost converter. The fundamental switching method is used for inverter. With suggested current control technique grid synchronization condition (i.e. grid voltage and grid current) can be obtained. Harmonic elimination in inverter is a major issue which is solved by ant colony optimization (ACO) method. The switching angles for inverter are calculated using ACO optimization which will not only improve the harmonic profile but also reduce the size of filter inductor. The performance of the proposed system is simulated in MATLAB/Simulink environment and the results are validated through experimental results.

This study investigates the relationship between output of wheat crop, area under wheat crop and agricultural GDP in Pakistan using a time series data ranging from 1970 to 2015. In this study, Phillips Perron (PP) unit root test have been used for checking the stationary of the data. Johansen Co-integration test have been used to investigate the long run relationship between dependent and independent variables. An econometric, Ordinary Least Square (OLS) method has been applied to estimate the relationship among output of wheat crop, area under wheat crop and agricultural GDP. The estimated results of Johansen Co-integration test revealed that there exists a long-run relationship among output of wheat crop, area under wheat crop and agricultural GDP of Pakistan. Regression results indicate that output of wheat crop has a strong positive and significant relationship with agricultural GDP while area under wheat crop has negative relationship with agricultural GDP.

Contrast enhancement is an important step in medical image analysis. It assists to make diagnosis more accurate. In this paper, a new local texture descriptor, which is so-called local intensity area descriptor (LIAD), is proposed and applied to enhance contrast in the lung CT image. The proposed contrast enhancement method consisted of two stages. In the first stage, the image was enhanced by LIAD. In the second stage, the enhanced image was deblurred using the Lucy‒Richardson filter. The method was evaluated in terms of improvement in contrast based on local binary patterns, contrast change rate, lightness change rate, and Tenengrad criterion in lung CT images. The results clearly indicated that our method was better than five conventional contrast enhancement methods. The proposed method not only improved the contrast of the lung CT images, but also retained information essential for clinical diagnosis. Thus, it is expected to assist in improving the accuracy of diagnosis by physicians and computer-aided detection/diagnosis systems.