We show how two circular polarization components of a linearly polarized pulse, propagating through a coherently driven dilute atomic vapor, can be well resolved in time domain by weak measurement. Slower group velocity of one of the components due to electromagnetically induced transparency leads to a differential group delay between the two components. For low number density, this delay may not be large enough to temporally resolve the two components. We show how this can be enhanced in terms of mean time of arrival of the output pulse through a post-selected polarizer. We demonstrate the idea with all the analytical and numerical results, with a specific example of alkali atoms.
We show how a single linearly polarized control field can produce a sharply tunable group velocity of a weak probe field at resonance in a four-level atomic configuration of alkali vapors. The dispersion can be switched from normal to anomalous along with vanishing absorption, just by changing intensity of the resonant control field. In addition, by allowing different intensities of the different polarization components of the control field, the anomalous dispersion can be switched back to the normal. This thereby creates a valley of anomaly in group index variation and offers two sets of control field intensities, for which the system behaves like a vacuum. The explicit analytical expressions for the probe coherence are provided along with all physical explanations. We demonstrate our results in $J = 1/2 leftrightarrow J = 1/2$ transition for D_1 lines in alkali atoms, in which one can obtain a group index as large as $3.2times10^{8}$ and as negative as $-1.5times10^{5}$ using a control field with power as low as 0.017 mW/cm$^2$ and 9.56 mW/cm$^2$ .
DSS serve the management, operations, and planning levels of an organization and help to make decisions, which may be rapidly changing and not easily specified in advance. Data mining has a vital role to extract important information to help in decision making of a decision support system. Integration of data mining and decision support systems (DSS) can lead to the improved performance and can enable the tackling of new types of problems. Artificial Intelligence methods are improving the quality of decision support, and have become embedded in many applications ranges from ant locking automobile brakes to these days interactive search engines. It provides various machine learning techniques to support data mining. The classification is one of the main and valuable tasks of data mining. Several types of classification algorithms have been suggested, tested and compared to determine the future trends based on unseen data. There has been no single algorithm found to be superior over all others for all data sets. The objective of this paper is to compare various classification algorithms that have been frequently used in data mining for decision support systems. Three decision trees based algorithms, one artificial neural network, one statistical, one support vector machines with and without ada boost and one clustering algorithm are tested and compared on four data sets from different domains in terms of predictive accuracy, error rate, classification index, comprehensibility and training time. Experimental results demonstrate that Genetic Algorithm (GA) and support vector machines based algorithms are better in terms of predictive accuracy. SVM without adaboost shall be the first choice in context of speed and predictive accuracy. Adaboost improves the accuracy of SVM but on the cost of large training time.
