With their highly robust nature and simple design, switched reluctance machines are finding their way into numerous modern day applications. However, they produce oscillatory torque that generates torque ripple and mechanical vibrations. A double rotor structure to maximize the flux linkage and thereby increase the torque generating capability is proposed. As the machine operates close to saturation, the torque computation depends heavily on the energy conversion as the rotor rolls over the stator for a fixed pole pitch. The flux linkage characteristics are highly non-linear, hence estimation of the magnetic and mechanical parameters is extremely cumbersome. Magnetic circuit analysis by interpretation of the number of flux tubes using integration techniques at different positions of the machine to develop the flux linkage characteristics of the double rotor structure is presented. Computation of the inductances during the movement of rotor from unaligned to aligned is crucial in determining the generated torque. Relevant equations of calculations for inductance and flux linkages in the aligned, partially aligned and unaligned positions are computed. The partially aligned computation is based on the average on two intermediate positions, namely the 1/4th aligned and 3/4th aligned conditions. The static torque characteristics based on the energy conversion principles are used to compute the torque value. Results from simulation and experiments used for performance evaluation of the proposed flux tube analysis for computation of the electro-magnetic torque are presented. OPEN ACCESSEnergies 2012, 5 4009

Image representation is one of the major aspects of automatic classification algorithms. In this paper, different feature extraction techniques have been utilized to represent medical X-ray images. They are categorized into two groups; (i) low-level image representation such as Gray Level Co-occurrence Matrix(GLCM), Canny Edge Operator, Local Binary Pattern(LBP) , pixel value, and (ii) local patch-based image representation such as Bag of Words (BoW). These features have been exploited in different algorithms for automatic classification of medical Xray images. We then analyzed the classification performance obtained with regard to the image representation techniques used. These experiments were evaluated on ImageCLEF 2007 database consists of 11000 medical X-ray images with 116 classes. Experimental results showed the classification performance obtained by exploiting LBP and BoW outperformed the other algorithms with respect to the image representation techniques used.

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