A hybrid framework composed of two stages for gene selection and classification of DNA microarray data is proposed. At the first stage, five traditional statistical methods are combined for preliminary gene selection (Multiple Fusion Filter). Then, different relevant gene subsets are selected by using an embedded Genetic Algorithm (GA), Tabu Search (TS), and Support Vector Machine (SVM). A gene subset, consisting of the most relevant genes, is obtained from this process, by analyzing the frequency of each gene in the different gene subsets. Finally, the most frequent genes are evaluated by the embedded approach to obtain a final relevant small gene subset with high performance. The proposed method is tested in four DNA microarray datasets. From simulation study, it is observed that the proposed approach works better than other methods reported in the literature.

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In this paper a wide-speed-range predictive direct torque control scheme for surface-mounted permanent magnet synchronous machines without rotational transducer is presented. At very low and zero speeds of operation, the identification of the rotor position is carried out by applying test voltage signals within the regular commutation process for predictive torque control, in order to detect the machine saliency produced by the stator magnetic saturation. Then, the acquired signals that are function of the rotor position are digitally processed through a Quadrature Phase-Locked Loop tracking observer. At middle-and-high speeds of operation the angular position of the rotor is estimated by using a predictive sliding-mode observer of the stator flux. A changeover algorithm is programmed for coupling both estimated values of the angular position of the rotor. Experimental results in a wide-speed-range obtained by using a hybrid digital system which consist of a digital signal processor (DSP) and of a fieldprogrammable-gate-array (FPGA) verify the effectiveness of the proposed encoderless predictive control scheme.

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