A novel space vector modulation (SVM) method is proposed with a new approach to judge the sector of the SVM . To predigest complicated calculations of the SVM, the phase voltage signals are used to judge the sector. Discontinuous modulating function is used to reduce the switchings. The switching loss of the SVM is optimized, and the complicated judgement of the sector is avoided. The simulation and experimental results are presented to validate the SVM.
Segmentation of brain tissues from magnetic resonance (MR) images plays a crucial role in medical image processing. In this paper, we propose an automatic unsupervised segmentation method integrating wavelet transform with self-organizing map for brain MR image. Firstly, a multi-dimensional feature vector is constructed based on the intensity, the low-frequency subband of wavelet transform and spatial position information. Then, an adaptive growing self-organizing tree map (AGSOTM) is presented, which adaptively captures the complicated spatial layout of the individual tissues, and overcomes the problem of overlapping grey-scale intensities for different tissues. The proposed method is validated by extensive experiments using both simulated and real T1-weighted MR images, and compared with other algorithms.
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