Van-Truong Pham

Global Minimum Torque Ripple Design for Direct Torque Control of Induction Motor Drives

This paper proposes a simple but effective method to reduce the torque ripple for direct torque control (DTC) of induction motor drives. The proposed DTC provides a global minimum torque ripple, which satisfies the root-mean-square (rms) criteria of torque ripple. Such a global minimum torque ripple DTC has not been derived before. The proposed global minimum torque ripple DTC is a two-step design. The first step drives the torque error to zero at the end of the control period. Then, the second step reduces the torque bias and rms ripple by modifying the asymmetry switching patterns of the applied voltage vectors of the first step into symmetry ones. Theoretical analysis is provided to show that the torque ripple of the proposed DTC is a global minimum rms ripple. Furthermore, to verify the effectiveness of this study, a DSP-based experimental induction motor DTC drive system is built. Simulation and experimental results verify that the torque ripple performance has been improved.

Unsupervised active contours driven by density distance and local fitting energy with applications to medical image segmentation

This study presents an efficient variational region-based active contour model for segmenting images without priori knowledge about the object or background. In order to handle intensity inhomogeneities and noise, we propose to integrate into the region-based local intensity model a global density distance inspired by the Bhattacharyya flow. The local term based on local information of segmented image allows the model to deal with bias field artifact, which arises in data acquisition processes. The global term, which is based on the density distance between the probability distribution functions of image intensity inside and outside the active contour, provides information for accurate segmentation, keeps the curve from spilling, and addresses noise in the image. Intensive 2D and 3D experiments on many imaging modalities of medical fields such as computed tomography, magnetic resonance imaging, and ultrasound images demonstrate the effectiveness of the model when dealing with images with blurred object boundary, intensity inhomogeneities, and noise.

Active contour with selective local or global segmentation for intensity inhomogeneous image

In this paper, a novel algorithm for intensity inhomogeneous image segmentation is proposed. The presented method introduces a signed pressure force function using the local information of the image to be segmented. Thus, this model can work with heterogeneous images. In addition, by taking the advantages of Geodesic active contour (GAC) and Chan-Vese (C-V) model, the mehod could deal with objects even with discrete /blur boundaries and gives exact results in detecting object boundaries. Experimental results demonstrate that the proposed model is effective in segmenting inhomogeneous images and promising in pattern recognition.

Multiphase B-spline level set and incremental shape priors with applications to segmentation and tracking of left ventricle in cardiac MR images

This paper presents a new multiphase active contour model for object segmentation and tracking. The paper introduces an energy functional which incorporates image feature information to drive contours toward desired boundaries, and shape priors to constrain the evolution of the contours with respect to reference shapes. The shape priors, in the model, are constructed by performing the incremental principal component analysis (iPCA) on a set of training shapes and newly available shapes which are the resulted shapes derived from preceding segmented images. By performing iPCA, the shape priors are updated without repeatedly performing PCA on the entire training set including the existing shapes and the newly available shapes. In addition, by incrementally updating the resulted shape information of consecutive frames, the approach allows to encode shape priors even when the database of training shapes is not available. Moreover, in shape alignment steps, we exploit the shape normalization procedure, which takes into account the affine transformation, to directly calculate pose transformations instead of solving a set of coupled partial differential equations as in gradient descent-based approaches. Besides, we represent the level set functions as linear combinations of continuous basic functions expressed on B-spline basics for a fast convergence to the segmentation solution. The model is applied to simultaneously segment/track both the endocardium and epicardium of left ventricle from cardiac magnetic resonance (MR) images. Experimental results show the desired performances of the proposed model.

Moment-based alignment for shape prior with variational B-spline level set

This paper presents a new shape prior-based implicit active contour model for image segmentation. The paper proposes an energy functional including a data term and a shape prior term. The data term, inspired from the region-based active contour approach, evolves the contour based on the region information of the image to segment. The shape prior term, defined as the distance between the evolving shape and a reference shape, constraints the evolution of the contour with respect to the reference shape. Especially, in this paper, we present shapes via geometric moments, and utilize the shape normalization procedure, which takes into account the affine transformation, to align the evolving shape with the reference one. By this way, we could directly calculate the shape transformation, instead of solving a set of coupled partial differential equations as in the gradient descent approach. In addition, we represent the level-set function in the proposed energy functional as a linear combination of continuous basic functions expressed on a B-spline basic. This allows a fast convergence to the segmentation solution. Experiment results on synthetic, real, and medical images show that the proposed model is able to extract object boundaries even in the presence of clutter and occlusion.

Evaluation of active contour on medical inhomogeneous image segmentation

Segmentation is an important step in medical image analysis. This process is crucial but challenging due to inhomogeneneity in intensity of images. In addition, the images are often corrupted by noise and with contrast edges. There are some approaches aiming to cope with this kind of images such as: region growing, region competition, watershed segmentation, global thresholding, and active contour methods. Among them, active contour methods, especially level set-based active contour is widely used for image segmentation by their advantageous properties such as topology adaptability, and robustness to initialization. In this paper, we present and demonstrate the effectiveness of some recently active contour models for segmenting medical images with inhomogeneity in intensity. Among these techniques, the local binary fitting based model is validated as a promising method for medical image segmentation.

Zernike moment and local distribution fitting fuzzy energy-based active contours for image segmentation

This paper presents a new region-based active contour model for extracting the object boundaries in an image, based on techniques of curve evolution. The proposed model introduces an energy functional that involves intensity distributions in local image regions and fuzzy membership functions. The local image intensity distribution information used to guide the motion of the contour, in the paper, is derived by Hueckel operator in the neighborhood of each image point. The parameters of Hueckel operator are estimated by a set of orthogonal Zernike moments before curve evolution. Meanwhile, the fuzzy membership functions are used to measure the association degree of each image pixel to the region outside and inside the contour. To minimize the energy functional, instead of solving the Euler–Lagrange equation of the underlying problem, the paper employs a direct method to compute the energy alterations. As a result, the model can deal with images with intensity inhomogeneity. In addition, the model effectively alleviates the sensitivity to contour initialization. Moreover, the model reduces computational cost, avoids problems associated with choosing time steps as well as allows fast convergence to the segmentation solutions. Experimental results on synthetic, real images and comparisons with other models show the desired performances of the proposed model.

MRI image segmentation based on fast global minimization of snake model

Snakes, or active contours, have been widely used to locate boundaries of image segmentation and computer vision. Problem associated with the existence of the local minima in the active contour energy function makes snakes have poor convergence in segmentation process; therefore, the poor convergence has limited applications. In this work, a fast minimization of snake model is used for an MRI knee image segmentation. This method provides a satisfied result. As a result, it is a good candidate for MRI image segmentation approach.

Acoustic-integrated dynamic MR imaging for a patient with obstructive sleep apnea

Obstructive sleep apnea syndrome (OSAS) is caused by multi-level upper airway obstruction. Anatomic changes at the sites of obstruction may modify the physical or acoustic properties of snores. The surgical success of OSA depends upon precise localization of obstructed levels. We present a case of OSAS who received simultaneous dynamic MRI and snore acoustic recordings. The synchronized image and acoustic information successfully characterize the sites of temporal obstruction during sleep-disordered breathing events.

Synchronized imaging and acoustic analysis of the upper airway in patients with sleep-disordered breathing.

Progressive narrowing of the upper airway increases airflow resistance and can produce snoring sounds and apnea/hypopnea events associated with sleep-disordered breathing due to airway collapse. Recent studies have shown that acoustic properties during snoring can be altered with anatomic changes at the site of obstruction. To evaluate the instantaneous association between acoustic features of snoring and the anatomic sites of obstruction, a novel method was developed and applied in nine patients to extract the snoring sounds during sleep while performing dynamic magnetic resonance imaging (MRI). The degree of airway narrowing during the snoring events was then quantified by the collapse index (ratio of airway diameter preceding and during the events) and correlated with the synchronized acoustic features. A total of 201 snoring events (102 pure retropalatal and 99 combined retropalatal and retroglossal events) were recorded, and the collapse index as well as the soft tissue vibration time were significantly different between pure retropalatal (collapse index, 2 ± 11%; vibration time, 0.2 ± 0.3 s) and combined (retropalatal and retroglossal) snores (collapse index, 13 ± 7% [P ≤ 0.0001]; vibration time, 1.2 ± 0.7 s [P ≤ 0.0001]). The synchronized dynamic MRI and acoustic recordings successfully characterized the sites of obstruction and established the dynamic relationship between the anatomic site of obstruction and snoring acoustics.