Imaging of physiological functions using magnetic resonance imaging is limited due its slow data acquisition speed. Previously various techniques based on data sharing in the spatiotemporal k-space or sparse recovery methods have been proposed to increase imaging speeds in dynamic MRI. This paper presents a novel formulation for fast dynamic MRI which combines the generic linear dynamical system based spatiotemporal model with sparse recovery techniques. Specifically, the formulation uses a known prior time-evolution model for the physiological function implicitly and enforces the model errors (innovations) to be sparse. The preliminary results of dynamic MRI recovery experiments on an in-vivo myocardial perfusion dataset show that the proposed approach preserves details like edges and fine structures in recovered images better than previous k-space data-sharing and sparse recovery techniques individually.

High spatial resolution in functional magnetic resonance imaging improves its sensitivity to brain activation signals by reducing partial volume effects. However, the long acquisition times required for high spatial resolution limit the temporal resolution in fMRI studies. Consequently, the low temporal sampling bandwidth leads to increase in physiological noise and poor modeling of the functional activation dynamics. Thus, fast techniques capable of recovering fMRI time-series from under-sampled data are desirable to improve the sensitivity and specificity of fMRI for functional brain mapping. This paper presents an under-sampled fMRI recovery using low-rank plus sparse matrix decomposition signal model. This model is suited for blocked or slow event-related fMRI studies, where the low-rank matrix captures the temporally static T*2-weighted image patterns and, the sparse matrix captures the pseudo-periodic brain activation signal. The preliminary results of under-sampled recovery on in-vivo fMRI data show recovery of BOLD activation in human superior colliculus with contrast-to-noise ratio ≥ 4.4 (85% of reference) up to acceleration factors of 3.

A critical requirement for dynamic magnetic resonance imaging (MRI) is to reduce image acquisition times while maintaining high spatial resolutions to capture the underlying process with high-information rates. This paper presents a sparse signal recovery based fast MRI method which uses: 1) dictionary learning for sparse representation of signals for encoding of data redundancy in physiological functions and, 2) a tagging radio-frequency pulses based novel MR signal encoding formulation to uniformly sample the k-space, even at high acceleration factors. The preliminary results of dynamic MR image recovery experiments using tagging based MR signal acquisition method on an in-vivo myocardial perfusion dataset outperforms the equivalent dynamic MRI method implemented with variable density k-space under-sampling.

Segmentation of liver from volumetric images forms the basis for surgical planning required for living donor transplantations and tumor resections surgeries. This paper introduces a novel idea of using sparse representations of liver shapes in a learned structured dictionary to produce an accurate preliminary segmentation, which is further evolved using a joint image and shape based level-set framework to obtain the final segmented volume. Structured dictionary for liver shapes can be learned from an available training dataset. The proposed approach requires only 3 orthogonal segmented masks as user-input, which is less than half the number required by current state-of-the-art interaction-based methods. The increased accuracy of the preliminary segmentation translates into faster convergence of the evolution step and highly accurate final segmentations with mean average symmetric surface distances (ASSD) [1] of (1.03±0.3)mm when tested on a challenging dataset containing 62 volumes. Our approach segments a volume on an average of 5 mins and, is 25% (approx.) faster than comparably performing techniques.

This paper presents a novel approach for segmented rapid magnetic resonance (MRI) imaging using structured sparse representations. In recent years, MR imaging speed has been improved through techniques relying on reduced data acquisition either to allow less-perceivable artifacts or to exploit the data-redundancy. But, these techniques require design of randomized sampling patterns which prove to be sub-optimal from the point of attaining high acceleration gains. The proposed approach treats the imaging problem as a segmented imaging problem constrained to structured dictionaries learned from a small training dataset. The solution to which, is further relaxed to allow recovery of segmented component images from the under-sampled Fourier space. The under-sampling corresponds to traversing few commonly used sampling trajectories only (such as: spirals and radials), i.e., no special sampling pattern design is needed. This highly reduces the number of trajectory acquisitions needed per image formation and, reconstruction in learned dictionaries leads to recovery of automatic segmented images. Experimental results on a real-patient cardiac cine-MR data-set recover high quality segmented images at an acceleration factor of R=13.5 using only 2-spiral sampling trajectories.

A criterion for the global asymptotic stability of 2D discrete systems described by the Roesser model employing state saturation arithmetic is presented. A comparison of the present criterion with the previous criteria and an illustrative example showing the effectiveness of the present criterion are given.

A computationally tractable, i.e., linear matrix inequality (LMI)-based criterion for the global asymptotic stability of uncertain two-dimensional digital filters described by the Fornasini-Marchesini second local state-space model with saturation overflow arithmetic is presented. The criterion is compared with an earlier LMI-based criterion.

A computationally tractable, i.e., linear matrix inequality (LMI)-based criterion for the elimination of overflow oscillations in two-dimensional (2-D) state-space digital filters described by the Roesser model using saturation arithmetic is presented. The criterion is a generalization over an earlier LMI-based criterion.

A novel linear matrix inequality (LMI)-based criterion for the global asymptotic stability and uniqueness of the equilibrium point of a class of delayed cellular neural networks (CNNs) is presented. The criterion turns out to be a generalization and improvement over some previous criteria.

A frequency-domain criterion for the overflow stability of a class of state-space digital filters is presented. The result is derived using a Lyapunov technique and the associated MKY machinery. Some consequences of the present derivation are discussed.

This work, which is part of the benchmark series in electrical engineering and computer science, is a compilation of the research papers representing the major advances in the area of nonlinear systems stability. It contains a total of 28 papers that pertain to both Lyapunov-like approach applied to ordinary differential equations and functional-analytic approach applied to feedback systems. The papers are categorized into eight parts, listed below.

Buku ini membahas tentang: 1. Pengantar untuk mengontrol sistem 2.Transformasi Laplace 3.Sistem perwakilan dan komponen 4.Metode akar lokus 5.Sistem untuk analisis sistem kontrol,dll.