Narrow Results

The application of the multicanonical simulation method to small proteins and peptides seems to be feasible and should be undertaken. In this work, the three-dimensional structures of five common tetrapeptide sequences (QPGQ, QSGQ, YPTS, SPQQ and QPGY, in one letter code) in the repetitive central domain of HMW glutenin subunits are investigated by using the multicanonical simulation procedure. Ramachandran plots were prepared and analyzed to predict the relative occurrence probabilities of β-turn and γ-turn structures and helical states. Structural predictions of the five tetrapeptide sequences indicated the presence of high level of β-turns and considerable level of γ-turns. It was also possible to distinguish different type of turns and their occurrence probabilities.

The three-dimensional structures of two hexapeptide repeat motifs (PGQGQQ and SGQGQQ, in one letter code) in the repetitive central domain of HMW glutenin subunits are investigated by using the multicanonical simulation procedure. Ramachandran plots were prepared and analyzed to predict the relative occurrence probabilities of β-turn and γ-turn structures and helical state. Structural predictions of PGQGQQ repeat motif indicated the presence of high level of β-turns and considerable level of γ-turns. Simulations of the repeat motifs in the repetitive central domain of HMW glutenin subunits indicated that these structures take important part in the three-dimensional structures of repeat motifs.

We propose a hybrid algorithm, which combines the features of the energy landscape paving (ELP) and Monte Carlo Minimization (MCM) methods. We have tested its performance in studying the low-energy conformations of the heptapeptide deltorphin.

The three-dimensional structures of the heptapeptide deltorphin (H-Tyr¹-D-Met²-Phe³-His⁴-Leu⁵-Met⁶-Asp⁷-NH₂) are studied in aqueous solution using Energy Landscape Paving (ELP) method. The effect of a solvation energy term on the conformations are determined by analyzing Ramachandran plots. The structures are compared with experimental NMR data. By minimizing the energy structures, the low-energy microstates of the molecule in aqueous solution are determined.

Conformational structures of two common repeat motifs Val¹-Pro²-Gly³-Val⁴-Gly⁵ and Gly¹-Leu²-Gly³-Gly⁴ of tropoelastin are investigated by using the multicanonical simulation procedure with solvation effects included energy force field. The effects of solvation energy term on the conformations are determined by analyzing Ramachandran plots. By minimizing the energy structures along the trajectory, the thermodynamically most stable low-energy microstates of the molecule in aqueous solution are determined and the root mean square deviations of these structures with respect to the global minimum are calculated.

We have employed the steepest descent method to optimize the variational groundstate quantum Monte Carlo wave function for He, Li, Be, B and C atoms. We have used both the direct energy minimization and the variance minimization approaches. Our calculations show that in spite of receiving insufficient attention, the steepest descent method can successfully minimize the wave function. All the derivatives of the trial wave function respect to spatial coordinates and variational parameters have been computed analytically. Our groundstate energies are in a very good agreement with those obtained with diffusion quantum Monte Carlo method (DMC) and the exact results.

Several deterministic and stochastic multi-variable global optimization algorithms (Conjugate Gradient, Nelder–Mead, Quasi-Newton and global) are investigated in conjunction with energy minimization principle to resolve the pressure and volumetric flow rate fields in single ducts and networks of interconnected ducts. The algorithms are tested with seven types of fluid: Newtonian, power law, Bingham, Herschel–Bulkley, Ellis, Ree–Eyring and Casson. The results obtained from all those algorithms for all these types of fluid agree very well with the analytically derived solutions as obtained from the traditional methods which are based on the conservation principles and fluid constitutive relations. The results confirm and generalize the findings of our previous investigations that the energy minimization principle is at the heart of the flow dynamics systems. The investigation also enriches the methods of computational fluid dynamics for solving the flow fields in tubes and networks for various types of Newtonian and non-Newtonian fluids.

This paper presents an efficient algorithm to track the object contour in image sequences. Firstly, the probability distributions of color feature and texture feature are approximated by different schemes. Secondly, the object and the background pixels are distinguished by calculating their posterior probability in light of Bayesian formula based on the two models. Finally, an energy functional is proposed and solved by gradient descent flow to evolve the initial contour which is estimated by motion feature, so that minimum energy contour corresponds to the object contour. In order to save the computational resource, a new texture descriptor is applied to analyze pixels merely in the neighborhood of a contour rather than the whole image. Furthermore, the algorithm's implementation based on the narrow band level set scheme can help to reduce the processing time further. In some experiments, the contours of various objects such as human, car and body organ are successfully tracked by our algorithm and the processing time is short enough for practical application.

In this paper, we propose a robust variational segmentation model capable of overcoming the problem of the negative effects of outliers. The proposed method is based on the combination of the characteristics of the generalized mean with the concept of the active contours. The optimization problem raising in this combination employs a power method technique. We demonstrate the performance of the proposed model on a series of sample images from diverse modalities and show an outperforming proposed model in comparison with the state-of-the-art methods. The proposed method shows better or equivalent performance in terms of accuracy and robustness than the conventional state-of-the-art models. The validation of the efficiency of the proposed two-phase algorithm is further extended to vector-valued images and multi-phase formulation.

Three-dimensional (3D) shape recovery from a monocular image is the inverse process of optical projection and necessitates the use of additional constraints on 3D features of the objects. We have proposed the method of shape recovery based on some hypothetical constraints and its realization by energy minimization using a parallel network. In this paper, we will discuss an extension of our proposal with the aim of correcting noise and errors in an input image through the process of shape recovery. It is hardly possible to extract edges and vertexes free from noise and errors from a real image. For the robustness of the system, we must provide our system with the ability to correct such noise and errors. In this extension, we can also modulate the inclination of the process toward model-driven or data-driven.

This paper proposes a robust method for tracking an object contour in a sequence of images. In this method, both object extraction and tracking problems can be solved simultaneously. Furthermore, it is applicable to the tracking of arbitrary shapes since it does not need a priori knowledge about the object shapes. In the contour tracking, energy-minimizing elastic contour models are utilized, which is newly presented in this paper. The contour tracking is formulated as an optimization problem to find the position that minimizes both the elastic energy of its model and the potential energy derived from the edge potential image that includes a target object contour. We also present an algorithm which efficiently solves energy minimization problems within a dynamic programming framework. The algorithm enables us to obtain optimal solution even when the variables to be optimized are not ordered. We show the validity and usefulness of the proposed method with some experimental results.

Fast growing technologies have made pervasive computing elements part of our routine life. These computing elements dissipate a lot of energy, and power optimization is one of the critical design aspects of such systems. In this work an intelligent laboratory automation and resource management system (iLARMS) is proposed, which incorporates power-aware strategies activated in software with hardware support. In the current work, investigations are carried out to observe the energy issues of the application and the processors used. Various software techniques for energy saving are incorporated in the application level and in the processor core level. The hardware implementation of the proposed resource management system is carried out using PIC16F877A and ARM7 LPC2148 microcontrollers and Intel Core 2 Duo processors. An average energy saving of the order of 48.78% was achieved with iLARMS when compared to a conventional resource management system.

Fluid scheduling allows tasks to be allocated with fractional processing capacity, which significantly improves the schedulability performance. For dual-criticality systems (DCS), dual-rate fluid-based scheduling has been widely studied, e.g., the state-of-the-art approaches mixed-criticality fluid scheduling (MCF) and MC-Sort. However, most of the existing works on DCS either only focus on the schedulability analysis or minimize the energy consumption treating leakage power as a constant. To this end, this paper considers the effect of temperature on leakage power and proposes a thermal and power aware fluid scheduling strategy, referred to as thermal and energy aware (TA)-MCF which minimizes both the energy consumption and temperature, while ensuring a comparable schedulability ratio compared with the MCF and MC-Sort. Extensive experiments validate the efficiency of TA-MCF.

We study the lower semicontinuity for functionals of the form K → ∫_K φ (x, ν)dℋ¹ defined on compact sets in ℝ² with a finite number of connected components and finite ℋ¹ measure and apply the result to the study of quasi-static growth of brittle fractures in linearly elastic inhomogeneous and anisotropic bodies.

We study a variant of the variational model for the quasi-static growth of brittle fractures proposed by Francfort and Marigo.⁹ The main feature of our model is that, in the discrete-time formulation, in each step we do not consider absolute minimizers of the energy, but, in a sense, we look for local minimizers which are sufficiently close to the approximate solution obtained in the previous step. This is done by introducing in the variational problem an additional term which penalizes the L²-distance between the approximate solutions at two consecutive times. We study the continuous-time version of this model, obtained by passing to the limit as the time step tends to zero, and show that it satisfies (for almost every time) some minimality conditions which are slightly different from those considered in Refs. 9 and 8, but are still enough to prove (under suitable regularity assumptions on the crack path) that the classical Griffith's criterion holds at the crack tips. We also prove that, if no initial crack is present and if the data of the problem are sufficiently smooth, no crack will develop in this model, provided the penalization term is large enough.

A multiscale model and numerical method for computing microstructures with large and inhomogeneous deformation is established, in which the microscopic and macroscopic information is recovered by coupling the finite order rank-one convex envelope and the finite element method. The method is capable of computing microstructures which are locally finite order laminates. Numerical experiments on a double-well problem show that plenty of stress free large deformations can be achieved by microstructures consisting of piecewise simple twin laminates.

We propose a time-space discretization of a general notion of quasistatic growth of brittle fractures in elastic bodies proposed by Dal Maso, Francfort and Toader,¹⁴ which takes into account body forces and surface loads. We employ adaptive triangulations and prove convergence results for the total, elastic and surface energies. In the case in which the elastic energy is strictly convex, we also prove a convergence result for the deformations.

An iso-parametric finite element method is introduced in this paper to study cavitations and configurational forces in nonlinear elasticity. The method is shown to be highly efficient in capturing the cavitation phenomenon, especially in dealing with multiple cavities of various sizes and shapes. Our numerical experiments verified and extended, for a class of nonlinear elasticity materials, the theory of Sivaloganathan and Spector on the configurational forces of cavities, as well as justified a crucial hypothesis of the theory on the cavities. Numerical experiments on configurational forces indicate that, in the case of a round reference configuration with radially symmetric stretch on the boundary, the cavitation centered at the origin is the unique energy minimizer. Numerical experiments also reveal an interesting size effect phenomenon: for macro-scale pre-existing-defects, the cavitation process is dominated by the relatively larger pre-existing-defects, and the cavitation tendency of much smaller pre-existing-defects is significantly suppressed.

Surface extraction from noisy volumetric images is a problem commonly encountered in medical image analysis. Deformable surface models can, in principle, solve the problem in an automatic manner. However, it is often essential that a reasonably close initialization and good parameter values for deformable models are provided. In this paper, novel algorithms for global minimization of the energy of deformable meshes are presented. We demonstrate that global optimization by these algorithms reduces the sensitivity of the deformable mesh to its initialization and its parameter values. Consequently, it becomes easier to automate the initialization process and the selection of parameter values. As the second contribution, the internal energy function is derived in a novel way in the framework of deformable surface models. The construction of the internal energy in this way features a simple way to derive the variants of our global optimization algorithm. The experiments with synthetic images are performed to compare variants of the proposed optimization algorithm. Also, we present a practical application of our deformable model to automatic segmentation of positron emission tomography images.

Classification of remote sensing spatial information from multi spectral satellite imagery can be used to obtain multiple representation of the image and capture different structure lineaments. Pixels are grouped using clustering and morphology based segmentation for region based spatial information. This is used to calculate the spatial features of the contiguous regions by classifying the region into the statistics of the pixel properties. In the proposed work, analysis of Google Earth images for identification of morphological patterns of the river flow is done for remote sensing image using graph-cuts. Multi-temporal satellite images acquired from Google Earth to identify the digital elevation is used to formulate the energy function from images to compare the displacement in pixel value using similarity measure. A method is proposed to solve non-rigid image transformation via graph-cuts algorithm by modeling the registration process as a discrete labeling problem. A displacement vector associated to each pixel in the source image indicates the corresponding position in the moving image. The transformation matrix produced from change in the intensity of the pixels for a region is then optimized for energy minimization by using the graph-cuts algorithm and demon registration technique. The proposed study enhances the advantages of regional segmentation in order to know homogeneous areas for optimal image segmentation and digital footprints for change in the river bed patterns by identifying the change in LANDSAT data from temporal satellite images. By applying the proposed multi-level registration method, the number of labels used in each level is greatly reduced due to lower image resolution being used in coarser levels. The results demonstrate that the lineament detection for better accuracy compared to traditional sources of lineament identification methods. It has provided better geotectonic understanding of Cudappah rock in Ahobhilam with Quartzite. The imprints of Eastern Ghat orogeny are seen in upper stream section through a graph cut based segmentation approach.