Exploration of Reaction Pathways and Chemical Transformation Networks

For the investigation of chemical reaction networks, the identification of all relevant intermediates and elementary reactions is mandatory. Many algorithmic approaches exist that perform explorations efficiently and automatedly. These approaches differ in their application range, the level of completeness of the exploration, as well as the amount of heuristics and human intervention required. Here, we describe and compare the different approaches based on these criteria. Future directions leveraging the strengths of chemical heuristics, human interaction, and physical rigor are discussed.Comment: 48 pages, 4 figure

Segmentation non supervisée des images par arbres de Markov couple

Nous traitons dans cet article de la segmentation statistique non supervisée d'images de synthèse en utilisant le modèle récent des arbres de Markov couple. L'objectif de cet article est de montrer que la stricte généralisation du modèle des arbres de Markov cachés apporte, notamment dans le cas non supervisé où un algorithme original de type ICE est proposé, un gain appréciable au niveau de la qualité de la segmentation. Les exemples traités montrent en effet que le modèle des diarbres de Markov couple permet d'améliorer les résultats obtenus pour les diarbres de Markov cachés

Solution of 3-dimensional time-dependent viscous flows. Part 3: Application to turbulent and unsteady flows

A numerical scheme is developed for solving the time dependent, three dimensional compressible viscous flow equations to be used as an aid in the design of helicopter rotors. In order to further investigate the numerical procedure, the computer code developed to solve an approximate form of the three dimensional unsteady Navier-Stokes equations employing a linearized block implicit technique in conjunction with a QR operator scheme is tested. Results of calculations are presented for several two dimensional boundary layer flows including steady turbulent and unsteady laminar cases. A comparison of fourth order and second order solutions indicate that increased accuracy can be obtained without any significant increases in cost (run time). The results of the computations also indicate that the computer code can be applied to more complex flows such as those encountered on rotating airfoils. The geometry of a symmetric NACA four digit airfoil is considered and the appropriate geometrical properties are computed

Vision-based techniques for gait recognition

Global security concerns have raised a proliferation of video surveillance devices. Intelligent surveillance systems seek to discover possible threats automatically and raise alerts. Being able to identify the surveyed object can help determine its threat level. The current generation of devices provide digital video data to be analysed for time varying features to assist in the identification process. Commonly, people queue up to access a facility and approach a video camera in full frontal view. In this environment, a variety of biometrics are available - for example, gait which includes temporal features like stride period. Gait can be measured unobtrusively at a distance. The video data will also include face features, which are short-range biometrics. In this way, one can combine biometrics naturally using one set of data. In this paper we survey current techniques of gait recognition and modelling with the environment in which the research was conducted. We also discuss in detail the issues arising from deriving gait data, such as perspective and occlusion effects, together with the associated computer vision challenges of reliable tracking of human movement. Then, after highlighting these issues and challenges related to gait processing, we proceed to discuss the frameworks combining gait with other biometrics. We then provide motivations for a novel paradigm in biometrics-based human recognition, i.e. the use of the fronto-normal view of gait as a far-range biometrics combined with biometrics operating at a near distance

Flow and pressure measurement using phase-contrast MRI : experiments in stenotic phantom models.

Peripheral Arterial Disease (PAD) is a progressive atherosclerotic disorder which is defined as any pathologic process obstructing the blood flow of the arteries supplying the lower extremities. Moderate stenoses mayor may not be hemodynamically significant, and intravascular pressure measurements have been recommended to evaluate whether these lesions are clinically significant. Phase-contrast MRI (PC-MRI) provides a powerful and non-invasive method to acquire spatially registered blood velocity. The velocity field, then, can be used to derive other clinically useful hemodynamic parameters, such as blood flow and blood pressure gradients. Herein, a series of detailed experiments are reported for the validation of MR measurements of steady and pulsatile flows with stereoscopic particle image velocimetry (SPIV). Agreement between PC-MRI and SPIV was demonstrated for both steady and pulsatile flow measurements at the inlet by evaluating the linear regression between the two methods, which showed a correlation coefficient of\u3e 0.99 and\u3e 0.96 for steady and pulsatile flows, respectively. Experiments revealed that the most accurate measures of flow by PC-MRI are found at the throat of the stenosis (error \u3c 5% for both steady and pulsatile mean flows). The flow rate error distal to the stenosis was shown to be a function of narrowing severity. Furthermore, pressure differences across an axisymmetric stenotic phantom model were estimated by solving the pressure-Poisson equation (iterative method) and a non-iterative method based on harmonics-based orthogonal projection using PC-MRI velocity data. Results were compared with the values obtained from other techniques including SPIV, computational fluid dynamic (CFD) simulations, and direct pressure measurements. Using the pressure obtained from CFD as the ground truth and PC-MRI velocity data as the input, the relative error in pressure drop for iterative and non-iterative techniques were 13.1 % and 12.5% for steady flow, 4.0% and 22.1 % for pulsatile flow at peak-systole, and 194.5% and 155.2% at end-diastole, respectively. It was concluded that pressure drop calculation using PC-MRI is more promising for steady cases and pulsatile cases at peak-systole compared to pulsatile flow cases at end-diastole

Automatic 3D facial modelling with deformable models.

Facial modelling and animation has been an active research subject in computer graphics since the 1970s. Due to extremely complex biomechanical structures of human faces and peoples visual familiarity with human faces, modelling and animating realistic human faces is still one of greatest challenges in computer graphics. Since we are so familiar with human faces and very sensitive to unnatural subtle changes in human faces, it usually requires a tremendous amount of artistry and manual work to create a convincing facial model and animation. There is a clear need of developing automatic techniques for facial modelling in order to reduce manual labouring. In order to obtain a realistic facial model of an individual, it is now common to make use of 3D scanners to capture range scans from the individual and then fit a template to the range scans. However, most existing template-fitting methods require manually selected landmarks to warp the template to the range scans. It would be tedious to select landmarks by hand over a large set of range scans. Another way to reduce repeated work is synthesis by reusing existing data. One example is expression cloning, which copies facial expression from one face to another instead of creating them from scratch. This aim of this study is to develop a fully automatic framework for template-based facial modelling, facial expression transferring and facial expression tracking from range scans. In this thesis, the author developed an extension of the iterative closest points (ICP) algorithm, which is able to match a template with range scans in different scales, and a deformable model, which can be used to recover the shapes of range scans and to establish correspondences between facial models. With the registration method and the deformable model, the author proposed a fully automatic approach to reconstructing facial models and textures from range scans without re-quiring any manual interventions. In order to reuse existing data for facial modelling, the author formulated and solved the problem of facial expression transferring in the framework of discrete differential geometry. The author also applied his methods to face tracking for 4D range scans. The results demonstrated the robustness of the registration method and the capabilities of the deformable model. A number of possible directions for future work were pointed out

Feature-Based Image Registration

Image registration is the fundamental task used to match two or more partially overlapping images taken, for example, at different times, from different sensors, or from different viewpoints and stitch these images into one panoramic image comprising the whole scene. It is a fundamental image processing technique and is very useful in integrating information from different sensors, finding changes in images taken at different times, inferring three-dimensional information from stereo images, and recognizing model-based objects. Some techniques are proposed to find a geometrical transformation that relates the points of an image to their corresponding points of another image. To register two images, the coordinate transformation between a pair of images must be found. In this thesis, a feature-based method is developed to efficiently estimate an eight-parametric projective transformation model between pairs of images. The proposed approach applies wavelet transform to extract a number of feature points as the basis for registration. Each selected feature point is an edge point whose edge response is the maximum within a neighborhood. During the real matching process, we check each candidate pair in advance to see if it can possibly become a correct matching pair. Due to this checking, many unnecessary calculations involving cross-correlations can be screened in advance. Therefore, the search time for obtaining correct matching pairs is reduced significantly. Finally, based on the set of correctly matched feature point pairs, the transformation between two partially overlapping images can be decided

Application of Deep Learning Methods in Monitoring and Optimization of Electric Power Systems

This PhD thesis thoroughly examines the utilization of deep learning techniques as a means to advance the algorithms employed in the monitoring and optimization of electric power systems. The first major contribution of this thesis involves the application of graph neural networks to enhance power system state estimation. The second key aspect of this thesis focuses on utilizing reinforcement learning for dynamic distribution network reconfiguration. The effectiveness of the proposed methods is affirmed through extensive experimentation and simulations.Comment: PhD thesi

The workshop on iterative methods for large scale nonlinear problems

The aim of the workshop was to bring together researchers working on large scale applications with numerical specialists of various kinds. Applications that were addressed included reactive flows (combustion and other chemically reacting flows, tokamak modeling), porous media flows, cardiac modeling, chemical vapor deposition, image restoration, macromolecular modeling, and population dynamics. Numerical areas included Newton iterative (truncated Newton) methods, Krylov subspace methods, domain decomposition and other preconditioning methods, large scale optimization and optimal control, and parallel implementations and software. This report offers a brief summary of workshop activities and information about the participants. Interested readers are encouraged to look into an online proceedings available at http://www.usi.utah.edu/logan.proceedings. In this, the material offered here is augmented with hypertext abstracts that include links to locations such as speakers` home pages, PostScript copies of talks and papers, cross-references to related talks, and other information about topics addresses at the workshop

ROC comparison of acquisition parameters for two PET/CT scanners based on lesion detectability in a torso phantom

Positron emission tomography (PET) and computed tomography (CT) are well established and powerful tools for medical diagnostics but even integrated PET/CT scanner images still lack the necessary quality and resolution that would make medical diagnoses flawless. In this thesis experiments were performed to statistically determine the effect that image acquisition parameters have upon diagnostic accuracy. Images from different PET/CT scanners were assessed by comparing subject human diagnostic accuracy from a sample of both professional and student volunteers. The assessment results were compared to the objective NEMA-standards performance data provided by the manufacturers for each scanner. The data analysis method is the receiver operating characteristic (ROC) curves. We hypothesize that human performance in making accurate diagnoses from PET images correlates with the system performance. The data shows that human diagnostic performance correlates to spatial resolution and sensitivity of the PET imaging systems