Post-processing Visualization Research Articles

PHyL v1.0: A parallel, flexible, and advanced software for hydrological and slope stability modeling at a regional scale

Physically-based hydrological-geotechnical modeling at large scales is difficult, especially due to the time-consuming nature of flow routing and 3D soil stability models. Although parallelization techniques are commonly used for each model individually, there is currently no concurrent parallelization strategy for both. This study proposed an open-source, Parallelized, and modular modeling software for regional Hydrologic processes and Landslides simulation and prediction (PHyL v1.0). It offers parallel computation in both hydrological and 3D slope stability modules, cross-scale modeling ability via a soil moisture downscaling method, and advanced input/output (I/O) and post-processing visualization. Additionally, PHyL v1.0 is flexible and extensible, making it compatible with all mainstream operating systems. We applied PHyL v1.0 in the Yuehe River Basin, where the computational efficiencies, parallel performance, parameter sensitivity analysis, and predictive capabilities were evaluated. The PHyL v1.0 is therefore appropriately used as an advanced software for high-resolution and complex simulations of regional floods and landslides.

A local surface roughness mapping method for post-failure interpretation of brittle fracture propagation

Tensile fractures in brittle materials may exhibit striation features running along the surface that indicate the trajectory of fracture propagation. Because of the directional persistence of these features, directional roughness characterization methods can be used on subsampled fracture surface data to map localized roughness features that follow these striations. These maps can aid in the interpretation of fracture initiation sites and crack front positions with the use of post-processing visualization techniques such as vector maps and streamlines. Tensile fractures in rock and synthetic rock analogues are used to demonstrate the various fracture propagation patterns that emerge following this analysis. This mapping method and streamline analysis can be further used to analyze elevation profiles that follow fracture propagation along non-linear paths which may inform about the mechanisms that can associate surface features with the fracture process.

The Effect of Post-processing Visualization on The Image Quality of A Panoramic Dental Digital System

The Effect of Post-processing Visualization on The Image Quality of A Panoramic Dental Digital System

Effects of control zone, exhaust rate and station extent on smoke dispersal during emergency concourse fires in underground stations

Effective mass rapid transit plays an essential role in public transportation for sustainable urban development. This study investigates the temporary spreading behavior of smoke in the concourses of underground stations. The computational approach implements the software package FDS® 6.7.4 along with a post-processing visualization module (Smokeview) to simulate the smoke dispersal processes for determining the unsteady distributions of temperature and visibility during emergency concourse fires. The interfacial temperature between the lower air layer and upper smoke layer is employed to estimate the smoke layer height, as guided by NFPA-92B. The FDS predictions agree reasonably well with the measured fire plume temperatures at the heights of 7.0 m and 13.0 m above the fire source from the available literature in the time duration of 600 s for model verification. FDS calculations are then extended utilizing the validated computational model to explore the effects of the control area, exhaust rate and station size on the smoke spreading progressions. The simulated results reveal the effectiveness of installing static barriers to restrict smoke dispersal within a specific control space; however, the implementation of suitable mechanical exhaust system is required to extract the dense smoke out of the concourse. For the scenario of a high volume MRT underground station, the design adopting the smoke control area of 20 m × 15 m in conjunction with the exhaust rate of 15 m3/s can attain clear visibilities for emergency evacuation. However, the simulations show the inappropriateness of arranging the same design layout of control area and exhaust rate as safety measures of underground fires for a medium volume station.

DHM-viewer: Comprehensive, open-source image processing suite for digital holographic microscopy.

In recent years, digital holographic microscopy (DHM) has witnessed extensive use in quantitative phase imaging (QPI) applications such as live cell dynamics imaging, and 3D particle tracking owing to its ability to reconstruct optical fields based on a single exposure. As a computational imaging technique, DHM is highly specific to the processing pipeline depending on optical configuration and processing requirements. Commercial DHM systems additionally rely on proprietary user interfaces and specific workflows that prohibit user modification, limiting their appeal for users who wish to explore different optical configurations or implement the latest processing algorithms. To address these demands, we present a comprehensive open-source software suite (DHM-viewer) compatible with most commercially available and bespoke DHM systems. DHM-viewer provides user-facing functionalities such as optical alignment assistance, stage actuation control, real-time live image acquisition, and post-processing visualization with a Graphical User Interface (GUI) system. Notably, the GUI is driven by a Standard Operation Procedure (SOP) processing layout that assist users and can accommodate various optical configurations and operating modes. From a system control perspective, DHM-viewer exploits the Micro-Manager framework to ensure compatibility with many existing microscope automation toolkits. From a post-processing perspective, the core software supports a variety of state-of-the-art phase aberration correction and Fourier filtering algorithms as well as deep learning-accelerated phase unwrapping and 3D tracking functionality leveraging the computational power of GPU-based processing. Finally, the DHM-viewer backend supports large batch offline processing through dedicated server-side distribution. The functionality and usability of the software were verified on three different DHM platforms, including one hybrid DHM-fluorescence system.

GPU-Based In Situ Visualization for Large-Scale Discrete Element Simulations

The result of discrete element simulations has tens of thousands of time frames and hundreds of billions of interacting particles in each frame, which may lead to very large data sets. The existing postprocessing visualization technology cannot deal with such large-scale data, which will bring a negative impact to the simulation, affect the simulation speed, and make it difficult to realize the runtime steering. In this work, an attempt has been made to optimize visualization methods by directly accessing DEM simulation results, data exchanging straightly between GPUs, and accelerating pixel composition by GPU. The key feature of this work is that the entire process from DEM simulation to graphical rendering and image composition is done inside the same GPU.

Visualization, Quantification, and Alignment of Spectral Drift in Population Scale Untargeted Metabolomics Data.

Untargeted liquid-chromatography-mass spectrometry (LC-MS)-based metabolomics analysis of human biospecimens has become among the most promising strategies for probing the underpinnings of human health and disease. Analysis of spectral data across population scale cohorts, however, is precluded by day-to-day nonlinear signal drifts in LC retention time or batch effects that complicate comparison of thousands of untargeted peaks. To date, there exists no efficient means of visualization and quantitative assessment of signal drift, correction of drift when present, and automated filtering of unstable spectral features, particularly across thousands of data files in population scale experiments. Herein, we report the development of a set of R-based scripts that allow for pre- and postprocessing of raw LC-MS data. These methods can be integrated with existing data analysis workflows by providing initial preprocessing bulk nonlinear retention time correction at the raw data level. Further, this approach provides postprocessing visualization and quantification of peak alignment accuracy, as well as peak-reliability-based parsing of processed data through hierarchical clustering of signal profiles. In a metabolomics data set derived from ∼3000 human plasma samples, we find that application of our alignment tools resulted in substantial improvement in peak alignment accuracy, automated data filtering, and ultimately statistical power for detection of metabolite correlates of clinical measures. These tools will enable metabolomics studies of population scale cohorts.

Collaborative simulation and scientific big data analysis: Illustration for sustainability in natural hazards management and chemical process engineering

Classical approaches for remote visualization and collaboration used in Computer-Aided Design and Engineering (CAD/E) applications are no longer appropriate due to the increasing amount of data generated, especially using standard networks. We introduce a lightweight and computing platform for scientific simulation, collaboration in engineering, 3D visualization and big data management. This ICT based platform provides scientists an “easy-to-integrate” generic tool, thus enabling worldwide collaboration and remote processing for any kind of data. The service-oriented architecture is based on the cloud computing paradigm and relies on standard internet technologies to be efficient on a large panel of networks and clients. In this paper, we discuss the need of innovations in (i) pre and post processing visualization services, (ii) 3D large scientific data set scalable compression and transmission methods, (iii) collaborative virtual environments, and (iv) collaboration in multi-domains of CAD/E. We propose our open platform for collaborative simulation and scientific big data analysis. This platform is now available as an open project with all core components licensed under LGPL V2.1. We provide two examples of usage of the platform in CAD/E for sustainability engineering from one academic application and one industrial case study. Firstly, we consider chemical process engineering showing the development of a domain specific service. With the rise of global warming issues and with growing importance granted to sustainable development, chemical process engineering has turned to think more and more environmentally. Indeed, the chemical engineer has now taken into account not only the engineering and economic criteria of the process, but also its environmental and social performances. Secondly, an example of natural hazards management illustrates the efficiency of our approach for remote collaboration that involves big data exchange and analysis between distant locations. Finally we underline the platform benefits and we open our platform through next activities in innovation techniques and inventive design.

Image quality assessment in panoramic dental radiography: a comparative study between conventional and digital systems.

This study is designed to compare and evaluate the diagnostic image quality of dental panoramic radiography between conventional and digital systems. Fifty-four panoramic images were collected and divided into three groups consisting of conventional, digital with and without post processing image. Each image was printed out and scored subjectively by two experienced dentists who were blinded to the exposure parameters and system protocols. The evaluation covers of anatomical coverage and structures, density and image contrast. The overall image quality score revealed that digital panoramic with post-processing scored the highest of 3.45±0.19, followed by digital panoramic system without post-processing and conventional panoramic system with corresponding scores of 3.33±0.33 and 2.06±0.40. In conclusion, images produced by digital panoramic system are better in diagnostic image quality than that from conventional panoramic system. Digital post-processing visualization can improve diagnostic quality significantly in terms of radiographic density and contrast.

Web-based post-processing visualization system for finite element analysis

Abstract In this paper, we propose and implement a website of post-processing system for finite element analysis (WebDFEA). Finite element analysis is a computer-aided engineering tool and is popular for static/dynamic structure analysis. It includes three processing systems where post-processing system is to graphically demonstrate the analysis result of a structure model analyzed by finite element method. WebDFEA performs as a website. It is cross-platform because it can auto-detect a client computer platform and auto-download proper OpenGL API for drawing computer graphics. It can draw precise graphics on webpage which can be free controlled by the mouse as a manner in professional software. A database server is involved to store finite element model data and its analysis result. The graphic user interface (GUI) of WebDFEA is a flexible GUI comprising three parts: the switch buttons designed by HTML, the display board and the color bar both developed in Java. The three components are independent and cooperative with each other. They can be recombined without running errors for different purposes. A ship hull section with half a hatch is chosen as the study case to test WebDFEA website. Its finite element model comprises 11,442 triangle elements (shapes). The timeframe starting when WebDFEA is connected to the end when the model is demonstrated is acceptable.

Satisfactory rate of post-processing visualization of fetal cerebral axial, sagittal, and coronal planes from three-dimensional volumes acquired in routine second trimester ultrasound practice by sonographers of peripheral centers

Objective. The aim of this study was to evaluate the feasibility to visualize central nervous system (CNS) diagnostic planes from three-dimensional (3D) brain volumes obtained in ultrasound facilities with no specific experience in fetal neurosonography.Methods. Five sonographers prospectively recorded transabdominal 3D CNS volumes starting from an axial approach on 500 consecutive pregnancies at 19–24 weeks of gestation undergoing routine ultrasound examination. Volumes were sent to the referral center (Department of Obstetrics and Gynecology, Università Roma Tor Vergata, Italy) and two independent reviewers with experience in 3D ultrasound assessed their quality in the display of axial, coronal, and sagittal planes.Results. CNS volumes were acquired in 491/500 pregnancies (98.2%). The two reviewers acknowledged the presence of satisfactory images with a visualization rate ranging respectively between 95.1% and 97.14% for axial planes, 73.72% and 87.16% for coronal planes, and 78.41% and 94.29% for sagittal planes. The agreement rate between the two reviewers as expressed by Cohen's kappa coefficient was >0.87 for axial planes, >0.89 for coronal planes, and >0.94 for sagittal planes. The presence of a maternal body mass index >30 alters the probability of achieving satisfactory CNS views, while existence of previous maternal lower abdomen surgery does not affect the quality of the reconstructed planes.Conclusions. CNS volumes acquired by 3D ultrasonography in peripheral centers showed a quality high enough to allow a detailed fetal neurosonogram.

Graph Based Representations of Density Distribution and Distances for Self-Organizing Maps

The self-organizing map (SOM) is a powerful method for manifold learning because of producing a 2-D spatially ordered quantization of a higher dimensional data space on a rigid lattice and adaptively determining optimal approximation of the (unknown) density distribution of the data. However, a postprocessing visualization scheme is often required to capture the data manifold. A recent visualization scheme CONNvis, which is shown effective for clustering, uses a topology representing graph that shows detailed local data distribution within receptive fields. This brief proposes that this graph representation can be adapted to show local distances. The proposed graphs of local density and local distances provide tools to analyze the correlation between these two information and to merge them in various ways to achieve an advanced visualization. The brief also gives comparisons for several synthetic data sets.

Automated Methodology for Determination of Stress Distribution in Human Abdominal Aortic Aneurysm

Knowledge of impending abdominal aortic aneurysm (AAA) rupture can help in surgical planning. Typically, aneurysm diameter is used as the indicator of rupture, but recent studies have hypothesized that pressure-induced biomechanical stress may be a better predictor Verification of this hypothesis on a large study population with ruptured and unruptured AAA is vital if stress is to be reliably used as a clinical prognosticator for AAA rupture risk. We have developed an automated algorithm to calculate the peak stress in patient-specific AAA models. The algorithm contains a mesh refinement module, finite element analysis module, and a postprocessing visualization module. Several aspects of the methodology used are an improvement over past reported approaches. The entire analysis may be run from a single command and is completed in less than 1 h with the peak wall stress recorded for statistical analysis. We have used our algorithm for stress analysis of numerous ruptured and unruptured AAA models and report some of our results here. By current estimates, peak stress in the aortic wall appears to be a better predictor of rupture than AAA diameter. Further use of our algorithm is ongoing on larger study populations to convincingly verify these findings.

Computer simulations of blood flow with mass transport through the carotid artery bifurcation

The current paradigm for clinical diagnostic for the treatment of vascular disease relies exclusively on diagnostic imaging data to define the present state of the patient, empirical data to evaluate the efficacy of prior treatments for similar patients. These techniques are insufficient to predict the outcome of a given treatment for an individual patient. We here propose a new paradigm of predictive medicine where physician could use computational simulation to construct and evaluate a specific geometrical/anatomical model to predict the outcome for an individual patient. For this purpose it is necessary to develop a complex software system which combines user friendly interface, automatic solid modeling, automatic finite mesh generation, computational fluid dynamics and post-processing visualization. The flow dynamics is defined according to the incompressible Navier-Stokes equations for Newtonian and non-Newtonian fluids. Mass transport of oxygen and macromolecules is modeled by the convection diffusion equation and coupled with flow dynamics. The computer simulations are based upon finite element analysis where the new computer methods for coupling oxygen transport and fluid flow are described. The comparison results shows a good agreement between clinical observation for critical zones of flow separation, flow recirculation, low wall shear stresses which may contribute to the development of atherosclerotic diseases.

没入型ディスプレイを用いた可視化

Two visualization systems using Immersive Projection Technology (IPT) are presented. One is CABIN (Computer Augmented Booth for Image Navigation) of IML, University of Tokyo, which has five 2.5m square screens. In CABIN, a visual computing system is implemented as well as the post processing visualization system. The other is a one slanted screen system, which is located in Kawagoe campus, Tokyo University. The author's in-house post-processor runs on this system. Both systems show the advantage of the use of virtual reality (VR) technology.

A Fuzzy - Based Conceptual KDD Approach: The SaintEtiQ System

Knowledge Discovery in Databases (KDD) systems are basically designed to extract knowledge nuggets from data, i.e. a very precise and hidden knowledge, rather than to provide a global view on database. Moreover, knowledge representation is often unintelligible for the user, such that a post-processing visualization step is necessary. Therefore, we propose a fuzzy-based summarization system named S AINTETlQ, providing different levels of summaries covering all the database. Summaries are output concepts of an incremental conceptual clustering algorithm performed on database records. Concept formation is the fundamental activity which structures objects into a concise form of knowledge that can be efficiently used in the future. It includes the classification of new objects based on a subset of their properties (the prediction ability), as well as the qualitative understanding of those objects based on the generated knowledge (the observation ability). In our approach, database records could be either crisp or fuzzy—imprecise, uncertain or missing. Their representation is then extended to fuzzy sets. Moreover, fuzzy background knowledge represented by fuzzy relational thesauri (FRT) on each attribute is essential to the generalization step of the system. Indeed, this fuzzy-based domain knowledge allows us to induce higher-level intents of concepts, representing part of the database. FRT are both built a priori on numerical and nominal attributes by domain experts, and coupled with a fuzzy discretization process performed on numerical attributes with Zadeh's linguistic variables. Using background knowledge into a concept learning process presents the Data Mining II, C.A. Brebbia & N.F.F. Ebecken (Editors) © 2000 WIT Press, www.witpress.com, ISBN 1-85312-821-X