Transfer function design based on user selected samples for intuitive multivariate volume exploration

pre-printMultivariate volumetric datasets are important to both science and medicine. We propose a transfer function (TF) design approach based on user selected samples in the spatial domain to make multivariate volumetric data visualization more accessible for domain users. Specifically, the user starts the visualization by probing features of interest on slices and the data values are instantly queried by user selection. The queried sample values are then used to automatically and robustly generate high dimensional transfer functions (HDTFs) via kernel density estimation (KDE). Alternatively, 2D Gaussian TFs can be automatically generated in the dimensionality reduced space using these samples. With the extracted features rendered in the volume rendering view, the user can further refine these features using segmentation brushes. Interactivity is achieved in our system and different views are tightly linked. Use cases show that our system has been successfully applied for simulation and complicated seismic data sets

Improved pattern extraction scheme for clustering multidimensional data

Multidimensional data refers to data that contains at least three attributes or dimensions. The availability of huge amount of multidimensional data that has been collected over the years has greatly challenged the ability to digest the data and to gain useful knowledge that would otherwise be lost. Clustering technique has enabled the manipulation of this knowledge to gain an interesting pattern analysis that could benefit the relevant parties. In this study, three crucial challenges in extracting the pattern of the multidimensional data are highlighted: the dimension of huge multidimensional data requires efficient exploration method for the pattern extraction, the need for better mechanisms to test and validate clustering results and the need for more informative visualization to interpret the “best” clusters. Densitybased clustering algorithms such as density-based spatial clustering application with noise (DBSCAN), density clustering (DENCLUE) and kernel fuzzy C-means (KFCM) that use probabilistic similarity function have been introduced by previous works to determine the number of clusters automatically. However, they have difficulties in dealing with clusters of different densities, shapes and size. In addition, they require many parameter inputs that are difficult to determine. Kernel-nearestneighbor (KNN)-density-based clustering including kernel-nearest-neighbor-based clustering (KNNClust) has been proposed to solve the problems of determining smoothing parameters for multidimensional data and to discover cluster with arbitrary shape and densities. However, KNNClust faces problem on clustering data with different size. Therefore, this research proposed a new pattern extraction scheme integrating triangular kernel function and local average density technique called TKC to improve KNN-density-based clustering algorithm. The improved scheme has been validated experimentally with two scenarios: using real multidimensional spatio-temporal data and using various classification datasets. Four different measurements were used to validate the clustering results; Dunn and Silhouette index to assess the quality, F-measure to evaluate the performance of approach in terms of accuracy, ANOVA test to analyze the cluster distribution, and processing time to measure the efficiency. The proposed scheme was benchmarked with other well-known clustering methods including KNNClust, Iterative Local Gaussian Clustering (ILGC), basic k-means, KFCM, DBSCAN and DENCLUE. The results on the classification dataset demonstrated that TKC produced clusters with higher accuracy and more efficient than other clustering methods. In addition, the analysis of the results showed that the proposed TKC scheme is capable of handling multidimensional data, validated by Silhouette and Dunn index which was close to one, indicating reliable results

Multi-dimensional transfer function design based on flexible dimension projection embedded in parallel coordinates

In this paper, we present an effective transfer function (TF) design for multivariate volume, providing tightly coupled views of parallel coordinates plot (PCP), MDS-based dimension projection plots, and volume rendered image space. In our design, the PCP showing the data distribution of each variate dimension and the MDS showing reduced dimensional features are integrated seamlessly to provide flexible feature classification for the user without context switching between different data presentations. Our proposed interface enables users to identify interested clusters and assign optical properties with lassos, magic wand and other tools. Furthermore, sketching directly on the volume rendered images has been implemented to probe and edit features. To achieve interactivity, octree partitioning with Gaussian Mixture Model (GMM), and other data reduction techniques are applied. Our experiments show that the proposed method is effective for multidimensional TF design and data exploration.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000316816300003&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Computer Science, Theory & MethodsEngineering, Electrical & ElectronicEICPCI-S(ISTP)1

Natural ventilation design attributes application effect on, indoor natural ventilation performance of a double storey, single unit residential building

In establishing a good indoor thermal condition, air movement is one of the important parameter to be considered to provide indoor fresh air for occupants. Due to the public awareness on environment impact, people has been increasingly attentive to passive design in achieving good condition of indoor building ventilation. Throughout case studies, significant building attributes were found giving effect on building indoor natural ventilation performance. The studies were categorized under vernacular houses, contemporary houses with vernacular element and contemporary houses. The indoor air movement of every each spaces in the houses were compared with the outdoor air movement surrounding the houses to indicate the space’s indoor natural ventilation performance. Analysis found the wind catcher element appears to be the most significant attribute to contribute most to indoor natural ventilation. Wide opening was also found to be significant especially those with louvers. Whereas it is also interesting to find indoor layout design is also significantly giving impact on the performance. The finding indicates that a good indoor natural ventilation is not only dictated by having proper openings at proper location of a building, but also on how the incoming air movement is managed throughout the interior spaces by proper layout. Understanding on the air pressure distribution caused by indoor windward and leeward side is important in directing the air flow to desired spaces in producing an overall good indoor natural ventilation performance

Doctor of Philosophy

dissertationVisualization and exploration of volumetric datasets has been an active area of research for over two decades. During this period, volumetric datasets used by domain users have evolved from univariate to multivariate. The volume datasets are typically explored and classified via transfer function design and visualized using direct volume rendering. To improve classification results and to enable the exploration of multivariate volume datasets, multivariate transfer functions emerge. In this dissertation, we describe our research on multivariate transfer function design. To improve the classification of univariate volumes, various one-dimensional (1D) or two-dimensional (2D) transfer function spaces have been proposed; however, these methods work on only some datasets. We propose a novel transfer function method that provides better classifications by combining different transfer function spaces. Methods have been proposed for exploring multivariate simulations; however, these approaches are not suitable for complex real-world datasets and may be unintuitive for domain users. To this end, we propose a method based on user-selected samples in the spatial domain to make complex multivariate volume data visualization more accessible for domain users. However, this method still requires users to fine-tune transfer functions in parameter space transfer function widgets, which may not be familiar to them. We therefore propose GuideME, a novel slice-guided semiautomatic multivariate volume exploration approach. GuideME provides the user, an easy-to-use, slice-based user interface that suggests the feature boundaries and allows the user to select features via click and drag, and then an optimal transfer function is automatically generated by optimizing a response function. Throughout the exploration process, the user does not need to interact with the parameter views at all. Finally, real-world multivariate volume datasets are also usually of large size, which is larger than the GPU memory and even the main memory of standard work stations. We propose a ray-guided out-of-core, interactive volume rendering and efficient query method to support large and complex multivariate volumes on standard work stations

Redução de dimensionalidade e visualização interativa de dados multimensionais utilizando processamento paralelo em GPU

Orientador : Prof. Dr. Sérgio ScheerTese (doutorado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Métodos Numéricos em Engenharia. Defesa: Curitiba, 29/08/2016Inclui referências : f. 102-105Resumo: O método de apresentação de um conjunto de dados influencia os processos de análise e tomada de decisão acerca de seu conteúdo. Portanto, o processo de visualização deve representar, da melhor forma possível, as relações existentes entre seus elementos. Fenômenos ou processos reais apresentam conjuntos de dados multidimensionais, para os quais seria ideal utilizar representações visuais com o maior número de características possível, o que nem sempre é viável devido a limitações nos dispositivos e pelo fato de que a compreensão de um conjunto com mais de três dimensões não é natural. O problema abordado é a visualização de um grande conjunto de dados, como os resultantes de simulações numéricas ou do sensoriamento de uma estrutura, processo ou mesmo fenômeno natural a partir de um conjunto de diferentes tipos de sensores, utilizando um ambiente computacional de baixo custo. Considerando estes casos, são necessárias ferramentas que auxiliem na visualização e análise dos dados produzidos, facilitando sua compreensão pelos distintos profissionais envolvidos. A partir destas considerações, esta pesquisa tem por objetivo propor uma abordagem para realizar a visualização e análise interativas de um volume de dados multidimensional, de modo que todo o conjunto de dados esteja representado na imagem resultante. Para isso utilizar processamento paralelo baseado em processadores gráficos para implementar as técnicas de Redução Dimensional (RD): Multidimensional Scaling (MDS) e transformação por Coordenadas Estrela, de modo a produzir imagens que representem o conteúdo do volume multidimensional (n-dimensional) de dados. Quatro abordagens para realizar a visualização de dados multidimensionais são descritas e, posteriormente, testadas em um protótipo utilizando General-Purpose Computation on Graphics Processing Units (GPGPU). Os resultados de processamento indicam a viabilidade de se realizar a visualização de um volume de dados n-dimensional utilizando uma técnica de RD em um computador de baixo custo equipado com uma placa gráfica. Palavras-chave: Escala multidimensional, Processamento paralelo, Coordenadas Estrela, Redução Dimensional (RD), Imagem tridimensional.Abstract: The method of presenting a data set influences the analysis and decision-making processes, about its contents. So the visualization process should represent in the best possible way the different relations between its elements. Phenomena or real processes present multidimensional data sets, for which it would be ideal to use visual representations with as many features as possible, which is not always feasible due to limitations in the devices and the fact that the understanding of a range of more than three dimensions is not natural. The problem addressed is the view of a large data set, as a result of numerical simulations or the sensoring of a structure, process or natural phenomenon from a number of different types of sensors, using for this a low cost computing environment. Considering these cases, tools are needed to assist in the visualization and analysis of the data produced, facilitating their comprehension by the various professionals involved. Based on these considerations, this research aims to propose an approach to perform interactive visualization and analysis of a multidimensional data volume, so that the entire data set is represented in the resulting image. Using for this parallel processing based on graphical processing units to implement: the MDS and the Star Coordinates transformation Dimensional Reduction (DR) techniques to produce images that represent the contents of the n-dimensional data volume. Four approaches to perform multidimensional data visualization are described and subsequently tested in a prototype using GPGPU. The processing results indicate the feasibility of performing the visualization of a n-dimensional data volume using a DR technique in a low cost computer equipped with a video card Keywords: Dimensional scale, Parallel processing, Star coordinates, Dimensional Reduction (DR), Tridimensional image