Implicit Multidimensional Projection of Local Subspaces

We propose a visualization method to understand the effect of multidimensional projection on local subspaces, using implicit function differentiation. Here, we understand the local subspace as the multidimensional local neighborhood of data points. Existing methods focus on the projection of multidimensional data points, and the neighborhood information is ignored. Our method is able to analyze the shape and directional information of the local subspace to gain more insights into the global structure of the data through the perception of local structures. Local subspaces are fitted by multidimensional ellipses that are spanned by basis vectors. An accurate and efficient vector transformation method is proposed based on analytical differentiation of multidimensional projections formulated as implicit functions. The results are visualized as glyphs and analyzed using a full set of specifically-designed interactions supported in our efficient web-based visualization tool. The usefulness of our method is demonstrated using various multi- and high-dimensional benchmark datasets. Our implicit differentiation vector transformation is evaluated through numerical comparisons; the overall method is evaluated through exploration examples and use cases

Improved integration of information to reduce subsurface model bias

Subsurface modeling deals with data-related issues like cognitive and sampling biases, and model-related challenges including statistical assumptions, misspecification, and algorithmic biases. These challenges introduce four critical implications during subsurface modeling. Firstly, subsurface sampling is subject to sampling bias, which compromises statistical representativeness. Secondly, analog selection methodologies rely on multivariate statistics and expert judgment that overlook spatial information and data dimensionality. Thirdly, subsurface inferential workflows that utilize dimensionality reduction seldom provide repeatable frameworks that maintain model stability and are invariant to Euclidean transformations. Lastly, deep learning methods for dimensionality reduction, characterized as black-box models, lack interpretability and robust evaluation metrics, increasing susceptibility to algorithmic bias. Consequently, neglecting these challenges in subsurface modeling could lead to erroneous predictions, inconsistent inferences, diminished model reliability, and suboptimal decision-making that impacts project economics. This dissertation integrates information within subsurface models to reduce model bias and significantly improve their accuracy, robustness, and generalizability. First, I create spatial declustering methods to debias spatial datasets with single and multiscale preferential sampling in stationary populations. Second, I introduce a novel geostatistics-based machine learning method for identifying subsurface resource analogs that integrate spatial information in subsurface datasets with high dimensionality. Next, I efficiently combine machine learning and computational geometry methods to stabilize lower dimensional spaces for uncertainty quantification and interpretation. Finally, I create a methodology to assess, evaluate, and interpret the stability of deep learning latent feature spaces. These novel methodologies demonstrate the importance of improved techniques for information integration in subsurface modeling and show better results over naïve methods. This results in objective sampling debiasing in spatial stationary populations with single or multiple data scales, improving statistical representativity. Also, the results show better generalization and accurate identification of spatial analogs in high-dimensional datasets. Moreover, the methods yield Euclidean transformation-invariant lower-dimensional spaces, ensuring unique and repeatable solutions that improve model reliability and interpretability, for rational comparisons. Finally, the results indicate that deep learning models for dimensionality reduction exhibit algorithmic biases and instabilities, including sample, structural, and inferential instability, affecting their reliability and interpretability. Together, these innovations ultimately reduce model bias and significantly improve subsurface modeling.Petroleum and Geosystems Engineerin

Estimating Anthropometric Marker Locations from 3-D LADAR Point Clouds

An area of interest for improving the identification portion of the system is in extracting anthropometric markers from a Laser Detection and Ranging (LADAR) point cloud. Analyzing anthropometrics markers is a common means of studying how a human moves and has been shown to provide good results in determining certain demographic information about the subject. This research examines a marker extraction method utilizing principal component analysis (PCA), self-organizing maps (SOM), alpha hulls, and basic anthropometric knowledge. The performance of the extraction algorithm is tested by performing gender classification with the calculated markers

Mutable Objects, Spatial Manipulation and Performance Optimization

Contemporary digital design techniques are powerful, but disjoint. There are myriad emerging ways of manipulating design components, and generating both functional forms and formal functions. With the combination of selective agglomeration, sequencing, and heuristics, it is possible to use these techniques to focus on optimizing performance criteria, and selecting for defined characteristics. With these techniques, complex, performance oriented systems can emerge, with minimal input and high effectiveness and e""ciency. These processes depend on iterative loops for stability and directionality, and are the basis for optimization and refinement. They begin to approach cybernetic principles of self-organization and equilibrium. By rapidly looping this process, design ‘attractors’– shared solution components–become visible and accessible. In the past, we have been dedicated to selecting the contents of the design space. With these tools, we can now ask, what are the inputs to the design process, what is the continuum or spectrum of design inputs, and what are the selection criteria for the success of a design-aspect? These new questions allow for a greater coherence within a particular cognitive model for the designed and desired object. There are ways of using optimization criteria that enable design freedom within these boundaries, while enforcing constraints and maintaining consistency for selected processes and product aspects. The identification and codification of new rules for the process support both flexibility and the potential for cognitive restructuring of the process and sequences of design

Novel neural approaches to data topology analysis and telemedicine

1noL'abstract è presente nell'allegato / the abstract is in the attachmentopen676. INGEGNERIA ELETTRICAnoopenRandazzo, Vincenz

An investigation into automated processes for generating focus maps

The use of geographic information for mobile applications such as wayfinding has increased rapidly, enabling users to view information on their current position in relation to the neighbouring environment. This is due to the ubiquity of small devices like mobile phones, coupled with location finding devices utilising global positioning system. However, such applications are still not attractive to users because of the difficulties in viewing and identifying the details of the immediate surroundings that help users to follow directions along a route. This results from a lack of presentation techniques to highlight the salient features (such as landmarks) among other unique features. Another problem is that since such applications do not provide any eye-catching distinction between information about the region of interest along the route and the background information, users are not tempted to focus and engage with wayfinding applications. Although several approaches have previously been attempted to solve these deficiencies by developing focus maps, such applications still need to be improved in order to provide users with a visually appealing presentation of information to assist them in wayfinding. The primary goal of this research is to investigate the processes involved in generating a visual representation that allows key features in an area of interest to stand out from the background in focus maps for wayfinding users. In order to achieve this, the automated processes in four key areas - spatial data structuring, spatial data enrichment, automatic map generalization and spatial data mining - have been thoroughly investigated by testing existing algorithms and tools. Having identified the gaps that need to be filled in these processes, the research has developed new algorithms and tools in each area through thorough testing and validation. Thus, a new triangulation data structure is developed to retrieve the adjacency relationship between polygon features required for data enrichment and automatic map generalization. Further, a new hierarchical clustering algorithm is developed to group polygon features under data enrichment required in the automatic generalization process. In addition, two generalization algorithms for polygon merging are developed for generating a generalized background for focus maps, and finally a decision tree algorithm - C4.5 - is customised for deriving salient features, including the development of a new framework to validate derived landmark saliency in order to improve the representation of focus maps

Preserving Measured Structure During Generation and Reduction of Multivariate Point Configurations

Inherent in any multivariate data is structure, which describes the general shape and distribution of the underlying point configuration. While there are potentially many types of structure that could be of interest, consider restricting interest to two general types: geometric structure, the general shape of a point configuration, and probabilistic structure, the general distribution of points within the configuration. The ability to quantify geometric structure is an important step in many common statistical analyses. For instance, general neighbourhood structure is captured using a k-nearest neighbour graph in dimension reduction techniques such as isomap and locally-linear embedding. Neighbourhood graphs are also used in sensor network localization, which has applications in fields such as environmental habitat monitoring and wildlife monitoring. Another geometric graph, the convex hull, is also used in wildlife monitoring as a rough estimate of an animal's home range. The identification of areas of high and low density is one example of measuring the probability structure of a configuration, which can be done using a wide variety of methods. One such method is using kernel density estimation, which can be viewed as a weighted sum of nearby points. Kernel density estimation has widely varying applications, including in regression analysis, and is used in general to assess certain features of the data (modality, skewness, etc.). Related to the idea of measuring structure is the concept of "Cognostics", which has been formalized as scatterplot diagnostics (or scagnostics). Scagnostics provides a framework through which interesting structure can be measured in a configuration. The central idea is to numerically summarize the structure of a large number of two-dimensional point configurations via measures calculated on geometric graphs. This allows the interesting views to be quickly identified, and ultimately examined visually, while the views deemed to be uninteresting are simply discarded. While a good starting point, several issues in the current framework need to be addressed. For instance, while each measure is designed to be in [0,1], there are some that, when measured over tens of thousands of configurations, fail to achieve this range. In addition, there is a lot of structure that could be considered interesting that is not captured by the current framework. These issues, among others, will be addressed and rectified so that the current scagnostic framework can continue to be built upon. With tools to measure structure, attention is turned to making use of the structural information contained in the configuration. Consider the problem of preserving measured structure during the task of data aggregation, more commonly known as binning. Existing methods of data aggregation tend to exist on two ends of the structure retention spectrum. Through experimentation, methods such as equal width and hexagonal binning will be shown to tend to retain the shape of the configuration, at the expense of the density, while methods such as equal frequency and random sampling tend to retain relative density at the expense of overall shape. Tree-based binning, a general binning framework inspired by classification and regression trees, is proposed to bridge the gap between these sets of specialist algorithms. GapBin, a specially designed tree-based binning algorithm, will be shown through experimentation to provide a trade-off in low dimensional space between geometric structure retention and probabilistic structure retention. In higher dimensions, it will be shown to be the superior algorithm in terms of structure retention among those considered. Next, the general problem of constructing a configuration with a given underlying structure is considered. For example, the minimal spanning tree is known to carry important clustering information. Of interest then, is the generation of configurations with a given minimal spanning tree structure. The problem of generating a configuration with a known minimal spanning tree is equivalent to completing a Euclidean distance matrix where the only known entries are those in the minimal spanning tree. For this problem, there are several solutions, including those of Alfakih et. al., Fang & O'Leary, and Trosset. None of these algorithms, however, are designed to retain the structure of the minimal spanning tree. In addition, the sparsity of the Euclidean distance matrix containing only the minimal spanning tree results in completions that are not accurate as compared to the known completion. This leads to issues in the point configurations of the resulting completions. To resolve these, two new algorithms are proposed which are designed to retain the structure of the minimal spanning tree, leading to more accurate completions of these sparse matrices. To complement the algorithms presented, implementation of these algorithms in the statistical programming language R will also be discussed. In particular, the R package treebinr for tree-based binning, and edmcr for Euclidean distance matrix completions will be presented

Real-time spatial modeling to detect and track resources on construction sites

For more than 10 years the U.S. construction industry has experienced over 1,000 fatalities annually. Many fatalities may have been prevented had the individuals and equipment involved been more aware of and alert to the physical state of the environment around them. Awareness may be improved by automatic 3D (three-dimensional) sensing and modeling of the job site environment in real-time. Existing 3D modeling approaches based on range scanning techniques are capable of modeling static objects only, and thus cannot model in real-time dynamic objects in an environment comprised of moving humans, equipment, and materials. Emerging prototype 3D video range cameras offer another alternative by facilitating affordable, wide field of view, automated static and dynamic object detection and tracking at frame rates better than 1Hz (real-time). This dissertation presents an imperical work and methodology to rapidly create a spatial model of construction sites and in particular to detect, model, and track the position, dimension, direction, and velocity of static and moving project resources in real-time, based on range data obtained from a three-dimensional video range camera in a static or moving position. Existing construction site 3D modeling approaches based on optical range sensing technologies (laser scanners, rangefinders, etc.) and 3D modeling approaches (dense, sparse, etc.) that offered potential solutions for this research are reviewed. The choice of an emerging sensing tool and preliminary experiments with this prototype sensing technology are discussed. These findings led to the development of a range data processing algorithm based on three-dimensional occupancy grids which is demonstrated in detail. Testing and validation of the proposed algorithms have been conducted to quantify the performance of sensor and algorithm through extensive experimentation involving static and moving objects. Experiments in indoor laboratory and outdoor construction environments have been conducted with construction resources such as humans, equipment, materials, or structures to verify the accuracy of the occupancy grid modeling approach. Results show that modeling objects and measuring their position, dimension, direction, and speed had an accuracy level compatible to the requirements of active safety features for construction. Results demonstrate that video rate 3D data acquisition and analysis of construction environments can support effective detection, tracking, and convex hull modeling of objects. Exploiting rapidly generated three-dimensional models for improved visualization, communications, and process control has inherent value, broad application, and potential impact, e.g. as-built vs. as-planned comparison, condition assessment, maintenance, operations, and construction activities control. In combination with effective management practices, this sensing approach has the potential to assist equipment operators to avoid incidents that result in reduce human injury, death, or collateral damage on construction sites.Civil, Architectural, and Environmental Engineerin

Management of spatial data for visualization on mobile devices

Vector-based mapping is emerging as a preferred format in Location-based Services(LBS), because it can deliver an up-to-date and interactive map visualization. The Progressive Transmission(PT) technique has been developed to enable the ecient transmission of vector data over the internet by delivering various incremental levels of detail(LoD). However, it is still challenging to apply this technique in a mobile context due to many inherent limitations of mobile devices, such as small screen size, slow processors and limited memory. Taking account of these limitations, PT has been extended by developing a framework of ecient data management for the visualization of spatial data on mobile devices. A data generalization framework is proposed and implemented in a software application. This application can signicantly reduce the volume of data for transmission and enable quick access to a simplied version of data while preserving appropriate visualization quality. Using volunteered geographic information as a case-study, the framework shows exibility in delivering up-to-date spatial information from dynamic data sources. Three models of PT are designed and implemented to transmit the additional LoD renements: a full scale PT as an inverse of generalisation, a viewdependent PT, and a heuristic optimised view-dependent PT. These models are evaluated with user trials and application examples. The heuristic optimised view-dependent PT has shown a signicant enhancement over the traditional PT in terms of bandwidth-saving and smoothness of transitions. A parallel data management strategy associated with three corresponding algorithms has been developed to handle LoD spatial data on mobile clients. This strategy enables the map rendering to be performed in parallel with a process which retrieves the data for the next map location the user will require. A viewdependent approach has been integrated to monitor the volume of each LoD for visible area. The demonstration of a exible rendering style shows its potential use in visualizing dynamic geoprocessed data. Future work may extend this to integrate topological constraints and semantic constraints for enhancing the vector map visualization