Semiparametric Regression During 2003–2007

Semiparametric regression is a fusion between parametric regression and nonparametric regression and the title of a book that we published on the topic in early 2003. We review developments in the field during the five year period since the book was written. We find semiparametric regression to be a vibrant field with substantial involvement and activity, continual enhancement and widespread application

Spatiotemporal visual analysis of human actions

In this dissertation we propose four methods for the recognition of human activities. In all four of them, the representation of the activities is based on spatiotemporal features that are automatically detected at areas where there is a significant amount of independent motion, that is, motion that is due to ongoing activities in the scene. We propose the use of spatiotemporal salient points as features throughout this dissertation. The algorithms presented, however, can be used with any kind of features, as long as the latter are well localized and have a well-defined area of support in space and time. We introduce the utilized spatiotemporal salient points in the first method presented in this dissertation. By extending previous work on spatial saliency, we measure the variations in the information content of pixel neighborhoods both in space and time, and detect the points at the locations and scales for which this information content is locally maximized. In this way, an activity is represented as a collection of spatiotemporal salient points. We propose an iterative linear space-time warping technique in order to align the representations in space and time and propose to use Relevance Vector Machines (RVM) in order to classify each example into an action category. In the second method proposed in this dissertation we propose to enhance the acquired representations of the first method. More specifically, we propose to track each detected point in time, and create representations based on sets of trajectories, where each trajectory expresses how the information engulfed by each salient point evolves over time. In order to deal with imperfect localization of the detected points, we augment the observation model of the tracker with background information, acquired using a fully automatic background estimation algorithm. In this way, the tracker favors solutions that contain a large number of foreground pixels. In addition, we perform experiments where the tracked templates are localized on specific parts of the body, like the hands and the head, and we further augment the tracker’s observation model using a human skin color model. Finally, we use a variant of the Longest Common Subsequence algorithm (LCSS) in order to acquire a similarity measure between the resulting trajectory representations, and RVMs for classification. In the third method that we propose, we assume that neighboring salient points follow a similar motion. This is in contrast to the previous method, where each salient point was tracked independently of its neighbors. More specifically, we propose to extract a novel set of visual descriptors that are based on geometrical properties of three-dimensional piece-wise polynomials. The latter are fitted on the spatiotemporal locations of salient points that fall within local spatiotemporal neighborhoods, and are assumed to follow a similar motion. The extracted descriptors are invariant in translation and scaling in space-time. Coupling the neighborhood dimensions to the scale at which the corresponding spatiotemporal salient points are detected ensures the latter. The descriptors that are extracted across the whole dataset are subsequently clustered in order to create a codebook, which is used in order to represent the overall motion of the subjects within small temporal windows.Finally,we use boosting in order to select the most discriminative of these windows for each class, and RVMs for classification. The fourth and last method addresses the joint problem of localization and recognition of human activities depicted in unsegmented image sequences. Its main contribution is the use of an implicit representation of the spatiotemporal shape of the activity, which relies on the spatiotemporal localization of characteristic ensembles of spatiotemporal features. The latter are localized around automatically detected salient points. Evidence for the spatiotemporal localization of the activity is accumulated in a probabilistic spatiotemporal voting scheme. During training, we use boosting in order to create codebooks of characteristic feature ensembles for each class. Subsequently, we construct class-specific spatiotemporal models, which encode where in space and time each codeword ensemble appears in the training set. During testing, each activated codeword ensemble casts probabilistic votes concerning the spatiotemporal localization of the activity, according to the information stored during training. We use a Mean Shift Mode estimation algorithm in order to extract the most probable hypotheses from each resulting voting space. Each hypothesis corresponds to a spatiotemporal volume which potentially engulfs the activity, and is verified by performing action category classification with an RVM classifier

Dynamic Thermal Imaging for Intraoperative Monitoring of Neuronal Activity and Cortical Perfusion

Neurosurgery is a demanding medical discipline that requires a complex interplay of several neuroimaging techniques. This allows structural as well as functional information to be recovered and then visualized to the surgeon. In the case of tumor resections this approach allows more fine-grained differentiation of healthy and pathological tissue which positively influences the postoperative outcome as well as the patient's quality of life. In this work, we will discuss several approaches to establish thermal imaging as a novel neuroimaging technique to primarily visualize neural activity and perfusion state in case of ischaemic stroke. Both applications require novel methods for data-preprocessing, visualization, pattern recognition as well as regression analysis of intraoperative thermal imaging. Online multimodal integration of preoperative and intraoperative data is accomplished by a 2D-3D image registration and image fusion framework with an average accuracy of 2.46 mm. In navigated surgeries, the proposed framework generally provides all necessary tools to project intraoperative 2D imaging data onto preoperative 3D volumetric datasets like 3D MR or CT imaging. Additionally, a fast machine learning framework for the recognition of cortical NaCl rinsings will be discussed throughout this thesis. Hereby, the standardized quantification of tissue perfusion by means of an approximated heating model can be achieved. Classifying the parameters of these models yields a map of connected areas, for which we have shown that these areas correlate with the demarcation caused by an ischaemic stroke segmented in postoperative CT datasets. Finally, a semiparametric regression model has been developed for intraoperative neural activity monitoring of the somatosensory cortex by somatosensory evoked potentials. These results were correlated with neural activity of optical imaging. We found that thermal imaging yields comparable results, yet doesn't share the limitations of optical imaging. In this thesis we would like to emphasize that thermal imaging depicts a novel and valid tool for both intraoperative functional and structural neuroimaging

Non-intrusive reduced order modelling for aerodynamic applications

During the design and optimisation of aerodynamic components, the simulations to be performed involve a large number of parameters related to the geometry and flow conditions. In this scenario, the simulation of all possible configurations is not af-fordable. To overcome this problem, the present work proposes a novel multi-output neural network (NN) for the prediction of aerodynamic coefficients of aerofoils and wings using compressible flow data. Contrary to existing NNs that are designed to predict aerodynamic quantities of interest, the proposed network considers as output the pressure or stresses at a number of selected points on the aerodynamic surface. The proposed approach is compared against the more traditional networks where the aero-dynamic coefficients are directly the outputs of the network. Furthermore, a detailed comparison of the proposed NN against the popular proper orthogonal decomposi-tion (POD) method is presented. The numerical results, involving high dimensional problems with flow and geometric parameters, show the benefits of the proposed ap-proach.The proposed NN is used to accelerate the evaluation of the objective function in an inverse aerodynamic shape design problem. The optimisation algorithm uses the gradient-free modified cuckoo search method. Applications in two and three dimen-sions are shown, demonstrating the potential of the proposed framework in the con-text of both optimisation and inverse design problems. The performance of the pro-posed optimisation framework is also compared against existing frameworks where the more traditional NNs are employed

A Parametric Sound Object Model for Sound Texture Synthesis

This thesis deals with the analysis and synthesis of sound textures based on parametric sound objects. An overview is provided about the acoustic and perceptual principles of textural acoustic scenes, and technical challenges for analysis and synthesis are considered. Four essential processing steps for sound texture analysis are identifi ed, and existing sound texture systems are reviewed, using the four-step model as a guideline. A theoretical framework for analysis and synthesis is proposed. A parametric sound object synthesis (PSOS) model is introduced, which is able to describe individual recorded sounds through a fi xed set of parameters. The model, which applies to harmonic and noisy sounds, is an extension of spectral modeling and uses spline curves to approximate spectral envelopes, as well as the evolution of parameters over time. In contrast to standard spectral modeling techniques, this representation uses the concept of objects instead of concatenated frames, and it provides a direct mapping between sounds of diff erent length. Methods for automatic and manual conversion are shown. An evaluation is presented in which the ability of the model to encode a wide range of di fferent sounds has been examined. Although there are aspects of sounds that the model cannot accurately capture, such as polyphony and certain types of fast modulation, the results indicate that high quality synthesis can be achieved for many different acoustic phenomena, including instruments and animal vocalizations. In contrast to many other forms of sound encoding, the parametric model facilitates various techniques of machine learning and intelligent processing, including sound clustering and principal component analysis. Strengths and weaknesses of the proposed method are reviewed, and possibilities for future development are discussed

Comparative Analysis of Connection and Disconnection in the Human Brain Using Diffusion MRI: New Methods and Applications

Institute for Adaptive and Neural ComputationDiffusion magnetic resonance imaging (dmri) is a technique that can be used to examine the diffusion characteristics of water in the living brain. A recently developed application of this technique is tractography, in which information from brain images obtained using dmri is used to reconstruct the pathways which connect regions of the brain together. Proxy measures for the integrity, or coherence, of these pathways have also been defined using dmri-derived information. The disconnection hypothesis suggests that specific neurological impairments can arise from damage to these pathways as a consequence of the resulting interruption of information flow between relevant areas of cortex. The development of dmri and tractography have generated a considerable amount of renewed interest in the disconnectionist thesis, since they promise a means for testing the hypothesis in vivo in any number of pathological scenarios. However, in order to investigate the effects of pathology on particular pathways, it is necessary to be able to reliably locate them in three-dimensional dmri images. The aim of the work described in this thesis is to improve upon the robustness of existing methods for segmenting specific white matter tracts from image data, using tractography, and to demonstrate the utility of the novel methods for the comparative analysis of white matter integrity in groups of subjects. The thesis begins with an overview of probability theory, which will be a recurring theme throughout what follows, and its application to machine learning. After reviewing the principles of magnetic resonance in general, and dmri and tractography in particular, we then describe existing methods for segmenting particular tracts from group data, and introduce a novel approach. Our innovation is to use a reference tract to define the topological characteristics of the tract of interest, and then search a group of candidate tracts in the target brain volume for the best match to this reference. In order to assess how well two tracts match we define a heuristic but quantitative tract similarity measure. In later chapters we demonstrate that this method is capable of successfully segmenting tracts of interest in both young and old, healthy and unhealthy brains; and then describe a formalised version of the approach which uses machine learning methods to match tracts from different subjects. In this case the similarity between tracts is represented as a matching probability under an explicit model of topological variability between equivalent tracts in different brains. Finally, we examine the possibility of comparing the integrity of groups of white matter structures at a level more fine-grained than a whole tract

Dimensionally reduced modeling and gradient-based design of microchannel cooling networks

Microvascular composites constitute a novel class of biomemetic materials with the ability to perform multiple functions such as dynamic tuning of electromagnetic properties, self-healing and thermal management depending on the fluid circulated in the embedded microchannels. Recent breakthroughs in the vaporization of sacrificial component (VaSC) manufacturing technique have allowed for the creation of intricate microchannel networks and large scale production of these composites. As the design of these networks is key to the performance of the composites and designer's intuition is insufficient to achieve optimal performance, the development of ``automated" design tools is of paramount importance. The primary goal of this work is to fulfill that need in the specific application of thermal management. To that end, we develop three ingredients: dimensionally reduced thermal and hydraulic models, a numerical solver and a shape optimization scheme. Another goal of this project is to verify and validate the dimensionally reduced models against a commercial computational fluid dynamics software package and experiments. The final goal is to apply the design tool to various 2D and 3D problems. In the dimensionally reduced thermal model, the microchannels are collapsed into lines/curves to simplify mesh generation and their thermal impacts are added to the heat equation. Two versions of the thermal model are considered: (i) a linear model that does not involve radiative heat exchange or linearizes the Stefan-Boltzman radiation equation and (ii) a nonlinear model that incorporates the original radiation equation. The hydraulic model uses the Hagen-Poiseuille law to describe the flow rates and pressure drops in the microchannel networks. To capture the gradient discontinuity in the temperature field due to the microchannels, we employ the interface-enriched generalized finite element method (IGFEM) as the numerical solver, which greatly simplifies mesh generation by allowing for the use of meshes that do not conform to the microchannel network. While previous IGFEM works are based on polynomial enrichment functions, we demonstrate the flexibility of the IGFEM by developing non-uniform rational B-splines (NURBS) enrichment functions for branched network of curved microchannels. We then develop a method to address the convergence issue due to the singularity associated with the thermal model in 3D and combine that method with polynomial IGFEM. The thermal fields obtained from the resulting modified IGFEM agree with those of the significantly more complex and costly ANSYS FLUENT conjugate heat transfer simulations. The final ingredient involves the development of analytical IGFEM-based shape sensitivity analyses for both linear and nonlinear models. These analyses allow the design tool to efficiently exploit existing powerful gradient-based optimization algorithms, especially for large number of design parameters. We then apply the gradient-based shape optimization scheme to solve a diverse range of problems, which demonstrate two key advantages of the scheme due to the use of stationary non-conforming meshes by (i) eliminating the cumulative mesh generation cost and (ii) avoiding severe mesh distortion issues as the microchannel geometry evolves during the optimization process. The first problem involves parallel networks of microchannels for 2D microvascular composite battery cooling panels. Using a differentiable alternative to the maximum temperature (the $p$-norm of the temperature field) of a cooling panel as an objective function, we obtain optimized designs superior to the reference designs in terms of cooling performance. We also extensively validate the IGFEM solutions associated with the designs against ANSYS FLUENT simulations and experiments. We further extend the uses of the tool to include multi-objective optimization, pressure drop as objective function, channel diameters as design parameters and localized heat sources. In the multi-objective optimization, the Pareto fronts of the maximum temperatures and the pressure drops across the networks are generated using the normalized normal constraint method. Next, we apply the optimization scheme to design blockage-tolerant cooling networks embedded in 2D PDMS panels. In this novel application, a minmax problem that minimizes the worst case of a set of predetermined blockage scenarios is formulated and converted to a simpler single-objective optimization problem. In the worst blockage scenario, the designs optimized in this manner exhibit substantial reduction of cooling performance loss compared with designs optimized without considering blockages, with greater reduction as the redundancy of the network decreases. The designs are also validated against experiments. Another novel application of the optimization scheme is related to the design of 2D microvascular panels for nanosatellite. In this application, the sensitivity analysis based on the nonlinear thermal model is used since the nonlinear effect of radiation cannot be neglected. Taking advantage of the optimization tool, two formulations are proposed to satisfy the design constraints. We perform extensive benchmarking of the results obtained from the dimensionally reduced models against those from ANSYS FLUENT, and provide analytical estimates of the thermal performance of optimized designs. In the final application, we design multiple parallel microchannels embedded in 3D microvascular panels using the modified-IGFEM-based optimization scheme. Due to the importance of the straight microchannel design, we propose a semi-analytical model of the maximum temperature in a panel with multiple straight channels

Conceptual design of a concrete staircase based on topology optimization : A feasibility study on the development and application of topology optimization on a concrete staircase

Master's thesis in Civil and structural engineering (BYG508