Topologically reliable approximation of curves and surfaces

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Ocean Engineering, 1997.Includes bibliographical references (p. [213]-222).by Wonjoon Cho.Ph.D

Neural Spectro-polarimetric Fields

Modeling the spatial radiance distribution of light rays in a scene has been extensively explored for applications, including view synthesis. Spectrum and polarization, the wave properties of light, are often neglected due to their integration into three RGB spectral bands and their non-perceptibility to human vision. Despite this, these properties encompass substantial material and geometric information about a scene. In this work, we propose to model spectro-polarimetric fields, the spatial Stokes-vector distribution of any light ray at an arbitrary wavelength. We present Neural Spectro-polarimetric Fields (NeSpoF), a neural representation that models the physically-valid Stokes vector at given continuous variables of position, direction, and wavelength. NeSpoF manages inherently noisy raw measurements, showcases memory efficiency, and preserves physically vital signals, factors that are crucial for representing the high-dimensional signal of a spectro-polarimetric field. To validate NeSpoF, we introduce the first multi-view hyperspectral-polarimetric image dataset, comprised of both synthetic and real-world scenes. These were captured using our compact hyperspectral-polarimetric imaging system, which has been calibrated for robustness against system imperfections. We demonstrate the capabilities of NeSpoF on diverse scenes

Design of Field Experiments for Adaptive Sampling of the Ocean with Autonomous Vehicles

Due to the highly non-linear and dynamical nature of oceanic phenomena, the predictive capability of various ocean models depends on the availability of operational data. A practical method to improve the accuracy of the ocean forecast is to use a data assimilation methodology to combine in-situ measured and remotely acquired data with numerical forecast models of the physical environment. Autonomous surface and underwater vehicles with various sensors are economic and efficient tools for exploring and sampling the ocean for data assimilation; however there is an energy limitation to such vehicles, and thus effective resource allocation for adaptive sampling is required to optimize the efficiency of exploration. In this paper, we use physical oceanography forecasts of the coastal zone of Singapore for the design of a set of field experiments to acquire useful data for model calibration and data assimilation. The design process of our experiments relied on the oceanography forecast including the current speed, its gradient, and vorticity in a given region of interest for which permits for field experiments could be obtained and for time intervals that correspond to strong tidal currents. Based on these maps, resources available to our experimental team, including Autonomous Surface Craft (ASC) are allocated so as to capture the oceanic features that result from jets and vortices behind bluff bodies (e.g., islands) in the tidal current. Results are summarized from this resource allocation process and field experiments conducted in January 2009.Singapore. National Research Foundatio

Multi-layer model simulation and data assimilation in the Serangoon Harbor of Singapore

In June of 2009, a sea trial was carried out around Singapore to study and monitor physical, biological and chemical oceanographic parameters. Temperature, salinity and velocities were collected from multiple vehicles. The extensive data set collected in the Serangoon Harbour provides an opportunity to study barotropic and baroclinic circulation in the harbour and to apply data assimilation methods in the estuarine area. In this study, a three-dimensional, primitive equation coastal ocean model (FVCOM) with a number of vertical layers is used to simulate barotropic and baroclinic flows and reconstruct the vertical velocity structures. The model results are validated with in situ ADCP observations to assess the realism of the model simulations. EnKF data assimilation method is successively implemented to assimilate all the available ADCP data, and thus correct for the model forecast deficiencies.Singapore. National Research FoundationSingapore-MIT AllianceSingapore-MIT Alliance. Center for Environmental Sensing and Monitorin

Multi-vehicle oceanographic feature exploration

URL to conference page. Scroll down to 2009 conference (June 21-26), click "Paper and session list," and search under Patrikalakis' name.Oceanographic features such as jets and vortices are often found downstream of obstacles and landforms such as islands or peninsulas. Such features have high spatial and temporal variability and are, hence, interesting but difficult to measure and quantify. This paper discusses an experiment to identify and resolve such oceanographic features in Selat Pauh, in the Straits of Singapore. The deployment formation for multiple robotic vehicles (Autonomous Surface Craft - ASC), the measurement instruments, and the algorithms developed in extracting oceanographic field variables are described. These were based on two ocean field predictions from well-known geophysical flow dynamic models. Field experiments were carried out and comparison of the forecasts with measurements was attempted. To investigate an unexpected behaviour of one ASC, hindcasts with wind effects and simulation with vortex feature extraction on a larger domain with more involved bathymetry were also partially carried out.Singapore-MIT Alliance for Research and TechnologySingapore. National Research Foundation (SMART/CENSAM initiative

Chiral Biomaterials for Nanomedicines: From Molecules to Supraparticles

Chirality, the property whereby an object or a system cannot be superimposed on its mirror image, prevails amongst nature over various scales. Especially in biology, numerous chiral building blocks and chiral-specific interactions are involved in many essential biological activities. Despite the prevalence of chirality in nature, it has been no longer than 70 years since the mechanisms of chiral-specific interactions drew scientific attention and began to be studied. Owing to the advent of chiral-sensitive equipment such as circular dichroism spectrometers or chiral liquid columns for chromatography, it has recently been possible to achieve a deeper understanding of the chiral-specific interactions and consequential impacts on the functionality and efficiency of nanomedicine. From this point of view, it is worthwhile to examine previously reported chiral biomaterials with their compositions and possible applications to achieve new paradigms of biomaterials. This review discusses chiral materials on various scales and their biological applications

Interpreting Internal Activation Patterns in Deep Temporal Neural Networks by Finding Prototypes

Deep neural networks have demonstrated competitive performance in classification tasks for sequential data. However, it remains difficult to understand which temporal patterns the internal channels of deep neural networks capture for decision-making in sequential data. To address this issue, we propose a new framework with which to visualize temporal representations learned in deep neural networks without hand-crafted segmentation labels. Given input data, our framework extracts highly activated temporal regions that contribute to activating internal nodes and characterizes such regions by prototype selection method based on Maximum Mean Discrepancy. Representative temporal patterns referred to here as Prototypes of Temporally Activated Patterns (PTAP) provide core examples of subsequences in the sequential data for interpretability. We also analyze the role of each channel by Value-LRP plots using representative prototypes and the distribution of the input attribution. Input attribution plots give visual information to recognize the shapes focused on by the channel for decision-making

Topologically reliable approximation of trimmed polynomial surface patches

We present an unstructured triangular mesh generation algorithm that approximates a set of mutually nonintersecting simple trimmed polynomial parametric surface patches within a user specified geometric tolerance. The proposed method uses numerically robust interval geometric representations/computations and also addresses the problem of topological consistency (homeomorphism) between the exact geometry and its approximation. Those are among the most important outstanding issues in geometry approximation problems. We also extract important differential geometric features of input geometry for use in the approximation. Our surface tessellation algorithm is based on the unstructured Delaunay mesh approach which leads to an efficient adaptive triangulation. A robust decision criterion is introduced to prevent possible failures in the conventional Delaunay triangulation. To satisfy the prescribed geometric tolerance, an adaptive node insertion algorithm is employed and furthermore, an efficient method to compute a tight upper bound of the approximation error is proposed. Unstructured triangular meshes for free-form surfaces frequently involve triangles with high aspect ratio and, accordingly, result in ill-conditioned meshing. Our proposed algorithm constructs 2D triangulation domains which sufficiently preserve the shape of triangles when mapped into 3D space and, furthermore, the algorithm provides an efficient method that explicitly controls the aspect ratio of the triangular elements