Spatially-adaptive sensing in nonparametric regression

While adaptive sensing has provided improved rates of convergence in sparse regression and classification, results in nonparametric regression have so far been restricted to quite specific classes of functions. In this paper, we describe an adaptive-sensing algorithm which is applicable to general nonparametric-regression problems. The algorithm is spatially adaptive, and achieves improved rates of convergence over spatially inhomogeneous functions. Over standard function classes, it likewise retains the spatial adaptivity properties of a uniform design

On-site surface reflectometry

The rapid development of Augmented Reality (AR) and Virtual Reality (VR) applications over the past years has created the need to quickly and accurately scan the real world to populate immersive, realistic virtual environments for the end user to enjoy. While geometry processing has already gone a long way towards that goal, with self-contained solutions commercially available for on-site acquisition of large scale 3D models, capturing the appearance of the materials that compose those models remains an open problem in general uncontrolled environments. The appearance of a material is indeed a complex function of its geometry, intrinsic physical properties and furthermore depends on the illumination conditions in which it is observed, thus traditionally limiting the scope of reflectometry to highly controlled lighting conditions in a laboratory setup. With the rapid development of digital photography, especially on mobile devices, a new trend in the appearance modelling community has emerged, that investigates novel acquisition methods and algorithms to relax the hard constraints imposed by laboratory-like setups, for easy use by digital artists. While arguably not as accurate, we demonstrate the ability of such self-contained methods to enable quick and easy solutions for on-site reflectometry, able to produce compelling, photo-realistic imagery. In particular, this dissertation investigates novel methods for on-site acquisition of surface reflectance based on off-the-shelf, commodity hardware. We successfully demonstrate how a mobile device can be utilised to capture high quality reflectance maps of spatially-varying planar surfaces in general indoor lighting conditions. We further present a novel methodology for the acquisition of highly detailed reflectance maps of permanent on-site, outdoor surfaces by exploiting polarisation from reflection under natural illumination. We demonstrate the versatility of the presented approaches by scanning various surfaces from the real world and show good qualitative and quantitative agreement with existing methods for appearance acquisition employing controlled or semi-controlled illumination setups.Open Acces

Parameter estimation for peaky altimetric waveforms

Much attention has been recently devoted to the analysis of coastal altimetric waveforms. When approaching the coast, altimetric waveforms are sometimes corrupted by peaks caused by high reflective areas inside the illuminated land surfaces or by the modification of the sea state close to the shoreline. This paper introduces a new parametric model for these peaky altimetric waveforms. This model assumes that the received altimetric waveform is the sum of a Brown echo and an asymmetric Gaussian peak. The asymmetric Gaussian peak is parameterized by a location, an amplitude, a width, and an asymmetry coefficient. A maximum-likelihood estimator is studied to estimate the Brown plus peak model parameters. The Cramér–Rao lower bounds of the model parameters are then derived providing minimum variances for any unbiased estimator, i.e., a reference in terms of estimation error. The performance of the proposed model and the resulting estimation strategy are evaluated via many simulations conducted on synthetic and real data. Results obtained in this paper show that the proposed model can be used to retrack efficiently standard oceanic Brown echoes as well as coastal echoes corrupted by symmetric or asymmetric Gaussian peaks. Thus, the Brown with Gaussian peak model is useful for analyzing altimetric easurements closer to the coast

Studying scratching/buffing mechanism of the clearcoats and improving the technique and devices/materials in automobile refinish work

As automotive coating technology advances, the manufacturers have developed new generations of after-market coatings as well as OEM (Original Equipment Manufacturing) coatings. The new coatings possess much stronger mar/scratch resistance either with raised elastic modulus and hardness or with the ability of self-healing. These pose new challenges to the auto-body refinish work. In the refinishing process, the after-market coatings are applied to the bodies (surfaces), and then the imperfections, which are inevitable during the application process due to the environmental dust in the shops, will be buffed out to make a smooth and shining surface. The new coatings are very difficult to work with in the refinish process. Sponsored by 3M Incorporation, the largest producer of the refinish devices and materials, we have carried out a systematic study of the scratching/buffing mechanism of the clearcoats in the refinish work. We measured modulus and hardness at depths of nano/micron meters at the coating surface, checked the heating effect on the mars and scratches at the coating surface, and examined the surface morphologies in nano and micro scales of different coatings at different stages in the refinish process. We also studied the variations of the mechanical and tribological properties of the coatings during the curing process after the baking, etc., using newly developed nano instruments, a Nano-Indenter XP and a Scanning Probe Microscope (SPM). This study has increased our knowledge of the scratching and buffing mechanism of coatings and the refinish process, thus shedding light on the direction for improvement of the technique and devices/materials in the refinish work

Dynamics of Contact Angles and Hemiwicking on Rock Fracture Faces

The dynamics of contact angles and capillary wicking (hemiwicking) were investigated on rock fracture surfaces from a selection of low-porosity rocks with different mineralogy: Burlington Limestone, Crossville Sandstone, Mancos Shale, Sierra White Granite, Vermilion Bay Granite, and Westerly Granite. Wetting height data for rough fracture faces were acquired in a parallel view using dynamic neutron radiography at the Oak Ridge National Laboratory Neutron Imaging Facility. Hemiwicking rates on the rock fracture surfaces were determined using a high-speed optical setup with a perpendicular viewpoint. Wetting height versus time relationships for both methods were delineated through changepoint analysis. The contact angle of the fracture surface (����) was then quantified based on the maximum wetting height. Statistical significance was assessed at the 95% confidence level. Analysis of variance indicated statistically significant differences in mean ���� values between rock types. Regression analyses between ���� and the contact angles of polished rock surfaces (����) and the Wenzel Roughness Factor yielded statistically non-significant relationships. Linear regression showed that the median wetting height during hemiwicking behaved linearly with respect to the square root of time. Surface sorptivity was quantified by the proportionality constant between the height of capillary wetting and the square root of time. Analysis of variance indicated statistically significant differences between rock types in mean surface sorptivity values. A statistically significant negative relationship was observed between surface sorptivity and ����, while non-significant relationships were observed between surface sorptivity and ����, and the Wenzel Roughness Factor. An analysis of variance of the interquartile range (IQR) for wetting height revealed statistically significant dependencies on both rock type and time, with no interaction. Overall, the results point to differences in mineralogy, rather than roughness, as the main control of contact angle and hemiwicking dynamics on rock fracture faces

The Iray Light Transport Simulation and Rendering System

While ray tracing has become increasingly common and path tracing is well understood by now, a major challenge lies in crafting an easy-to-use and efficient system implementing these technologies. Following a purely physically-based paradigm while still allowing for artistic workflows, the Iray light transport simulation and rendering system allows for rendering complex scenes by the push of a button and thus makes accurate light transport simulation widely available. In this document we discuss the challenges and implementation choices that follow from our primary design decisions, demonstrating that such a rendering system can be made a practical, scalable, and efficient real-world application that has been adopted by various companies across many fields and is in use by many industry professionals today

In Search of a Binding Agent: Nano-Scale Evidence of Preferential Carbon Associations with Poorly-Crystalline Mineral Phases in Physically-Stable, Clay-Sized Aggregates

Mechanisms of protecting soil carbon (C) are still poorly understood despite growing needs to predict and manage the changes in soil C or organic matter (OM) under anticipated climate change. A fundamental question is how the submicron-scale interaction between OM and soil minerals, especially poorly-crystalline phases, affects soil physical aggregation and C stabilization. Nano-sized composites rich in OM and poorly-crystalline mineral phases were presumed to account for high aggregate stability in the Andisol we previously studied. Here we searched for these nanocomposites within a sonication-resistant aggregate using scanning transmission X-ray microscopy (STXM) and near-edge X-ray absorption fine structure (NEXAFS) as well as electron microscopy (SEM, TEM). Specifically, we hypothesized that nanometer-scale spatial distribution of OM is controlled by poorly-crystalline minerals as both co-exist as physically-stable nanocomposites. After maximum dispersion of the cultivated Andisol A-horizon sample in water, one aggregate (a few p.m in diameter) was isolated from 0.2-2 mu m size fraction which accounted for 44-47% of total C and N and 50% of poorly-crystalline minerals in bulk soil. This fraction as well as 2 mu m size fractions, implying high abundance of the nanocomposites in the smaller fractions. The isolated aggregate showed a mosaic of two distinctive regions. Smooth surface regions showed low adsorption intensity of carbon K-edge photon energy (284-290 eV) with well-crystalline mineralogy, whereas rough surface regions had features indicative of the nanocomposites: aggregated nanostructure, high C intensity, X-ray amorphous mineral phase, and the dominance of Si, O, Al, and Fe based on SEM/EDX and TEM/EDX. Carbon functional group chemistry assessed by NEXAFS showed the dominance of amide and carboxyl C over aromatic and aliphatic C with some variation among the four rough surface regions. Together with C and N isotopic patterns among the size fractions (relatively low C:N ratio, high N-15 natural abundance, and more positive Delta C-14 of the <2 mu m fractions), our results provided the direct evidence of preferential binding of microbially-altered, potentially-labile C with poorly-crystalline mineral phases at submicron scale. The role of the nanocomposite inferred from this study may help to bridge the knowledge gap between physical aggregation process and biogeochemical reactions taking place within the soil physical structure

Photometric Depth Super-Resolution

This study explores the use of photometric techniques (shape-from-shading and uncalibrated photometric stereo) for upsampling the low-resolution depth map from an RGB-D sensor to the higher resolution of the companion RGB image. A single-shot variational approach is first put forward, which is effective as long as the target's reflectance is piecewise-constant. It is then shown that this dependency upon a specific reflectance model can be relaxed by focusing on a specific class of objects (e.g., faces), and delegate reflectance estimation to a deep neural network. A multi-shot strategy based on randomly varying lighting conditions is eventually discussed. It requires no training or prior on the reflectance, yet this comes at the price of a dedicated acquisition setup. Both quantitative and qualitative evaluations illustrate the effectiveness of the proposed methods on synthetic and real-world scenarios.Comment: IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 2019. First three authors contribute equall