Perceptual Qualities of Optically Mixed Materials

We present a novel setup in which real objects made of two different materials can be mixed optically in a linearly weighted manner. We conducted a psychophysical experiment in which observers rated optical mixtures of the three combinations of glossy, matte, and velvety green birds. The observers rated the materials on four scales: matte–glossy, hard–soft, cold–warm, and light-heavy. The judgments were found to be consistent and varied systematically with the weights of the contributions. This record was migrated from the OpenDepot repository service in June, 2017 before shutting down

Practical Multiple Scattering for Rough Surfaces

Microfacet theory concisely models light transport over rough surfaces. Specular reflection is the result of single mirror reflections on each facet, while exact computation of multiple scattering is either neglected, or modeled using costly importance sampling techniques. Practical but accurate simulation of multiple scattering in microfacet theory thus remains an open challenge. In this work, we revisit the traditional V-groove cavity model and derive an analytical, cost-effective solution for multiple scattering in rough surfaces. Our kaleidoscopic model is made up of both real and virtual V-grooves, and allows us to calculate higher-order scattering in the microfacets in an analytical fashion. We then extend our model to include nonsymmetric grooves, allowing for additional degrees of freedom on the surface geometry, improving multiple reflections at grazing angles with backward compatibility to traditional normal distribution functions. We validate the accuracy of our model against ground-truth Monte Carlo simulations, and demonstrate its flexibility on anisotropic and textured materials. Our model is analytical, does not introduce significant cost and variance, can be seamless integrated in any rendering engine, preserves reciprocity and energy conservation, and is suitable for bidirectional methods

Fabricating BRDFs at high spatial resolution using wave optics

Recent attempts to fabricate surfaces with custom reflectance functions boast impressive angular resolution, yet their spatial resolution is limited. In this paper we present a method to construct spatially varying reflectance at a high resolution of up to 220dpi, orders of magnitude greater than previous attempts, albeit with a lower angular resolution. The resolution of previous approaches is limited by the machining, but more fundamentally, by the geometric optics model on which they are built. Beyond a certain scale geometric optics models break down and wave effects must be taken into account. We present an analysis of incoherent reflectance based on wave optics and gain important insights into reflectance design. We further suggest and demonstrate a practical method, which takes into account the limitations of existing micro-fabrication techniques such as photolithography to design and fabricate a range of reflection effects, based on wave interference.United States-Israel Binational Science FoundationIntel Corporation (Intel Collaborative Research Institute for Computational Intelligence)National Science Foundation (U.S.) (CGV 1116303

Deciphering Surfaces of Trans-Neptunian and Kuiper Belt Objects using Radiative Scattering Models, Machine Learning, and Laboratory Experiments

Decoding surface-atmospheric interactions and volatile transport mechanisms on trans-Neptunian objects (TNOs) and Kuiper Belt objects (KBOs) involves an in-depth understanding of physical and thermal properties and spatial distribution of surface constituents – nitrogen (N2), methane (CH4), carbon monoxide (CO), and water (H2O) ices. This thesis implements a combination of radiative scattering models, machine learning techniques, and laboratory experiments to investigate the uncertainties in grain size estimation of ices, the spatial distribution of surface compositions on Pluto, and the thermal properties of volatiles found on TNOs and KBOs. Radiative scattering models (Mie theory and Hapke approximations) were used to compare single scattering albedos of N2, CH4, and H2O ices from their optical constants at near-infrared wavelengths (1 – 5 µm). Based on the results of Chapters 2 and 3, this thesis recommends using the Mie model for unknown spectra of outer solar system bodies in estimating grain sizes of surface ices. When using an approximation for radiative transfer models (RTMs), we recommend using the Hapke slab approximation model over the internal scattering model. In Chapter 4, this thesis utilizes near-infrared (NIR) spectral observations of the LEISA/Ralph instrument onboard NASA’s New Horizons spacecraft. Hyperspectral LEISA data were used to map the geographic distribution of ices on Pluto’s surface by implementing the principal component reduced Gaussian mixture model (PC-GMM), an unsupervised machine learning technique. The distribution of ices reveals a latitudinal pattern with distinct surface compositions of volatiles. The PC-GMM method was able to recognize local-scale variations in surface compositions of geological features. The mapped distribution of surface units and their compositions are consistent with existing literature and help in an improved understanding of the volatile transport mechanism on the dwarf planet. In Chapter 5, we propose a method to estimate thermal conductivity, volumetric heat capacity, thermal diffusivity, and thermal inertia of N2, CH4, and CO ices, and mixtures thereof in a simulated laboratory setting at temperatures of 20 to 60 K – relevant to TNOs and KBOs. A new laboratory experimental facility – named the Outer Solar System Astrophysics Lab (OSSAL) – was built to implement the proposed method. This thesis provides detailed technical specifications of that laboratory with an emphasis on facilitating the design of similar cryogenic facilities in the future. Thus, this research was able to incorporate a set of methods, tools, and techniques for an improved understanding of ices found in the Kuiper Belt and to decipher surface-atmospheric interactions and volatile transport mechanisms on planetary bodies in the outer solar system

Deciphering Surfaces of Trans-Neptunian and Kuiper Belt Objects using Radiative Scattering Models, Machine Learning, and Laboratory Experiments

Decoding surface-atmospheric interactions and volatile transport mechanisms on trans-Neptunian objects (TNOs) and Kuiper Belt objects (KBOs) involves an in-depth understanding of physical and thermal properties and spatial distribution of surface constituents – nitrogen (N2), methane (CH4), carbon monoxide (CO), and water (H2O) ices. This thesis implements a combination of radiative scattering models, machine learning techniques, and laboratory experiments to investigate the uncertainties in grain size estimation of ices, the spatial distribution of surface compositions on Pluto, and the thermal properties of volatiles found on TNOs and KBOs. Radiative scattering models (Mie theory and Hapke approximations) were used to compare single scattering albedos of N2, CH4, and H2O ices from their optical constants at near-infrared wavelengths (1 – 5 µm). Based on the results of Chapters 2 and 3, this thesis recommends using the Mie model for unknown spectra of outer solar system bodies in estimating grain sizes of surface ices. When using an approximation for radiative transfer models (RTMs), we recommend using the Hapke slab approximation model over the internal scattering model. In Chapter 4, this thesis utilizes near-infrared (NIR) spectral observations of the LEISA/Ralph instrument onboard NASA’s New Horizons spacecraft. Hyperspectral LEISA data were used to map the geographic distribution of ices on Pluto’s surface by implementing the principal component reduced Gaussian mixture model (PC-GMM), an unsupervised machine learning technique. The distribution of ices reveals a latitudinal pattern with distinct surface compositions of volatiles. The PC-GMM method was able to recognize local-scale variations in surface compositions of geological features. The mapped distribution of surface units and their compositions are consistent with existing literature and help in an improved understanding of the volatile transport mechanism on the dwarf planet. In Chapter 5, we propose a method to estimate thermal conductivity, volumetric heat capacity, thermal diffusivity, and thermal inertia of N2, CH4, and CO ices, and mixtures thereof in a simulated laboratory setting at temperatures of 20 to 60 K – relevant to TNOs and KBOs. A new laboratory experimental facility – named the Outer Solar System Astrophysics Lab (OSSAL) – was built to implement the proposed method. This thesis provides detailed technical specifications of that laboratory with an emphasis on facilitating the design of similar cryogenic facilities in the future. Thus, this research was able to incorporate a set of methods, tools, and techniques for an improved understanding of ices found in the Kuiper Belt and to decipher surface-atmospheric interactions and volatile transport mechanisms on planetary bodies in the outer solar system

The construction of novelty in computer science papers

Novelty is a concept of great importance in the writing of a research paper, given that the author has to persuade the audience of the news value of the reported research, which makes it worth publishing. In this paper I use a corpus of computer science papers to investigate how novelty is created in this discipline. I analyse how the author uses evaluation and lexical cohesion to integrate his/her research into the existing knowledge structure of the field. Evaluation in computer science papers is closely associated with the Problem-Solution pattern which structures most of the papers: authors claim that the technology they introduce is the best solution to a problem that they have previously identified. Lexical cohesion highlights the novelty of the research by establishing a semantic relation of contrast between the fragment of text reporting previous research in the field and that reporting the authors' own research

Investigation of land-use spectral signatures

A technique was developed to obtain bidirectional reflectance data from natural surfaces by using a folding mirror to transfer the reflected energy from the test surface to a spectroradiometer. The folding mirror was a first surface reflector made by stretching Mylar vacuum coated with aluminum over a light weight frame. The optically folding mirror was positioned over the test surfaces with a moveable platform for both laboratory and field tests. Field tests were conducted using a tethered balloon system to position the folding mirror. A spectroradiometer was designed and built specifically for this investigation. The spectroradiometer had an angular field of view of twenty-four minutes in one axis and ten minutes in the other axis. The radiometer was capable of detecting energies in small bandwidths throughout the electromagnetic spectrum from 0.3 microns to 3.0 microns. Bidirectional reflectance data and variations in the data with source angles were obtained for Saint Augustine grass, Bermuda grass, and a black alluvium soil from the Mississippi River delta