A Variational Stereo Method for the Three-Dimensional Reconstruction of Ocean Waves

We develop a novel remote sensing technique for the observation of waves on the ocean surface. Our method infers the 3-D waveform and radiance of oceanic sea states via a variational stereo imagery formulation. In this setting, the shape and radiance of the wave surface are given by minimizers of a composite energy functional that combines a photometric matching term along with regularization terms involving the smoothness of the unknowns. The desired ocean surface shape and radiance are the solution of a system of coupled partial differential equations derived from the optimality conditions of the energy functional. The proposed method is naturally extended to study the spatiotemporal dynamics of ocean waves and applied to three sets of stereo video data. Statistical and spectral analysis are carried out. Our results provide evidence that the observed omnidirectional wavenumber spectrum S(k) decays as k-2.5 is in agreement with Zakharov's theory (1999). Furthermore, the 3-D spectrum of the reconstructed wave surface is exploited to estimate wave dispersion and currents

A simple variational method for calculating energy and quantum capacitance of an electron gas with screened interactions

We describe a variational procedure for calculating the energy of an electron gas in which the long-range Coulomb interaction is truncated by the screening effect of a nearby metallic gate. We use this procedure to compute the quantum capacitance of the system as a function of electron density and spin polarization. The accuracy of the method is verified against published Monte-Carlo data. The results compare favorably with a recent experiment.Comment: 4 pages, 3 figure

Visual-Quality-Driven Learning for Underwater Vision Enhancement

The image processing community has witnessed remarkable advances in enhancing and restoring images. Nevertheless, restoring the visual quality of underwater images remains a great challenge. End-to-end frameworks might fail to enhance the visual quality of underwater images since in several scenarios it is not feasible to provide the ground truth of the scene radiance. In this work, we propose a CNN-based approach that does not require ground truth data since it uses a set of image quality metrics to guide the restoration learning process. The experiments showed that our method improved the visual quality of underwater images preserving their edges and also performed well considering the UCIQE metric.Comment: Accepted for publication and presented in 2018 IEEE International Conference on Image Processing (ICIP

A particle filter to reconstruct a free-surface flow from a depth camera

We investigate the combined use of a Kinect depth sensor and of a stochastic data assimilation method to recover free-surface flows. More specifically, we use a Weighted ensemble Kalman filter method to reconstruct the complete state of free-surface flows from a sequence of depth images only. This particle filter accounts for model and observations errors. This data assimilation scheme is enhanced with the use of two observations instead of one classically. We evaluate the developed approach on two numerical test cases: a collapse of a water column as a toy-example and a flow in an suddenly expanding flume as a more realistic flow. The robustness of the method to depth data errors and also to initial and inflow conditions is considered. We illustrate the interest of using two observations instead of one observation into the correction step, especially for unknown inflow boundary conditions. Then, the performance of the Kinect sensor to capture temporal sequences of depth observations is investigated. Finally, the efficiency of the algorithm is qualified for a wave in a real rectangular flat bottom tank. It is shown that for basic initial conditions, the particle filter rapidly and remarkably reconstructs velocity and height of the free surface flow based on noisy measurements of the elevation alone

D-wave correlated Critical Bose Liquids in two dimensions

We develop a description of a new quantum liquid phase of interacting bosons in 2d which possesses relative D-wave two-body correlations and which we call a D-wave Bose Liquid (DBL). The DBL has no broken symmetries, supports gapless boson excitations residing on "Bose surfaces" in momentum space, and exhibits power law correlations with continuously variable exponents. While the DBL can be constructed for bosons in the 2d continuum, the state only respects the point group symmetries of the square lattice. On the lattice the DBL respects all symmetries and does not require a particular filling. But lattice effects allow a second distinct phase, a quasi-local variant which we call a D-wave Local Bose Liquid (DLBL). Remarkably, the DLBL has short-range boson correlations and hence no Bose surfaces, despite sharing gapless excitations and other critical signatures with the DBL. Moreover, both phases are metals with a resistance that vanishes as a power of the temperature. We establish these results by constructing a class of many-particle wavefunctions for the DBL, which are time reversal invariant analogs of Laughlin's quantum Hall wavefunction for bosons at $\nu=1/2$. A gauge theory formulation leads to a simple mean field theory, and an N-flavor generalization enables incorporation of gauge field fluctuations to deduce the properties of the DBL/DLBL; various equal time correlation functions are in qualitative accord with the properties inferred from the wavefunctions. We also identify a promising Hamiltonian which might manifest the DBL or DLBL, and perform a variational study comparing to other competing phases. We suggest how the DBL wavefunction can be generalized to describe an itinerant non-Fermi liquid phase of electrons on the square lattice with a no double occupancy constraint, a D-wave metal phase.Comment: 33 pages, 17 figure

Autoencoding a Soft Touch to Learn Grasping from On-land to Underwater

Robots play a critical role as the physical agent of human operators in exploring the ocean. However, it remains challenging to grasp objects reliably while fully submerging under a highly pressurized aquatic environment with little visible light, mainly due to the fluidic interference on the tactile mechanics between the finger and object surfaces. This study investigates the transferability of grasping knowledge from on-land to underwater via a vision-based soft robotic finger that learns 6D forces and torques (FT) using a Supervised Variational Autoencoder (SVAE). A high-framerate camera captures the whole-body deformations while a soft robotic finger interacts with physical objects on-land and underwater. Results show that the trained SVAE model learned a series of latent representations of the soft mechanics transferrable from land to water, presenting a superior adaptation to the changing environments against commercial FT sensors. Soft, delicate, and reactive grasping enabled by tactile intelligence enhances the gripper's underwater interaction with improved reliability and robustness at a much-reduced cost, paving the path for learning-based intelligent grasping to support fundamental scientific discoveries in environmental and ocean research.Comment: 17 pages, 5 figures, 1 table, submitted to Advanced Intelligent Systems for revie