This paper presents a multidisciplinary research on the thermodynamic and geophysical effects of crude oil released underneath thin sea ice, and further evaluates the ability of a combined frequency- and time-domain approach toward its detection. To this end, a controlled oil release experiment in an artificially grown sea ice mesocosm was performed during the winter of 2017 at the Sea-Ice Research Environmental Facility located at the University of Manitoba. Ice cores extracted during the evolution of the sea ice prior and post oil injection allowed the investigation of the profile's properties and the oil distribution. Furthermore, chemical composition and microstructure analysis were performed via a gas chromatography-time-of-flight mass spectrometry and X-ray, respectively. The time-series radar signature of the profile was measured utilizing ground penetration radar at 500 MHz and a C-band scatterometer. For this experiment, it was shown that the retrieval of the oil presence underneath the young sea ice layer was feasible, provided that the measured data were utilized simultaneously in a unified cost function.

An oil-in-sea ice mesocosm experiment was conducted at the University of Manitoba Sea-Ice Environmental Research Facility from January to March 2016 in which geophysical and electromagnetic parameters of the ice were measured, and general observations about the oil-contaminated ice were made. From the experimental measurements, the presence of crude oil appears to affect the temperature and bulk salinity profiles as well as the normalized radar cross section (NRCS) of the contaminated young sea ice. The measured temperature and bulk salinity profiles of the ice, as well as the crude oil distribution within the ice, were used to model the permittivity profile of the oil-contaminated ice by adapting two mixture models commonly used to describe sea ice to account for the presence of oil. Permittivity modeling results were used to simulate the NRCS of the oil-contaminated sea ice in an effort to determine the accuracy of the models. In addition, the application of X-ray microtomography in modeling the dielectric profile of oil-contaminated sea ice was examined. The sensitivity of the permittivity models for oil-contaminated sea ice to changes in temperature, frequency, and oil volume fraction was also examined.

This paper presents a multidisciplinary case study on a crude oil injection experiment in an artificially grown young sea ice environment under controlled conditions. In particular, the changes in the geophysical and electromagnetic responses of the sea ice to oil introduction are investigated for this experiment. Furthermore, we perform a preliminary study on the detection of oil spills utilizing the normalized radar cross section (NRCS) data collected by a C-band scatterometer is presented. To this end, an inversion scheme is introduced that retrieves the effective complex permittivity of the domain prior and after oil injection by comparing the simulated and calibrated measured NRCS data, while roughness parameters calculated using lidar are utilized as prior information. Once the complex permittivity values are obtained, the volume fraction of oil within the sea ice is found using a mixture formula. Based on this volume fraction, a binary detection of oil presence seems to be possible for this test case. Finally, the possible sources of error in the retrieved effective volume fraction, which is an overestimate of the actual value, are identified and discussed by macrolevel and microlevel analyses through bulk salinity measurements and X-ray imagery of the samples, as well as a brief chemical analysis.

This paper investigates the inversion of the time-series single-frequency normalized radar cross section (NRCS) data collected from first-year snow-covered sea ice in Cambridge Bay, Nunavut, Canada. The discrepancy between the reconstructed profiles and the true profiles, obtained by in situ measurements, are speculated to be due to the discrepancy between the model used in the inversion algorithm, and the actual electromagnetic wave interactions with the profiles.

A nonlinear electromagnetic inverse scattering algorithm is presented to reconstruct snow-covered sea-ice dielectric profiles using multiple-frequency multipolarimetric normalized radar cross-sectional data. This inversion algorithm utilizes a global optimization technique in conjunction with the boundary-perturbation-theory electromagnetic forward solver to retrieve the complex permittivities, thickness values, and roughness parameters of the snow-covered sea-ice profiles of interest. The proposed algorithm is then tested against synthetically collected data in both bistatic and monostatic configurations. The achievable quantitative accuracy of the inversion results is reasonable as long as sufficient scattering data are given to the inversion algorithm.

The goal of this paper is to investigate the sensitivity of the normalized radar cross section data collected from a given arctic profile of interest with respect to snow and ice complex permittivities and their thickness values. To calculate this sensitivity, the recently-developed boundary perturbation theory is used as a forward scattering solver. This sensitivity analysis is then used to set some guideline for appropriate data collection schemes to be used for arctic dielectric profile inversion. Finally, the inversion of two different data sets having different sensitivities to the unknown model parameters are presented and compared.

Although Krylov subspace methods provide fast regularization techniques for the microwave imaging problem, they cannot preserve the edges of the object being imaged and may result in an oscillatory reconstruction. To suppress these spurious oscillations and to provide an edge-preserving regularization, we use a multiplicative regularizer which improves the reconstruction results significantly while adding little computational complexity to the inversion algorithm. We show the inversion results for a real human forearm assuming the 2-D transverse magnetic illumination and a cylindrical object assuming the 2-D transverse electric illumination.

This paper describes a prototype and analytical studies of a spherical rolling robot, a new design of an omnidirectional robot system. The robot can arbitrarily begin to move in any direction to the target, and autonomously roll and reach any desired position. Our design considers a spherical robot with an internal mechanism for propulsion. The propulsion mechanism distributes weights radially along spokes fixed inside the sphere and enables the robot to accelerate, decelerate, and move with constant velocity. A mathematical model of the robot's dynamic and motion is instructed. An algorithmic motion planning is developed and, partly, pseudo-code of that is presented. For a number of missions, it is shown experimentally that the model agrees well with the results.