Three techniques to retrieve information on sea ice thickness from both active and passive radar backscatter data are presented. The first inversion model is a combination of the radiative transfer theory with dense medium phase and amplitude correction theory (DMPACT), and the Levenberg-Marquardt optimization algorithm. The radiative transfer theory was applied as the forward model to generate radar backscatter data, while the DMPACT was included to account for the close spacing effect among the scatterers within the medium. The Levenberg-Marquardt optimization algorithm was then applied to reduce the error between the model generated radar backscatter data and the measured radar backscatter data from satellite images so that the sea ice thickness could be estimated. The second method presented was the neural network inversion method which utilizes a chain of neurons with variable weights. Once the network was fully operational it would be possible to predict the sea ice thickness, provided sufficient training data are given. The last method was the genetic algorithm which is a search technique used in order to predict the approximate sea ice thickness from the measured data. Data from ground truth measurements carried out in Ross Island, Antarctica, together with radar backscatter data extracted from purchased satellite images were used as input to verify the models. All three models were tested and successfully predicted sea ice thickness from actual terrain using the ground truth measurement data, with several constraints and assumptions placed to avoid problems during the retrieval process. While the models still have their own limitations, the potential use of the models for actual sea ice thickness retrieval was confirmed.
Virtual Reality (VR) is a technology that makes use of computer graphics,
algorithms and special hardware to simulate the real world in real time. There
are four main elements required to make a VR system a success, namely
virtual world, immersion, sensory feedback and interactivity. The virtual
world created must be as real as possible. Users should have a sense of
immersion in the virtual world. Position tracking is usually incorporated into
a VR system for visual, sound and force feedback on the users and the virtual
objects in the VR world must be interact-able with the users. VR has proven
to be effective in training and widely used in many areas, for example medical
surgery, dental treatment, psychology treatment for phobia, engineering
design, maintenance and repair, sports and many more. By implementing VR
technology in training, users are able to reduce the training cost and time. VR
training is also safer for users, as harsh environments can be simulated despite
the environment and/or human factors. On the other hand, the physical
facilities and infrastructures of the track cycling are very costly. In track
cycling, the game field, known as a velodrome, requires a large space of area.
It requires a huge budget and professional manpower to maintain these
facilities. Therefore, the proposed spatial immersive track cycling simulator
is invented to overcome these issues. The aim of this study is to simulate the
velodrome track cycling in VR environment and synchronize with a 6 degreeof-freedom
motion platform. The simulator is aimed to be low cost and
minimal space requirement compared to actual velodrome. A trainee who
undergoes VR track cycling simulator training wears a head-mounted-display (HMD) to visualize the VR environment. An actual bike will be mounted on
the 6-DOF motion platform, which the platform will synchronize with the VR
environment to simulate the track condition for the training purposes. An
encoder is placed at the bicycle wheel to feedback the moving speed and
synchronize the visualize feedback to the HMD.