3-D immersive screen experiments

We are currently piloting a range of computer simulated science experiments as 3-D virtual environments. These are rendered on a PC in 3-D and use photographs of specic parts of the actual apparatus as textures to add realism to the simulation. In particular, photographs are used to represent the consequential views of an experiment. These particular views may also be animated depending on the state of the experiment. The work combines the photographic approach of the Interactive Screen Experiments (ISEs) with the advantages of a fully simulated 3-D environment where the user can interact with the apparatus in a more natural and intuitive way. The potential advantages are that users can quickly adapt to the environment and in particular the controls. They gain realistic views of the physicality of the experiment as they are not just seeing it from a particular viewpoint, but from wherever they see t to place themselves within the experiment's scene. They are immersed in the experiment in a way that mitigates some of the objections to online as opposed to real laboratory experimentation. It is also the case that the results of an initial calibration or setup carry over into the main part of the experiment. This is perceived as an extremely important teaching element of Physics practicals as the user learns that care in setting up an experiment is an essential part of being able to get good results. Furthermore there is no need to represent scales, read-outs or controls as separate parts of the interface; these can all be rendered at their correct physical positions within the experiment. The rst of these experiments based on the use of a diffraction grating has been fully implemented and has been evaluated with a Physics A level class. The application and its evaluation will be presented. A more complicated experiment using a spectrometer has also been modelled which raises issues of complexity. These issues will also be discussed

Observation of nano-indent induced strain fields and dislocation generation in silicon wafers using micro-raman spectroscopy and white beam x-ray topography

In the semiconductor manufacturing industry, wafer handling introduces micro-cracks at the wafer edge. During heat treatment these can produce larger, long-range cracks in the wafer which can cause wafer breakage during manufacture. Two complimentary techniques, micro-Raman spectroscopy (μRS) and White Beam Synchrotron X-ray Topography (WBSXRT) were employed to study both the micro-cracks and the associated strain fields produced by nano-indentations in Si wafers, which were used as a means of introducing controlled strain in the wafers. It is shown that both the spatial lateral and depth distribution of these long range strain fields are relatively isotropic in nature. The Raman spectra suggest the presence of a region under tensile strain beneath the indents, which can indicate a crack beneath the indent and the data strongly suggests that there exists a minimum critical applied load below which cracking will not initiate

Detecting binocular 3-D motion in static 3-D noise: No effect of viewing distance.

Relative binocular disparity cannot tell us the absolute 3-D shape of an object, nor its 3-D trajectory if it is moving, unless the visual system has independent access to how far away the object is at any moment. Indeed, as the viewing distance is changed, the same disparate retinal motions will correspond to very different real 3-D trajectories. In this paper we were interested in whether binocular 3-D motion detection is affected by viewing distance. We used a visual search task in which the observer is asked to detect a target dot, moving in 3-D, amidst 3-D stationary distractor dots. We found that distance does not affect detection performance. Motion-in-depth is consistently harder to detect than the equivalent lateral motion, for all viewing distances. For a constant retinal motion with both lateral and motion-in-depth components, detection performance is constant despite variations in viewing distance that produce large changes in the direction of the 3-D trajectory. We conclude that binocular 3-D motion detection relies on retinal, not absolute visual signals

Rapid Prototyping Using 3-D Welding

Rapid prototyping systems are based, almost exclusively on polymer, or paper materials. The dimensions of the parts produced are limited by the volume of the processing area within the machine, and parts tend to warp or distort due to shrinkage and lack of support. Also the mechanical properties of the part are restricted to those of the processable materials and thus, in many cases, required 'engineering properties' cannot be obtainedMechanical Engineerin

From surround to true 3-D

To progress from surround sound to true 3-D requires an updating of the psychoacoustical theories which underlie current technologies. This paper shows how J.J.Gibson’s ecological approach to perception can be applied to audio perception and used to derive 3-D audio technologies based on intelligent pattern recognition and active hypothesis testing. These technologies are suggested as methods which can be used to generate audio environments that are believable and can be explored

3-D Sound: Massive and minute

A Technical, perceptual and aesthetic exploration of cellular "multi-scale" artificial auditory environment

3-D Perturbations in Conformal Turbulence

The effects of three-dimensional perturbations in two-dimensional turbulence are investigated, through a conformal field theory approach. We compute scaling exponents for the energy spectra of enstrophy and energy cascades, in a strong coupling limit, and compare them to the values found in recent experiments. The extension of unperturbed conformal turbulence to the present situation is performed by means of a simple physical picture in which the existence of small scale random forces is closely related to deviations of the exact two-dimensional fluid motion.Comment: Discussion of intermittency improved. Figure include

Towards diagnosis of rotator cuff tears in 3-D MRI using 3-D convolutional neural networks

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