Matching form with content

Successful computer-supported distance education requires that its enabling technologies are accessible and usable anywhere. They should work seamlessly inside and outside the information superhighway, wherever the target learners are located, without obtruding on the learning activity. It has long been recognised that the usability of interactive computer systems is inversely related to the visibility of the implementing technologies. Reducing the visibility of technology is especially challenging in the area of online language learning systems, which require high levels of interactivity and communication along multiple dimensions such as speaking, listening, reading and writing. In this article, the authors review the concept of invisibility as it applies to the design of interactive technologies and appliances. They describe a specialised appliance matched to the requirements for distance second language learning, and report on a successful multi-phase evaluation process, including initial field testing at a Thai open university.

Matching form with content

Successful computer-supported distance education requires that its enabling technologies are accessible and usable anywhere. They should work seamlessly inside and outside the information superhighway, wherever the target learners are located, without obtruding on the learning activity. It has long been recognised that the usability of interactive computer systems is inversely related to the visibility of the implementing technologies. Reducing the visibility of technology is especially challenging in the area of online language learning systems, which require high levels of interactivity and communication along multiple dimensions such as speaking, listening, reading and writing. In this article, the authors review the concept of invisibility as it applies to the design of interactive technologies and appliances. They describe a specialised appliance matched to the requirements for distance second language learning, and report on a successful multi-phase evaluation process, including initial field testing at a Thai open university.

Collision Detection: Broad Phase Adaptation from Multi-Core to Multi-GPU Architecture

We present in this paper several contributions on the collision detection optimization centered on hardware performance. We focus on the broad phase which is the first step of the collision detection process and propose three new ways of parallelization of the well-known Sweep and Prune algorithm. We first developed a multi-core model takes into account the number of available cores. Multi-core architecture enables us to distribute geometric computations with use of multi-threading. Critical writing section and threads idling have been minimized by introducing new data structures for each thread. Programming with directives, like OpenMP, appears to be a good compromise for code portability. We then proposed a new GPU-based algorithm also based on the "Sweep and Prune" that has been adapted to multi-GPU architectures. Our technique is based on a spatial subdivision method used to distribute computations among GPUs. Results show that significant speed-up can be obtained by passing from 1 to 4 GPUs in a large-scale environment.

Multi-Mode Tensor Representation of Motion Data

In this paper, we investigate how a multilinear model can be used to represent human motion data. Based on technical modes (referring to degrees of freedom and number of frames) and natural modes that typically appear in the context of a motion capture session (referring to actor, style, and repetition), the motion data is encoded in form of a high-order tensor. This tensor is then reduced by using N-mode singular value decomposition. Our experiments show that the reduced model approximates the original motion better then previously introduced PCA-based approaches. Furthermore, we discuss how the tensor representation may be used as a valuable tool for the synthesis of new motions.

Registration of Sub-Sequence and Multi-Camera Reconstructions for Camera Motion Estimation

This paper presents different application scenarios for which the registration of sub-sequence reconstructions or multi-camera reconstructions is essential for successful camera motion estimation and 3D reconstruction from video. The registration is achieved by merging unconnected feature point tracks between the reconstructions. One application is drift removal for sequential camera motion estimation of long sequences. The state-of-the-art in drift removal is to apply a RANSAC approach to find unconnected feature point tracks. In this paper an alternative spectral algorithm for pairwise matching of unconnected feature point tracks is used. It is then shown that the algorithms can be combined and applied to novel scenarios where independent camera motion estimations must be registered into a common global coordinate system. In the first scenario multiple moving cameras, which capture the same scene simultaneously, are registered. A second new scenario occurs in situations where the tracking of feature points during sequential camera motion estimation fails completely, e.g., due to large occluding objects in the foreground, and the unconnected tracks of the independent reconstructions must be merged. In the third scenario image sequences of the same scene, which are captured under different illuminations, are registered. Several experiments with challenging real video sequences demonstrate that the presented techniques work in practice.