Blood pressure measurement by auscultatory method is a compulsory skill that is required by all healthcare practitioners. During the measurement, they must concentrate on recognizing the Korotkoff sounds, looking at the sphygmomanometer scale, and constantly deflating the cuff pressure simultaneously. This complex operation is difficult for the new learners and they need a lot of practice with the supervisor in order to guide them on their measurements. However, the supervisor is not always available and consequently, they always face the problem of lack of enough training. In order to help them mastering the skill of measuring blood pressure by auscultatory method more efficiently and effectively, we propose using a sensor device to capture the signals of Korotkoff sounds and cuff pressure during the measurement, and display the signal changes on a visualization tool through wireless connection. At the end of the measurement, the learners can verify their skill on deflation speed and recognition of Korotkoff sounds using the graphical view, and compare their measurements with the machine instantly. By using this device, the new learners do not need to wait for their supervisor for training but can practice with their colleagues more frequently. As a result, they will be able to acquire the skill in a shorter time and be more confident with their measurements.

Because motion capture system enabled us to capture a number of human motions, the demand for a method to easily browse the captured motion database has been increasing. In this paper, we propose a method to generate simple visual outlines of motion clips, for the purpose of efficient motion data browsing. Our method unfolds a motion clip into a 2D stripe of keyframes along a timeline that is based on semantic keyframe extraction and the best view point selection for each keyframes. With our visualization, timing and order of actions in the motions are clearly visible and the contents of multiple motions are easily comparable. In addition, because our method is applicable for a wide variety of motions, it can generate outlines for a large amount of motions fully automatically.

Volume Graphics Clusters (VG Clusters) have proven to be efficient in a wide range of visualization applications and have also shown promise in some other applications where the image composition device could be fully utilized. The main differentiating feature from other graphics clusters is a specialized image composition device, commercially available as the MPC Image Compositor, which enables the building of do-it-yourself VG Clusters. Although this device is highly scalable, the unidirectional composition flow limits the data subdivision to the quantity of physically available rendering nodes. In addition, the limited buffer memory limits the maximum capable image composition size, therefore limiting its use in large-scale data visualization and high-resolution visualization. To overcome these limitations, we propose and evaluate an image composition mechanism in which additional hardware is used for assisting the image composition process. Because of the synergistic use of two distinct image composition hardware devices we named it "Hybrid Image Composition". Some encouraging results were obtained showing the effectiveness of this solution in improving the VG Cluster 's potential. A low-cost parallel port based hardware barrier is also presented as an efficient method for further enhancing this kind of small-scale VG Cluster. Moreover, this solution has proven to be especially useful in clusters built using low-speed networks, such as Fast Ethernet, which are still in common use.

Self-organizing map is a widely used tool in high-dimensional data visualization. However, despite its benefits of plotting very high-dimensional data on a low-dimensional grid, browsing and understanding the meaning of a trained map turn to be a difficult task -- specially when number of nodes or the size of data increases. Though there are some well-known techniques to visualize SOMs, they mainly deals with cluster boundaries and they fail to consider raw information available in original data in browsing SOMs. In this paper, we propose our Factor controlled Hierarchical SOM that enables us select number of data to train and label a particular map based on a pre-defined factor and provides consistent hierarchical SOM browsing.