Interactive classification for pre-integrated volume rendering of high-precision volume data

The pre-integrated volume rendering technique is widely used for creating high quality images. It produces good images even though the transfer function is nonlinear. Because the size of the pre-integration lookup table is proportional to the square of data precision, the required storage and computation load steeply increase for rendering of high-precision volume data. In this paper, we propose a method that approximates the pre-integration function proportional to the data precision. Using the arithmetic mean instead of the geometric mean and storing opacity instead of extinction density, this technique reduces the size and the update time of the pre-integration lookup table so that it classifies high-precision volume data interactively. We demonstrate performance gains for typical renderings of volume datasets.     

Dreamware: edutainment system for children with developmental disability

We designed and implemented an edutainment platform, Dreamware. It effectively helps education, sensibility treatment and intelligence training for children with developmental disability. We developed special toy-like hardware and software tools: a content authoring tool that makes a variety of multimedia content operating on the hardware, a management tool that provides training results to instructors, and a real-time monitoring tool to observe the current state of learning. The hardware is designed to consider the characteristics of children with developmental disability. It can provide sensibility training, such as visual, auditory, and tactile senses to them. The easy-to-use authoring tool enables teachers and non-specialists to make educational content conveniently. In addition, the real-time monitoring tool helps observe the user’s status, even outside the classroom. The management tool stores training results and provides the result for further steps. Efficient repetitive training is possible without restriction of time and location using this platform. We can recognize that our system is effective in improving their concentration and learning.     

An efficient classification and rendering method using tagged distance maps

Several high-speed volume rendering methods generate spatial data structures for speedup. Although they are useful for improving rendering speed, much time may be required to regenerate them. We propose an efficient classification and rendering method that supports fast classification. While original space-leaping needs to perform distance transformations for all voxels, our method modifies the values of some parts of the entire map by assigning predefined tag values when a voxels transparency is changed. The rendering algorithm is an extension of the space-leaping method and it determines the next sampling position by interpreting the values of those tagged voxels. This allows us to reclassify the volume quickly and to render datasets without loss of image quality.     

A linear switched reluctance motor: converter and control

The converter topology with a minimum number of power devices and control implementations to facilitate the pulsation-free force control of the linear switched reluctance machines are investigated for the first time in this paper. The minimization of the devices offers cost reduction, compact packaging, and enhanced overall reliability. With that in view, a topology with 3N_sc+3 devices is chosen where N_sc is the number of sectors in the linear machine. The propulsion force with conventional control of single-phase excitation has high-commutation torque pulsation and it is overcome with a multiphase excitation strategy, proposed in this paper. Further, the proposed control strategy reduces the normal force pulsation. A systematic step-by-step design procedure of the switching strategy for the converter known as unipolar switching strategy, proportional plus integral current controller, and gating control strategy of a long linear switched reluctance machine is presented. Experimental correlation of the proposed converter arrangement and control strategy is presented with a 4.8 m-long linear switched reluctance machine in achieving the stated objectives     

Automatic Generation of Ortho-Photo Texture from Digital Elevation Model

We propose the automatic generation of the ortho-photo data which support realistic scenes for DEM by texture mapping. This ortho-photo data is automatically generated by pattern recognition techniques using Bayesian classifier which uses the features extracted from a DEM and its geo-referenced ortho-photo data as training sets. We defined the various features of each texel such as its height, slope angle, slope direction, surface curvature, hue, saturation and brightness from the training datasets. The proposed method makes possible for mapping texture of a realistic ortho-photo data to virtual terrain data which are unable to take satellite photo or aerial photo. These case are often in of computer game and digital movie area. Also, generating ortho-photo with the enlarged DEM, it does not cause the aliasing from the difference of resolution. It makes very similar images with real photography by shading and efficiently handles ortho-photo data and elevation data occupied enormous storage in cloud computing environment.     

Group decision making procedure considering preference strength under incomplete information

This article deals with the multiple criteria decision making problem with incomplete information when multiple decision makers (Multiple Criteria Group Decision Making: MCGDM) are involved. Usually decision makers (DMs) are willing or able to provide only incomplete information, because of time pressure, lack of knowledge or data, and their limited expertise related to the problem domain. There have only been a few studies considering incomplete information in group settings. We also consider the case where importance weights are given incompletely. This article suggests the possibility that individually optimized results can be used to build group consensus. Individual optimization results by pairwise dominance, contain useful information in forming consensus, such as, ordinal rankings or preference intensity of an alternative over the others. Rather than using ordinal rankings for aggregation which do not consider preference strength, we suggest a procedure that takes account of individual DMs' preference strength.     

Distributed Trusted Computing for Blockchain-Based Crowdsourcing

A centralized trusted execution environment (TEE) has been extensively studied to provide secure and trusted computing. However, a TEE might become a throughput bottleneck if it is used to evaluate data quality when collecting large-scale data in a crowdsourcing system. It may also have security problems compromised by attackers. Here, we propose a scheme, named dTEE, for building a platform for providing distributed trusted computing by leveraging TEEs. The platform is used as an infrastructure of trusted computations for blockchain-based crowdsourcing systems, especially to securely evaluate data quality and manage remuneration: these operations are handled by a TEE group. First, dTEE uses a public blockchain with smart contracts to manage TEEs without reliance on any trusted third parties. Second, to update TEE registration information and rule out zombie TEEs, dTEE uses a reporting mechanism. To attract TEE owners to join in and provide service of trusted computations, it uses a fair monetary incentive mechanism. Third, to account for malicious attackers, we design a model with Byzantine fault tolerance, not limited to a crash-failure model. Finally, we conduct an extensive evaluation of our design on a local cluster. The results show that dTEE finishes evaluating 10,000 images within one minute and achieves about 65 tps throughput when evaluating Sudoku solution data with collective signatures both in a group of 120 TEEs.     

An image division approach for volume ray casting in multi-threading environment

For an efficient parallel volume ray casting suitable for recent multi-core CPUs, we propose an image-ordered approach by using a cost function to allocate loaded tasks impartially per each processing node. At the first frame, we divide an image space evenly, and we compute a cost function. By applying the frame coherence property, we divide the image space unevenly using the computed previous cost function since the next frame. Conventional image-ordered parallel approaches have focused on dividing and compositing volume datasets. However, the divisions and accumulations are negligible for recent multi-core CPUs because they are performed inside one physical CPU. As a result, we can reduce the rendering time without deteriorating the image quality by applying a cost function reflecting on all time-consuming steps of the volume ray casting.