Rapid scalar value classification and volume clipping for?interactive 3D medical image visualization

In many clinical scenarios, medical data visualization and interaction are important to physicians for exploring inner anatomical structures and extracting meaningful diagnostic information. Real-time high-quality volume rendering, artifact-free clipping, and rapid scalar value classification are important techniques employed in this process. Unfortunately, in practice, it is still difficult to achieve an optimal balance. In this paper, we present some strategies to address this issue, which are based on the calculation of segment-based post color attenuation and dynamic ray–plane intersection (RPI) respectively. When implemented within our visualization system, the new classification algorithm can deliver real-time performance while avoiding the “color over-accumulation” artifacts suffered by the commonly used acceleration algorithms that employ pre-integrated classification. Our new strategy can achieve an optimized balance between image quality and classification speed. Next, the RPI algorithm is used with opacity adjustment technique to effectively remove the “striping” artifacts on the clipping plane caused by the nonuniform integration length. Furthermore, we present techniques for multiple transfer function (TF) based anatomical feature enhancement and “keyhole” based endoscopic inner structure view. Finally, the algorithms are evaluated subjectively by radiologists and quantitatively compared using image power spectrum analysis.     

Technological determinism in educational technology research: some alternative ways of thinking about the relationship between learning and technology

This paper argues that research on the educational uses of technology frequently overemphasizes the influence of technology. Research in the field is considered a form of critical perspective, and assumptions about technology are questioned. Technological determinism is introduced, and different positions on this concept are identified. These are used to discuss the ways in which work within the field might be described as technologically deterministic. Four theoretical perspectives (activity theory, communities of practice, actor–network theory, and the social construction of technology) are then briefly characterized, demonstrating that alternative positions are viable, and positioning each in relation to the earlier discussion of technological determinism. The paper concludes by arguing that research, building on such alternative conceptions of technology, is important in developing our understanding of the relationship between technology and learning, as well as identifying potential methodological implications.     

On the Use of Enumeration for Investigating the Performance of Hypothesis Tests for Economic Models with a Discrete Response Variable

This article notes that it is now practical to use the method of enumerationto analyse the performance of estimators and hypothesis tests of fullyparametric binary data models. The general method is presented and thenemployed to investigate the power performance of a common misspecificationtest for the Probit model. The advantages, disadvantages and limitations ofenumeration compared with standard Monte Carlo simulation are thendiscussed. Finally, an example from experimental economics is used todemonstrate that the methodology can also be used in small empirical studies.     

Rough Neural Computing in Signal Analysis

This paper introduces an application of a particular form of rough neural computing in signal analysis. The form of rough neural network used in this study is based on rough sets, rough membership functions, and decision rules. Two forms of neurons are found in such a network: rough membership function neurons and decider neurons. Each rough membership function neuron constructs upper and lower approximation equivalence classes in response to input signals as an aid to classifying inputs. In this paper, the output of a rough membership function neuron results from the computation performed by a rough membership function in determining degree of overlap between an upper approximation set representing approximate knowledge about inputs and a set of measurements representing certain knowledge about a particular class of objects. Decider neurons implement granules derived from decision rules extracted from data sets using rough set theory. A decider neuron instantiates approximate reasoning in assessing rough membership function values gleaned from input data. An introduction to the basic concepts underlying rough membership neural networks is briefly given. An application of rough neural computing in classifying the power system faults is considered.     

Using narratives in conferences to improve the CMC learning environment

Abstract This paper reports on the use of short stories in Internet discussions to promote student learning. It describes off-campus teacher education students CMC discussions of short stories concerning issues in human development. The content of students' discussions is analysed, as is their perceptions of the value of the discussion stories. The results indicate that the use of narratives can improve the social environment of online conferences and contribute to collaborative student learning.     

Supporting ordering and consistency in a distributed Event Heap for Ubiquitous Computing

The Stanford Event Heap has been shown to provide appropriate support for constructing interactive workspace applications. Given this success it is natural to consider the Event Heap as a platform to support other classes of Ubiquitous Computing applications. In this paper we argue that the distributed, spontaneous nature of these applications places additional demands on the Event Heap that require extensions to both the engineering and API. Suitable extensions are described and their use to support a typical Ubicomp application is discussed.

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Investigation of soybean oil as a diesel fuel extender: Endurance tests

Engine performance and crankcase lubricant viscosity were followed with 1∶2 and 1∶1 fuel mixtures of degummed soybean oil in No. 2 diesel fuel in tests with a John Deere 6-cylinder, 404 cubic in. displacement, direct-injection, turbocharged engine for a total of 600 running hours. A crankcase oil contamination problem resulting in an unacceptable thickening and a potential for gelling did exist with a 50/50 blend or a greater concentration of soybean oil, but it did not occur with the 1∶2 blend. The data accumulated during the initial 600 hr running time indicates that a fuel blend of one-third degummed soybean oil and two-thirds No. 2 diesel (1∶2 blend) may be a suitable fuel for agricultural equipment during periods of diesel fuel shortages or allocations. Additional data are being accumulated and will be analyzed in the future.