CONTEXTUALIZING LEARNING OBJECTS USING ONTOLOGIES

Educational research over the past three years has intensified such that the context of learning resources needs to be properly modeled. Many researchers have described and even mandated the use of ontologies in the research being conducted, yet the process of actually connecting one or more ontologies to a learning object has not been extensively discussed. This paper describes a practical model for associating multiple ontologies with learning objects while making full use of the IEEE LOM specification. The model categorizes these ontologies according to five major categories of context based on the most popular fields of study actively being pursued by the educational research community: Thematic context, Pedagogical context, Learner context, Organizational context, and Historical/Statistical context.     

A hybrid technique of modeling of cracks using displacement discontinuity and direct boundary element method

A hybrid technique to model two dimensional fracture problems which makes use of displacement discontinuity and direct boundary element method is presented. Direct boundary element method is used to model the finite domain of the body, while displacement discontinuity elements are utilized to represent the cracks. Thus the advantages of the component methods are effectively combined. This method has been implemented in a computer program and numerical results which show the accuracy of the present method are presented. The cases of bodies containing edge cracks as well as multiple cracks are considered. A direct method and an iterative technique are described. The present hybrid method is most suitable for modeling problems involving crack propagation.     

Effect of cooking on the chemical composition of lentils,rice and their blend (Koshary)

Lentils, rice and their blend (Koshary) were cooked by traditional Egyptian methods. Cooking resulted in slight changes in the total nitrogen, as well as in the non-protein nitrogen (NPN), ether extract and crude fibre of the three products, compared with raw grains. In contrast, significant decreases were found in the mineral elements, indicating losses through leaching, on washing and cooking. The mineral composition of lentils' cooking liquor confirmed this.     

Window materials for high power gyrotron

The room temperature application of sapphire as window material at higher frequencies is not feasible since its absorption coefficient increases almost linearly with increasing frequency in the millimeter wavelength region. At cryogenic temperature the absorption coefficient value decreases only by a few factors (factor of 2 to 3) in the 90 – 200 GHz region. The earlier reported temperature squared dependence (decrease) in the absorption coefficient or the loss tangent value is totally absent in our broad band continuous wave data we are reporting here (at 6.5 K, 35K, 77K and 300K) and one we reported at conferences earlier. Our results are verified by another technique. We utilize our precision millimeter wave dispersive Fourier transform spectroscopic techniques at room temperature and at cryogenic temperatures The extra high resistivity single crystal compensated silicon is no doubt the lowest loss material available at room temperature in the entire millimeter wavelength region At higher millimeter wave frequencies an extra high resistivity silicon window or an window made with extra high resistivity silicon coated with diamond film would certainly make a better candidate in the future. A single free standing synthetic diamond window seems to have higher absorption coefficient values at millimeter wavelength region at this time although it is claimed that it possesses good mechanical strength and higher thermal conductivity characteristics. It certainly does not rule out the use of diamond film on a single crystal high resistivity silicon to improve its mechanical strength and thermal conductivity     

MC-CDMA with exponential power profile in Nakagami fading channel

A refined study of multi-carrier code division multiple access transmission over a Nakagami fading channel is proposed. The channel power is modeled with an exponential power profile rather than the uniform profile prevalent in other papers. An expression for the bit error rate (BER) is then derived. Numerical results show that MC-CDMA performances depend on the power dispersion of interferers around the desired user power.     

The electrical properties of post-deposition annealed and as-deposited In-doped CdTe thin films

The electrical properties of CdTe thin film have been studied and discussed, including, the conduction mechanism, and the effect of temperature and indium doping on the current passing through the CdTe film and hence on the film conductivity. It is observed that the CdTe film is of the modified Poole–Frenkel conduction mechanism and the resistivity of the film can be lowered by more than one order of magnitude due to indium doping.     

An incentive scheduling mechanism for peer-to-peer video streaming

P2P video streaming networks are found as a scalable solution and an alternative for traditional client–server based video streaming over the Internet. One of the significant issues affecting the success of any P2P streaming network is cooperation between peers. Practical observations have proved the prevalence of free riders in P2P networks that degrade their performance. To solve this problem, using incentive mechanisms, which encourage peers to contribute more in the network, is necessary. In this paper, we designed and proposed a distributed and scalable incentive mechanism for mesh based P2P video streaming networks. In the proposed approach the contribution of the peers is measured and maintained in a distributed fashion. Furthermore, we proposed an incentive sending side scheduler in which peers are served based on their contribution in the network. Our simulation evaluations show the efficiency of the proposed approach in improving the overall perceived video quality by the non-free rider nodes and consequently in the whole network.     

File size models for shared content over the BitTorrent Peer-to-Peer network

Peer-to-Peer (P2P) traffic has increased rapidly over the past few years, with file sharing providing the main drive behind such traffic. In this work we perform a measurement study of the content shared over the popular BitTorrent P2P file sharing network. We mathematically model the file size distributions of shared files after categorizing them into Audio, Video, Archive and CD image classes. For each of these categories we look into the most popular shared file formats and investigate their file size statistics. This provides an important milestone to building a realistic simulation framework for P2P systems, and for future analytical modeling of P2P networks.     

Automatic Channel Detection Using DNN on 2D Seismic Data

Geologists interpret seismic data to understand subsurface properties and subsequently to locate underground hydrocarbon resources. Channels are among the most important geological features interpreters analyze to locate petroleum reservoirs. However, manual channel picking is both time consuming and tedious. Moreover, similar to any other process dependent on human intervention, manual channel picking is error prone and inconsistent. To address these issues, automatic channel detection is both necessary and important for efficient and accurate seismic interpretation. Modern systems make use of real-time image processing techniques for different tasks. Automatic channel detection is a combination of different mathematical methods in digital image processing that can identify streaks within the images called channels that are important to the oil companies. In this paper, we propose an innovative automatic channel detection algorithm based on machine learning techniques. The new algorithm can identify channels in seismic data/images fully automatically and tremendously increases the efficiency and accuracy of the interpretation process. The algorithm uses deep neural network to train the classifier with both the channel and non-channel patches. We provide a field data example to demonstrate the performance of the new algorithm. The training phase gave a maximum accuracy of 84.6% for the classifier and it performed even better in the testing phase, giving a maximum accuracy of 90%.     

Visual tracking using convolutional features with sparse coding

Artificial Intelligence Review - Visual object tracking has become one of the most active research topics in computer vision, and it has been applied in several commercial...