A Content Vector Model for Text Classification

As a popular rank-reduced vector space approach, Latent Semantic Indexing (LSI) has been used in information retrieval and other applications. In this paper, an LSI-based content vector model for text classification is presented, which constructs multiple augmented category LSI spaces and classifies text by their content. The model integrates the class discriminative information from the training data and is equipped with several pertinent feature selection and text classification algorithms. The proposed classifier has been applied to email classification and its experiments on a benchmark spam testing corpus (PU1) have shown that the approach represents a competitive alternative to other email classifiers based on the well-known SVM and naïve Bayes algorithms.

Learning Based On Computer Science Unplugged in Computer Science Education: Design, Development, and Assessment

Although, all high school students in Japan are required to learn informatics, many of them do not learn this topic sufficiently. In response to this situation, we propose a support package for high school informatics classes. To examine what students learned and if they sufficiently understood the context of the lessons, a questionnaire survey was distributed to 186 students. We analyzed the results of the questionnaire and determined the weakest units, which were “basic computer configuration” and “memory and secondary storage”. We then developed a package for teaching these units. We propose that our package be applied in high school classrooms.

Lattice Monte Carlo Analyses of Thermal Diffusion in Laminar Flow

Lattice Monte Carlo methods are an excellent choice for the simulation of non-linear thermal diffusion problems. In this paper, and for the first time, Lattice Monte Carlo analysis is performed on thermal diffusion combined with convective heat transfer. Laminar flow of water modeled as an incompressible fluid inside a copper pipe with a constant surface temperature is considered. For the simulation of thermal conduction, the temperature dependence of the thermal conductivity of the water is accounted for. Using the novel Lattice Monte Carlo approach, temperature distributions and energy fluxes are obtained.

RANFIS : Rough Adaptive Neuro-Fuzzy Inference System

The paper presents a new hybridization methodology involving Neural, Fuzzy and Rough Computing. A Rough Sets based approximation technique has been proposed based on a certain Neuro – Fuzzy architecture. A New Rough Neuron composition consisting of a combination of a Lower Bound neuron and a Boundary neuron has also been described. The conventional convergence of error in back propagation has been given away for a new framework based on 'Output Excitation Factor' and an inverse input transfer function. The paper also presents a brief comparison of performances, of the existing Rough Neural Networks and ANFIS architecture against the proposed methodology. It can be observed that the rough approximation based neuro-fuzzy architecture is superior to its counterparts.

Damage Evolution of Underground Structural Reinforced Concrete Small-Scale Static-Loading Experiments

Small-scale RC models of both piles and tunnel ducts were produced as mockups of reality and loaded under soil confinement conditionsto investigate the damage evolution of structural RC interacting with soil. Experimental verifications usinga 3D nonlinear FE analysis program called COM3D, which was developed at the University of Tokyo, are introduced. This analysis has been used in practice for seismic performance assessment of underground ducts and in-ground LNG storage tanks in consideration of soil-structure interactionunder static and dynamic loading. Varying modes of failure of RCpilessubjected to different magnitudes of soil confinement were successfully reproduced in the proposed small-scale experiments and numerically simulated as well. Analytical simulation was applied to RC tunnel mockups under a wide variety of depth and soil confinement conditions, and reasonable matching was confirmed.

Antioxidants Reveal Protection against the Biochemical Changes in Liver, Kidney and Blood Profiles after Clindamycin / Ibuprofen Administration in Dental Patients

The adverse effects of Clindamycin (Clind.) / Ibuprofen (Ibu.) combination on liver, kidney, blood elements and the significances of antioxidants (N-acetylcysteine and Zinc) against these effects were evaluated. The study includes: Group I; control n=30, Group II; patients on Clind.300mg/Ibu.400mg twice daily for a week n=30, Group III; patients on Clind.300mg/Ibu.400mg+Nacetylcysteine 200mg twice daily for a week n=15 and Group IV; patients on Clind.300mg/Ibu.400mg+Zinc50mg twice daily for a week n=15. Serum malondialdehyde (MDA), alanine transferase (ALT), aspartate transferase (AST), γ glutamyl transferase (GGT), creatinine, blood urea nitrogen (BUN) were measured. Applying one way ANOVA followed by Tuckey Kramer post test, Group II showed significant increase in ALT, AST, GGT, BUN and decrease in Hb, RBCs, platelets than Group I. Group III showed significant decrease in ALT, AST, GGT, BUN than Group II. Moreover, Group IV showed significant decrease in ALT, AST, GGT and increase in Hb, RBCs, and platelets than Group II. Conclusively, Adding Zinc or Nacetylcysteine buffer the oxidative stress and improve the therapeutic outcome of Clindamycin/Ibuprofen combination.

Experimental Study of Frequency Behavior for a Circular Cylinder behind an Airfoil

The interaction between wakes of bluff body and airfoil have profound influences on system performance in many industrial applications, e.g., turbo-machinery and cooling fan. The present work investigates the effect of configuration include; airfoil-s angle of attack, transverse and inline spacing of the models, on frequency behavior of the cylinder-s near-wake. The experiments carried on under subcritical flow regime, using the hot-wire anemometry (HWA). The relationship between the Strouhal numbers and arrangements provide an insight into the global physical processes of wake interaction and vortex shedding.

Using Artificial Neural Network and Leudeking-Piret Model in the Kinetic Modeling of Microbial Production of Poly-β- Hydroxybutyrate

Poly-β-hydroxybutyrate (PHB) is one of the most famous biopolymers that has various applications in production of biodegradable carriers. The most important strategy for enhancing efficiency in production process and reducing the price of PHB, is the accurate expression of kinetic model of products formation and parameters that are effective on it, such as Dry Cell Weight (DCW) and substrate consumption. Considering the high capabilities of artificial neural networks in modeling and simulation of non-linear systems such as biological and chemical industries that mainly are multivariable systems, kinetic modeling of microbial production of PHB that is a complex and non-linear biological process, the three layers perceptron neural network model was used in this study. Artificial neural network educates itself and finds the hidden laws behind the data with mapping based on experimental data, of dry cell weight, substrate concentration as input and PHB concentration as output. For training the network, a series of experimental data for PHB production from Hydrogenophaga Pseudoflava by glucose carbon source was used. After training the network, two other experimental data sets that have not intervened in the network education, including dry cell concentration and substrate concentration were applied as inputs to the network, and PHB concentration was predicted by the network. Comparison of predicted data by network and experimental data, indicated a high precision predicted for both fructose and whey carbon sources. Also in present study for better understanding of the ability of neural network in modeling of biological processes, microbial production kinetic of PHB by Leudeking-Piret experimental equation was modeled. The Observed result indicated an accurate prediction of PHB concentration by artificial neural network higher than Leudeking- Piret model.

Post-Compression Consideration in Video Watermarking for Wireless Communication

A simple but effective digital watermarking scheme utilizing a context adaptive variable length coding (CAVLC) method is presented for wireless communication system. In the proposed approach, the watermark bits are embedded in the final non-zero quantized coefficient of each DCT block, thereby yielding a potential reduction in the length of the coded block. As a result, the watermarking scheme not only provides the means to check the authenticity and integrity of the video stream, but also improves the compression ratio and therefore reduces both the transmission time and the storage space requirements of the coded video sequence. The results confirm that the proposed scheme enables the detection of malicious tampering attacks and reduces the size of the coded H.264 file. Therefore, the current study is feasible to apply in the video applications of wireless communication such as 3G system

Application of the Neural Network to the Synthesis of Multibeam Antennas Arrays

In this paper, we intend to study the synthesis of the multibeam arrays. The synthesis implementation-s method for this type of arrays permits to approach the appropriated radiance-s diagram. The used approach is based on neural network that are capable to model the multibeam arrays, consider predetermined general criteria-s, and finally it permits to predict the appropriated diagram from the neural model. Our main contribution in this paper is the extension of a synthesis model of these multibeam arrays.

A Comparative Analysis of Activity-Based Costing and Traditional Costing

Activity-Based Costing (ABC) which has become an important aspect of manufacturing/service organizations can be defined as a methodology that measures the cost and performance of activities, resources and cost objects. It can be considered as an alternative paradigm to traditional cost-based accounting systems. The objective of this paper is to illustrate an application of ABC method and to compare the results of ABC with traditional costing methods. The results of the application highlight the weak points of traditional costing methods and an S-Curve obtained is used to identify the undercosted and overcosted products of the firm.

Comparison of Detrending Methods in Spectral Analysis of Heart Rate Variability

Non-stationary trend in R-R interval series is considered as a main factor that could highly influence the evaluation of spectral analysis. It is suggested to remove trends in order to obtain reliable results. In this study, three detrending methods, the smoothness prior approach, the wavelet and the empirical mode decomposition, were compared on artificial R-R interval series with four types of simulated trends. The Lomb-Scargle periodogram was used for spectral analysis of R-R interval series. Results indicated that the wavelet method showed a better overall performance than the other two methods, and more time-saving, too. Therefore it was selected for spectral analysis of real R-R interval series of thirty-seven healthy subjects. Significant decreases (19.94±5.87% in the low frequency band and 18.97±5.78% in the ratio (p

A Method for Modeling Multiple Antenna Channels

In this paper we propose a method for modeling the correlation between the received signals by two or more antennas operating in a multipath environment. Considering the maximum excess delay in the channel being modeled, an elliptical region surrounding both transmitter and receiver antennas is produced. A number of scatterers are randomly distributed in this region and scatter the incoming waves. The amplitude and phase of incoming waves are computed and used to obtain statistical properties of the received signals. This model has the distinguishable advantage of being applicable for any configuration of antennas. Furthermore the common PDF (Probability Distribution Function) of received wave amplitudes for any pair of antennas can be calculated and used to produce statistical parameters of received signals.

2D Human Motion Regeneration with Stick Figure Animation Using Accelerometers

This paper explores the opportunity of using tri-axial wireless accelerometers for supervised monitoring of sports movements. A motion analysis system for the upper extremities of lawn bowlers in particular is developed. Accelerometers are placed on parts of human body such as the chest to represent the shoulder movements, the back to capture the trunk motion, back of the hand, the wrist and one above the elbow, to capture arm movements. These sensors placement are carefully designed in order to avoid restricting bowler-s movements. Data is acquired from these sensors in soft-real time using virtual instrumentation; the acquired data is then conditioned and converted into required parameters for motion regeneration. A user interface was also created to facilitate in the acquisition of data, and broadcasting of commands to the wireless accelerometers. All motion regeneration in this paper deals with the motion of the human body segment in the X and Y direction, looking into the motion of the anterior/ posterior and lateral directions respectively.

Binarization of Text Region based on Fuzzy Clustering and Histogram Distribution in Signboards

In this paper, we present a novel approach to accurately detect text regions including shop name in signboard images with complex background for mobile system applications. The proposed method is based on the combination of text detection using edge profile and region segmentation using fuzzy c-means method. In the first step, we perform an elaborate canny edge operator to extract all possible object edges. Then, edge profile analysis with vertical and horizontal direction is performed on these edge pixels to detect potential text region existing shop name in a signboard. The edge profile and geometrical characteristics of each object contour are carefully examined to construct candidate text regions and classify the main text region from background. Finally, the fuzzy c-means algorithm is performed to segment and detected binarize text region. Experimental results show that our proposed method is robust in text detection with respect to different character size and color and can provide reliable text binarization result.

Design Process and Real-Time Validation of an Innovative Autonomous Mid-Air Flight and Landing System

This paper describes the design process and the realtime validation of an innovative autonomous mid-air flight and landing system developed by the Italian Aerospace Research Center in the framework of the Italian national funded project TECVOL (Technologies for the Autonomous Flight). In the paper it is provided an insight of the whole development process of the system under study. In particular, the project framework is illustrated at first, then the functional context and the adopted design and testing approach are described, and finally the on-ground validation test rig on purpose designed is addressed in details. Furthermore, the hardwarein- the-loop validation of the autonomous mid-air flight and landing system by means of the real-time test rig is described and discussed.

The Impact of Product Package Information on Consumer Behavior toward Genetically Modified Foods

Genetically modified (GM) technology in food production continued to generate controversies. Consumers were concerned with the GM foods about the healthy and environmental risks. While consumers- acceptance was a critical factor affecting how widely this technology be used. According to the research review, consumers- lack of information was one of the reasons to explain consumers- low acceptance toward GM foods. The objective for this study wanted to find out would informative product package affect consumers- behavior toward GM foods. An experiment was designed to investigate consumer behavior toward different product package information. The results indicated that the product package information influenced consumer product trust toward GM foods. Compared with the traceability production system information, the information about the GM rice was approved by authorized organizations could increase consumers product trust in GM foods. Consumers in Taiwan saw the information provided by authorized organizations more credible than other information.

Nonconforming Control Charts for Zero-Inflated Poisson Distribution

This paper developed the c-Chart based on a Zero- Inflated Poisson (ZIP) processes that approximated by a geometric distribution with parameter p. The p estimated that fit for ZIP distribution used in calculated the mean, median, and variance of geometric distribution for constructed the c-Chart by three difference methods. For cg-Chart, developed c-Chart by used the mean and variance of the geometric distribution constructed control limits. For cmg-Chart, the mean used for constructed the control limits. The cme- Chart, developed control limits of c-Chart from median and variance values of geometric distribution. The performance of charts considered from the Average Run Length and Average Coverage Probability. We found that for an in-control process, the cg-Chart is superior for low level of mean at all level of proportion zero. For an out-of-control process, the cmg-Chart and cme-Chart are the best for mean = 2, 3 and 4 at all level of parameter.

Meta-analysis of Performance: Summarizing Research for Implementation of Reconfigurability

The aim of this study is to identify the conditions of implementation for reconfigurability in summarizing past flexible manufacturing systems (FMS) research by drawing overall conclusions from many separate High Performance Manufacturing (HPM) studies. Meta-analysis will be applied to links between HPM programs and their practices related to FMS and manufacturing performance with particular reference to responsiveness performance. More specifically, an application of meta-analysis will be made with reference to two of the main steps towards the development of an empirically-tested theory: testing the adequacy of the measurement of variables and testing the linkages between the variables.

Blind Impulse Response Identification of Frequency Radio Channels: Application to Bran A Channel

This paper describes a blind algorithm for estimating a time varying and frequency selective fading channel. In order to identify blindly the impulse response of these channels, we have used Higher Order Statistics (HOS) to build our algorithm. In this paper, we have selected two theoretical frequency selective channels as the Proakis-s 'B' channel and the Macchi-s channel, and one practical frequency selective fading channel called Broadband Radio Access Network (BRAN A). The simulation results in noisy environment and for different data input channel, demonstrate that the proposed method could estimate the phase and magnitude of these channels blindly and without any information about the input, except that the input excitation is i.i.d (Identically and Independent Distributed) and non-Gaussian.