Earticle

A new approach is presented to improve the face recognition accuracy. This approach is based on the contrast enhancement using high-frequency emphasize filtering and histogram equalization. In the presented method, image contrast and the global (or local) visualization are enhanced using digital filtering and equalizing the histogram of the pixel values over entire image. For this, first the face images are transformed into a high-frequency domain and then the global thresholding technique, by Otsu method, is applied to the image. Then, the values lower than threshold has only been considered. For dimension reduction and also feature extraction purpose the linear method such as two dimensional principle component analysis (2DPCA) and two dimensional linear discriminant analysis (2DLDA) are adopted. In the last stage of the algorithm, the simple minimum distance method is exploited for the classification. Experimental trials demonstrate that the presented method is leading to the promising recognition rates and noticeable improvement in the face recognition system.

Synchrotron radiation (SR) sources provide very high photon flux light in a very narrow opening angle with wavelength ranging from visible to hard X-rays for use in experiments related to material science, physics, chemistry and biology. In the beam lines (BL) the SR position is highly dependent on the electron beam position and angle at the source point. The tuning of accelerator for getting the desired electron beam position and angle at the source point is a time consuming and regular job done during commissioning of new BL or when accelerator is operated at new operating point. This paper presents a novel intelligent agent based operator support and beam orbit control scheme for accelerator control. The proposed multi-agent based scheme is well suited for the multilayer control system architectures of synchrotron radiation sources. The scheme successfully distributes the orbit control job to multiple low complexity reactive agents that work simultaneously and control the local orbit for individual BL and insertion devices (ID) in an optimized manner. The proposed scheme of beam orbit control in particular is very useful for machines like INDUS-2, where new BL are in the process of commissioning as this scheme reduces the operator efforts and accelerator tuning time for providing beam to new BL. It also extends the beam availability to other BL (already installed and in use) as the agent tunes the accelerator in systematic way and under constraints on local orbit bump leakage thereby enabling the use of other BL for routine experiments which otherwise was not possible. The effectiveness of the scheme is shown through simulation results obtained by applying the stated scheme on INDUS-2 storage ring model.

This paper was conducted to apply Analytical Hierarcy Process (AHP), applied as Decision Support System (DSS) model in selection of lecturer at STAIN Batsangkar. Data collected by through observation and interview done in shares of administration academic data center at college. Here in data analyzed to learn the pattern from method used and added with the reference from literature. Experiment done using Microsoft Excel and Expert Choice Software, known that method can yield the optimal decision in selection of lecturer. There by the method recommended to be applied to getting optimal result in decision making.

Identification of expert to domain knowledge in any field of interest is essential for consulting in industry, academia and scientific community. The objective of this study is to address the expert-finding task in contemporary communities. We proposed Multifaceted Web Mining Architecture (MfWMA) and implemented a tool with data extracted from Growbag, dblpXML and web authors home page resource to identify personnel with specific expertise. We mined two thousand and five hundred author's personal web pages with the underlying criteria of a dozen of key parameters; while parsing on each page in pursuit of 8 thousand topics. This study corroborate this quantification in terms of a measure of expertise. The prototype provides its users to distinguish the level of expertise in a particular area; thus resulting in the capability to mark people with broader expertise. Through this extension to the web enabling technique, we have demonstrated that the proposed architecture presents a novel web mining approximation with realistic results.

Floating Point (FP) addition, subtraction and multiplication are widely used in large set of scientific and signal processing computation. A high speed floating point double precision adder/subtractor and multiplier are implemented on a Virtex-6 FPGA. In addition, the proposed designs are compliant with IEEE-754 format and handles over flow, under flow, rounding and various exception conditions. The adder/subtractor and multiplier designs achieved the operating frequencies of 363.76 MHz and 414.714 MHz with an area of 660 and 648 slices respectively.

Concrete compressive strength is one of the most important factors leading to building construction, in the civil engineering context. While evaluating such data, quantitative analysis required. As it is known that, concrete as a non-homogeneous material, consists of separate phases. The more complicated the concrete, the higher is the compressive strength. But if missing value exists in the microstructure of concrete, then it may provide some unusual effect on the compressive strength of concrete. Thus it is required to deal with the analysis of missing values. In this study traditional and modern estimation techniques of missing values are performed and the effect of these methods on correlation matrix is observed along with their comparison. The result shows that, modern techniques provide efficient estimates compared to traditional method. .The analysis described here were undertaken in the SPSS 13.0 packages.

Automatic traffic scene analysis which has been used for real-time on-road vehicle detection system is essential to many areas of ITS (Intelligent Transport Systems). In order to improve the detection time and accuracy of detection performance, various image processing techniques have been used for real-time vehicle detection. Moreover, Neural Networks have been increasingly and successfully applied to many problems for ITS research topics. Support Vector Machines (SVMs) are currently another efficient approach to vehicle detection because of their remarkable performance. In this research, two different models, Backpropagation which is the best-known neural network model and SVMs have been studied to compare their performance in predictive accuracy, through experiment with real world image data of traffic scenes. Experimental results show that SVMs can provide higher performance in terms of predictive performance than the well-known Backpropagation neural network model.

In this fast entertainment era Twenty-20 cricket becomes one of the most popular entertaining sports in all aged persons. Multi-criteria analysis plays a vital role to measure the performance of cricketers and Decision tree technique helps us to classify in very efficient manner. This paper makes use of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method to produce the overall performance of the all-rounder of Indian Premier League (IPL) T-20 session-III cricket tournament. The results of TOPSIS method are further used to classify all-rounder in four different categories by using Decision tree. Finally, this paper proposed a multi-criteria decision tree approach which provides accurate & efficient data classification upon the players’ performance.

Search Engine Optimization (SEO) relates to the activity of optimizing individual web-pages or the entire website to make them friendlier to acquire higher raking in the search results. All the major search engines such as Google, Yahoo, Ask, Bing, etc. rank web-pages based on certain factors that affect its ranking; therefore, SEO aims at generating the right types of signals on the web-pages. The core methodology used in SEO is to upgrade both content and associated coding of the website to improve its visibility and prominence in organic searches made by the search engines. The optimized websites obtain better ranks, and typically get a higher number of visitors. This research is based on reviewing different optimization techniques for individual web-pages or the entire website to make them search engine friendly. Besides, this study also critically analyzes and summarizes the core techniques proposed in the contemporary literature. This paper offers a comparative study of the previous research work regarding the techniques used in SEO and pinpoints certain gaps in the known search engine optimization techniques. Finally, we put forward some pertinent guidelines for optimizing the websites.

Diesel engines are widely used in almost all walks of life and cannot be dispensed with in the near future. As the fossil fuels now mainly used in diesel engine and continually depleting accompanied by increasing consumption and prices day by day, there is a need to find out an alternative fuel to fulfill the energy demand of the world. Alternative fuels like biodiesel, are being used as an effective alternative to diesel. The feasibility of biodiesel production from palm oil was investigated with respect to its fuel properties. Though biodiesel can replace diesel satisfactorily, problems related to fuel properties persist. In this study an oxygenated additive diethyl ether (DEE) was blended with palm oil biodiesel (POME) in the ratios of 2%, 4%, 6% and 8% and tested for their properties improvement. These blends were tested for energy content and various fuel properties according to ASTM standards. Qualifying of the effect of additive on palm biodiesel fuel properties can serve the researchers who work on biodiesel fuels to indicate the fuel suitability for diesel engines according to fuel standards. Blends of DEE in POME resulted in an improvement in acid value, viscosity, density and pour point with increasing content of DEE, accompanied by a slight decrease in energy content of biodiesel.

In this paper, lesion areas affected by anthracnose are segmented using segmentation techniques, graded based on percentage of affected area and neural network classifier is used to classify normal and anthracnose affected on fruits. We have considered three types of fruit namely mango, grape and pomegranate for our work. The developed processing scheme consists of two phases. In the first phase, segmentation techniques namely thresholding, region growing, K-means clustering and watershed are employed for separating anthracnose affected lesion areas from normal area. Then these affected areas are graded by calculating the percentage of affected area. In the second phase texture features are extracted using Runlength Matrix. These features are then used for classification purpose using ANN classifier. We have conducted experimentation on a dataset of 600 fruits’ image samples. The classification accuracies for normal and affected anthracnose fruit types are 84.65% and 76.6% respectively. The work finds application in developing a machine vision system in horticulture field.