Volume 6 | Issue 2 |

 Abstract

ABSTRACT Fiber Reinforced Concrete can be defined as a composite material consisting of mixtures of cement, mortar or concrete and discontinuous, discrete, uniformly dispersed suitable fibers. . It contains short discrete fibers that are uniformly distributed and randomly oriented. In order to achieve the desired goal, different dosages of steel fiber (0.5%, 1%, 1.5% & 2%) in the concrete grade of M-40.According to the results of experimental study, it is concluded that with the addition of steel fibers, The concrete mix M2, M3 M4 and M-5 shows percentage increment in compressive strength by 4.86%, 9.34%, 11.64% and 18.76% at 7 days of curing period with respect to concrete mix M1. The compressive strength by CTM (destructive testing technique) is found to increase with increase in percentage of steel fibers in the conventional concrete. The concrete mix M2, M3 M4 and M-5 showed increment in compressive strength by 4.54%, 7.32%, 9.08% and 11.72% at 28 day of curing period with respect to concrete mix M1. Tensile strength of cylinder specimen is maximum at 2% of steel fibers which having strength of 3.18 and 4.69 Mpa after 14 and 28 days respectively. Percentage increase for 2% steel fiber of 14 and 28 days is 25.19% and 18.43% respectively.Maximum flexural strength after 28 days is 10.43 MPa. Percentage increase for 2% of steel fiber is 13.36% as comparison to M-1 mix so it can be analyse that this variation of steel fiber is good for flexural strength of concrete. This experimental study shows that the steel fibers improve the properties of the conventional concrete and it enhances the tensile strength of concrete. 1. INTRODUCTION

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Steel Fibre CTM

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Index Terms - Index Terms: - clone attack, RED, witness distribution, oblivious, performance, WSN

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Index Terms - Wireless Sensor Network, Residual Energy, Clustering & Neighbour Weight

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Abstract : In this paper, color histograms, texture using wavelet transform and shape feature using thresholding is used to retrieve images from the database .The database includes 500 images of five different categories. Moreover success of this content based image retrieval depends on the method used to create feature vector and relativity measure, the accuracy of wavelet transform engaged. Retrieving images can be increases the accuracy of the system based on color, feature and shape relativity. For relativity measure between the database images and query image Mahalanobis, Minkowiski, Chebyshev, Cosine Relativity, Spearman correlation are used ,at the end aggregation of mean and relative deviation is done and aggregation is then arranged in increasing order i.e. minimum to maximum. This Arranging order shows images having minimum values are more accurate retrieved image. The Proposed system can better the retrieval accuracy that has been shown by the experimental results. The system is tested on 500 images of five different categories downloaded from the internet

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Index Terms - Histograms, Support Vector Machine; Mahalanobis, Minkowiski, Chebyshev, Cosine Relativity, Spearman correlation

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Index Terms: Computer Vision, MS Tracker, Local Binary Pattern (LBP), Object Tracking, Histogram, Intensity, Edge, Color, Uniform Pattern (Up)

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Textual Features Rating Features

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Abstract: In this paper we consider EOQ model with constant deterioration rate and selling price & time dependent demand. We considered holding cost as inventory dependent. Shortages are not allowed. Mathematical formulation is provided to find the optimal solution. We show total profit is a convex function with respect to cycle time. Numerical examples and sensitivity analysis are given to validate the proposed model in the paper.

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Key words: Selling price, Deterioration rate, EOQ model, holding cost, Time dependent, Inventory

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Abstract: Energy efficiency is one of most important parameter to consider while designing the wireless sensor networks. In this paper, we proposed robust energy management routing scheme based on two methods using artificial neural network (ANN). First is Most Significant Sensor Node prediction and another is Group Head selection. We discuss both of these problems in any WSN and seek possible solutions using neural networks, which will actually use to determine the shortest routing path in any WSN for minimizing the energy consumption. Usually WSNs life-time ends by having a single sensor node which uses all its energy and the other sensors consuming the remaining energy. This sensor (which is the cause of the networks end of lifetime) is most likely located in a very significant sensor node which always is in the routing path of many nodes to the base station. By predicting these significant nodes, it is possible to allocate tasks to the nodes in a more efficient way and thus increase the lifetime of the network. Selecting Group Heads amongst all the nodes is also energy conserving scheme for a WSN is proposed herewith. Sensor nodes are initially powered by batteries with full capacities. Each sensor collects data which are typically associated with other sensors in its neighborhood, and then the associated data is sent to the Base Station through Group Head for evaluating the tasks more efficiently. The following sections describe briefly energy saving scheme for network life time optimization, the experiments performed and results achieved in this work, and the main conclusions drawn from our experimental results.

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Index Terms- Wireless Sensor Network (WSN), Artificial Neural Network (ANN), Feed Forward Network (FFN), Base Station (BS)

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Key words- Design, Estimation, source, renewable and radiations

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Abstract In this paper, an edge cracked rectangular specimen subjected to symmetric three-point bending and asymmetric four point bending were used to determine the mode I and mode II fracture toughness of walnut particulate and coconut fibre reinforced biocomposite. Epoxy resin was used as matrix material and 10 wt % of coconut fibre and 20 wt % of walnut shell content were as reinforcing material. Scanning electron microscopy (SEM) shows that the walnut particles were well dispersed in the epoxy resin matrix. Addition of walnut particles increased the modulus of elasticity of the bio biocomposite. The fracture toughness (KIC) and mechanical test were conducted in a servo hydraulic universal testing machine and the results were analysed and discussed.

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Keywords: Fracture Toughness, Biocomposites, Walnut particle, Coconut fibre, SEM

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