Vol. 03, No. 01 [January 2017]

Self compacting concrete requires no external or internal compaction as it is leveled and compacted under its own weight. Self compacting concrete is highly engineered concrete with much higher fluidity without segregation and bleeding. The three main requirement of self compacting concrete are filling ability, passing ability and resistance to segregation. Plenty of availability natural resources in the past have become a dream for present day engineering society due to large scale consumptions. To overcome the problem of scarcity of natural aggregates and to save the environment from the pollution of dumping, civil engineers opined that there is significance potential for reuse of slag in value added application to maximize economic and environment benefit. Here an attempt has been made in this investigation to determine the strength characteristics of slag for application in self compacting concrete (SCC). It is found that ACI-1985 predicts closely to the test values while the others overestimate in 7 days test. The predicated values by Yun Wang Choi (2004) and Hueste et al (2004) overestimate while ACI-1985, ACI-1992, and ACI-1995 underestimate the test results in 28 days. Flexural strength is found to be increasing with increasing in the percentage of slag. ACI-1992,overestimate the values, IS-456-2000 and ACI-1985 well predicts the test values while Foster F Z-1995 underestimates the values.

Data mining (DM), also called Knowledge-Discovery in Databases (KDD), is the process of automatically searching large volumes of data for patterns using specific DM technique. The efficient discovery of previously unknown, valid, potentially useful, understandable patterns in large datasets. The analysis of (often large) observational data sets to find unsuspected relationships and to summarize the data in novel ways that are both understandable and useful to the data owner. Association rule mining represents a data mining technique and its goal is to find interesting association or correlation relationships among a large set of data items. With massive amounts of data continuously being collected and stored in databases, many companies are becoming interested in mining association rules from their databases to increase their profits. For example, the discovery of interesting association relationships among huge amounts of business transaction records can help catalog design, cross marketing, loss leader analysis, and other business decision making processes. If/then statements that help uncover relationships between seemingly unrelated data in a relational database or other information repository A typical example of association rule mining is market basket analysis. This process analyzes customer buying habits by finding associations between the different items that customers place in their "shopping baskets" using confidence and support factors.
Key words: data mining, association rule, market basket analysis, knowledge discovery, support and confidence factors.

In this paper there is a comparative study of different algorithms for path planning of a mobile robot to successfully reach a target in a known environment. The algorithms are A*Algorithm and Genetic Algorithm (GA) respectively which allows a mobile robot to navigate through static obstacles and find the path in order to reach target without collision. Here the mobile robot concerned is an AUV. The above strategy is designed in a 2-D grid map form of known environment and static obstacles. When the mission is executed it is necessary to plan an optimal or feasible path for itself avoiding obstructions on its way and minimise a cost such as computational time and path distance. The algorithms are implemented in Matlab and the results are compared.
Keywords: Path Planning, A*Algorithm, Genetic Algorithm, Mobile robot, AUV

Data has become an essential part of every Economy, Production, Organization, Business function and individual. The amount of data in world is growing day by day because of use of internet, Smartphone, social network, fine tuning of ubiquitous computing and many other technological advancements. Big Data is a term used to identify the datasets that whose size is beyond the ability of typical database software tools to store, manage and analyses. Generally size of the data is Petabyte and Exabyte. Most of the data is partly structured, unstructured or semi structured and it is heterogeneous in nature. Due to its specific nature, Big Data is stored in distributed file system architectures. Hadoop and HDFS by Apache are widely used for storing and managing Big Data. In the same way, Cloud computing has changed the entire process that distributed computing used to present e.g. Grid computing, server client computing. Cloud computing security is an important aspect of quality of service from cloud service providers. Security concerns arise as soon as one begins to run applications beyond the designated firewall and move closer towards the public domain. In violation of security in any component in the cloud can be disaster for the organization (the customer) as well as for the provider. Likewise using the cloud storage, users store their data on the cloud without the burden of data storage and maintenance and services and high-quality applications from a shared pool of configurable computing resources. Cryptography is probably the most important aspect of communications security and is becoming increasingly important as a basic building block for computer security, as data sharing is an important functionality in cloud storage. As a result in this paper, we propose a new approach towards big data in secured cloud computing along with efficient system for scalable data sharing to develop and enhance smart cities.

SRAM are the most promising high speed memory elements which are used in most of the Processors and controllers. Although the effect of leakage currents in SRAM memories is a great problem for the future architectures. In this paper we are going to redesign the SRAM for the purpose of more power & area reduction than the existing type of designs as well as the new design which is combined of NBTI Stability Improvement with read Error Reduction Logic and with Process Variation Tolerance Effect is Proposed and it‟s compared with the existing technologies & the nanometer technology. The simulations were carried out using Tanner EDA Tools. The Simulations are done under TSMC018 technology rules.
Keywords: NBTI Reduction, Recovery Boosting, SRAM Design, Process Variation Tolerance.

The paper used ARIMA model to predict the change of stock price of Vinamilk joint-stock company. The stock price data have investigated from 2006 to 2015. Because this chain of figures was non-constant, we made most difference of the figures. Then, we got new chain of figure whose fluctuation trended around the average. Also, when looking into auto-correlation diagram and partial correlation diagram, after making the most difference, we got figure chain that had stationary because the figure fluctuated around the fixed average. Then, we continually used stata 12 software and determined ARIMA (0,1,1) model was the most suitable model to predict stock price of Vinamilk joint-stock company in the future. The results showed that the stock price of Vinamilk joint-stock company have tended to increase and stable in the next few years, with the increase level from 100 to 140. The result can be used as foundation for the investors who can feel secure for investigating for this stock.
Keywords: stock price, predict, ARIMA model, Box - Jenkins methodology

Desalination methods are used to convert saline/brackish water to drinkable freshwater. Major processes use either thermal energy (conventional distillation) or pressure energy (Reverse osmosis). Different methods of desalination are discussed and their influence on overall water production has been highlighted. With the increase in appreciation for a green technology, desalination methods using renewable/waste energy are drawing significant attention in recent years
Keywords: Desalination, Distillation, MED MSF and Thermal method.

This paper presents the aspects of finding the coordinates of the robot end-effector target point. In manufacturing industries were using robots for many applications like assembling, drilling etc.in this processes object recognition, object tracking are necessary operations to perform. Object tracking in this area constitutes an important issue for vision assisted robot control. This paper describes the real time computation of relative pose estimation of robot end effector with respect to the targeted objects using image information and an algorithm to find position and orientation of the target. The algorithm should use as few feature points as possible during estimation such as centre of gravity (cog) of blobs, pixel coordinates in the image plane. The pose estimation is done through Dementhon linear approach and the iterations are done to get the pose of the objects till converges to the solution with lower error vector.
Keywords: pose, eye in hand, perspective projection.