Informatics Publishing Limited

S. Manikandan ¹

Affiliations
1 Department of IT, EGS Pillay Engineering College, Nagapattinam, Tamil Nadu, IN

Abstract

Adaptive join algorithms have recently attracted a lot of attention in emerging applications where data are provided by autonomous data sources through heterogeneous network environments. Their main advantage over traditional join techniques is that they can start producing join results as soon as the first input tuples are available, thus, improving pipelining by smoothing join result production and by masking source or network delays. In this paper, we first propose DINER (Double Index NEsted-loops Reactive join), a new adaptive two-way join algorithm for result rate maximization. DINER combines two key elements: an intuitive flushing policy that aims to increase the productivity of in-memory tuples in producing results during the online phase of the join, and a novel reentrant join technique that allows the algorithm to rapidly switch between processing in-memory and disk-resident tuples, thus, better exploiting temporary delays when new data are not available. We then extend the applicability of the proposed technique for a more challenging setup: handling more than two inputs. Multi Active Relational join Algorithm (MARA) is a multiway join operator that inherits its principles from DINER. Our experiments using real and synthetic data sets demonstrate that TARA outperforms previous adaptive join algorithms in producing result tuples at a significantly higher rate, while making better use of the available memory. Our experiments also shows that in the presence of multiple inputs, MARA manages to produce a high percentage of early results, outperforming existing techniques for adaptive multiway join.

Keywords

Query Processing, DINER, MARA, Join.

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S. Manikandan ¹, S. Ramanathan ², S. Krishnamohan ³, D. Vasudevan ¹

Affiliations
1 Department of Mechanical Engineering, Annamalai University, Annamalai Nagar-608002, Tamilnadu, IN
2 Department of Manufacturing Engineering, Annamalai University, Annamalai Nagar-608002, Tamilnadu, IN
3 Department of Mechanical Engineering, E.G.S Pillay Engineering College, Nagapattinam-611002, Tamilnadu, IN

Abstract

Titanium is recognized for its strategic importance as a unique lightweight, high strength alloyed structurally efficient metal for critical, high-performance aircraft, such as jet engine and airframe components. Titanium is called as the "space age metal" and is recognized for its high strength-to-weight ratio. Today, titanium alloys are common, readily available engineered metals that compete directly with stainless and Specialty steels, copper alloys, nickel based alloys and composites. Titanium alloys are needed to be heat treated in order to reduce residual stress developed during fabrication and to increase the strength. Titanium (Ti-6Al-4V) alloy is an alpha beta alloy which is subjected to annealing and solution treatment to attain beta phase. This beta phase is maintained by quenching and subsequent aging to increase strength. Thermal cycling process was carried out for heat treated Ti-6Al-4V specimens. Forced air used for cooling. This paper reports on the investigation of tensile behaviour of different Heat treated and thermal cycled Titanium (Ti-6Al-4V) alloy and the micro structural changes.

Keywords

Thermal Cycling, Heat Treatment, Solutionizing, Aging, Tensile Strength.

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S. Manikandan ¹

Affiliations
1 The Institute of Companies Secretaries of India, Mumbai, IN

Abstract

Thiruvalluvar's Thirukural has today come to be documented as a classic in the literature of the entire world. In Tamil "Thiru" means "holy" or "sacred," and "Kural" means a short poem. Thirukural is in a couplet form and each Kural is composed of 7 words spread across 2 lines (4&3 words). In fact, it is the shortest form of poetry in the Tamil language.

Keywords

Sangam Literature, Thiruvalluvar and His philosophy, Palm Leaf Manuscript, Economic and management in Thirukural.

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V. Jayaraj ¹, D. Ebenezer ², S. Manikandan ³

Affiliations
1 Department of Electronics and Communication Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, IN
2 Department of Electronics and Communication Engineering, RMK Engineering College, Chennai, IN
3 DRDO, IN

Abstract

A Non-linear adaptive decision based algorithm with robust motion estimation technique is proposed for removal of impulse noise, Gaussian noise and mixed noise (impulse and Gaussian) with edge and fine detail preservation in images and videos. The algorithm includes detection of corrupted pixels and the estimation of values for replacing the corrupted pixels. The main advantage of the proposed algorithm is that an appropriate filter is used for replacing the corrupted pixel based on the estimation of the noise variance present in the filtering window. This leads to reduced blurring and better fine detail preservation even at the high mixed noise density. It performs both spatial and temporal filtering for removal of the noises in the filter window of the videos. The Improved Cross Diamond Search Motion Estimation technique uses Least Median Square as a cost function, which shows improved performance than other motion estimation techniques with existing cost functions. The results show that the proposed algorithm outperforms the other algorithms in the visual point of view and in Peak Signal to Noise Ratio, Mean Square Error and Image Enhancement Factor.

Keywords

Impulse Noise, Gaussian Noise, Motion Estimation, Median Based Filter, Minimum Median Square Error.

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V. Padma Supriya ¹, S. Manikandan ², T. Ramakrishnan ³, P. Radha ⁴

Affiliations
1 Dept. of Electronics and Instrumentation Engg. National Engineering College, Kovilpatti, Tamil Nadu -628 503, IN
2 Dept. of Bio medical Engineering Saveetha Engineering College, Thandalam Chennai – 602 105, IN
3 Dept. of Electronics and Instrumentation Engg., National Engineering College, Kovilpatti, Tamil Nadu -628 503, IN
4 Siddha Clinical Research Unit (SCRU) Government Siddha Medical College Campus, Palayamkottai, Tamil Nadu-627002, IN

Abstract

Herbal plants have been used for medicinal purposes since the ages. These plants also play a major role in medicines, food, perfumes and cosmetics. At present, the identification of herbal plants is purely based on the human perception of their knowledge. It may be probability of human error occurring. An efficient herb species classification system should be automatic and a convenient recognition of herbal plants which reduces the human error. The present research aims to predict the herbal plants in a very convenient and accurate way. This approach is based on the leaf shape, texture, color and its feature. Bacteria Foraging Optimization (BFO) for feature selection and Fuzzy Relevance Vector Machine (FRVM) for the classification of herbal plants are used in the proposed system. The data required for classification are computed using the MATLAB software. In the present work, ten different types of herbal leaves and twenty samples of each have been considered for the process and the classification accuracy is achieved as maximum with an efficient intelligence technique. The efficiency of the proposed method of classifying the different herbal plants gives better performance.

Keywords

Detection, GLCM Texture Feature Extraction, BFO, FRVM Classifier.

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