Informatics Publishing Limited

S. Anbazhagan ¹, R. Neelakantan ¹, S. Arivazhagan ¹, G. Vanaraju ¹

Affiliations
1 Department of Geology, Periyar University, Salem-636011, IN

Abstract

Kolli hills is one of the small tourist spots in Tamil Nadu falls in Eastern Ghats. In general, improper land management, intensive weathering and rainfall trigger frequent slope failures and landslide in hill systems. During December 2005, similar slope failure had occuued in the Southeastern part of Kolli hills. Fractures and open cracks had developed along with horizontal and vertical displacements. Continuous heavy rainfall deforestation agricultural practices and obstruction of natural flow are observed to be the causative factors for such failure. Field investigations and other parameters were studied in detail. Abandoning of current agriculture practices and permitting natural flow of the streams are immediate steps recommended for stabilising such vulnerable slope.

Keywords

Fractures, Land Subsidence Landslide, Kolli Hills, Tamil Nadu.

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R. Ahila Priyadharshini ¹, S. Arivazhagan ¹

Affiliations
1 Mepco Schlenk Engineering College, Sivakasi, IN

Abstract

This paper elaborately discusses on effective method for object classification. In the intricate process of object recognition, it so happens that images have often to be classified based on objects which constitute only a very limited part of the image. By using Patches (local features) properly, the properties of certain regions of an image can be described in detail. Then the object parts can be modeled based on the image patches extracted with regard to each salient point, where the information content is high. The information collected thus are, then, properly stored in an histogram. It is proposed to use a sparse representation of the histograms, i.e., only those bins whose content is not empty are stored. The distances between the histograms of the test image and training images are computed by using an appropriate classifier and finally they are classified. The experimental evaluation of the proposed method is carried out using the Caltech database.

Keywords

Salient Points, Principal Component Analysis, Sparse Histogram, Cross-Bin Distance Measures.

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V. Kejalakshmi ¹, R. Thilagavathy ², S. Arivazhagan ³

Affiliations
1 Anna University of Technology, Trichy, IN
2 K.L.N College of Engineering Anna University, Chennai, IN
3 College of Engineering, Guindy, Anna University, Chennai, IN

Abstract

Particle Swarm Optimization algorithm (PSO) is used in the optimization of Multi user Multi Input and Multi Output (MU-MIMO) communication systems. Linear precoding is employed in MU-MIMO communication system to improve the system capacity and to minimize the receiver complexity. The previous works on optimization algorithm to design a linear precoder to maximize the system capacity is assumed to have perfect channel state information (CSI) at the base station (BS) and antenna size 4x4. However the CSI available at the BS is imperfect due to channel estimation errors. Hence, in this paper, the precoder is designed to maximize system capacity by considering the channel estimation error. Further, the PSO algorithm is used to find the precoding matrix and also analyze the performance of capacity and bit error rate with different variance values and with different antenna sizes. The simulation results show that the system capacity performance of the proposed precoder which considers channel estimation error outperforms the previously proposed precoder.

Keywords

CSI, PSO, SDMA, SINR, DPDC, THP, BD, ZF, MMSE.

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S. Arivazhagan ¹, W. Sylvia Lilly Jebarani ¹, S. T. Veena ¹, M. Shanmugaraj ¹

Affiliations
1 Department of Electronics and Communication Engineering, Mepco Schlenk Engineering College, Sivakasi - 626005, Tamil Nadu, IN

Abstract

Data hiding techniques whenever used to hide mammoth payloads disturb statistical properties of the cover medium thus leaving a characteristic artifact. These artifacts can provide useful information to the watchful eyes of the steganalyst to identify potential carriers. But the probability of detection sharply declines when the amount of data getting embedded is reduced. Intelligent steganographers as a measure of evading significant artifacts hide only minimal amount of data. This work is an effort to differentiate stego images from innocuous cover images especially when they carry very minimal payloads. A novel low dimensional feature set has been used along with an ensemble classifier.

Keywords

Composite Feature Set, Ensemble Classifier, Payload, Steganalysis, Steganography.

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S. Arivazhagan ¹, R. Newlin Shebiah ¹, J. Salome Magdalene ¹, G. Sushmitha ¹

Affiliations
1 Department of Electronics and Communication Engineering, Mepco Schlenk Engineering College, IN

Abstract

Derailments take place when a train runs off its rails and are seriously hazardous to human safety. Most of the Railway Track defects which lead to derailment are detected manually by trained human operators walking along the track. To overcome this difficulty, an Automatic Railway Track Derailment Inspection System using Machine Vision Algorithm to detect the cracks in the railway track is proposed here. The input image is decomposed by Gabor filter and texture features were extracted using Segmentation based Fractal Texture Analysis (SFTA) and the features are classified as defect and defect free classes using AdaBoost Classifier. The proposed algorithm is tested on a set of real time samples collected and the classification rate obtained was satisfactory.

Keywords

Crack Detection, Gabor Wavelets, Texture Analysis, AdaBoost Classifier.

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S. Arivazhagan ¹, L. Ganesan ², C. N. Savithri ¹

Affiliations
1 Departmentof Electronics and Communication Engineering, Mepco Schlenk Engineering College, Tamil Nadu, IN
2 Department of Computer Science and Engineering, Alagappa Chettiar College of Engineering and Technology, Tamil Nadu, IN

Abstract

Texture classification is important in applications of computer image analysis for characterization or classification of images based on local spatial variations of intensity or color. Texture can be defined as consisting of mutually related elements. This paper proposes an experimental approach for identification of suitable multi-resolution transform for characterization and classification of different texture groups based on statistical and co-occurrence features derived from multi-resolution transformed sub bands. The statistical and co-occurrence feature sets are extracted for various multi-resolution transforms such as Discrete Wavelet Transform (DWT), Stationary Wavelet Transform (SWT), Double Density Wavelet Transform (DDWT) and Dual Tree Complex Wavelet Transform (DTCWT) and then, the transform that maximizes the texture classification performance for the particular texture group is identified.

Keywords

Texture, Multi-Resolution Transforms, Statistical and Co-Occurrence Features.

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S. Arivazhagan ¹, W. Sylvia Lilly Jebarani ¹, S. Bagavath ¹

Affiliations
1 Department of Electronics and Communication Engineering, Mepco Schlenk Engineering College, Tamil Nadu, IN

Abstract

Steganographic algorithms in the recent past have been producing stego images with perceptual invisibility, better secrecy and certain robustness against attacks like cropping, filtering etc. Recovering a good quality secret from a good quality stego image may not always be possible. The method proposed in this paper works in transform domain and attempts to extract the secret almost as same as the embedded one maintaining minimal changes to the cover image by using techniques like median maintenance, offset and quantization.

Keywords

Arnold Transform, Median maintenance, Discrete Wavelet Transform, Improved LSB, Offset, Quantization.

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K. Kavitha ¹, S. Arivazhagan ², R. Dhivya Priya ¹, I. Kanaga Sangeetha ¹

Affiliations
1 Deptt. of E.C.E., Mepco Schlenk Engineering College, Sivakasi, Tamilnadu, IN
2 Mepco Schlenk Engineering College, Sivakasi, Tamilnadu, IN

Abstract

Classification of heterogeneous classes present in the Hyperspectral image is one of the recent research issues in the field of remote sensing. This work presents a novel technique that classifies Hyperspectral images that contain number of classes by making use of the image moments. Recently, researchers have introduced a number of neural network models and structured output based methods for classification of these Hyperspectral images they however suffers with the problem of confusion between the classes that are having similar characteristics and hence provides imbalanced solution for the classes with less number of pixels. The polynomial features such as Zernike moments are extracted from the Hyperspectral image and is used for classification. Support Vector Machines with Binary Hierarchical Tree is used for classification of the Hyperspectral data by One Against All methodology. Then, the performance of Zernike moments in Hyperspectral image classification is evaluated.

Keywords

Zernike Moments, Hyperspectral Image Classification, Multi-Class Classifier, Support Vector Machine, AVIRIS.

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S. Arivazhagan ¹, M. Kirubakaran ²

Affiliations
1 Centre for Applied Geology, Gandhigram Rural Institute-Deemed University, Gandhigram, Dindigul District, Tamil Nadu-624302, IN
2 Department of Civil Engineering, Dhirajlal Gandhi College of Technology, Salem, Tamilnadu-636309, IN

Abstract

Unplanned city growth is an indicator of rapid industrialization, which usually reduces the quality of the environmental health of a region-sometimes disastrously. Monitoring provides the planners and decision-makers with required information about the current state of development and the nature of changes that have occurred. The vegetation, urban sprawl, water bodies and road networks are taken as the key features to map the changes in Tiruchengode taluk, Namakkal, Tamil Nadu. The satellite images obtained in the two different periods such as LANDSAT 7 (1999) and LANDSAT 8 (2013) were used in the present study along with the town and country planning map (1994) and topo-sheets (1972) as ancillary data. Change detection is an important tool in many remote sensing applications. Using such tools we can compare satellite data from different times to assess damage from natural disasters, understand the ways in which humans alter the land, and characterize climatic and seasonal changes to the landscape. Mapping of the urban changes in the study area have been interpreted in view of developing urban land with different classes. The identification of suitable region for urban development is also quite important for planners to do sustainable development. So current study attempts to identify the suitable site for urban development in Tiruchengode taluk, Namakkal using integrated GIS and multi influence factor (MIF) techniques.

Keywords

LANDSAT, GIS, Multi IF Technique, Tiruchengode.

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