Contribution of SAR Radar Imagery in the Study of the Dynamics of the Danube Island System, Giurgiu-Călăraşi Sector, Romania

SAR radar imagery allows spatial and temporal, regular and precise tracking of surfaces. The response of the radar signal is particularly sensitive to the topography, the roughness and the humidity of the grounds, thus making it possible to detect possible changes occurring between different acquisitions. The surface of the islands is formed especially of alluvial materials and covered by vegetation. The latter contributes greatly to the stability of the islands. The aim of this work is to show the contribution of radar imagery in the study of the dynamics of the Danube island system, Giurgiu-Călăraşi sector, in Romania. This study, based on amplitude images, showed the possibility of determining, locating, inventorying and mapping 11 islands (including 19 sub-islands). The radar images delimit perfectly the latter due to their sensitivity to roughness. Their surface is very rough and wet represented by a strong radiometry. On the other hand, the water surface is smooth thus giving values of low radiometry. The first results obtained show that over this period between 1995 and 2009, the changes are due mainly to weather conditions, precipitation and very high wind speeds. They also reveal an increase in sediment over this 14-year period.

Assessment of Suitable Image Fusion Method for CARTOSAT-2E Satellite Urban Imagery for Automatic Feature Extraction

The purpose of this study is to investigate the best suitable pan sharpening method for CARTOSAT-2E satellite launched by ISRO (Indian Space Research Organisation). This satellite provides high resolution images that are being used for many urban applications such as mapping, feature extraction, facility management etc. The synthesized image using pan sharpening method enables users to take the combined advantage of the best available spatial and spectral resolutions. In this paper, various pan sharpening methods based on component substitution (CS) and Multi Resolution Analysis (MRA) are applied on the CARTOSAT-2E images and the resultant images are tested for their qualitative and quantitative performance. Qualitative analysis is carried out based on image blur and spectral distortion and quantitative evaluation is performed using image metrics by comparing the synthesized image with the original image. The results show that the High-Pass Filter (HPF) method offers the good spectral fidelity. However, due to its inherent stair-casing effect in the resultant image; modified-IHS followed by PRACS method is found to be preferable for automatic urban feature extraction from CARTOSAT-2E images.

A Prototype Model to Detect Elephants near the Railway Tracks

Artificial Neural Network (ANN) technologies are becoming very popular. It has been used almost in all the research areas. Approach in this paper is to develop an automated expert system to drive away Elephants found near the railway tracks to stop the casualties of these animals on the railway tracks. In this paper, a prototype model has been designed using Geophone Sensors which recognizes the vibrations of the Elephants roaming near the railway tracks. These vibrations are sent to the nearby servers with the help of Arduino. The server runs software based on the ANN model developed here. It detects the exact position of the Elephants present near the railway tracks and raises an alarm to drive them away.

Hybrid Architecture for Distributed Intrusion Detection System Using Semi-supervised Classifiers in Ensemble Approach

Security of data is becoming a big treat today because of modern attacks. All the data passing through network is at risk as intruders can easily access and modify data. Security to the network is provided using Intrusion Detection System (IDS) which helps to monitor and analyze each packet entering or passing through the network. In this paper hybrid architecture for IDS is proposed which can work as an intelligent system in distributed environment. Proposed system makes use of semi-supervised machine learning classifiers into an ensemble approach. Classifiers used are Support vector machine, decision tree and k-nearest neighbor. Ensemble of this classifier is done and final prediction is given by majority voting algorithm. This system makes use of feature selection technique to reduce number of features used for training various classifiers. Experiments are conducted on NSL-KDD dataset. From results it is observed that ensemble technique increases accuracy by 3% and reduces false alarm rate by 0.05. System performance improves if used in ensemble approach as compare to individual classifier.

3D Forced Convection in a Box Containing Two Cylindrical Heat Sources

Forced convection is a special type of heat transfer in which fluids are forced to move, in order to increase the heat transfer. The aim of this study is to investigate in a numerical simulation of forced convection in a cubic cavity containing two cylindrical heat sources in the middle. The cavity is subjected to an inflow of air inlet from the left and an outlet flow from the right side with the same dimensions of the opening doors (0.1 H). The walls of the cavity are maintained adiabatic. The conservation equations governing the flow of forced convection are solved using the finite volume method that is used by FLUENT –ANSYS Software. Also, a comparison was performed shows good agreement between the numerical results with those available in the literature (numerical, analytical and experimental). The effect of the dimension of cavity (number of Rayleigh) and the velocity of ventilation of the air (number of Reynolds), as well as the dimension between sources cylindrical are presented and discussed. The results obtained show a strong dependence between the geometrical conditions and the structure of the dynamic and thermal field of the flow of forced convection.