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Seasonal impact on beach morphology and the status of heavy mineral deposition a？？ central Tamil Nadu coast, India
N Chandrasekar,V Joevivek
- , 2014,
Abstract:
Generating Finite Dimensional Integrable Nonlinear Dynamical Systems
M. Lakshmanan,V. K. Chandrasekar
Physics , 2013, DOI: 10.1140/epjst/e2013-01871-6
Abstract: In this article, we present a brief overview of some of the recent progress made in identifying and generating finite dimensional integrable nonlinear dynamical systems, exhibiting interesting oscillatory and other solution properties, including quantum aspects. Particularly we concentrate on Lienard type nonlinear oscillators and their generalizations and coupled versions. Specific systems include Mathews-Lakshmanan oscillators, modified Emden equations, isochronous oscillators and generalizations. Nonstandard Lagrangian and Hamiltonian formulations of some of these systems are also briefly touched upon. Nonlocal transformations and linearization aspects are also discussed.
Enhanced Genetic Algorithm for Optimal Electric Power Flow using TCSC and TCPS
K. Kalaiselvi,V. Suresh Kumar,K. Chandrasekar
Lecture Notes in Engineering and Computer Science , 2010,
Abstract:
Investigating rainfall estimation from radar measurements using neural networks
A. Alqudah,V. Chandrasekar,M. Le
Natural Hazards and Earth System Sciences (NHESS) & Discussions (NHESSD) , 2013, DOI: 10.5194/nhess-13-535-2013
Abstract: Rainfall observed on the ground is dependent on the four dimensional structure of precipitation aloft. Scanning radars can observe the four dimensional structure of precipitation. Neural network is a nonparametric method to represent the nonlinear relationship between radar measurements and rainfall rate. The relationship is derived directly from a dataset consisting of radar measurements and rain gauge measurements. The performance of neural network based rainfall estimation is subject to many factors, such as the representativeness and sufficiency of the training dataset, the generalization capability of the network to new data, seasonal changes, and regional changes. Improving the performance of the neural network for real time applications is of great interest. The goal of this paper is to investigate the performance of rainfall estimation based on Radial Basis Function (RBF) neural networks using radar reflectivity as input and rain gauge as the target. Data from Melbourne, Florida NEXRAD (Next Generation Weather Radar) ground radar (KMLB) over different years along with rain gauge measurements are used to conduct various investigations related to this problem. A direct gauge comparison study is done to demonstrate the improvement brought in by the neural networks and to show the feasibility of this system. The principal components analysis (PCA) technique is also used to reduce the dimensionality of the training dataset. Reducing the dimensionality of the input training data will reduce the training time as well as reduce the network complexity which will also avoid over fitting.
Passing VBR in Mobile Ad Hoc Networks – for effective live video Streaming
V. Saravanan,Dr.C.Chandrasekar
International Journal of Advanced Computer Sciences and Applications , 2013,
Abstract: Mobile ad hoc networks (often referred to as MANETs) consist of wireless hosts that communicate with each other in the absence of a fixed infrastructure. This technique can be used effectively in disaster management, intellectualconference and also in the battlefield environments. It has the significant attention in the recent years. This research paper depicts the remuneration of using suggestion tracking for selecting energy-conserving routes in delay-tolerant applications and it sends Variable Bit Rate delivery. The previous investigation set up from earlier period surveillance that delay can be traded for energy efficiency in selecting a path. The Prior objective is to find an experiential upper bound on the energy savings by assuming that each node accurately knows or predicts its future path. It examines the effect of varying the amount of future information on routing. Such a bound may prove useful in deciding how far to look in advance, and thus how much convolution to provide in mobility tracking.
The CASA quantitative precipitation estimation system: a five year validation study
V. Chandrasekar,Y. Wang,H. Chen
Natural Hazards and Earth System Sciences (NHESS) & Discussions (NHESSD) , 2012, DOI: 10.5194/nhess-12-2811-2012
Abstract: Flooding is one of the most common natural hazards that produce substantial loss of life and property. The QPE products that are derived at high spatiotemporal resolution, which is enabled by the deployment of a dense radar network, have the potential to improve the prediction of flash-flooding threats when coupled with hydrological models. The US National Science Foundation Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) is dedicated to revolutionizing our ability to observe, understand, predict, and respond to hazardous weather events, especially in the lower atmosphere. CASA's technology enables precipitation observation close to the ground and QPE is one of the important products generated by the system. This paper describes the CASA QPE system built on the various underlying technologies of networked X-band radar systems providing high-resolution (in space and time) measurements, using the rainfall products from the radar. Evaluation of the networked rainfall product using 5 yr of data from the CASA IP-1 test bed is presented. Cross validation of the product using 5 yr of data with a gauge network is also provided. The validation shows the excellent performance of the CASA QPE system with a standard error of 25% and a low bias of 3.7%. Examples of various CASA rainfall products including instantaneous and hourly rainfall accumulations are shown.
In Media Streaming Security - Focus Data leakage and Modifiers in Mobile Environment
V.Saravanan,C.Chandrasekar
International Journal of Computer Science Issues , 2012,
Abstract: Generally, in the emblematic deployment of mobile networks, where a huge number of nodes are aimlessly deployed in a two dimensional area. Packet dropping and modification are frequent attacks that can be launched by an adversary to disrupt communication in even based mobile networks. Several schemes have been wished-for to take the edge off or put up with such attacks but very few can effectively and efficiently identify the intruders. To address this problem and make a security without affecting an even based streaming process, we propose a simple however valuable method, which can classify mischievous forwarders that drop or modify packets. Extensive analysis and simulations have been conducted to verify the effectiveness and efficiency of the scheme. Most of the bad nodes can be identified by our heuristic ranking algorithms with little artificial encouraging.
Improving Seismic Monitoring System for Small to Intermediate Earthquake Detection
V. Joevivek, N. Chandrasekar & Y.Srinivas
International Journal of Computer Science and Security , 2010,
Abstract: Efficient and successful seismic event detection is an important and challengingissue in many disciplines, especially in tectonics studies and geo-seismicsciences. In this paper, we propose a fast, efficient, and useful feature extractiontechnique for maximally separable class events. Support vector machineclassifier algorithm with an adjustable learning rate has been utilized toadaptively and accurately estimate small level seismic events. The algorithm hasless computation, and thereby increased high economic impact on analyzing thedatabase. Experimental results demonstrate the strength and robustness of themethod.
Determining toothache severity in pediatric patients: A study
Gupta V,Chandrasekar T,Ramani P,Anuja
Journal of the Indian Society of Pedodontics and Preventive Dentistry , 2006,
Abstract: To correlate sodium-potassium levels in saliva of pediatric patients having different intensities of toothache assessed by Visual Analogue Scale (VAS) in age group 3-14 yrs. A prospective study of 50 children having different intensities of pain was carried out in the Dept. of Pedodontics, Sareetha Dental College and Hospital. 50 children (aged 3-14 yrs) having different intensities of toothache including normal children (control) were included in the study. Saliva samples were collected and Na+, K+ levels in saliva were measured by using Na+, k+ colorimeter kit. Photographs were taken using Digital camera and VAS was prepared accordingly. Sodium levels decreased with increasing pain intensity and potassium levels increased, facial expressions correlated with Na+, K+ levels. Correlation between Na+, K+ levels and pain intensity exists. Also, VAS is a valid measure for pain.
On the general solution for the modified Emden type equation $\ddot{x}+αx\dot{x}+βx^3=0$
V K Chandrasekar,M Senthilvelan,M Lakshmanan
Physics , 2006,
Abstract: In this paper, we demonstrate that the modified Emden type equation (MEE), $\ddot{x}+\alpha x\dot{x}+\beta x^3=0$, is integrable either explicitly or by quadrature for any value of $\alpha$ and $\beta$. We also prove that the MEE possesses appropriate time-independent Hamiltonian function for the full range of parameters $\alpha$ and $\beta$. In addition, we show that the MEE is intimately connected with two well known nonlinear models, namely the force-free Duffing type oscillator equation and the two dimensional Lotka-Volterra (LV) equation and thus the complete integrability of the latter two models can also be understood in terms of the MEE.
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