Extensive Labview based Power Quality Monitoring and Protection System
Power quality issues and mitigation techniques became hot research topics soon after the introduction of solid state devices in power system. The equipments of non-linear nature introduce power quality issues such as harmonics, reduction in power factor, voltage unbalance, transients etc. and cause malfunction or damage of power system equipments. In this paper, harmonics, noises, reactive power etc. are considered as major issues. There is an ever increasing need for power quality monitoring systems due to the growing number of sources of disturbances in AC power systems. Monitoring of power quality is essential to maintain proper functioning of utilities, customer services and equipments. The authors surveyed different existing methods of power quality monitoring already in use and available in literature and arrived at the conclusion that an improved and affordable power quality monitoring system is the need of the hour. This paper presents the development of a simple power quality system for the purpose of measurement by designing virtual instruments using LabVIEW software. The real time data of hardware are acquired and fed to the software using Arduino for interfacing with LabVIEW. All power quality parameters are also measured by fluke power analyzer for validation. Observations taken from the hardware under test depict the importance of power quality monitoring, and also the accuracy and the precision of the developed system. The testing results and analysis indicate that the proposed method is feasible and practical for analyzing power quality disturbances.
Published by: Anurag Verma, Mrs. Shimi S.L
Author: Anurag Verma
Paper ID: V2I4-1147
Paper Status: published
Published: July 13, 2016
Credit Card Fraud Detection and False Alarms Reduction using Support Vector Machines
In day to day life credit cards are used for purchasing goods and services with the help of virtual card for online transaction or physical card for offline transaction. In a physical-card based purchase, the cardholder presents his card physically to a merchant for making a payment. To carry out fraudulent transactions in this kind of purchase; an attacker has to steal the credit card. To commit fraud in these types of purchases, a fraudster simply needs to know the card details. Most of the time, the genuine cardholder is not aware that someone else has seen or stolen his card information. The only way to detect this kind of fraud is to analyze the spending patterns on every card and to figure out any inconsistency with respect to the “usual” spending patterns. To commit fraud in these types of purchases, a fraudster simply needs to know the card details. Most of the time, the genuine cardholder is not aware that someone else has seen or stolen his card information. The only way to detect this kind of fraud is to analyze the spending patterns on every card and to figure out any inconsistency with respect to the “usual” spending patterns. Fraud detection based on the analysis of existing purchase data of cardholder is a promising way to reduce the rate of successful credit card frauds. As manually processing credit card transactions is a time-consuming and resource-demanding task, credit card issuers search for high-performing and efficient algorithms that automatically look for anomalies in the set of incoming transactions. Data mining is a well-known and often suitable solution to big data problems involving risk such as credit risk modelling, churn prediction and survival analysis. Nevertheless, fraud detection in general is an atypical prediction task which requires a tailored approach to address and predict future fraud. Though most of the fraud detection systems show good results in detecting fraudulent transactions, they also lead to the generation of too many false alarms. This assumes significance especially in the domain of credit card fraud detection where a credit card company needs to minimize its losses but, at the same time, does not wish the cardholder to feel restricted too often. In this work, we propose a novel credit card fraud detection system based on the integration support vector machines.
Published by: Mehak Kamboj, Shankey Gupta
Author: Mehak Kamboj
Paper ID: V2I4-1145
Paper Status: published
Published: July 12, 2016
A Hybrid Approach for Enhancing Security in RFID Networks
RFID (Radio-Frequency Identification) is a technology for automatic identification of things and people. Human beings are skillful at identifying things under many different challenge circumstances. A bleary-eyed person can quickly pick a cup out of coffee on a cluttered breakfast dining table each day, as an example. Computer sight, though, executes jobs which are such. RFID might be considered an easy method of explicitly objects that are labeling facilitate their “perception” by processing devices. An RFID device frequently only called an RFID label isa microchip that is small for wireless information transmission. It is generally mounted on an antenna in a package that resembles an adhesive sticker that is ordinary. The word “RFID” to denote any RF device whose function that is main identification of an object or person. Thisdefinition excludes simple products like retail stock tags, which simply indicate their particular presence and on/off condition during the standard end of the practical range. It also excludes products being transportable smart phones, which do a lot more than merely identify by themselves or their particular bearers. Numerous cryptographic models of security neglect to show crucial features of RFID systems. A straightforward design that is cryptographic as an example, catches the top-layer communication protocol between a tag and audience. In the reduced layers are anticollision protocols along with other RF that is basic notably enumerate the safety dilemmas present at multiple interaction layers in RFID methods. This work proposes a hybrid that is brand new and AES based Encryption mechanism for RFID program.
Published by: Bhawna Sharma, Dr. R.K. Chauhan
Author: Bhawna Sharma
Paper ID: V2I4-1144
Paper Status: published
Published: July 12, 2016
Performance Analysis of Multi-Hop Parallel Free-Space Optical Systems over Exponentiated Weibull Fading Channels Optimize by Particle Swarm Optimization
The performance of multihop parallel free- space optical (FSO) communication systems with decode-and-forward (DF) protocol over exponentiated Weibull (EW) fading channels has been investigated. The ABER and outage probability performance are analyzed under different turbulence conditions, receiver aperture sizes and structure parameters (R, C). The ABER and outage probability for FSO system is derived based on PSO. The ABER performance of the considered systems are investigated systematically combined with MC simulations. The comparison between EW fading model and PSO based EW fading channel demonstrates that performance of the both systems could be enhanced by large aperture sizes with the structure parameters R and C. With the particle swarm optimization (PSO) optimize path selection, fast average bit error rate (ABER) and outage probability reduction are achieved.
Published by: Babita, Dr. Manjit Singh Bhamrah,
Author: Babita
Paper ID: V2I4-1143
Paper Status: published
Published: July 9, 2016
Implementation of OLSR Protocol in MANET
Mobile ad hoc networks (MANETs) are autonomously self-organized networks without infrastructure support. In a mobile ad hoc network, nodes move arbitrarily; therefore the network may experience rapid and unpredictable topology changes. Because nodes in a MANET normally have limited transmission ranges, some nodes cannot communicate directly with each other. Hence, routing paths in mobile ad hoc networks potentially contain multiple hops, and every node in mobile ad hoc networks has the responsibility to act as a router. In this paper, we implement the OLSR Protocol in MANET to know how much data sent by the OLSR in bits/sec.
Published by: Rohit Katoch, Anuj Gupta
Author: Rohit Katoch
Paper ID: V2I4-1142
Paper Status: published
Published: July 7, 2016
Tumor Segmentation and Automated Training for Liver Cancer Isolation
Image segmentation is the process of subdividing the image to into its parts that are constituent and is considered one of the most difficult tasks in image processing. It plays a task that is a must any application and its particular success is based on the effective implementation of the segmentation technique. For numerous applications, segmentation reduces to locating an object in an image. This involves partitioning the image into two classes, background or object. Into the individual system that is visual segmentation happens obviously. Our company is experts on detecting patterns, lines, edges and forms, and making decisions based upon the information that is visual. At that time that is same we have been overwhelmed by the quantity of image information which can be captured by technology, as it is not feasible to manually process all such images.Automatic segmentation of tumor faction from medical pictures is difficult due to size, shape, place and presence of other objects with the intensity that is exact same in the image. Therefore, cancer segments from the liver where tumor persists cannot be easily segmented accurately from medical scans utilizing approaches that are traditional. The performance of ANN been examined in classifying the Liver Tumor in this research. An approach for segmentation of tumor and liver from medical pictures is principally used for computer aided diagnosis of liver is required. The method is use contour detection with optimized threshold algorithm. The liver is segmented region that is utilizing technique efficiently close around the liver tumors. The whole process is a learning that is supervised; the classifiers require training information set which can be segmented. The classifier that is last evaluated with test set total error in tumor segmentation of this liver is be calculated. Algorithm should be based on segmentation of abnormal regions in the liver. The category regarding the regions can be carried out based on shape categorization and lots of other features using methods such artificial networks that are neural.
Published by: Shikha Mandhan, Kiran Jain
Author: Shikha Mandhan
Paper ID: V2I4-1141
Paper Status: published
Published: July 5, 2016
