64 research outputs found
Investigating MapReduce framework extensions for efficient processing of geographically scattered datasets
No full textWe observe two important trends brought about by the evolution of Internet in recent years. Firstly to improve end-to-end application performance in presence of bottlenecks in the wide-area Internet communication, modern day Internet services are designed in a decentralized fashion involving geographically distributed data-centers connected through the Internet. Secondly the pervasive nature of Internet services has resulted into an exponential growth in the size of digital information created, captured or replicated. Organizations are keenly interested in mining this information to uncover trends, statistics and other actionable information which can give them competitive advantage. These two trends necessitate the design of a large-scale data processing system which can operate efficiently in a distributed environment involving multiple datacenters connected through the Internet. In recent years, MapReduce programming model and specifically its open source implementation Hadoop is gaining a lot of traction for performing large-scale data processing in a centralized environment. Our evaluation of different real-world usage scenarios of Hadoop deployments revealed that the organizations with the distributed datasets are required to copy the entire dataset to a centralized location so that it can be efficiently processed by the Hadoop MapReduce framework. As the Internet evolves growth in the size of distributed datasets would outpace the improvements in the network bandwidth available in the Internet. At that point the approach of copying the entire dataset to a single location using Internet would become infeasible. In this thesis, we have investigated the possibility of extending the MapReduce and specifically Hadoop framework to operate in a distributed environment involving multiple datacenters connected through the Internet. We also have proposed policies to improve the performance of Hadoop MapReduce framework in a distributed environment. We have observed that our policies improve the performance of Hadoop framework substantially.M.S.Includes bibliographical referencesby Hrishikesh Gadr
Framework for crawling and local event detection using twitter data
No full textTwitter is a popular social media service, with millions of registered users as of December 2010. Twitter hosts substantial amounts of user-contributed data of real-world events. Twitter‟s core functions represent a simple social awareness stream model. Twitter users share information about upcoming events, the events the users are attending and events being broadcasted. The users also specify their location in their profile on Twitter. We can programmatically collect data from Twitter using their API and detect top terms and events in the data. Researchers can use this program to collect any kind of data from social networks easily. Journalists can get a real time the list of events detected by this method. In this thesis, we propose a solution to tackle the problem above. We wrote scripts that collected Twitter data through Twitter API. The scripts collect data according to user location and by search keywords. We built a web interface that provides mechanism to manage the collection of data. The web interface allows addition of new locations and keywords to the data collection. We collected Twitter data for important locations across the United States of America and the world using these tools. We use two approaches to detect trends in the data. In the first approach, we detected spikes in data by looking at overall rate of tweets at each location over a period of time. In the second approach, .we indexed the data according to location and time of the day. Then, we identified trends in the indexed data by ranking the terms according to spikes in term frequency. Using our framework, we can detect the top events and trends for a given time period and location according to Twitter data.M.S.Includes bibliographical referencesby Hrishikesh Baksh
Design of Mems Decoupled Gyroscope.
No full textMEMS (Micro-electro-mechanical systems) gyroscopes are widely used as an inertial measurement unit in different industrial applications. MEMS are the device that combines mechanical and electrical components on a small silicon wafer with components sized in the range of a micro meter. Amongst all the gyroscopes available, vibratory gyroscopes are most simple in construction and are widely used. In vibratory rate gyroscope, sensitivity is achieved by reducing frequency mismatch and this is mostly done by selecting the common beam topology. However, due to the common beam, mechanical coupling effect is introduced, which might affect the overall stability of the device. Thus, this is a challenging part for a designer to achieve higher sensitivity, while maintaining stability. In this thesis a new 2-DOF vibratory rate gyroscope is proposed that has two independent axis of vibration with a mechanical coupling measure. Design includes selection of structural parameter, as well as driving and sensing topology. The structural design parameters of drive and sense beams are carefully selected so as to achieve maximum sensitivity, while reducing the overall coupling between the drive and sense mechanism. The design showed improved decoupling and sensing sensitivity. From the simulation, the displacement sensitivity is in the range of 3nm/(°/sec) and capacitance change is in the range of femto Farad. Furthermore, the angular rate table and capacitance results are provided in this paper to verify device performance.978133928000
Structural and optical behavior of thin films of protein (BSA)-Polyelectrolyte (PAA, PSS) complexes
No full textInfluence of Honeycomb Cellular Meso-structure on Frontal Crash Analysis for Passenger Vehicle
No full textNumerical Study On The Impact Of Self Induced Gravity Waves On Offshore Wind Farms
No full textThe present work studies the impact of self-induced Atmospheric Gravity Waves (AGWs) excited by an moderately sized offshore wind farm immersed in a Conventionally Neutral Boundary Layer (CNBL). A wind farm of finite span-wise length, consisting of 25 NREL 15MW wind turbines laid out in (5 X 5 Aligned manner) is considered. In order to achieve the same, two customized open source CFD RANS solvers are used, based on the OpenFOAM and SOWFA environments. The study primarily focuses on the impact of thermal stratification in the Free Atmosphere, the strength of the capping inversion and height of the same on the characteristics of the AGWs excited. Further, the work also evaluates the impact of the excited AGWs on the wind farm in the form of velocity deficits at each individual turbine column and also power down the analysis.The study finds that for a higher capping inversion strength, the AGWs excited by the wind farms are trapped and propagate horizontally along the capping inversion along with its vertically propagating counterpart. These cause velocity fluctuations at the hub height of the turbine, which results in AGW induced wind farm blockage that lowers the amount of incoming kinetic energy at the first turbine row and an increased velocity at the last two rows. Furthermore, the trapped AGWs cause the collective wake of the wind farm to recover faster. Moreover, the study proves that the height of the capping inversion determines the extent of AGW excitation by the wind farm. It was found lower capping inversion heights led to a higher AGW excitation and higher AGW induced flow blockage and wake recovery. Lastly, the study also finds that the thermal stratification in the Free Atmosphere determines the vertical wavelength of the AGWs propagating in the free atmosphere. Furthermore, the study also reports that for a higher Free Atmosphere stratification, a lower AGW induced blockage is observed.Aerospace Engineerin
Balanced-force numerical method for two phase flow at the onset of instability
No full textThe Interface Capturing method, which is a finite volume method as formulated by Queutey and Vissoneau for free surface immiscible, incompressible multiphase flows employs a collocated arrangement of unknownsand achieves a discrete force balance for the case when the interface coincides with the faces of the control volumes in the computational domain. This constraint limits the applicability of the method. Furthermore,the authors do not provide the exact formulation of the operators involved in the pressure velocity coupling. In the present research, a balanced-force numerical method is formulated, applicable for an interface thatneither has to coincide nor be aligned with the faces of the control volumes. The approach consists of the reconstruction of the values of the flow variables at the interface based on the interface jump conditions, with which the limit values of the normal derivatives at the interface are calculated. Furthermore, the construction of the operators of the discrete system is delineated to achieve a discrete force balance, by incorporating the reconstructed flow variables and employing a discretization which complies with the interface jump conditions. It is sufficient for a stationary discrete formulation to comply with the differential equation and the interface jump conditions. However, to apply this approach to solve unsteady flow problems the influence of the reformulated operators on the stability properties of the system should also be investigated.The properties of the individual operators are analyzed as well as their behaviour when they are embedded in the complete solver algorithm. Results are shown for both steady and unsteady test cases and compared with numerical results obtained with OpenFOAM. The resulting framework avoids the occurrence ofspurious velocities as it discretely complies with the interface conditions.<br/
Optimization of Data using Artificial Bee Colony Optimization with Map Reduce
No full textWe are developing an idea through this paper which would give any question the perfect and best answer. Existing system is not capable of classifying according to different patterns. This compromises with efficiency of the system and quality of final result. In this project Parallel Clustering optimization method is formed by amalgamation of Map-Reduce with Ant Bee Colony Optimization Technique for improving the efficiency and success of the data science method. In addition, related running services on a Hadoop network are predicted with the help of Map Reduce algorithm
Infant’s MRI Brain Tissue Segmentation using Integrated CNN Feature Extractor and Random Forest
No full textInfant MRI brain soft tissue segmentation become more difficult task compare with adult MRI brain tissue segmentation, due to Infant’s brain have a very low Signal to noise ratio among the white matter_WM and the gray matter _GM. Due the fast improvement of the overall brain at this time , the overall shape and appearance of the brain differs significantly. Manual segmentation of anomalous tissues is time-consuming and unpleasant. Essential Feature extraction in traditional machine algorithm is based on experts, required prior knowledge and also system sensitivity has change. Recently, bio-medical image segmentation based on deep learning has presented significant potential in becoming an important element of the clinical assessment process. Inspired by the mentioned objective, we introduce a methodology for analysing infant image in order to appropriately segment tissue of infant MRI images. In this paper, we integrated random forest classifier along with deep convolutional neural networks (CNN) for segmentation of infants MRI of Iseg 2017 dataset. We segmented infants MRI brain images into such as WM- white matter, GM-gray matter and CSF-cerebrospinal fluid tissues, the obtained result show that the recommended integrated CNN-RF method outperforms and archives a superior DSC-Dice similarity coefficient, MHD-Modified Hausdorff distance and ASD-Average surface distance for respective segmented tissue of infants brain MRI
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