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Load balancing with availability checker and load reporters (LB-ACLRs) for improved performance in distributed systems
Distributed system has quite a lot of servers to attain increased availability of service and for fault tolerance. Balancing the load among these servers is an important task to achieve better performance. There are various hardware and software based load balancing solutions available. However there is always an overhead on Servers and the Load Balancer while communicating with each other and sharing their availability and the current load status information. Load balancer is always busy in listening to clients' request and redirecting them. It also needs to collect the servers' availability status frequently, to keep itself up-to-date. Servers are busy in not only providing service to clients but also sharing their current load information with load balancing algorithms. In this paper we have proposed and discussed the concept and system model for software based load balancer along with Availability-Checker and Load Reporters (LB-ACLRs) which reduces the overhead on server and the load balancer. We have also described the architectural components with their roles and responsibilities. We have presented a detailed analysis to show how our proposed Availability Checker significantly increases the performance of the system. 2014 IEEE. -
Cataloging of happy facial affect using a radial basis function neural network
The paper entitled "Cataloging of Happy facial Affect using a Radial Basis Function Neural Network" has developed an affect recognition system for identifying happy affect from faces using a RBF neural network. The methodology adapted by this research is a four step process: image preprocessing, marking of region of interest, feature extraction and a classification network. The emotion recognition system has been a momentous field in human-computer interaction. Though it is considerably a challenging field to make a system intelligent that is able to identify and understand human emotions for various vital purposes, e.g. security, society, entertainment but many research work has been done and going on, in order to produce an accurate and effective emotion recognition system. Emotion recognition system can be classified into facial emotion recognition and speech emotion recognition. This work is on facial emotion recognition that identifies one of the seven basic emotions i.e. happy affect. This is carried out by extracting unique facial expression feature; calculating euclidean distance, and building the feature vector. For classification radial basis function neural network is used. The deployment was done in Matlab. The happy affect recognition system gave satisfactory results. 2013 Springer. -
Depletion studies in the interstellar medium
We report interstellar Si depletion and dust-phase column densities of Si along 131 Galactic sight lines using previously reported gas-phase Si II column densities, after correcting for the differences in oscillator strengths. With our large sample, we could reproduce the previously reported correlations between depletion of Si and average density of hydrogen along the line of sight () as well as molecular fraction of hydrogen (f(H2). We have also studied the variation of amount of Si incorporated in dust with respect to different extinction parameters. With the limitations we have with the quality of data, we could find a strong relation between the Si dust and extinction. While we cannot predict the density dependent distribution of size of Si grains, we discuss about the large depletion fraction of Si and the bigger size of the silicate grains. 2013 AIP Publishing LLC. -
GPR based subsurface geotechnical exploration
The Seismic refraction technique (SRT) and Electrical resistivity technique (ERT) have long been in use in geotechnical exploration. A relatively recent technique is Ground penetrating radar (GPR). The study presented in this paper is on GPR-aided geotechnical subsurface exploration. The usual method of exploration is drilling, which gives much-needed site-specific information, but is expensive and restricted to a few point locations. The possibilities of non-invasive investigation offered by GPR make it useful for supplementing geotechnical investigations. The present work describes GPR survey at a construction site in Mumbai. The objective was to derive subsurface logs from GPR signals. Conventionally, subsurface logging is done using boreholes. First, the extracted soil and rock samples are examined visually. Second, additional information such as Core recovery ratios (CRR), Rock quality designation (RQD) and Standard penetration test (SPT) N values are collected and strata are demarcated. In comparison, the amplitude variations of GPR signals may not correspond directly to variations of these physical properties with depth. However, the study shows that fairly good correlations do exist with the subsurface stratification and transformed signals. -
An approach for document pre-processing and K Means algorithm implementation
The web mining is a cutting edge technology, which includes information gathering and classification of information over web. This paper puts forth the concepts of document pre-processing, which is achieved by extraction of keywords from the documents fetched from the web, processing it and generating a term-document matrix, TF-IDF and the different approaches of TF-IDF (term frequency Inverse document frequency) for each respective document. The last step is the clustering of these results through K Means algorithm, by comparing the performance of each approach used. The algorithm is realized on an X64 architecture and coded on Java and Matlab platform. The results are tabulated. 2014 IEEE. -
An ettective dynamic scheduler tor reconfigurable high speed computing system
High Speed Computing is a promising technology that meets ever increasing real-time computational demands through leveraging of flexibility and parallelism. This paper introduces a reconfigurable fabric named Reconfigurable High Speed Computing System (RHSCS) and offers high degree of flexibility and parallelism. RHSCS contains Field Programmable Gate Array (FPGA) as a Processing Element (PE). Thus, RHSCS made to share the FPGA resources among the tasks within single application. In this paper an efficient dynamic scheduler is proposed to get full advantage of hardware utilization and also to speed up the application execution. The addressed scheduler distributes the tasks of an application to the resources of RHSCS platform based on the cost function called Minimum Laxity First (MLF). Finally, comparative study has been made for designed scheduling technique with the existing techniques. The proposed platform RHSCS and scheduler with Minimum Laxity First (MLF) as cost function, enhances the speed of an application up to 80.30%. 2014 IEEE. -
User profiling based on keyword clusters for improved recommendations
Recommender Systems (RS) have risen in popularity over the years, and their ability to ease decision-making for the user in various domains has made them ubiquitous. However, the sparsity of data continues to be one of the biggest shortcomings of the suggestions offered. Recommendation algorithms typically model user preferences in the form of a profile, which is then used to match user preferences to items of their interest. Consequently, the quality of recommendations is directly related to the level of detail contained in these profiles. Several attempts at enriching the user profiles leveraging both user preference data and item content details have been explored in the past. We propose a method of constructing a user profile, specifically for the movie domain, based on user preference for keyword clusters, which indirectly captures preferences for various narrative styles. These profiles are then utilized to perform both content-based (CB) filtering as well as collaborative filtering (CF). The proposed approach scores over the direct keyword-matching, genre-based user profiling and the traditional CF methods under sparse data scenarios as established by various experiments. It has the advantage of a compact user model representation, while at the same time capturing the essence of the styles or genres preferred by the user. The identification of implicit genres is captured effectively through clustering without requiring labeled data for training. 2014 Springer International Publishing Switzerland. -
Verification and validation of Parallel Support Vector Machine algorithm based on MapReduce Program model on Hadoop cluster
From the recent years the large volume of data is growing bigger and bigger. It is difficult to measure the total volume of structured and unstructured data that require machine-based systems and technologies in order to be fully analyzed. Efficient implementation techniques are the key to meeting the scalability and performance requirements entailed in such scientific data analysis. So for the same in this paper the Sequential Support Vector Machine in WEKA and various MapReduce Programs including Parallel Support Vector Machine on Hadoop cluster is analyzed and thus, in this way Algorithms are Verified and Validated on Hadoop Cluster using the Concept of MapReduce. In this paper, the performance of above applications has been shown with respect to execution time/training time and number of nodes. Experimental Results shows that as the number of nodes increases the execution time decreases. This experiment is basically a research study of above MapReduce applications. 2013 IEEE. -
Mapping extinction using GALEX and SDSS photometric observations
The primary objective of this work is to create an all sky extinction map of the Milky Way galaxy. We have cross-matched the Sloan Digital Sky Survey (SDSS data release 8) photometric observations with that of Galaxy Evolution Explorer (GALEX data release 6). This provides a wide range of wavelength coverage from Far Ultra-Violet through the optical spectrum and gives one unique SDSS source for every GALEX source. We discuss a sample of ?32000 objects in the north galactic pole (?75 latitude) from this combined database. The Castelli and Kurucz Atlas was fit to the photometric observations of each star, best fit being determined using a chi-square test. Best fit parameters provide the spectral type and extinction towards each of the objects. The shift in magnitude obtained during the best-fit can be used to determine the distance to each of the stars. With this data, a comprehensive extinction map can be made for the high-latitude objects and later extended to all-sky. 2013 AIP Publishing LLC. -
Message efficient ring leader election in distributed systems
Leader Election Algorithm, not only in distributed systems but in any communication network, is an essential matter for discussion. Tremendous amount of work are happening in the research community on election as network protocols are in need of co-ordinator process for the smooth running of the system. These so called Coordinator processes are responsible for the synchronization of the system otherwise, the system loses its reliability. Furthermore, if the leader process crashes, the new leader process should take the charge as early as possible. New leader is one among the currently running processes with the highest process id. In this paper we have presented a modified version of ring algorithm. Our work involves substantial modifications of the existing ring election algorithm and the comparison of message complexity with the original algorithm. Simulation results show that our algorithm minimizes the number of messages even in worst case scenario. 2013 Springer Science+Business Media. -
A distributed randomization framework for privacy preservation in big data
The privacy preservation is a big challenge for data generated from various sources such as social networking sites, online transaction, weather forecast to name a few. Due to the socialization of the internet and cloud computing pica bytes of unstructured data is generated online with intrinsic values. The inflow of big data and the requirement to move this information throughout an organization has become a new target for hackers. This data is subject to privacy laws and should be protected. The proposed protocol is one step toward the security in case of above circumstances where data is coming from multiple participants and all are concerned about individual privacy and confidentiality. 2014 IEEE. -
Verification and validation of MapReduce program model for parallel K-means algorithm on Hadoop cluster
With the development of information technology, a large volume of data is growing and getting stored electronically. Thus, the data volumes processing by many applications will routinely cross the petabyte threshold range, in that case it would increase the computational requirements. Efficient processing algorithms and implementation techniques are the key in meeting the scalability and performance requirements in such scientific data analyses. So for the same here, we have p analyzed the various MapReduce Programs and a parallel clustering algorithm (PKMeans) on Hadoop cluster, using the Concept of MapReduce. Here, in this experiment we have verified and validated various MapReduce applications like wordcount, grep, terasort and parallel K-Means Clustering Algorithm. We have found that as the number of nodes increases the execution time decreases, but also some of the interesting cases has been found during the experiment and recorded the various performance change and drawn different performance graphs. This experiment is basically a research study of above MapReduce applications and also to verify and validate the MapReduce Program model for Parallel K-Means algorithm on Hadoop Cluster having four nodes. 2013 IEEE. -
Computationally efficient wavelet domain solver for florescence diffuse optical tomography
Estrogen induced proliferation of mutant cells is a growth signal hallmark of breast cancer. Fluorescent molecule that can tag Estrogen Receptor (ER) can be effectively used for detecting cancerous tissue at an early stage. A novel targetspecific NIRf dye conjugate aimed at measuring ER status was synthesized by ester formation between 17-? estradiol and a hydrophilic derivative of ICG, cyanine dye, bis-1,1-(4-sulfobutyl) indotricarbocyanine-5-carboxylic acid, sodium salt. In-vitro studies provided specific binding on ER+ve [MCF-7] cells clearly indicating nuclear localization of the dye for ER+ve as compared to plasma level staining for MDAMB-231. Furthermore, cancer prone cells showed 4.5-fold increase in fluorescence signal intensity compared to control.; A model of breast phantom was simulated to study the in-vivo efficiency of dye with the parameters of dye obtained from photo-physical and in-vitro studies. The excitation (754 nm) and emission (787 nm) equation are solved independently using parallel processing strategies. The results were obtained by carrying out wavelet transformation on forward and the inverse data sets. An improvisation of the Information content of system matrix was suggested in wavelet domain. The inverse problem was addressed using LevenbergMarquardt (LM) procedure with the minimization of objective function using Tikhonov approach. The multi resolution property of wavelet transform was explored in reducing error and increasing computational efficiency. Our results were compared with the single resolution approach on various parameters like computational time, error function, and Normalized Root Mean Square (NRMS) error. A model with background absorption coefficient of 0.01 mm-1 with anomalies of 0.02 mm-1 with constant reduced scattering of 2.0 mm for different concentration of dye was compared in the result. The reconstructed optical properties were in concurrence with the tissue property at 787 nm. We intend our future plans on in-vivo study on developing a complete instrumentation for imaging a target specific lipophilic dye. Springer International Publishing Switzerland 2014. -
Secure multi-party computation protocol using asymmetric encryption
Privacy preservation is very essential in various real life applications such as medical science and financial analysis. This paper focuses on implementation of an asymmetric secure multi-party computation protocol using anonymization and public-key encryption where all parties have access to trusted third party (TTP) who (1) doesn't add any contribution to computation (2) doesn't know who is the owner of the input received (3) has large number of resources (4) decryption key is known to trusted third party (TTP) to get the actual input for computation of final result. In this environment, concern is to design a protocol which deploys TTP for computation. It is proposed that the protocol is very proficient (in terms of secure computation and individual privacy) for the parties than the other available protocols. The solution incorporates protocol using asymmetric encryption scheme where any party can encrypt a message with the public key but decryption can be done by only the possessor of the decryption key (private key). As the protocol works on asymmetric encryption and packetization it ensures following: (1) Confidentiality (Anonymity) (2) Security (3) Privacy (Data). 2014 IEEE. -
X -ray diffraction and microhardness studies of tin monoselenide
Tin Monoselenide (SnSe) crystals have been grown by the Physical Vapour Deposition (PVD) method. X-ray diffraction studies were carried out on the as grown crystals and the lattice parameters were found to be a=11.506 b=4.149and c=4.447 The values were found to be comparable with that reported in the PDF card for SnSe. The microhardness of the crystals has been determined by using Vickers microhardness indenter. 2011 American Institute of Physics. -
Prominent label identification and multi-label classification for cancer prognosis prediction
Cancer prognosis prediction improves the quality of treatment and increases the survivability of the patients. Conventional methods of cancer prediction deal with single class by limiting the prognosis prediction to one response variable. The SEER Public Use cancer database has more prominent variables that support better prediction approach. The objective of this paper is to find the prominent labels from cancer databases and use them in a multi-class environment. The implementation consist of three phases namely, pre-processing, prominent label identification and multi-label classification. Breast, Colorectal and Respiratory Cancer Data sets have been used for the experimentation. Also random samples from all three data sets are generated to form a mixed cancer data. Patient survival, number of primaries and age at diagnosis are the prominent labels identified from others using the Decision tree, Nae Bayes and KNN algorithms. The three prominent labels have been tested using multi-label RAkEL algorithm to find the relations between them. The results of the empirical study are comparatively better than the traditional way of cancer prediction. 2012 IEEE. -
Occupancy improvement in serviced apartments: Customer profiling
Sustaining and improving higher occupancy and generating steady revenue by bringing the experience of 'Home away from Home'for the Customers is the business model of ServicedApartments Industry. Serviced Apartment Industry has to be highly competitive. Its performance is governed by many factors such as competition, technology, social factors and lastly Customers themselves. This study focuses only on Customer profile. To achieve results, the Serviced Apartment Owners/Managers will need to study Customers' profile and their needs. Customer satisfaction and retention lead to better customer loyalty, occupancy rates, and revenue. In this paper a methodological framework to analyze and profile Serviced Apartment Customers is discussed, focusing on the factors and particularly the Customer information which could help in increasing the Occupancy. There is a trend that would normally go unnoticed if analysis of data is taken at the aggregate level but looking at them individually, it provides interesting information. 2012 Taylor & Francis Group. -
Usage of online educational courses by undergraduate engineering students in Karnataka
Increasing availability of low-cost technology has enabled many students to use online courses to supplement their studies. The emergence of MOOCs (Massively Open Online Courses) has also brought about a great revolution in the teaching and learning methods. In case of Indian students, since most of the online courses available are not customized according to the syllabus, the students do not find them completely useful. In this case, Massively Empowered Classrooms (MEC) provides curriculum based video lectures and quizzes to students free of cost. The students are able to gain a good understanding of the subject and also score well in exams. This paper is based on an exploratory study conducted to analyze the usage of online courses and MEC by the undergraduate engineering students in Karnataka, India. The paper also describes some expectations from students and teachers to improve the reach and impact of online education. 2013 IEEE. -
Growth and characterization of chalcogenide crystals by vapour method
A horizontal linear gradient two zone furnace was designed and employed to grow single crystals of indium telluride by Physical Vapour Deposition (PVD) method. It was calibrated for various trials including, series and parallel combinations of coils, and set temperatures. Systematic growth runs for chalcogenide crystals were performed by varying the source and growth temperatures. Crystals of different sizes and morphologies were obtained. The morphology and chemical analysis of the grown crystals were investigated by Scanning Electron Microscope (SEM) and Energy Dispersive Analysis using X-rays (EDAX). The hardness of the crystals was estimated using a Vickers microhardness tester. 2011 American Institute of Physics. -
Mesoporous iron aluminophosphate: An efficient catalyst for one pot synthesis of amides by ester-amide exchange reaction
A series of metal aluminophosphates (MAlP: M = V, Fe, Co, Ni & Cu) were prepared by co-precipitation method. All the materials were characterized by various physico-chemical techniques. The materials were found to be mesoporous and moderately acidic. The catalytic activity of the materials was investigated in the synthesis of benzamides in a single pot reaction under solvent free refluxing conditions from methyl benzoate and different amines. Iron aluminophosphate was found to be the most effective catalyst for the synthesis of benzamides with 100% selectivity. The isolated yield of benzamide varied from 46% to 100% depending on the nature of amine. A possible reaction mechanism has been proposed which correlates the surface acidity and catalytic activity of the catalyst. The catalyst could be recycled for about three times without any appreciable loss in activity, thus making the method ecofriendly and economical.