Robust Distributed Estimation over Multiple Access Channels with Constant Modulus Signaling

A distributed estimation scheme where the sensors transmit with constant modulus signals over a multiple access channel is considered. The proposed estimator is shown to be strongly consistent for any sensing noise distribution in the i.i.d. case both for a per-sensor power constraint, and a total power constraint. When the distributions of the sensing noise are not identical, a bound on the variances is shown to establish strong consistency. The estimator is shown to be asymptotically normal with a variance (AsV) that depends on the characteristic function of the sensing noise. Optimization of the AsV is considered with respect to a transmission phase parameter for a variety of noise distributions exhibiting differing levels of impulsive behavior. The robustness of the estimator to impulsive sensing noise distributions such as those with positive excess kurtosis, or those that do not have finite moments is shown. The proposed estimator is favorably compared with the amplify and forward scheme under an impulsive noise scenario. The effect of fading is shown to not affect the consistency of the estimator, but to scale the asymptotic variance by a constant fading penalty depending on the fading statistics. Simulations corroborate our analytical results.Comment: 28 pages, 10 figures, submitted to IEEE Transactions on Signal Processing for consideratio

Use and Rise of Social Media as Election Campaign Medium in India

The present research is to examining the relationship between the use and rise of social media as tools for political parties. election campaign medium in India. In Indian election system social media has becoming an important medium in campaign. These new forms of media is assume as a central role in attracting voters and especially the new generation of young adults. Social media helps political parties and candidates in more effectively reach large number of voters in a short amount of time. Unlike traditional media social media allow politician to actively engage with their potential voters. Social media allows political parties and candidates to share their beliefs; goals and accomplishments directly with voters, making them feel more directly involved with the campaign. Social media allows candidates to give their supporters a behind-thescenes view of their campaign and more easily track engagement. These new forms of media also facilitate the collection of news most relevant to crucial target groups. The increases in the use and rise of social media are shown as significant and it is essential to do research. The present research is to give an attention to the use and rise of social media in the arena of politics and its impact in general and political participation by the general public. First the study is to examine for rise of social media as medium to reach voters. Secondly why people were using social media during election campaign, based on these uses and gratification approach. The present study is to propose to analyze the rise and use of social media as election campaign medium during election time is based on uses and gratification theory

Television Advertisement and its Impact on Attitudes, Behaviors of Children-A Study

The present study is to examine the impact of television advertisements on children, and how it leads to consumerism of product. Advertising is the most influential and powerful medium in the present commercial society. As far as its influence on the society is concerned, advertising is second only to movies. An advertisement shapes the attitude and beliefs of the children and it encompass every aspect of children life. Children are exposed to the selling messages of advertisers through television. When they can remember and want what they see and when they are able to understand what the advertiser.s motive is to sell a product. Television is an integral piece of the household at least one set will be in each family. Thus children are born into a world in which television is present from the start. But at the point during early childhood does viewing actually begin. Television advertisements are designed to influence consumer behavior. The present study is to focus on impact of television advertisements on children attitudes, and behavior. An attempt has been made to know the children.s television viewing habit and effect of advertisements, to measure this impact descriptive research design is used as the study is followed structured design with predetermined objectives. The survey research is followed and data will be collected through questionnaire

Cultural Impact and Gender on Indian Young Adults in Using Social Networking Sites

The present research is an attempt to find the answer the impact and effect of social networking sites on Indian young adults in socialization, entertainment self-status seeking and information seeking. Social network sites have grown rapidly in recent years across world even in India too. These sites have quickly and deeply penetrated its users. everyday life. In India social networking sites like Facebook, YouTube, Orkut are more popular sites and also become part of daily life of Indian adults. There is no doubt these social networking sites have provides employment, marketing, personal growth, sharing of information, and entertainment. Yet little know about the impact of culture and gender on Indian adults. Based on a cross-sectional survey of 450 Indian young adults, the present study examines the impact of culture and gender on why Indian individuals use the social networking sites. The present study found that consistently interdependent self-construal can predict the four motives for Indian young adults to use social networking sites: socialization, entertainment, self-status seeking and information seeking. The study also found that the gender difference in information seeking and self-status seeking; with male adults being higher in self-status seeing and female adults higher in information seeking. Limitations and suggestions for futures research are provided

DATA-DRIVEN APPROACH TO IMAGE CLASSIFICATION

Image classification has been a core topic in the computer vision community. Its recent success with convolutional neural network (CNN) algorithm has led to various real world applications such as large scale management of photos/videos on cloud/social-media, image based search for online retailers, self-driving cars, building robots and healthcare. Image classification can be broadly categorized into binary, multi-class and multi-label classification problems. Binary classification involves assigning one of the two class labels to an instance. In multi-class classification problem, an instance should be categorized into one of more than two classes. Multi-label classification is a generalized version of the multi-class classification problem where each image is assigned multiple labels as opposed to a single label. In this work, we first present various methods that take advantage of deep representations (fully connected layer of pre-trained CNN on the ImageNet dataset) and yield better performance on multi-label classification when compared to methods that use over a dozen conventional visual features. Following the success of deep representations, we intend to build a generic end-to-end deep learning framework to address all three problem categories of image classification. However, there are still no well established guidelines (in terms of choosing the number of layers to go deeper, the number of kernels and the size, the type of regularizer, the choice of non-linear function, etc.) to build an efficient deep neural network and often network architecture design is specific to a problem/dataset. Hence, we present some initial efforts in building a computational framework called Deep Decision Network (DDN) which is completely data-driven. DDN is a tree-like structured built stage-wise. During the learning phase, starting from the root network node, DDN automatically builds a network that splits the data into disjoint clusters of classes which would be handled by the subsequent expert networks. This results in a tree-like structured network driven by the data. The proposed approach provides an insight into the data by identifying the group of classes that are hard to classify and require more attention when compared to others. This feature is crucial for people trying to solve the problem with little or no domain knowledge, especially for applications in medical domain. Initially, we evaluate DDN on a binary classification problem and later extend it to more challenging multi-class and multi-label classification problems. The extension of DDN to multi-class and multi-label involves some changes but they still operate under the same underlying principle. In all the three cases, the proposed approach is tested for its recognition performance and scalability on publicly available datasets providing comparison to other methods

C-conformal Metric Transformations on Finslerian Hypersurface

The purpose of the paper is to give some relation between the originalFinslerian hypersurface and other C-conformal Finslerian hypersufaces. In this pa-per we dene three types of hypersufaces, which were called a hyperplane of the 1stkind, hyperplane of the 2nd kind and hyperplane of the 3rd kind under considerationof C-conformal metric transformation.DOI : http://dx.doi.org/10.22342/jims.17.2.2.59-6

Electrodeposition of Zn-​Ni alloy from an acid sulfate bath containing ethanolamines

Electrodeposition of Zn-​Ni alloy contg. 10-​15​% Ni was carried out from an acid sulfate bath contg. ethanolamines. The effect of plating variables - such as metal ion ratio in the bath, pH, c.d., temp., stirring, concn. of ethanolamines in the bath and thickness - on the alloy compn. and cathodic c.d. was studied. The phase structure and the surface morphol. of electrodeposited Zn-​Ni alloy (SEM) and harness (on Vicker's scale) are reported

Exploring Scientific Application Performance Using Large Scale Object Storage

One of the major performance and scalability bottlenecks in large scientific applications is parallel reading and writing to supercomputer I/O systems. The usage of parallel file systems and consistency requirements of POSIX, that all the traditional HPC parallel I/O interfaces adhere to, pose limitations to the scalability of scientific applications. Object storage is a widely used storage technology in cloud computing and is more frequently proposed for HPC workload to address and improve the current scalability and performance of I/O in scientific applications. While object storage is a promising technology, it is still unclear how scientific applications will use object storage and what the main performance benefits will be. This work addresses these questions, by emulating an object storage used by a traditional scientific application and evaluating potential performance benefits. We show that scientific applications can benefit from the usage of object storage on large scales.Comment: Preprint submitted to WOPSSS workshop at ISC 201