In this article we examine the concepts of community, virtual community and virtual identity and consider if they have any continued relevancy as analytic terms in relation to social media. Firstly, we trace the origins of the term ‘community’ and contextualize its interpretation arguing that the term has political overtones. We then consider the notion of ‘virtual communities’ and suggest a working definition for this term. Thereafter we consider virtual identities. Finally, we contend that while the categories are deeply rooted in a particular understanding of communication, community and identity they do offer some very useful terms of analysis for social media.
Data Mining is a process of knowledge evaluation from large data sets. It is a very challenging filed of research in the recent past years. Extraction of knowledge is a critical issue for the researchers[1]. Many data mining tools currently fail to operate in extracting the needed information from the collection of data set it requires extra steps for extracting, and analyzing the data. Based on the type of extraction video is the particular media contains motion, sound, image, text and color information. Among these extracting the needed information is a challenging task. The usage of the image has been increasing day by day due to various factors. They are used not only for expressing knowledge and information but also used for the purpose of analyzing. For this purpose effective retrieval video dates are indexed. Here some of the existing indexing techniques are discussed. Based on the surveillance there may be a need for improvement in video indexing.
A students' prior technological background has an effect on his learning process and success in courses delivered in an online environment. The purpose of this paper is to examine the effect of three variables — previous computer knowledge & experience, internet skills and prior experience with online courses — on the improvement of thinking tendencies. The theoretical basis for this research relies on Perkins, Jay & Tishmans' tendency theory (1993). 285 bachelor and master students, studying in asynchronous and synchronous courses in the Fully On-line system, participated in the research. The research results show that previous personal computer knowledge & experience and internet skills affect thinking tendencies in varying positive degrees. However, no effect was found to be made by previous participation in on-line courses on thinking tendencies. The research conclusion shows that previous personal computer knowledge & experience and internet skills both contribute, separately and together, to the improvement of thinking tendencies, which form an important basis and a significant parameter for students' academic success.
Signature has been a distinguishing biometric feature through ages. Signature verification finds application in a large number of fields starting from online banking, passport verification systems, online exams etc. Human signatures can be handled as an image and recognized using computer vision and neural network techniques. This paper, proposes an off-line signature verification system using neural network. The system consists of three stages: the first is preprocessing stage, second is feature extraction stage and the last is verification stage using neural network. The objective of the work is to reduce two critical parameters, False Acceptance Rate (FAR) and False Rejection Rate (FRR).
High data rate implantable wireless systems come with many challenges, chief among them being low power operation and high path loss. LNAs designed for this application must include high gain, low noise figure (NF) and better linearity at low power consumption within the required frequency. In this paper, our design is based on Impulse Response (IR) Ultra Wide-Band (UWB) operating at (3.1 — 5) GHz. We report the design and measurement of an LNA with 2.4dB NF, 17.3dB of gain and input intercept point of 2dBm consuming 4mW, which make it suitable for implantable radio applications. The process technology used here is 0.25µm CMOS Silicon on Sapphire (SOS) process.
