In the recent past Internet has become the de-facto communication network. It is being prominently used by Telecommunication, Television and other such networks as a carrier network. The current Internet technology has matured enough to support both Non-Real Time and Real-Time Streaming applications. Recently, even the speed of the access network through which an end user accesses the Internet has also increased substantially. All these have given way to newer Applications for being ported on to the Internet. A similar attempt has been made here to extend the Networking Lab infrastructure to students who have enrolled for their higher education through Distance mode. These students who are spread across the country are able to access the Network Lab to perform their Lab Exercises Live on Network devices as part of their Practical Course.

Communication is the main motive in any Networks whether it is Wireless Sensor Network, Ad-Hoc networks, Mobile Networks, Wired Networks, Local Area Network, Metropolitan Area Network, Wireless Area Network etc, hence it must be energy efficient. The main parameters for energy efficient communication are maximizing network lifetime, saving energy at the different nodes, sending the packets in minimum time delay, higher throughput etc. This paper focuses mainly on the energy efficient communication with the help of Adjacency Matrix in the Wireless Sensor Networks. The energy efficient scheduling can be done by putting the idle node in to sleep node so energy at the idle node can be saved. The proposed model in this paper first forms the adjacency matrix and broadcasts the information about the total number of existing nodes with depths to the other nodes in the same cluster from controller node. When every node receives the node information about the other nodes for same cluster they communicate based on the shortest depths and schedules the idle node in to sleep mode for a specific time threshold so energy at the idle nodes can be saved.

In this paper two favorite artificial intelligence methods: ANN and SVM are proposed as a means to achieve accurate question level diagnosis, intelligent question classification and updates of the question model in intelligent learning environments such as E-Learning or distance education platforms. This paper reports the investigation of the effectiveness and performances of two favorite artificial intelligence methods: ANN and SVM within a web-based environment (E-Learning) in the testing part of an undergraduate course that is "History of Human Civilizations" to observe their question classification abilities depending on the item responses of students, item difficulties of questions and question levels that are determined by putting the item difficulties to Gaussian Normal Curve. 
The effective nesses of ANN and SVM methods were evaluated by comparing the performances and class correct nesses of the sample questions using the same 3 inputs as: item responses, item difficulties, question levels to 5018 rows of data that are the item responses of students in a test composed of 13 questions. The comparative test performance analysis conducted using the classification correctness revealed yielded better performances than the Artificial Neural Network (ANN) and Support Vector Machine (SVM).

In recent years there has been a massive growth in textual information in textual information especially in the internet. People now tend to read more e-books than hard copies of the books. While searching for some topic especially some new topic in the internet it will be easier if someone knows the pre-requisites and post- requisites of that topic. It will be easier for someone searching a new topic. Often the topics are found without any proper title and it becomes difficult later on to find which document was for which topic. A text categorization method can provide solution to this problem. In this paper domain based ontology is created so that users can relate to different topics of a domain and an automated text categorization technique is proposed that will categorize the uncategorized documents. The proposed idea is based on Term Frequency – Inverse Document Frequency (tf -idf) method and a dependency graph is also provided in the domain based ontology so that the users can visualize the relations among the terms.

Vides a proper solution to this limitation. There are broadly three main categories of Vector Space Model: term-document, word-content and pair-pattern matrices. The main aim of this paper is to discuss broadly the three main categories of VSM for semantic analysis of texts and make proper selection for automatic categorizing. The scenario taken up here is categorization of research papers for organizing a national or an international conference based on the proposed methodology. Computers do not understand human language and this makes it difficult when human wants the computer to do some specific task like categorization according to human need. Vector Space Model (VSM) for semantic analysis of texts and make proper selection of one of the three main categories for automatic categorizing of research papers for organizing a national or an international conference based on the proposed methodology.

Meditation refers to a family of self-regulation practices that focus on training attention and awareness in order to bring mental processes under greater voluntary control. The aim of this study is to evaluate the efficiency of two different classifiers, k-Nearest Neighbor (k-NN) and Radial Basis Function (RBF), on the heart rate signals in a specific psychological state. For this purpose, two types of heart rate time series (before, and during meditation) of 25 healthy women are collected in the meditation clinic in Mashhad. The spectral parameters are used to gain insight into the autonomic nervous system (ANS) response induced by meditation. Therefore, very low frequency, low frequency, high frequency, the LF/HF ratio and frequency of the highest spectral peak of heart rate signals are extracted and used as inputs of the classifiers. To evaluate performance of the classifiers, the classification accuracies and mean square error (MSE) of the classifiers were examined. The classification results of this study denote that the RBF classifier trained on spectral features obtains higher accuracy than that of k-NN classifier. The total classification accuracy of the RBF classifier is 92.3% with 0.026 classification error. However, k-Nearest Neighbor classifier gives encouraging results (86.5%). Experimental results verify that radial basis function is an efficient classifier for classifying heart rate signals in a specific psychological state.

No other electronic media has created as much mass impact as the TV. TV is both a personal as well as family/community device which makes it reach a large population. Obviously the immense popularity of TV has resulted in an unprecedented growth of TV viewing as well as technology. From the simple TV today one can have a smart TV with varying features satisfying all sections of society. The TV technology has grown in all aspects namely the TV studio technology, the TV transmitter & broadcast technology and the TV receiving device technology. Not only have the TV signals been converted from analog to digital, today one has high definition TV, the IPTV, the mobile TV and the 3D TV commercially available. It is very interesting and important to trace the evolution of TV technology from its basic form as in 1930s to date and to visualize their technical features at various stages of developments. This paper gives an overview of the developments in TV technology highlighting their important features.

The use of multimedia in teaching and learning leads to higher learning. Multimedia refers to any computer-mediated software or interactive application that integrates text, color, graphical images, animation, audio sound, and full motion video in a single application. Multimedia learning systems offer a potentially venue for improving student understanding about language. Teachers try to find the most effective way to create a better foreign language teaching and learning environment through multimedia technologies. In this paper, the researcher defines multimedia, elaborates the rationale for using multimedia, identifies multimedia learning, mentions principles of multimedia, explains theoretical basis of multimedia English teaching, reviews roles of teachers and learners in multimedia environment, discusses the relationship between multimedia and learning, and states the strength of multimedia English teaching. The review of literature shows that teachers need to make full use of multimedia to create an authentic language teaching and learning environment where students can easily acquire a language naturally and effectively.