Over the years, e-learning and e-examination has become standard in many institutions of higher learning. It has been observed that examination questions and results can be easily intercepted by invalid users, thus the security of resources shared among valid users is not guaranteed. In order to solve these problems as it relates to access control, a Role based Examination System (RBES) was designed, developed and evaluated. RBES attempted to solve the security issue by the combination of two authentication techniques: text-based authentication and graphical password authentication. The Text-based authentication utilizes two text-based parameters namely the username and password. The graphical password authentication makes use of a finite set of controls (RBES chooses radio buttons) which are identified by numbers. These numbers constitute the password used for graphical authentication. To improve on resource sharing among users in the examination system, RBES proposes role management (role creation, role update, role removal) and user management (user creation, user update and user removal). The developed system made use of asp.net, C#, IIS server, WAMP server, Mysql and other tools for its development. RBES was tested by some legitimate and illegitimate users and the performance of the system was found to be satisfactory, hence RBES shows an efficient and reliable scheme that can be deployed in any examination or e-learning system. Finally the potential threats to the system were modeled and the use of weak passwords was found to be the most likely threat the system could be vulnerable to.

Long-Term Evolution (LTE) is the next generation of current mobile telecommunication networks. LTE has a flat radio-network architecture and significant increase in spectrum efficiency, throughput and user capacity. In this paper, performance analysis of robust channel estimators for Downlink Long Term Evolution-Advanced (DL LTE-A) system using three Artificial Neural Networks: Feed-forward neural network (FFNN), Cascade-forward neural network (CFNN) and Layered Recurrent Neural Network (LRN) are trained separately using Back-Propagation Algorithm and also ANN is trained by Genetic Algorithm (GA). The methods use the information got by the received reference symbols to estimate the total frequency response of the channel in two important phases. In the first phase, the proposed ANN based method learns to adapt to the channel variations, and in the second phase it estimates the channel matrix to improve performance of LTE. The performance of the estimation methods is evaluated by simulations in Vienna LTE-A DL Link Level Simulator in MATLAB software. Performance of the proposed channel estimator, ANN trained by Genetic Algorithm (ANN-GA) is compared with traditional Least Square (LS) algorithm and ANN based other estimator like Feed-forward neural network, Layered Recurrent Neural Network and Cascade-forward neural network for Closed Loop Spatial Multiplexing (CLSM)-Single User Multi-input Multi-output (MIMO-2×2 and 4×4) in terms of throughput. Simulation result shows proposed ANN-GA gives better performance than other ANN based estimations methods and LS.

These days cloud computing is booming like no other technology. Every organization whether it’s small, mid-sized or big, wants to adapt this cutting edge technology for its business. As cloud technology becomes immensely popular among these businesses, the question arises: Which cloud model to consider for your business? There are four types of cloud models available in the market: Public, Private, Hybrid and Community. This review paper answers the question, which model would be most beneficial for your business. All the four models are defined, discussed and compared with the benefits and pitfalls, thus giving you a clear idea, which model to adopt for your organization.

Quasi Orthogonal Space Time Block Code (QO-STBC) can provide full-rate transmission and low decoding complexity. This paper deals with channel estimation for Quasi Orthogonal Space Time Block Code (QO-STBC) encoded Orthogonal Frequency Division Multiplexing (OFDM) based Multiple Input multiple Output (MIMO) Code Division Multiple Access (CDMA) system. Using the QO-STBC coding property, we analysis the weight performance that reduce the computational complexity of system. The design of channel estimation method is proposed by considering Minimum Mean Square Error (MMSE), Zero Forcing (ZF), and Singular Value Decomposition (SVD) that involves four transmit antennas and four receive antennas. Such filter facilitates the use of standard equalizer or decoder that has been designed to mitigate the Inter Symbol Interference (ISI) effect. In this paper analytical results show that the BER analysis of Minimum Mean Square Error (MMSE) algorithm using various modulation techniques outperforms as compared to other channel equalization techniques.

Obviously fair communication establishment in every technology increases the efficiency. As we know well, vehicles are used in day to day life of every human being to move from one location to another location. If network communication is formed between vehicles, mobile phones and home based telephones, it will increase the safety of the passengers by communicating with one another. In this paper, we propose GSM based network communication in vehicles, which will develop reliable network communication between vehicles, mobile phones and home based telephones. The added advantage GSM based network communication among vehicles will lead to safety of travel by tracking the vehicle’s location, since GSM based network communication is established in vehicles.

With the evolution of modern technology wireless sensor nodes are finding a lot of applications in day to day life starting from smart home system to military surveillance. The primary building block of a wireless sensor network is a spatially distributed set of autonomous sensor nodes or motes. In order to design a wireless sensor network it is necessary to understand the structure and working of a sensor node. The sensor nodes can be considered as tiny battery powered computers that consists of a computing subsystem, communication subsystem, sensor subsystem, power subsystem. In this paper we review the features of these subsystems so that it is easy for the application developer to quickly understand and select the type of component for building customized sensor node platform. In this paper we have studied the features of different microprocessors and transceivers properties used in sensor nodes. We also study the classifications of sensors based on applications, the relevant sensor parameters, and different storage devices with their properties. This paper can be a ready reference to beginners interested in this field. One more major problem of wireless sensor network application that should be addressed is the limited lifetime of sensor nodes due to energy constraints. We also review how energy harvesting can increase the lifetime of a wireless sensor network and the possible methods that can be implemented for energy harvesting.

Due to the exponential growth of hardware technology particularly in the field of electronic data storage media and processing such data, has raised serious issues related in order to protect the individual privacy like ethical, philosophical and legal. Data mining techniques are employed to ensure the privacy. Privacy Preserving Data Mining (PPDM) techniques aim at protecting the sensitive data and mining results. In this study, the different Clustering techniques via classification with and without anonym zed data using mining tool WEKA is presented. The aim of this study is to investigate the performance of different clustering methods for the diabetic data set and to compare the efficiency of privacy preserving mining. The accuracy of classification via clustering is evaluated using K-means, Expectation-Maximization (EM) and Density based clustering methods.

The technological developments in the fields of multimedia clinical applications and communication networks require a specific analysis to increase the efficiency of network based healthcare services. In this work, we computed the optimum transmission parameter (data packet size) for applications needed to guarantee the perceived quality of service in the proposed ubiquitous healthcare network. This has been carried out through NS2 based simulation of a state wide area network infrastructure implemented in Himachal Pradesh, a state with diverse geographical terrain situated in the Western Himalayan region of India. The various types of healthcare applications and services have been classified into different classes according to their perceived QOS requirements as per the guidelines in ITU report on network performance objectives.  The infrastructure specific optimum values of data packet size for these QoS classes have been computed. Network based healthcare applications and services running on both TCP and UDP type of traffic have been presented in this paper.