Journal of Advances in Computer Research
Volume:6 Issue: 2, Spring 2015

It’s not long since the advent of pervasive computing by Mark Weiser and one of its main usages is in healthcare systems. As this technology has been introduced only recently in this field and because we are dealing with peoples’ lives and their data, providing a high level of privacy and security is of great importance. Various studies have been performed in which a certain method has been used to reach the mentioned goals. In this paper we have two major goals, introducing this technology and its benefits in healthcare domains and studying and comparing the five most related articles done in this domain by some criteria. By comparing these works we can achieve a useful result in the current methods in order to provide privacy and establish security.

In recent years Wireless Sensor Networks have attracted researchers'' attention due to vast applications. One of the most prominent issues regarding these networks is coverage which is considered as a quality of Service parameter. In this study, we try to address energy shortage in WSNs through finding a set of optimal coverage nodes in a specific environment. This is performed utilizing effective removal of extra sensors and generating several networks with area coverage. In the first step we use graph based method to classify sensors. The area is divided into several smaller regions in a dynamic manner in such a way that the whole region is covered with sensors. In the next step each set analyzed using genetic algorithm so that optimal or semi-optimal number of active nodes which are necessary to cover the region could be selected. Finally, performance of sensors in coverage of the area is evaluated using Monte Carlo method. Decreasing the number of sensors in each set and generating multiple networks, this method saves more energy which prolongs lifetime of the network.

This paper presents a new coding method for image compression based on jpeg2000 and contourlet transform. Jpeg2000 standard is a common standard that uses Discrete Wavelet Transform (DWT) in the compression process. The main problems of DWT are failure to detect curved edges in image and its shortage representation of the ridge and furrow patterns which cause deficiency and block artifacts remain in the decompressed image. Contourlet transform however, is a new two dimensional extension of the wavelet transform with multidirectional and multiscale filter banks. In this paper, jpeg2000 standard is improved by using contourlet transform and scalar quantization. The results obtained are compared with those of the wavelet based ones which show the superiority of the proposed method. In addition for images containing fine-textured, PSNR obtained by contourlet transform is higher than that of the wavelet transform, while texture and edge are reconstructed better than that of the Jpeg2000 standard.

Content discovery is one of the fundamental issues that determines the architecture and performance of content distribution networks based on peer-to-peer (P2P) networks. To administrate the costs and discoveries, peers of a P2P network communicate with each other by one or more overlay layers. The condition of node’s relations in overlay networks, have a great impact in efficiency of networks. Therefore the suitability of an overlay network is so important. So it should be proposed some techniques to create the overlay layer in the best possible. Hence, this paper proposes a new method for designing an overlay layer that can improve the efficiency of it. For this purpose, It will be applied one of the mobility prediction patterns and change the Gnutella protocol. After evaluation, simulation results clarified the significant role of mobility prediction on performance of P2P content discovery protocol over mobile ad-hoc networks.

In this research work, the impact of user’s behavior on search engine results is discussed. It aims to improvement of search results which leads to the higher satisfaction of users. In other words, we are trying to present a personalized search engine for each user, based on his/her activity and search history. We base our hypothesis that the search history of each user in a specific time frame provides precious information to be used in case of offering customizable and more efficient results to the user. In order to evaluate our research project hypothesis, we designed and implemented an experimental search engine as a web platform. This search engine measures the level of user’s satisfaction by bringing putting extra information into account. According to the experimental results, consideration of the user’s behavior history has significant effect on the quality of the search results, leading to more satisfaction of the users.

This paper presents an accurate Differential Global Positioning System (DGPS) using multi-layered Neural Networks (NNs) based on the Back Propagation (BP) and Imperialistic Competition Algorithm (ICA) in order to predict the DGPS corrections for accurate positioning. Simulation results allowed us to optimize the NN performance in term of residual mean square error. We compare results obtained by the NN technique with BP and ICA. Results show a good improvement obtained by the application of the NN trained by the ICA. The experimental results on measurement data demonstrate that the prediction total RMS error using NN trained by the ICA learning algorithm are 0.8273 and 0.7143 m, before and after selective availability, respectively.

In this paper, the optimal design of a three phase surface mounted permanent magnet synchronous motor has been done by particle swarm optimization and bees algorithm. This machine has been designed for high speed applications, and an epoxy and glass fiber bandage is used for permanent magnet protection against centrifugal forces. The optimization has been done by new design equations to improve loss and torque in the machine. The results show the simultaneous improvement of loss and torque compared with the initial design of the motor.

Technology advancement improves the way we interact with computer systems. Keyboard, mouse, and touchpad provide an easier and more natural way of interaction with a computer system. Nowadays, by introducing low cost and high performance depth cameras like Kinect and Xtion, new opportunities are available for creating even more natural interfaces. In this paper, a new model was demonstrated for natural interaction with a projected screen on a surface by detecting hand gestures and touch points using depth camera. Because lighting conditions, color, and background have little impact on the performance of systems based on depth camera, these systems can be integrated in a variety of applications in daily life. In this paper, a model was proposed for Microsoft Office as goal presentation. The main steps of the proposed model can be categorized as follows: hand detection, gesture detection, touch point detection, and control software. The proposed model had reasonably low cost with minimum configuration and calibration prerequisite and can replace the expensive and not-convenient-for-moving tablets.

This paper surveys parallel scheduling problems and metrics correlated to and then applys metrics to make decision in comparison to other policy schedulers. Parallel processing is new trend in computer science especially in embedded and multicore systems whereas needs more power consumption to reach speed up. The QOS requirement for users is to have good responsiveness and for service providers or system owners to have high throughput and low power consumption in parallel processing or embedded multicore systems. Moreover, fairness is vital issue to make decision wether the scheduler is good or not. Using the metrics is very intricate because misleadling metrics will cause to lose performance and system utility that is why the metrics has been opted cautiously in this paper. However, satisfying all of the objects in which have potentially conflicts is computationally NP-Hard. So, tradeoff between metrics is needed. This paper indicates DEA FDH model based on linear programming that will select the optimal scheduling near to exact solution.

Optimal sitting and sizing of shunt capacitor banks at the distribution networks for the purpose of power quality improvement is drawn much attention of electric power utilities in the last decades. Determining the optimal number, location and sizing of capacitor using Bee Colony Optimization algorithm (BCO) is being presented in this paper. The BCO algorithm is a new population based meta-heuristic approach exalted by intelligent foraging behaviour of honey bee swarm. The objectives include improvement of reliability and voltage profile and minimization of line losses. Also effect of capacitor installation on reducing carbon dioxide emissions of the grid has been considered in the objective function. This method has been executed on a 33-bus test system and comparative studies are conducted before and after capacitor installation in the test system. Results illustrate significant losses and carbon dioxide emission reduction and voltage profile and reliability improvement with appropriate presence of capacitor banks in distribution system.