Journal of Advances in Computer Engineering and Technology
Volume:1 Issue: 1, Winter 2015

In wireless sensor networks, optimal consumptionof energy and having maximum life time are important factors. In this article attempt has been made to send the data packets with particular reliability from the beginning based on AODV protocol. In this way two new fields add to the routing packets and during routing and discovering of new routes, the lowest remained energy of nodes and route traffic based on the number of discarded packages, will store in this field as two variables. These two variables will be considered during choosing a suitable route for sending the data to that message which should be answered by sink. The efficiency of this protocol is based on the fact that, at the route request, it finds the routes with high energy and low traffic through which data are sent(information is sent).So data packets reach the destination with a higher probability and also the balance of energy consumption is considered in the network. From the energy point of view, not using weak nodes routes leads to not having off nodes at the end of the process. This fact affects balancing of energy consumption and reducing the variance of the energy remainder proportional to AODV model. Not using high traffic routes leads to reducing collision and sending fewer signaling packets; more data packets with lower delay reach the destination. In the case of high congestion, for meeting the desired reliability, which is among the main goals there may be more sending signaling packets, delay and collision. But this happens with sending more packets and a guaranteed reliability.

Realistic mobility models can demonstrate more precise evaluation results because their parameters are closer to the reality. In this paper a realistic Fuzzy Mobility Model has been proposed. This model has rules which are changeable depending on nodes and environmental conditions. It seems that this model is more complete than other mobility models.After simulation, it was found out that not only considering nodes movement as being imprecise (fuzzy) has a positive effects on most of ad hoc network parameters, but also, more importantly as they are closer to the real world condition, they can have a more positive effect on the implementation of ad hoc network protocols.

Template matching is a widely used technique in many of image processing and machine vision applications. In this paper we propose a new as well as a fast and reliable template matching algorithm which is invariant to Rotation, Scale, Translation and Brightness (RSTB) changes. For this purpose, we adopt the idea of ring projection transform (RPT) of image. In the proposed algorithm, two novel suggestions are offered that significantly increase the precision and performance of the previous methods. First, our algorithm works with Log-Spectrum of image instead of the image itself, this change increases the accuracy of matching, and secondly for boosting the speed of the searching strategy, a new and modified version of Imperialist Competitive Algorithm, MICA, is presented. This matching procedure avoids the searching algorithm from being trapped in local minimum by taking advantage of adding a modification step to ICA. The simulation results show the superiority of proposed method in comparison with the previous ones.

Schema matching is a critical step in many applications, such as data warehouse loading, Online Analytical Process (OLAP), Data mining, semantic web [2] and schema integration. This task is defined for finding the semantic correspondences between elements of two schemas. Recently, schema matching has found considerable interest in both research and practice. In this paper, we present a new improved solution for schema matching problem. An improvement hybrid semantic schema matching algorithm which semi automatically finds matching between two data representation schemas is introduced. The algorithm finds mappings based on the hierarchical organization of the elements of a term WordNet dictionary.

Cerebellar Model Articulation Controller Neural Network is a computational model of cerebellum which acts as a lookup table. The advantages of CMAC are fast learning convergence, and capability of mapping nonlinear functions due to its local generalization of weight updating, single structure and easy processing. In the training phase, the disadvantage of some CMAC models is unstable phenomenon or slower convergence speed due to larger fixed or smaller fixed learning rate respectively. The present research deals with offering two solutions for this problem. The original idea of the present research is using changeable learning rate at each state of training phase in the CMAC model. The first algorithm deals with a new learning rate based on reviation of learning rate. The second algorithm deals with number of training iteration and performance learning, with respect to this fact that error is compatible with inverse training time. Simulation results show that this algorithms have faster convergence and better performance in comparison to conventional CMAC model in all training  cycles.

In this paper, we propose a novel algorithm to enhance the noisy speech in the framework of dual-channel speech enhancement. The new method is a hybrid optimization algorithm, which employs the  combination of  the  conventional θ-PSO and the shuffled sub-swarms particle optimization (SSPSO) technique. It is known that the θ-PSO algorithm has better optimization performance than standard PSO algorithm, when dealing with some simple benchmark functions. To improve further the performance of the conventional PSO, the SSPSO algorithm has been suggested to increase the diversity of particles in the swarm. The proposed speech enhancement method, called θ-SSPSO, is a hybrid technique, which incorporates both θ-PSO and SSPSO, with the goal of exploiting the advantages of both algorithms. It is shown that the new θ-SSPSO algorithm is quite effective in achieving global convergence for adaptive filters, which results in a better suppression of noise from input speech signal. Experimental results indicate that the new algorithm outperforms the standard PSO, θ-PSO, and SSPSO in a sense of convergence rate and SNRimprovement.

Mobile ad hoc networks (MANET) are constructed by mobile nodes without access point. Since MANET has certain constraints, including power shortages, an unstable wireless environment and node mobility, more power-efficient and reliable routing protocols are needed. The OLSR protocol is an optimization of the classical link state algorithm. OLSR introduces an interesting concept, the multipoint relays (MPRs), to mitigate the message overhead during the flooding process. Although very efficient by many points, it suffers from the drawbacks of not taking into account QoS metrics such as delay or bandwidth. To overcome this pitfall, some QOLSR solutions have been designed. IN this paper, we introduce an algorithm for MPRs selection based on Battery Capacity and Link Stability. Simulation results show that our proposed protocol is able to enhance throughput and improve end-to-end delay.

Rate allocation has become a demanding task in data networks as diversity in users and traffics proliferate. Most commonly used algorithm in end hosts is TCP. This is a loss based scheme therefore it exhibits oscillatory behavior which reduces network performance. Moreover, since the price for all sessions is based on the aggregate throughput, losses that are caused by TCP affect other sessions as well and aggressively reduce their throughput and also have a drastic effect on the overall good put of the system. In this paper a new differentiated pricing method is proposed that not only reduces the loss phenomenon in the network, it improves the overall performance of the network and allows other sessions such as Proportional or Minimum Potential Delay schemes achieve more fair rates. Stability property of the algorithm is investigated and some numerical analysis is presented to verify the claims.