JOURNAL OF ADVANCES IN COMPUTER RESEARCH
Year:2017 | Volume:8 | Issue:4 (30)|Papers Count:9

Radio Frequency Identification (RFID) Systems are wireless identification technology that provide communication and identification of tagged objects through wireless communications. In some applications, a single reader is not enough to cover a specific identification area, so several readers are placed very close to each other for optimal connectivity and coverage. T his environment is called Dense Reader Environments (DREs). Such networks are susceptible to reader collision problems that cause decreases the performance of network. The reader-to-reader collision is an important challenge in dense reader RFID networks that lead to the reading throughput barrier and degrade the system performance. In this paper we propose a reader anti-collision algorithm based on the density of each reader in order to improve the network's throughput in dense RFID systems. Density Based Anti-Collision Algorithm (DBA) develops based on GDRA protocol but in this new algorithm each reader choice a time slot based on its density. Simulation results demonstrate the DBA algorithm provide 5.46% more throughput than GDRA.

Detecting optical characters is considered as the main responsibility to convert printed documents and manuscripts to digital format. In this article, detecting Persian handwritten letters using the combination of classifiers and features are assessed employing geometric and statistical sections' features. In order to detect each letter, it is divided into two parts; the major and the minor parts. Then, some features are presented for them. Preprocess algorithm prepares the possibility to unify dimension features for multiple words and delivers them to classifier for detection. The hierarchy classification can be obtained by separating the letters. In the following, the optimal answer will be reached by using GA method of different SVM, ML and KNN classifications. Extraction algorithm of required features is proved by using the evaluation of the basis of PCA. Empirical results represent classification of 94.3 and 92 accuracy in simple and multiple parts in 20 times repetition, respectively.

Micro grids are applied not only generate power, but also producing a sinusoidal output voltage and supplying nonlinear loads. In this paper, using A current control scheme for selective harmonic compensation is proposed for shunt active power filters. In the active power filters using voltage source converters that are capable of dual-use technology to improve the quality of the selected compensation can be paid. Using this system, an improved individual harmonic and THD with these requirements will be modified. Simulation results show the effectiveness of the proposed method for compensating current harmonics to an acceptable level.

The interactions among peers in the Peer-to-Peer systems as a distributed collaborative systems are based on asynchronous and unreliable communications. Trust is an essential and facilitating component in these interactions specially in the uncertain environments. Various attacks are possible due to large-scale nature and openness of these systems that affects the trust. Peers has not enough information about other peers thus they can use trust management to reason about future interactions with other peers. In fact any peer needs to know the exact prediction of the trustworthiness of other peers. In the proposed approach the Bayesian network based trust management model is used to infer the trust value of task processor peers based on various aspects of their behaviors. The previous behavior of peers is considered to determine meticulous trust value of these peers in completely distributed environment. Since the trust is multifaceted concept, each aspect is evaluated using a single Bayesian network to provide finer-grained inference of trust. In the proposed model, DisTriB (Dis tributed Tr ust Management Model Based on Goss i p Learning and B ayesian Networks), many aspects such as network link capacity and workload of task processor peers is used to construct the Bayesian network. The optimum time window size is found to obtain better performance too. Finally, a robust, asynchronous, gossip based protocol is proposed that can withstand high churn and failure rates, and can spread the trustworthiness of peers while the processing of tasks in the proposed collaborative computing system is performed. Simulation results shows that the proposed approach outperforms previous works.

Content-based image retrieval (CBIR) has been an active research topic in the last decade. In this paper we proposed an image retrieval method using global and local features. Firstly, for local features extraction, SURF algorithm produces a set of interest points for each image and a set of 64-dimensional descriptors for each interest points and then to use Bag of Visual Words model, a clustering algorithm is used to obtain the visual vocabulary and each resulted centroid represent a visual word. Then, images are viewed as BoVW represented as histogram. In order to improve retrieval performance, global feature is extracted by HSV color feature. Finally, this approach uses the combined local and global features as feature vectors to provide image retrieval. The COREL image database has been used for our experimental results. The experimental results show that the performance of the combination of both local and global features is much higher than each of them, which is applied separately.

With increasing speed of information and documents on the Web, our need to classify them in different categories and clusters is more necessary. Clustering tries to find related structures in data sets which they are not categorized, yet. Concerning the needs, a new approach for text documents categorization is presented in this paper which includes three phases: pre-processing documents and selection feature, K-Means clustering and Naïve Bayes (NB) optimization. The proposed model uses K-Means and NB algorithms that utilize K-Means algorithm to find minimum distances between features from the center of clusters and NB algorithm for computing the probability of each feature into documents and using them to cluster features, separately. The proposed model optimizes performance of K-Means algorithm by using NB properties in clustering. Therefore, the model overcomes to the challenges of labeling different documents and origin of K-Means algorithm which it refers to categorizing text documents as un-supervised model. Finally, the experiment results of proposed model and K-Means algorithms are evaluated based on evaluation methods and are compared in validated datasets.

Residue Number System is a numerical system which arithmetic operations are performed parallelly. One of the main factors that affect the system’s performance is the complexity of reverse converter. It should be noted that the complexity of this part should not affect the earned speed of parallelly performed arithmetic unit. Therefore in this paper a high speed converter for moduli set {2n-1.2n+1-1.2n} is proposed which is based on Two-Part RNS and Chinese Reminder Theorem. Using this method has increased the speed of reverse converter. To have an accurate comparison both unit gate model and synthesized silicon tools are used and their parameters are compared in terms of delay and area. Converters are implemented in hardware description language and correctness for various n values is verified by simulation and execution on Cadence. As the results show, the proposed circuit has lower delay by around 21% in comparison to previous presented converter.

The wireless sensor network (WSN) is typically comprised many tiny nodes equipped with processors, sender/receiver antenna and limited battery in which it is impossible or not economic to recharge. Meanwhile, network lifespan is one of the most critical issues because of limited and not renewal used battery in WSN. Several mechanisms have been proposed to prolong network lifespan such as LEACH, HEED and CHEF, but in all of them nodes consume energy continuously. One of the promising techniques is to apply dynamic sleep/wake up scheduling. In this paper, a novel sleep/wake up scheduling algorithm is proposed so-called FT-ECCKN. Each node executes sleep/wake up scheduling right after sending/receiving data where a node changes its status to sleep mode if it has at least k neighbors awake in its radius neighborhood with more residual energy in comparison with the node executing scheduler. Whenever the number of nodes is more than 2k, fuzzy TOPSIS method is used to rank nodes based on residual energy and coverage distance to select k out of number of nodes in ranking list. To evaluate the proposed algorithm, 25 scenarios are conducted in the experimental field 800X600 between 100 through 500 nodes increasing with 100 numbers and k belongs to {1, 2, …5}. Totally, our proposed algorithm outperforms 23.27 percent in term of network life time in comparison with EC-CKN method for overall scenarios. Remarkable results show that the proposed algorithm is beneficial for large scale fields with dense nodes along with smallest k.

Routing in computer networks has played a special role in recent years. The main reason is the role of routing in a performance of the networks. The quality of service and security is one of the most important challenges in routing due to lack of reliable methods. Routers use routing algorithms to find the best route to a particular destination. When talking about the best path, we consider parameters like the number of hops, change times, and communication cost of sending data packet. In this study we will try to improve the routing operations using local and global smart factors. The Ants Colony Algorithm is a multi-factor solution for optimization issues. This solution has models based on the ants’ collective intelligence and has attracted some users in computer networks through converting to an efficient technology. Although the Ant is a simple insect, but a colony of them are able to perform useful tasks such as finding the shortest path to the food source and to share this information with other ants through leaving back a chemical material called pheromone. This algorithm consists of three stages. The first phase is clustering nodes of the network to smaller colonies. This phase is conducted by using learning automata network in accordance with the need of the network; For example, putting nodes in one cluster which will have more close relations in near future. The second phase is finding the routes of the network by ants, and the third phase is sending network traffic to the destination through routs found by ants.