NASHRIYYAH -I MUHANDISI -I BARQ VA MUHANDISI -I KAMPYUTAR -I IRAN, B- MUHANDISI -I KAMPYUTAR
Year:2017 | Volume:14 | Issue:4|Papers Count:8

In recent years, target coverage is one of the important problems in visual sensor networks. An efficient use of energy is required in order to increase the network lifetime, while covering all the targets. In this paper, we address the Maximum Lifetime with Coverage Scheduling (MLCS) problem that maximizes the network lifetime. We develop a simulated annealing (SA) algorithm that divides the sensors’ Field-of-View (FoV) to a number of cover sets that can cover all the targets and then applies a sleep-wake scheduling algorithm. On the other hand, we have to identify the best possible FoV of sensors according to the targets’ location using rotating cameras, to reduce the solution space and find a near-optimal solution. It also provides the balanced distribution of energy consumption by introducing a new energy and neighbor generating function as well as escaping from local optima. Finally, we conduct some simulation experiments to evaluate the performance of our proposed method by comparing with well-known solutions in the literature such as greedy algorithms.

A fundamentally quantum model of computation based on quantum entanglement and quantum measurement is called one-way quantum computation model (1WQC). Computations are shown by measurement patterns (or simply patterns) in this model where an initial highly entangled state called a graph state is used to perform universal quantum computations. This graph together with the set of its input and output qubits is called the geometry of the pattern. Moreover, some optimization techniques have been introduced to simplify patterns.Previously, the 1WQC model has been applied to optimize quantum circuits. An approach for parallelizing quantum circuits has been proposed which takes a quantum circuit and then produces the corresponding pattern after performing the proposed optimization techniques for this model. Then it translates the optimized 1WQC patterns back to quantum circuits to parallelize the initial quantum circuit by using a set of rewriting rules.To improve previous works, in this paper, a new automatic approach is proposed to optimize patterns based on their geometries instead of using rewriting rules by applying optimization techniques simultaneously. Moreover, the optimized pattern is translated back to a quantum circuit and then this circuit is simplified by decreasing the number of auxiliary qubits. Results show that the quantum circuit cost metrics of the proposed approach is improved as compared to the previous ones.

The energy constraint is one of the key challenges in wireless sensor networks that directly affects the network lifetime. Clustering the sensor nodes is one of the possible approaches to improving the energy efficiency by uniformly distributing the energy consumption among the nodes. The number of appropriate clusters plays an important role in the network throughput. A Large number of clusters imply that packets pass more hops to reach the destination, which results in higher energy consumption. In this paper, we devise an energy and location aware clustering scheme that tries to optimize the number of required clusters. Moreover, the cluster heads are chosen according to their energy levels. The devised scheme partitions the network into concentric circles and calculates the appropriate number of clusters to provide an energy efficient network. A gossiping approach is used to provide information exchange mechanism. The performance of the devised approach is compared with ASH scheme. The simulation results show the network lifetime is improved from 25% to 40% in difference network scenarios.

One of the most important factors influencing the development of information technology on internet is steal the customer information. This security threat is known as phishing. With regarding to review and analysis of the published methods, lake of create the flexibility to effective attribute selection in the procedure of phishing websites detection, non- dynamic behavior of classification algorithm on target websites and also no attention to reduce the amount of computation for the large number of websites are the main gaps of these methods. To achieve the above-mentioned objectives, a new dynamic mechanism is planned to flexible attribute reduction based on designing threshold change of assessment in this paper. Then inclined planes optimization algorithm is memorized based soft reducing the effect of the embedded memory though high iterations and 12 fuzzy rules are defined in a fuzzy inference system for intelligent dynamiting the algorithm. The experimental results of the proposed intelligent algorithm and the comparison the algorithms with the best available algorithms; demonstrate the ability of the modified inclined planes optimization algorithm to detect phishing websites and satisfy the above mentioned objectives.

Pose and orientation of a person relative to the camera are the important and useful information in many applications, including surveillance systems. This information can be used in the behavior analysis of the person. Low quality of the recorded surveillance images, noisy data and cluttered backgrounds are some of the difficulties in this task. In the existing methods, histogram of orientation gradient (HOG) is used to estimate the orientation. The local properties of HOG is a weakness for orientation estimation. The edge surrounding the object, namely contour, is a useful information for orientation estimation. In this paper we present a general form of a contour. This hyper contour helps us to find the best contour which is matched to image of the person in a hierarchical fashion. These contours generated from a human 3D model. The matched contour as a high-level feature is combined with the low-level feature such as HOG, and considered as the final feature. The proposed feature is a linear combination of several types of contours with respect to different regions of the body. To show the impact of the proposed feature on orientation estimation, a support vector machine is trained on a hybrid feature space and then is evaluated on VIPeR dataset. The experimental results show that the accuracy of the orientation estimation is improved about 4% by using the extended feature.

In this paper an automated system based on feature extraction of new techniques is presented to detect the gender from the scanned images (off-line) handwriting samples. In order to show the difference between examples of handwriting, in the first step Radon transform is taken from the handwritten image, and then each handwriting sample features are extracted using symbolic dynamic filtering. Training and classification of extracted features from the samples are carried out by the multi-layer perceptron neural network. At the end, to determine the effectiveness of the proposed method, experiments are carried out on the Multi Script Handwritten Database (MSHD). In addition, two new challenges of text and script-independent gender detection are explored. Experiences show that the proposed method improves the detection rate compared to the previous works such as fractals, chain codes and textures. The best detection rate is able to achieve accuracy of 84.9% in experiences.

SIFT method is used to extract keypoints of the imagein order to overcome the problems of matching between the satellite and aerial images, including: difference in scale, rotation, brightness intensity and the geometric shape. Unfortunately, SIFT method extracts several unfavorable keypoints of satellite and aerial images because of the turbulence and the environmental factors which leads to unreliable matching and increasing complexity. In order to improve the quality of the extracted specific areas and the run time of the algorithm, first the edges of the original images are extracted by Sobel operator and thresholding, then by using the SIFT method, keypoints are extracted from the edge image. After extracting keypoints, using the rBREIF method, that have stability dependence with respect to atmospheric turbulence and rotation, descriptor for every point of the extracted points is created. Then by applying the bilateral image matching and the RANSAC method that removes the unfavorable adaptive points, the correct matching between the satellite and aerial images are found using the suggested method. The results of the proposed method on the real images show the superiority of this method in term of the accuracy and speed, compared to the some well-known matching methods such as SIFT.

Data quality affects on companies decision making, so that decisions based on data without quality incur companies high costs. Data quality has various dimensions and accuracy is the most important of these dimensions. Error detection is needed for data cleaning. Due to the huge volume of data, an automatic system is needed to perform this process without user interaction. In this paper an approach is proposed based on k-means clustering for error detection. Firstly data are clustered for each attribute. Then for each data in each cluster a method similar to k-nearest neighbor is used for detecting errors. The proposed method is able to detect multiple errors in one record. Also this approach is able to detect errors in fields with various attribute types. Experimental results show that this approach can detect 91% of errors in data on average. Also the proposed approach is compared with an automatic method which detects errors based on rule in various attribute types. Experimental results show that the proposed approach has on average 25%better performance to detect errors.