نشریه مهندسی مخابرات جنوب
پیاپی 36 (تابستان 1399)

Sensor networks have found many applications in different branches of science. A sensor grid is made up of a large number of small sensors. These sensors help each other to provide information about the sensory field. One of the major issues in sensor networks is the problem of coverage. The problem of coverage explores the answer to the question of how far the physical environment of a sensor network is properly monitored by the nodes of the network. The importance of this issue is to the extent that it is considered as one of the parameters of the quality of service in such networks. In all cases, the need for the methods that can accurately calculate network coverage is well known. In this paper, we try to provide an optimal solution to the problem of coverage in a wireless sensor network with the help of a modified gravitational search algorithm.

Refractive index sensors are one of the most widely used sensors in the bioelectronics and optoelectronics industries. The use of materials and structures that can detect the refractive index of unknown materials has important applications in biochemistry and medical engineering. In this paper, a polymer-based dual-core photon crystal fiber is introduced to measure the refractive index of a fluid. The size of the holes of the analyte cores and the light-transmitting solid core are engineered to be able to pass the main single mode. Due to its cheapness, high mechanical ability and ease of fabrication, the photon crystal fiber substrate is made of PMMA polymer. After simulating the structure and using the existing mathematical relations and examining the diffusion modes, for the complete transfer of light from the solid core to the core of the analyte channel, a fiber length of 0.13 cm has been obtained. The simulation results show that for a refractive index of 1.44, a sensitivity value of 1000  and a detection limit can be achieved by this sensor. All simulation steps in the field of FDTD are obtained in Lumerical software.

Intrusion detection systems for security in computer networks have been proposed to be crossed if the attacker from other security equipment, able to detect it and prevent it from advancing. One of the challenges of these systems, it is high dimensional data. In this study was to reduce the dimensions of a simple genetic algorithm with the length of the string variable we use. Then, according to selected characteristics, a meta-heuristic model for data classification, using ant colony algorithm offer. Classification model proposed by trying to divide the data into two samples is Hnjydh and Nahnjydh. The proposed method for evaluating the performance of database intrusion detection NSL-KDD than other data from the records of more realistic approach is used. The results of the experiments, the proposed method has better performance compared with other existing methods show.

In the division of remote sensing image pixels using Watershed segmentation, the image boundaries are not well defined. In this paper, an image clustering algorithm based on Watershed segmentation and Fuzzy C-Means clustering is presented. The method is that first the Watershed algorithm is used to segment the image obtained from the sum of the image derivative with the original image. Image derivation makes the borders of the image well-defined and does not overlap between borders. After segmentation, Fuzzy C-Means clustering is used to combine similar regions. Finally, in order to improve the clustering results, a new segmentation matrix is calculated for each area of the image, according to the characteristics of its neighboring areas. Due to the fact that remote sensing images contain a high level of noise, the proposed algorithm is more capable of dealing with noise compared to the conventional Watershed algorithm, and the edges of the image appear better. The test results of the proposed method on a sample of remote sensing image show the practicality and efficiency of the proposed algorithm.