This article proposed a novel human identification method based on retinal images. The proposed system composed of two main parts, feature extraction component and decision-making component. In feature extraction component, first blood vessels extracted and then they have been thinned by a morphological algorithm. Then, two feature vectors are constructed for each image, by utilizing angular and radial partitioning. In previous studies, Manhattan distance has been used as similarity measure between images. In this article, a fuzzy system with Manhattan distances of two feature vectors as input and similarity measure as output has been added to decisionmaking component. Simulations show that this system is about 99.75% accurate which make it superior to a great extent versus previous studies. In addition to high accuracy rate, rotation invariance and low computational overhead are other advantages of the proposed systems that make it ideal for real-time systems.
This study presents a new method based on the imperialist competitive algorithm (ICA-based) to solve the k-coverage and m-connected problem in wireless sensor networks (WSNs) through the least sensor node count, where the candidate positions for placing nodes are pre-specified. This dual featured problem in WSNs is a nondeterministic polynomial (NP)-hard problem therefore, ICA the social-inspired evolutionary algorithm is assessed and ICA-based scheme is designed to solve the problem. This newly proposed ICA-based scheme provides an efficient algorithm for representing the imperialistic competition among some of the best solutions to the problem in order to decrease the network cost. The mathematical formulation is presented for the node placement problem. The main issue of concern here is the deployed sensor node count. The simulation results confirm that ICA-based method can reduce the required sensor node count unlike other genetic-based and biogeography-based evolutionary algorithms. The experimental results are presented for WSN_Random and WSN_Grid scenarios.
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