Usually two-dimensional substitution is used for the production of integrated circuits. Due to heavy use of communications, two-dimensional substitution has high losses as well as, density of elements is low in it. To resolve this problem, three-dimensional substitution method was proposed. Rather than the two-dimensional arrangement of elements in a row, elements are layout in three dimensions in this substitution. In this paper, three-dimensional substitution algorithms using order used in two-dimensional substitution, three-dimensional substitution analysis by mPL and three-dimensional substitution simultaneous with two-dimensional substitution by mPL have been studied in terms of structure and function, and a butterfly processing element (PE) and an Advanced Encryption Standard (AES) block and a wireless MIMO decoder to assess them have been implemented with the mentioned methods. . Applying these methods shows that the use of face to face integration by microbuses in communications of substitution algorithm, on average, improves the maximum clock and block speed of AES encryption to 15. 3% and the maximum clock and block speed of PE module to 22. 6% as well as the maximum clock and speed of MIMO modules to 17. 1%, while the use of these methods has led to the average reduction in power consumption of 2. 6% for the AES module and the average reduction in power of 12. 9% for the PE module and the average reduction in power consumption of 5. 1% for MIMO module.

Location awareness is an important capability for a series of enhanced wireless businesses. sensor networks are dense wireless networks of small low cost sensors, which collect and disseminate environmental data, for monitoring, military application and so on. Localization is an unconstrained optimization problem. position estimation is based on various, distance / path measures, which include anchor and non-anchor nodes. Anchor positions, have been predetermined to help us localize other nodes. This study proposes using a combination of fuzzy techniques, and advanced APS method, to estimate unknown nodes. In a network with twenty hundred nodes of which twenty percent operates as anchors. These nodes localize the other one hundred and sixties. It is necessary to select the best four anchors for localizing. We suppose that the anchors neighbor to unknown nodes are the best. It is time consuming to find the distance of unknown anchors in such a widespread network. Using the fuzzy logic, putting the limitation of distance, and selecting the nearest anchor to the unknown node, the nearest four anchoress can be selected. In this case the rate of localization error will be decreased due to selecting neighbor anchors. Therefore, we can localize nodes by using ad-hoc positioning system.Fuzzy rules help us to estimate position in less than 2.4 seconds with mean normal positioning deviation of z=0.4597.

Potential evapotranspiration (ETp) rates are needed for irrigation scheduling. ETp rates are commonly estimated from weather parameters. The Fao-Penman Monteith, is now accepted for computation of ETp. This study examined the suitability of fuzzy logic for estimating daily potential evapotranspiration with grass reference crop compared with artificial neural networks, Fao-Penman Monteith and evapotranspiration pan. The daily climatic data of the Borojen station in Chaharmahale-Bakhtiari was used. The estimated ETp values from a fuzzy logic model using six input parameters resulted in RMSE=0.74 mm/day, R2=0.88 which has the highest correlation and the lowest error in comparison with other methods.

Because of the possibility of anonymity and impersonation in social networks, trust plays an important role in these networks. In social networks, trust can have two aspects: trust of users to social network and trust of users to other users. Pear to pear networks, by eliminating the supervisor roles, besides its benefit in decreasing management costs, have problems in trust and security of users. In these networks, trust evaluation is only related to the trust of pear to other pear and because of the direct relation between pears; each user should know the trust level of other users. However, trust evaluation in pear-to-pear networks (as an unsupervised network), only can be done based on the past relation between pears or trust evaluation of other pears. This kind of trust evaluation cannot give a comprehensive view to pears. In other word, if any pear is not in the friend cycle of a user or friend cycle of user's friends, he will not be able to assign appropriate trust level to this pear. In this research, by using social networks as supervised networks, trust level of each user is evaluated, then by identifying these users in unsupervised networks, appropriate trust level is assigned to them.

Network sensors consist of sensor nodes in which every node covers a limited area. The most common use of these networks is in unreachable fields.Sink is a node that collects data from other nodes. One of the main challenges in these networks is the limitation of nodes battery (power supply).Therefore, the use of topology control is required to decrease power consumption and increase network accessibility.In this paper, a network is modeled by a graph. Each vertex in the graph indicates a sensor node and the edges display the communication links between them. Changes in the graph show changes in network topology and a different path to the sink.In this research, “fuzzy logic” is used for topology control. As the fuzzy logic utilizes optimized sensing radius comparing with minimum-maximum sensing radius, we expect less dead nodes, more mean residual energy and relatively more load balance in the network. At first, 2-input fuzzy algorithm was chosen. However 3-input fuzzy algorithm was also observed due to reasons explained in the main text.In both algorithms, we have load balance in network and prolong network lifetime. Unreachable paths are less encountered with higher rates of packet delivery. The final standard deviation (STD) reaches to its minimum level, while the residual energy in sensors remains close to each other.

Network coding is an effective approach for improving the performance of wireless networks, such that a new generation of routing protocols, known as network coding-aware protocols, recognize coding opportunities during their route discovery process, and try to establish routes containing appropriate coding opportunities. Most of the research works being done around this type of routing, only try to maximize the number of coding opportunities along data transmission route. However, merely considering coding opportunities, may decrease the optimality and the throughput of routes, since sometimes, by considering only the number of coding opportunities without paying attention to the quality of links along routes, the quality of detected routes will be decreased. In this paper, in order to resolve the deficiencies of previously related works, a new network coding-aware routing protocol based on fuzzy logic is proposed, such that the delay of links besides the satus of all nodes along different paths are evaluated through utilizing fuzzy logic, and the overall value of all paths are determined. Finally, paths wirh better status in terms of the quality of links and nodes, are selected for data transmission. Simulation results demonstrate that the proposed protocol has better performance than previous approaches, in terms of throughput, packet delivery ratio, and delay.

Introduction: The goal of the present study was to develop an automatic and high-performance professional nutrition and diet advisor system to help the users to evaluate their nutrition conditions and get useful nutritional information. The study also aimed to provide users with a healthy meal plan based on their physical conditions such as weight, height, age, etc.Methods: This study was an applied research towards developing an intelligent diet advisor system. We focused on fuzzy logic and artificial neural networks as the means of implementation. The dataset was collected from one thousand patients’ files chosen randomly from the files of the patients referred to a diet clinic in Isfahan City, Iran, between 2011 and 2015. The collected data were entered into excel software during four months of study.Results: The designed three-layered artificial neural system with back propagation algorithm was able to diagnose the best dietary plan among the eleven proposed plans. The designed neural networks were able to work with 92% of accuracy, while the proposed fuzzy logic-based system carried out the procedure with 97% of accuracy.Conclusion: The results of this research indicated that this dietary proposal system using neural networks and the fuzzy logic was sufficient enough to be used to propose appropriate diet and meal plans to individuals. As a result, it could allow the users to receive the very efficient diet plans after entering their personal information easily, accurately, and almost free of charge.