Negative index of refraction has attracted a great attention in literatures. These materials are artificial structures named metamaterials has characteristics not found in nature. Microstrip antennas covered by metamaterial are very interesting areas of study. In this paper fractal Peano shape antenna is proposed and covered by two layers of modified ring resonator. The results show an enhancement in Reflection Coefficients, gain, and directivity.

In this paper, a combinational method is proposed to mitigate the peak-to-average power ratio problem in orthogonal frequency division multiplexing signals. The proposed algorithm is an intelligent combination of the constellation extension and probabilistic techniques. It is shown that proposed technique achieves the significant reduction in peak-to-average power on maintaining sufficient bit error rate with a slight increase in computational complexity compared to conventional schemes.

In wireless sensor networks, the issue of nodes localization has taken a wide area of research. Most applications need to know the position of sensor nodes for reasons of optimal and fast data routing. In this paper, a new distributed localization algorithm based on Self Organizing Maps (SOMs) is proposed to determine the location of a node in a wireless sensor network.
The proposed algorithm is classified as a range-free algorithm which uses only the connectivity information between nodes without the need to measure the time of arrival or signal strength as range-based algorithms require. It utilizes the neighborhood information and the well-known anchors' positions to calculate the estimated locations of nodes. Our algorithm is made up of two main stages. The initial estimated locations of nodes are calculated in the initialization stage, and fed to the learning stage in which a SOM is used to calculate the final estimated locations of nodes.
By using the neighborhood information at the first stage, the algorithm has significantly reduced the SOM learning time and the number of iterations to converge. On the other hand, starting with real data rather than random data maximized the accuracy of the resulted locations. Furthermore, the distributed implementation of the algorithm highly alleviated the pressure on the wireless nodes which are characterized with low power and limited capabilities.
The proposed algorithm has been implemented using MATLAB software and experimented by deploying different number of nodes in a specific area with different communication radio ranges. Extensive simulations evidently verified the performance of the algorithm and achieved a very good accuracy. Moreover, the algorithm proved its effectiveness with a lower average error and lower number of iterations compared to other related algorithms.