Amr H. Hussein

Optimum Design of Linear Antenna Arrays Using a Hybrid MoM/GA Algorithm

Synthesis of arbitrarily shaped radiation patterns using linear antenna arrays has a significant importance in many applications. Many attempts based on analytical schemes are exerted for this purpose. However, these analytical methods are developed for specific problems, and usually synthesis of the radiation pattern is subject to only one restriction. On the other hand, optimization algorithms are utilized for more general problems. However, these algorithms require more computational time. In this letter, a new hybrid technique for synthesizing arbitrary-shaped radiation pattern using a linear array is developed. The algorithm is based on a combination between the method of moments (MoM) and the genetic algorithm (GA). The proposed algorithm is applied to synthesis of both symmetric and asymmetric radiation pattern distributions with minimum number of elements. Excellent agreement is obtained by comparison to other analytical and optimization techniques.

Efficient selective image encryption

Selective image encryption has a significant importance in many applications as it offers significant savings in computations, cost, and time. Many attempts are exerted for this purpose as the traditional full image encryption/decryption algorithms may be too massive. In this paper, a new technique for selective image encryption is developed. The algorithm is based on a combination between the pseudo random number sequences, Arnold permutation, and the advanced encryption standard technique. The proposed technique is intended to reduce the execution time of the encryption process and increase the robustness of the encrypted image.

Energy Detection Performance Enhancement Using RLS and Wavelet De-noising Filters

The fast development in wireless communications and frequency bands assignments for every communication system limits the spectrum resources. Various techniques, for example, cognitive radio have occurred to tackle this issue by allowing unlicensed users to utilize the licensed bands. The most important component for establishing a reliable cognitive radio system is spectrum sensing. One of the ordinarily used spectrum sensing techniques is energy detection. It has low computational and usage complexities. But, for low signal-to-noise ratio (SNR) values it has a poor performance as it will not be able to differentiate the interference from noise and primary users. In this paper, a new energy detection technique for spectrum sensing is introduced. The proposed technique is based on utilization of de-noising filters such as recursive least square (RLS), 1-D wavelet de-noising filter, and 2-D wavelet de-noising filter. This technique is intended to achieve SNR gain, noise variance reduction, and enhance the detection threshold estimation. Furthermore, it exhibits noticeable increase in the throughput rather than that of the traditional detector. Simulation results revealed that the RLS de-noising filter exhibits much better performance than wavelet de-noising filters.

VAE/MPM/GA Technique for DoA Estimation Using Optimized Antenna Arrays

Many algorithms have been proposed to estimate the direction of arrival for the targets, but through using a large number of snapshots. In real time applications such as automotive radar, this is unacceptable as it causes delay and heavy processing. Instead, if only a small number of snapshots or, optimally, a single snapshot is available for DoA estimation, it will be fast and efficient. Single snapshot algorithms have a drawback as they require a large number of antenna elements, which considered a limiting factor. In this paper, a single snapshot DoA estimation technique is introduced by using optimized antenna arrays. The proposed algorithm is based on utilization of virtual array extension, matrix pencil method, and the genetic algorithm. The use of virtual array extension greatly improves the MPM performance. Furthermore, it exhibits a high DoA estimation accuracy by using a reduced number of antenna elements. The genetic algorithm is employed to determine the minimum number of antenna elements, which are required to estimate the DoAs with minimal root mean square error.