Dia I. Abu-Al-Nadi

Road traffic sign detection in color images

This work is a part of a smart system that can be used in autonomous vehicles or can assist drivers in locating road signs. The detection technique includes histogram equalization, light control and color segmentation. In this technique, both the HSV and the YUV color spaces are incorporated to achieve better segmentation results than that of one color space techniques.

Automated personal identification system based on human iris analysis

In general, a typical iris recognition system includes iris imaging, iris liveness detection, iris image quality assessment, and iris recognition. This paper presents an algorithm focusing on the last two steps. The novelty of this algorithm includes improving the speed and accuracy of the iris segmentation process, assessing the iris image quality such that only the clear images are accepted so as to reduce the recognition error, and producing a feature vector with discriminating texture features and a proper dimensionality so as to improve the recognition accuracy and computational efficiency. The Hough transform, polynomial fitting technique, and some morphological operations are used for the segmentation process. The phase data from 1D Log-Gabor filter is extracted and encoded efficiently to produce a proper feature vector. Experimental tests were performed using CASIA iris database (756 samples). These tests prove that the proposed algorithm has an encouraging performance.

Genetically evolved phase-aggregation technique for linear arrays control

This paper presents a modified Genetic Algorithm (GA) technique in which the large phase perturbations are calculated by aggregating small phase increments. The proposed aggregation GA technique overcomes the major drawback of the large solution space required by the classical GA techniques. The proposed method adopts small ranges for increments of the parameters and the optimality is reached via aggregation of the best increments of phases. Consequently, the GA searches in a smaller solution space and finds the solution with reduced number of iterations. Simulation results show the achieved improvement of the proposed technique over the classical GA. The suppressed sectors using phase-only control are accomplished with and without element failures. Problems like imposing symmetrical nulls around the mainbeam and compensation for the failure of center element have been achieved.

Design of Linear Phased Array for Interference Suppression Using Array Polynomial Method and Particle Swarm Optimization

In this work, a linear phased array pattern design with null steering is achieved using the array polynomial technique and the Particle Swarm Optimization (PSO) algorithm. The null steering for interference suppression is obtained by controlling some of the roots on the Schelkunoff’s unit circle while keeping the roots responsible for the main beam unchanged. The rest of the roots are controlled to minimize the Side Lobe Level (SLL) of the array pattern using the PSO algorithm. It has been demonstrated that this technique achieved more than 50% reduction in the parameters needed to be optimized compared with the conventional complex coefficients optimization techniques. Consequently, the fitness function is only responsible for the SLL as the prescribed controlled nulls and the mainbeam characteristics are solved analytically. The simulated results show the effectiveness of the proposed technique.

Genetically Evolved Phase-Aggregation Technique for Linear Arrays Control

This paper presents a modified Genetic Algorithm (GA) technique in which the large phase perturbations are calculated by aggregating small phase increments. The proposed aggregation GA technique overcomes the major drawback of the large solution space required by the classical GA techniques. The proposed method adopts small ranges for increments of the parameters and the optimality is reached via aggregation of the best increments of phases. Consequently, the GA searches in a smaller solution space and finds the solution with reduced number of iterations. Simulation results show the achieved improvement of the proposed technique over the classical GA. The suppressed sectors using phase-only control are accomplished with and without element failures. Problems like imposing symmetrical nulls around the mainbeam and compensation for the failure of center element have been achieved.

Genetic algorithm approach with frequency selectivity for model order reduction of MIMO systems

A novel genetic algorithm (GA) approach with frequency selectivity advantage for model order reduction (MOR) of multi-input–multi-output (MIMO) systems is presented in this article. Motivated by singular perturbation and other reduction techniques, the new MOR method is formulated using GAs, which can be applied to single-input–single-output (SISO)- or MIMO-type systems. The GA procedure is based on maximizing the fitness function corresponding to the response deviation between the full-order model and the reduced-order model with the option of substructure preservation. The proposed GA-MOR method is compared to the well-known reduction techniques, such as the Schur decomposition balanced truncation, proper orthogonal decomposition (POD) and state elimination through balancing-related frequency-weighted realization in addition to other recent methods. Simulation results validate the superiority and robustness of the new MOR technique as it can search the solution space for almost optimal solutions.

A General Configuration Antenna Array for Multi-User Systems with Genetic and Ant Colony Optimization

Antenna arrays are used in the CDMA-based cellular systems in order to increase the systems capacity. Different approaches benefit from the spatial separation between users. Smart and adaptive beam antennas are the best proposed solution for these systems. Several methods are used to provide the system with a radiation pattern that increases the signal-to-interference ratio (C/I). Antenna configuration is the way the array elements are distributed in space. This distribution can influence the design of the beam formation or the adaptation method, such that, the calculation of the different parameters requires a known array configuration. Antenna configuration is discussed in several research papers including planar arrays and circular arrays. Here, we introduce a general antenna array structure and derived its optimal parameters, and then we optimize the solution under different quantization errors using a genetic algorithm (GA) and an ant colony optimization (ACO). Results showed that we can configure the antenna array to provide an acceptable C/I. Also, results showed that we can obtain a good solution that is less sensitive to quantization errors using the ACO.

Pre-Coding for MIMO Systems in Frequency-Selective Fading Channels

Multiple input multiple output (MIMO) systems showed good utilization of channel characteristics. In MIMO systems multiple signals are transmitted using multiple antenna system. This provides each receiver the combined signals and hence, array processing techniques helps in reducing the effects of interference among them. In this paper we devise the use of pre-coded MIMO system to reduce the effects of frequency selectivity and hence, enhance the systems capacity and/or reduce the bit error rate. In this technique we introduce a temporal pre-coder on each antenna signal; this creates a deterministic multi-path signals similar to signals received when the channel is multi-path fading channel. The same antenna signal will arrive at each receiver forming orthogonal sub-space and the receiver will be simple add and delay of the received signals. Ant colony optimization is used in this paper to select the best pre-code. Results showed that we can diagonalize the channel matrix and practically eliminate the interference for frequency selective fading channel. Simulation of two transmitting two receiving antennas pre-coded MIMO system showed that the capacity can be doubled.

Pattern Synthesis with Phase-Only Control using Array Polynomial Technique

A new synthesis technique using the phase shifters of the linear array elements is developed using the array polynomial technique. The array factor is expressed as the product of sub polynomials such that their roots are located on the unit circle. All array elements are active for any arbitrary non-prime number of the array elements. The results show that the developed method in the form of an analytical solution can synthesize the prescribed patterns using the phase shifters of the linear antenna arrays.

Soft Computing Techniques for Reduced Order Modelling: Review and Application

AbstractAs the mathematical procedure of system modelling often leads to a comprehensive description, which causes significant difficulty in both analysis and control synthesis, it is necessary to find lower order models, which maintain the dominant characteristics of the original system. In this paper, different soft computing (named as artificial intelligence (AI)) techniques are presented, applied, and analysed for model order reduction (MOR) of multi time scale systems with the objective of substructure preservation. In addition to that, we investigate the firefly optimization technique for MOR with substructure preservation. The analysis is concerned with the optimization approach and quality of method performance.