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Inverse synthetic aperture radar (ISAR) imaging is an efficient technique to generate two‐dimensional spatial distributions of radar cross‐section of a target. The received signal of ISAR contains interphase errors resulting in blurring problem in the images due to motion effects of the target. In this study, a simple, stable, and robust method based on artificial bee colony (ABC) algorithm has been proposed for motion compensation (MoComp) to overcome the blurring problem in ISAR images. The minimum entropy was used as objective function in the ABC algorithm for optimally determining the parameters of velocity and acceleration. The proposed method was implemented to four different scenarios based on the two air target models to show performance of the ABC algorithm. The radar signal of the target without MoComp was given as inputs to the ABC algorithm to determine velocity and acceleration values so as to provide the clearest ISAR image. The proposed method was compared with a suggestion reported elsewhere using particle swarm optimization (PSO), PSO with island model, genetic algorithm. It is pointed out that the proposed method is more successful, stable and efficient in terms of the image quality, entropy values, and standard deviation. © 2014 Wiley Periodicals, Inc. Microwave Opt Technol Lett 56:2691–2698, 2014

In this study, the scattering map of the breast is reconstructed by applying the matchingpursuit algorithm (MPA) to the simulation data obtained by the monostatic inverse synthetic aperture radar (ISAR) principle, and the locations of the tumors are determined by considering the peaks on the scattering map. The MPA iteratively searches the true solution by assuming every discrete point in the solution space to be a scattering center by dividing the imaging region onto a discrete grid. In order to obtain images with better resolution, the fine granularity of the grid for accurate solutions is provided at the expense of increased processing times. First, our approach based on MPA is tested on simulated data generated by MATLAB for breast tumor detection and imaging. Perfect reconstruction for the locations of the hypothetical breast tumor points is attained. Then, a full-wave electromagnetic simulation software named CST Microwave Studio (CST MWS) is used to generate backscattered electric field data from a constructed scenario in which a tumor is located in a breast model. Next, we use the collected data from the defined scenarios as an input to our algorithm. Resultant images provide successful detection and imaging of the tumor region within the breast model. The accuracy of the MATLAB and the CST MWS simulation results demonstrate the availability of our MPA-based focusing algorithm to be used effectively in medical imaging.

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This paper proposes a novel and simple expression for effective radius of annular-ring microstrip antennas (ARMAs) obtained using a recently emerged optimization algorithm of artificial bee colony (ABC) in calculating the resonant frequency at dominant mode (TM11). A total of 80 ARMAs having different parameters related to antenna dimensions and dielectric constants was simulated in terms of the resonant frequency with the help of an electromagnetic simulation software called IE3D™ based on method of moment. The effective radius expression was constructed and the unknown coefficients belonging to the expression were then optimally determined with the use of ABC algorithm. The proposed expression was verified through comparisons with the methods of resonant frequency calculation reported elsewhere. Also, it was further validated on an ARMA fabricated in this study. The superiority of the presented approach over the other methods proposed in the literature is that it does not need any sophisticated computations while achieving the most accurate results in the resonant frequency calculation of ARMAs.

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In this work, a model constructed with the adaptive neuro -fuzzy inference system (ANFIS) for estimating the resonant frequency of rectangular ring compact microstrip antennas (RRCMAs) in UHF band is proposed. A total of 108 RRCMAs having different parameters related to the antenna dimensions and dielectric substrate were simulated with the help of electromagnetic packaged software IE3D TM based on method of moment (MoM) to generate the data pool for training and test processes of the ANFIS model. While 96 RRCMAs were employed for training, the remainders were used for test the ANFIS model. The resonant frequencies were computed with the average percentage errors (APE) as 0.014% and 0.666% for training and test, respectively. The accuracy of proposed model was successfully demonstrated by comparing with the results of a method over the simulated data previously published in the literature. Further to inspect the validity of the ANFIS model, a RRCMA operating at 2.44 GHz was designed and fabricated for this work, and the accurate results concerning the resonant frequency were achieved.

This contribution presents an approach for the modeling and prediction of surface roughness in the turning of AZ91D magnesium alloys using an artificial neural network. The experiments were conducted with CCGT, DCGT and VCGT cutting tools under minimum quantity lubrication and dry machining conditions. AZ91D alloys were machined at different cutting speeds and feed rates, and the depth of cut was kept constant. 15 out of 18 experimental data points were used for the training of the artificial neural network model and the remaining 3 were used for the testing process. The average percentage error was calculated as 0.000815 % and 0.663 % for training and testing, respectively. The model and target results were found to have extremely low error rates.