Abdurrahim Toktas

Simple Formulas for Calculating Resonant Frequencies of C and H Shaped Compact Microstrip Antennas Obtained by Using Artificial Bee Colony Algorithm

Accurate and simple formulas are presented for effective length in determining the resonant frequency of C and H shaped compact microstrip antennas (CMAs) operating on UHF band. For this purpose, 144 C-shaped CMAs and 216 H-shaped CMAs having different physical dimensions and various relative dielectric constants were simulated by packaged software called XFDTD based on finite difference time domain (FDTD) method. The physical and electrical parameters of the antennas and their respective resonant frequency values were given as inputs to artificial bee colony (ABC) algorithm to determine unknown coefficients of the effective length expression which provides the best fit to the resonant frequency. The resonant frequency results of the effective length expressions were compared with the different measurement, simulation and calculation results published earlier in the literature. It was shown that our results are in very good agreement with the simulated and measured results as compared to those of calculated by the other suggestions reported elsewhere. Moreover, C and H shaped CMAs were fabricated in this work and then the accuracy and validity of proposed expressions were tested and verified. Major benefit of the formulation proposed here is that it provides accurate results without necessitating any other calculations.

A Novel Expression in Calculating Resonant Frequency of H–Shaped Compact Microstrip Antennas Obtained by using Artificial Bee Colony Algorithm

In this paper, a new, simple and accurate expression for the resonant length in calculating resonant frequency of H–shaped compact microstrip antennas at UHF frequencies applications is presented. H–shaped compact microstrip antennas with a range of different physical dimensions built on various substrates are numerically simulated with a software package based on finitedifference time domain method. A closed-form expression for the resonant length is constructed with the aid of both the artificial bee colony algorithm and the simulation data. The resonant frequency is then calculated by means of this resonant length expression. The average percentage error is found to be less than 0.79% for 216 simulated antennas. Experimental and simulated results reported elsewhere have been employed to illustrate the validity and accuracy of the present formulation.

An Application of Artificial Neural Network to Compute the Resonant Frequency of E–Shaped Compact Microstrip Antennas

Abstract An application of artificial neural network (ANN) based on multilayer perceptrons (MLP) to compute the resonant frequency of E-shaped compact microstrip antennas (ECMAs) is presented in this paper. The resonant frequencies of 144 ECMAs with different dimensions and electrical parameters were firstly determined by using IE3D(tm) software based on the method of moments (MoM), then the ANN model for computing the resonant frequency was built by considering the simulation data. The parameters and respective resonant frequency values of 130 simulated ECMAs were employed for training and the remaining 14 ECMAs were used for testing the model. The computed resonant frequencies for training and testing by ANN were obtained with the average percentage errors (APE) of 0.257% and 0.523%, respectively. The validity and accuracy of the present approach was verified on the measurement results of an ECMA fabricated in this study. Furthermore, the effects of the slots loading method over the resonant frequency were investigated to explain the relationship between the slots and resonant frequency.

Design of wideband orthogonal MIMO antenna with improved correlation using a parasitic element for mobile handsets

In this paper, a novel design of compact wideband multiple-input multiple-output (MIMO) antenna operating over a frequency range of 1.8–4.0 GHz at 10 dB is presented for mobile terminals. The MIMO antenna design consists of two symmetrical and orthogonal radiating elements with a small size of 15.5 × 16.5 mm 2 printed on the corners of a mobile circuit board. The radiating element is composed of four meandered monopole branches with a strip-line fed by a probe. By triangularly trimming the corners of the common ground plane beneath the radiating elements, not only the mutual coupling is reduced, but also impedance bandwidth is increased. Although, the antenna in this form has sufficient correlation level between the radiating elements for MIMO operation, a novel design of plus-shaped parasitic element is inserted to the ground plane between those radiating elements in order to further enhance the isolation. The performance of the MIMO antenna is investigated in terms of s-parameters, radiation pattern, gain, envelope correlation coefficient (ECC), and total active reflection coefficient (TARC), and is verified through the measurements. The results demonstrate that the proposed MIMO antenna has good characteristics of wideband, isolation, gain, radiation pattern, and is compatible with LTE, WiMAX, and WLAN, besides it is small, compact, and embeddable in mobile terminals.

A novel and simple expression to accurately calculate the resonant frequency of annular-ring microstrip antennas

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 (TM 11 ). 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.

Computer vision-based method for classification of wheat grains using artificial neural network

BACKGROUND A simplified computer vision-based application using artificial neural network (ANN) depending on multilayer perceptron (MLP) for accurately classifying wheat grains into bread or durum is presented. The images of 100 bread and 100 durum wheat grains are taken via a high-resolution camera and subjected to pre-processing. The main visual features of four dimensions, three colors and five textures are acquired using image-processing techniques (IPTs). A total of 21 visual features are reproduced from the 12 main features to diversify the input population for training and testing the ANN model. The data sets of visual features are considered as input parameters of the ANN model. The ANN with four different input data subsets is modelled to classify the wheat grains into bread or durum. The ANN model is trained with 180 grains and its accuracy tested with 20 grains from a total of 200 wheat grains. RESULTS Seven input parameters that are most effective on the classifying results are determined using the correlation-based CfsSubsetEval algorithm to simplify the ANN model. The results of the ANN model are compared in terms of accuracy rate. The best result is achieved with a mean absolute error (MAE) of 9.8 × 10-6 by the simplified ANN model. CONCLUSION This shows that the proposed classifier based on computer vision can be successfully exploited to automatically classify a variety of grains.

Grain classifier with computer vision using adaptive neuro-fuzzy inference system: Grain classifier with computer vision using ANFIS

BACKGROUND A computer vision-based classifier using an adaptive neuro-fuzzy inference system (ANFIS) is designed for classifying wheat grains into bread or durum. To train and test the classifier, images of 200 wheat grains (100 for bread and 100 for durum) are taken by a high-resolution camera. Visual feature data of the grains related to dimension (#4), color (#3) and texture (#5) as inputs of the classifier are mainly acquired for each grain using image processing techniques (IPTs). In addition to these main data, nine features are reproduced from the main features to ensure a varied population. Thus four sub-sets including categorized features of reproduced data are constituted to examine their effects on the classification. In order to simplify the classifier, the most effective visual features on the results are investigated. RESULTS The data sets are compared with each other regarding classification accuracy. A simplified classifier having seven selected features is achieved with the best results. In the testing process, the simplified classifier computes the output with 99.46% accuracy and assorts the wheat grains with 100% accuracy. CONCLUSION A system which classifies wheat grains with higher accuracy is designed. The proposed classifier integrated to industrial applications can automatically classify a variety of wheat grains.

CS-based radar measurement of silos level

The amount of the grain in bulk silos is the most important issue in commercial care. Therefore many level measurement methods have been used to measure the level of solids in silos. Existing methods, however, are generally based on one-point measurement which makes the three dimensional (3D) level measurement impractical. Microwave radar based systems can be used to 3D perception but the multiple scatterings occurred from metallic walls of the silo, makes it impossible. In this study we present the preliminary results of our compressive sensing based reconstruction algorithm to enhance backscattering signals inside a grain silo. The method proposed here eliminates the effect of multiple scattering form silo wall and gives the accurate reading of the grain level. The effectiveness of the recommend CS-based reconstruction method, which will be able to extend to 3D level perception, was verified through a real data of bulk silo.

ANFIS model for determining resonant frequency of rectangular ring compact microstrip antennas

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.

Millimetre wave isar imaging technique based on sparse aperture data collection

The millimetre wave (MW) applications has become very popular in recent years due to the high-resolution requirement in inverse synthetic aperture radar (ISAR) imaging. The most important problem encountered in MW imaging method is the high data collection requirement. Compressed sensing (CS) is often used in MW applications because it allows processing of signals with a sampling number below the Nyquist rate. However, since existing techniques used in CS take random samples from all spatial-frequency ISAR data, too many data collection probes are needed. In this study, CS based ISAR image is reconstructed by taking random samples from only synthetic aperture data instead of all spatial-frequency ISAR data. So, this type of data collection mechanism offers a much more practical application area for CS based ISAR imaging. The proposed method was verified by simulation results and the quality of the images were evaluated calculating ISLR.