The design of multilayer radar absorber (MRA) has become quite popular in the last quarter century, and many studies have been presented to optimize different MRAs. In these studies, the number of layers (NL) has been predetermined, and thicknesses of the layers have been defined in the same parametric sequence. However, according to the type of objective function, the NL and thicknesses may vary, so this initial predetermination may prevent the discovery of the best overall solution. In this study, a double-stage artificial bee colony (DS-ABC) algorithm which evaluates the number and sequence of layers separately from thicknesses is proposed for the optimum design of MRA. Thus, the sequence and thickness of the MRAs are optimized to minimize the reflection coefficient and the total thickness for the desired frequency ranges (2-18 GHz), angles of incidence (0°-40°), and polarizations [transverse electric (TE)-transverse magnetic (TM)]. For the MRA designs, 16 predefined materials and recently published 21 up-to-date materials are used. The designed MRAs are compared with those optimized by other algorithms existing in the literature. The results show that the DS-ABC algorithm is more effective than other conventional metaheuristic search algorithms to find the optimal layer sequence and corresponding thicknesses.

The measurement of the moisture content of the stored grain in the silos provides the opportunity to take the necessary precautions to store the grain without spoiling. Since it is not possible to obtain all the moisture information of the stored grain with the current methods, in this study, a new method is proposed to determine the moisture content of the grain in real time during the loading processes. For this purpose, popular machine learning (ML) algorithms, i.e., KNN, SVR, and ANN, are used to predict the moisture content of the flowing grain. In order to measure the moisture content of the grain, a free-space electromagnetic measurement setup is constructed. Reflection and transmission coefficients are measured at 103 different frequency points between 1 and 2.48 GHz using a vector network analyzer (VNA) for three different grain types (Bulgur wheat, durum wheat, and corn silage kernel) with moisture content varying between 8% and 25%. In this way, three datasets are constituted as datasets 1–3 corresponding to each grain type. The $k$ -fold cross-validation ( $k$ -CV) technique is used to train and test the ML algorithms and the performance of the algorithms is evaluated with five different metrics. In addition, for each grain type, the error rates corresponding to each moisture content are evaluated separately and the relationship between moisture content and performance of algorithms is revealed. While the best results are obtained with KNN for durum wheat and corn silage kernel, SVR method gives the best results for bulgur wheat. This study reveals that the moisture content of flowing grain can be determined, thanks to proper modeling of ML algorithms and measurement setup.

In this study, the fusion of optical and synthetic aperture radar images with wavelet transform was investigated. Images are obtained from Sentinel-1 and sentinel-2 satellites. Images were decomposed by wavelet transform. The four main coefficients were obtained for different wavelet packages and up to ten decomposition levels. The coefficients were combined taking the maximum, minimum or mean. 1710 Fused images were obtained for all possible combinations in terms of different wavelet packets, decomposition levels and fusion rules. Fused images were evaluated according to the structural similarity index (SSI). It was seen that the missing regions in the optical images were improved in the fused images with the appropriate wavelet packets and highest SSI.

Since the grain is a crucial food source, the determination of the quantity of stored grain in silos is inevitable in terms of commercial and correct inventory planning. In this study, a convolutional neural network (CNN) is developed to determine grain quantity using the spectrograms of the radar backscattering data. The radar backscattering signals of different amounts of grain for different grain surface condition types are collected using a stepped frequency continuous-wave radar system. In the scaled model silo, a total of 5681 measurements are carried out for grain stacks with different surface patterns and different weights (0-20 kg). Then, the dataset is constituted by using the spectrograms of these radar measurements. Randomly selected 4261 data corresponding to 75% of the dataset are used for training and the remaining 1420 data are used for testing. The proposed method is compared with pretrained CNN. Accuracy of the methods is given with metric parameters for both classification and regression. The classification task results of the proposed method are obtained as 98.45% accuracy, 98.15% sensitivity, 99.07% specitivity, 98.77% precision, 98.45% F1-Score, and 97.62% Matthews correlation coefficient. The regression task results are calculated as 0.3228 mean absolute error, 0.5150 mean absolute percentage error (MAPE), 0.9649 mean squared error, and 0.9823 root-mean-squared error. The proposed method is also compared with previous studies in the literature (with 3.29 MAPE) and its superiority is demonstrated with metric parameters. The results point out that, if CNN is properly modeled and trained, the combination of CNN and proper signal processing can provide effective results in the quantity measurement applications of the grain stacks.

Main purpose of the design of multi-layer radar absorber (MRA) by means of metaheuristic optimization algorithms is to minimize both the total thickness (TT) of MRA and the maximum reflection coefficients for TE (RTE) & TM (RTM) polarizations at any oblique angle of incidence. For this purpose, sequence and thicknesses of layers of the MRA have been optimized by either single-objective approach based on combining all objectives or double-objective approach in which TT is evaluated separately from the reflection coefficients. In this study, triple-objective artificial bee colony (TO-ABC) algorithm integrated with Pareto front technique is proposed for fully optimized MRA design. Thus, both RTE, RTM and TT are simultaneously minimized by optimizing thickness, sequence and number of the layers. To demonstrate the superiority of TO-ABC, 3 types of MRAs operating at the frequency ranges of 2–18 GHz for each angle of incidence from 0⁰ to 60⁰ are optimized and compared with the literature. Furthermore, 4 different real MRAs are also optimized using real materials given in the literature. Thanks to the developed graphical user interface and TO-ABC algorithm, despite the limited number of materials, all possible solutions providing the specified parameters are easily achieved and successful MRA structures are designed.