Abstract-Detecting an on-the-ground object is a subject of interest for use in some applications. Foreign Object Detection (FOD), which is an important issue in aviation safety, is a possible application. In this way, radar imaging, has several inherent advantages over other on-the-ground object detection techniques. This paper will introduce a ground-based Circular Synthetic Aperture Radar, which detects and localizes various objects, based on their reflection properties of microwaves. Here, wideband Linear Frequency Modulated (LFM) chirp pulses are employed for the transmission and reception of reflection pulses, both to and from the object under test. Once the pulses are received by the radar, a processing algorithm (proposed later in this paper) is executed to confirm detection. In order to verify the validity of the model, a prototype was developed and a series of field experiments was carried out. The results show that the proposed system has the ability to detect and localize on-the-ground objects with dimensions as small as 2 cm high and 1 cm diameter, located several metres away. Furthermore, the resolution of the system was analysed and results indicate that the system is capable of distinguishing multiple objects in close proximity to each other, which therefore, makes it suitable for FOD applications by some small modifications.

Grapevine Fanleaf Virus (GFLV) is one of the most important viral diseases of grapes, which can damage up to 85% of the crop, if not treated at the right time. The aim of this study is to identify infected leaves with GFLV using artificial intelligent methods using an accessible database. To do this, some pictures are taken from infected and healthy leaves of grapes and labeled by technical specialists using conventional laboratory methods. In order to provide an intelligent method for distinguishing infected leaves from healthy ones, the area of unhealthy parts of each leaf is highlighted using Fuzzy C-mean Algorithm (FCM), and then the percentages of the first two segments area are fed to a Support Vector Machines (SVM). To increase the diagnostic reliability of the system, K-fold cross validation method with k = 3 and k =5 is applied. After applying the proposed method over all images using K-fold validation technique, average confusion matrix is extracted to show the True Positive, True Negative, False Positive and False Negative percentages of classification. The results show that specificity, as the ability of the algorithm to really detect healthy images, is 100%, and sensitivity, as the ability of the algorithm to correctly detect infected images is around 97.3%. The average accuracy of the system is around 98.6%. The results imply the ability of the proposed method compared to previous methods.

In this research an S-N doped Fe2O3 nanostructure is synthesized and its adsorption ability and photocatalytic activity were evaluated. The optimum experimental conditions were obtained and an ANN-GA model was proposed for predicting experimental values.

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Background: Magnetic resonance imaging (MRI) plays an important role in clinical diagnosis. The ability of fuzzy c-mean (FCM) algorithm in segmenting MR images has been proven. Some MR images are contaminated with noise. FCM performance is degraded in noisy images. Several efforts are done to overcome this weakness. Objectives: The aim of this study was to propose a new method for MR image segmentation which is more resistant than other methods when noisy MR images are confronted. Materials and Methods: In this study, simulated brain database prepared by BrainWeb was be used for analysis. First FCM and its improvements were analysed and their ability in segmenting noisy MR images were evaluated. Next, knowing that applying genetic algorithm on improver fuzzy c-mean (IFCM) could improve its performance, a new segmentation method was proposed by applying particle swarm optimization on IFCM. Results: The proposed algorithm was applied on some intentionally noise-added MR images. Similarity between the segmented image and the original one was measured using Dice index. Other off-the-shelf algorithms were also tested in the same conditions. The indices were presented together. In order to compare the algorithms' performances, the experiments were repeated using different noisy images. Conclusion: The obtained results show that the proposed algorithms have better performance in segmenting noisy MR images than existing methods.