Browsing by Author "Kakilli, Adnan"
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Article Citation Count: 8A Magnetic Measurement System and Identification Method for Buried Magnetic Materials Within Wet and Dry Soils(Ieee-inst Electrical Electronics Engineers inc, 2016) Nazlıbilek, Sedat; Ertürk, Korhan Levent; Kakilli, Adnan; Citak, Hakan; Kalender, Osman; Erturk, Korhan Levent; Karacor, Deniz; Information Systems Engineering; Department of Mechatronics EngineeringIn this paper, a new magnetic measurement system is developed to determine upper surfaces of buried magnetic materials, particularly land mines. This measurement system uses the magnetic-anomaly-detection method. It also has intelligent identification software based on an image matching algorithm. It is aimed to determine and identify the buried ferromagnetic materials with minimum energy consumption. It is concentrated on the detection and identification of the shapes of upper surfaces of buried magnetic materials in dry and wet conditions. The effect of humidity in the detection process for detection is tested. In this paper, we used sensor images to identify various ferromagnetic materials and similar objects. Sensor images of soils at various humidities covering the objects were obtained. We used the speeded-up-feature-transform algorithm in the comparison process of the images. Dry soil sample images match with the corresponding wet soil samples with the highest matching rate. The images for different objects can easily be distinguished by the matching process.Article Citation Count: 2New magnetic measurement system for determining metal covered mines by detecting magnetic anomaly using a sensor network(Natl inst Science Communication-niscair, 2015) Nazlıbilek, Sedat; Kakilli, Adnan; Citak, Hakan; Kalender, Osman; Nazlibilek, Sedat; Sensoy, Mehmet Gokhan; Department of Mechatronics EngineeringThe most commonly used remote sensing methods are used in such applications as the aquistic emmission, ground penetration radar (GPR) detection, electromagnetic induction spectroscopy, infrared imaging, thermal neutron activation, nuclear quadruple resonance, X-ray back scattering, neutron back scattering and magnetic anomaly detection. In deciding which type of method has to be used for detection, the variables such as the type of object, material used, position, geographical and environmental conditions, etc. play important roles. In recent years, studies are mainly concentrated on the improvement of detection distance, accuracy, power consumption aspects of remote sensing methods. In the present study, the same concerns are taken into account and a new magnetic measurement system is developed in this context. The system is made up of a sensor network consisting of high sensitive and low power anisotropic magneto-resistive KMZ51 sensors. The sensor network can detect the magnetic anomalies of vertical component of earth's magnetic field created by buried objects as metal covered mines. In the present paper, the effects of physical properties of metal covered materials to magnetic anomalies have been studied. The sensor network is composed of 24 sensors. The voltage levels of each sensor are measured one-by one and transferred to a digital computer where the distribution of the voltages in x-y plane is plotted as 3D graphics. Furthermore, the performance of the system on the detection of buried metallic mines and determination of their shapes have been investigated.Article Citation Count: 5A Study on the Performance of Magnetic Material Identification System by SIFT-BRISK and Neural Network Methods(Ieee-inst Electrical Electronics Engineers inc, 2015) Şengül, Gökhan; Nazlibilek, Sedat; Nazlıbilek, Sedat; Citak, Hakan; Kalender, Osman; Karacor, Deniz; Sengul, Gokhan; Computer Engineering; Department of Mechatronics EngineeringIndustry requires low-cost, low-power consumption, and autonomous remote sensing systems for detecting and identifying magnetic materials. Magnetic anomaly detection is one of the methods that meet these requirements. This paper aims to detect and identify magnetic materials by the use of magnetic anomalies of the Earth's magnetic field created by some buried materials. A new measurement system that can determine the images of the upper surfaces of buried magnetic materials is developed. The system consists of a platform whose position is automatically controlled in x-axis and y-axis and a KMZ51 anisotropic magneto-resistive sensor assembly with 24 sensors mounted on the platform. A new identification system based on scale-invariant feature transform (SIFT)-binary robust invariant scalable keypoints (BRISKs) as keypoint and descriptor, respectively, is developed for identification by matching the similar images of magnetic anomalies. The results are compared by the conventional principal component analysis and neural net algorithms. On the six selected samples and the combinations of these samples, 100% correct classification rates were obtained.