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Author: Kalender, OsmanScopus Q: Q1

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    Article
    Citation - WoS: 11
    Citation - Scopus: 12
    A Magnetic Measurement System and Identification Method for Buried Magnetic Materials Within Wet and Dry Soils
    (Ieee-inst Electrical Electronics Engineers inc, 2016) Ege, Yavuz; Nazlibilek, Sedat; Kakilli, Adnan; Citak, Hakan; Kalender, Osman; Erturk, Korhan Levent; Karacor, Deniz
    In 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.
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    Article
    Citation - WoS: 21
    Citation - Scopus: 22
    Mine Identification and Classification by Mobile Sensor Network Using Magnetic Anomaly
    (Ieee-inst Electrical Electronics Engineers inc, 2011) Nazlibilek, Sedat; Kalender, Osman; Ege, Yavuz
    In this paper, a new method is proposed to identify and classify the data obtained by the sensor network (SN) for the detection of mines. This method is used for the identification of antitank and antipersonnel mines and classification of buried objects within a target region. In this paper, a mobile SN is used to detect mines and some other objects buried and creating magnetic anomaly in and around the region where they are found, with the behavior of the individual sensors swarming onto the area under which a mine or any other object is buried. The process of collecting data by the SN and modeling it mathematically are explained in detail. The SN is modeled as a fictitious two-dimensional spatial impulse sampler. This paper is motivated by clearing the territories of mine fields to open them to agriculture. It is very important because, currently, in some countries, very fertile territories around the borders are covered by buried mines. The approach is basically based on magnetic anomaly measurements, which directly tackles the subregions corresponding to buried objects whether they represent objects that are separately located or occluded by other objects. It is based on a new developed method that is called "the back-most object detection and identification algorithm." This method is fully automatic, and there is no human intervention throughout the process. In this paper, classification of objects is based on their well-known shapes and dimensions. Therefore, there is no need for sophisticated learning algorithms to achieve classification. The experimental results are given both for detection and identification of a single mine and classification of a number of mines and any other objects that have a potential of giving false alarms in a target region.