Sedat Nazlibilek

Mine Identification and Classification by Mobile Sensor Network Using Magnetic Anomaly

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.

Numerical Analysis for Remote Identification of Materials With Magnetic Characteristics

There is a variety of methods used for remote sensing of objects such as acoustic, ground penetration radar detection, electromagnetic induction spectroscopy, infrared imaging, thermal neutron activation, core four-pole resonance, neutron backscattering, X-ray backscattering, and magnetic anomaly. The method that has to be used can be determined by the type of material, geographical location (underground or water), etc. Recent studies have been concentrated on the improvement of the criteria such as sensing distances, accuracy, and power consumption. In this paper, anomalies created by materials with magnetic characteristics at the perpendicular component of the Earth magnetic field have been detected by using a KMZ51 anisotropic magnetoresistive sensor with high sensitivity and low power consumption, and also, the effects of physical properties of materials on magnetic anomaly have been investigated. By analyzing the graphics obtained by 2-D motion of the sensor over the material, the most appropriate mathematical curves and formulas have been determined. Based on the physical properties of the magnetic material, the variations of the variables constituting the formulas of the curves have been analyzed. The contribution of this paper is the use of the results of these analyses for the purpose of identification of an unknown magnetic material. This is a new approach for the detection and determination of materials with magnetic characteristics by sensing the variation at the perpendicular component of the Earth magnetic field. The identification process has been explained in detail in this paper.

Electromagnetic Stirrer Operating in Double Axis

In this paper, a new rotating magnetic stirrer system that is controlled by a programmable-integrated-circuit (PIC) microcontroller and can stir in double axis is developed. In contrast to making a stirring action only at one point, as in the case of traditional electromagnetic stirrers, the system that is developed can rotate at two separate axes. One of the rotations is around the axis of the magnetic stir bar itself, and the other is over a circle defined by a rotating magnetic field. That is, the stirrer makes two rotational motions. This is the main contribution of this paper. The magnetic stirrer system is designed as a three-phase system, and a sinusoidal ramp signal is applied to the phases as the supply voltage. During the design stage, the mathematical model of the system was obtained, and the parameters affecting the design were determined. Based on these parameters, a parameter set was established. This parameter set can be used for subsequent design studies of the system. A PIC-based control circuit is used to control the frequency of the supply voltage. The structure of the double-rotating electromagnetic stirring system is explained. The physical conditions affecting the double-axis rotational motion of the magnetic stir bar are discussed in detail. It was observed that a more homogeneous stirring process could be achieved with this kind of double-axis rotation.

A Study on the Performance of Magnetic Material Identification System by SIFT–BRISK and Neural Network Methods

Industry 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 Earths 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.

A Magnetic Measurement System and Identification Method for Buried Magnetic Materials Within Wet and Dry Soils

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.

Discrete Lissajous Figures and Applications

In this paper, an innovative method based on an algorithm utilizing discrete convolutions of discrete-time functions is developed to obtain and represent discrete Lissajous and recton functions. They are actually discrete auto- and cross-correlation functions. The theory of discrete Lissajous figures is developed. The concept of rectons is introduced. The relation between the discrete Lissajous figures and autocorrelation functions is set. Some applications are described including phase, frequency, and period determination of periodic signals, time-domain characteristics (such as damping ratio) of a control system, and abnormality and spike detection within a signal, are described. In addition, an electrocardiogram signal with an abnormality of atrial fibrillation is given for abnormality detection by means of recton functions. An epileptic activity detection within an electroencephalography signal is also given.