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Publication Index: WoSSubject: Algorithm

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    Article
    Citation - WoS: 7
    Citation - Scopus: 15
    Lossless Text Compression Technique Using Syllable Based Morphology
    (Zarka Private Univ, 2011) Akman, Ibrahim; Bayindir, Hakan; Ozleme, Serkan; Akin, Zehra; Misra, Sanjay; Computer Engineering
    In this paper, we present a new lossless text compression technique which utilizes syllable-based morphology of multi-syllabic languages. The proposed algorithm is designed to partition words into its syllables and then to produce their shorter bit representations for compression. The method has six main components namely source file, filtering unit, syllable unit, compression unit, dictionary file and target file. The number of bits in coding syllables depends on the number of entries in the dictionary file. The proposed algorithm is implemented and tested using 20 different texts of different lengths collected from different fields. The results indicated a compression of up to 43%.
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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.