E

Egemen HALICI

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Software Developer at Mercury
Ankara, Türkiye

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Résumé

Jobs verified_user 0% verified
  • Stm
    Senior Software Developer
    Stm
    Oct 2024 - Current (2 years)
  • Mercury
    Software Developer
    Mercury
    Sep 2022 - Oct 2024 (2 years 2 months)
  • Thundra
    Software Developer
    Thundra
    Apr 2021 - Aug 2022 (1 year 5 months)
  • Tarım Kredi Technology
    Software Development Team Lead
    Tarım Kredi Technology
    Dec 2019 - Apr 2021 (1 year 5 months)
  • OPLOG
    Yazılım Geliştirici
    OPLOG
    Nov 2016 - Dec 2019 (3 years 2 months)
  • EŞKOM BİLGİSAYAR SAN TİC AŞ
    Software Developer
    EŞKOM BİLGİSAYAR SAN TİC AŞ
    Jun 2013 - Nov 2016 (3 years 6 months)
    To develop a software for municipality services.
Education verified_user 0% verified
  • Ankara University
    Doctor of Philosophy - PhD, Computer Engineering
    Ankara University
    Jan 2019 - Dec 2025 (7 years)
  • Ankara University
    Master's degree, Computer Engineering
    Ankara University
    Jan 2015 - Dec 2018 (4 years)
  • Kirikkale Üniversitesi
    Statistic
    Kirikkale Üniversitesi
    Jan 2009 - Dec 2014 (6 years)
    My majority was statistic when I was in University.We were doing analysis with collected data.
  • M
    High School, Science
    Mustafa Hakan Güvençer Anadolu Lisesi
    Jan 2009 - Dec 2013 (5 years)
    High School
Projects (professional or personal) verified_user 0% verified
  • B
    BiyoBanka
    Sep 2014 - Current (12 years 1 month)
    Biobank Automation system is built by me and Can Toker. We had developed this software for Dokuz Eylül University Hospital (Oncology Department). This software included 10.000+ cryo Bottle for our automation.
Publications verified_user 0% verified
  • I
    Evaluating the use of Interpolation Methods for Human Body Motion Modelling
    IJCTE Vol ISSN
    Mar 2018
    This paper proposes the use of interpolation methods rather that conventional learning algorithms such as Support Vector Machines (SVM) or Policy Learning by Weight Exploration with Return (POWER) for modelling human motion. The main aim was using a simpler model with less time and space complexity for later use in the recognition of certain actions. Three different polynomial interpolation methods, namely Lagrange, spline and cubic spline have been investigated. Parts of the dataset were used instead of the complete dataset using grouping techniques to reduce the training time. A non-parametric test known as Mc-Nemar's test was used to identify statistically significant performance differences between these methods. It was found that the c
  • I
    Analysis of Training Performances of Interpolation Methods for Modelling Human Body Motion
    ICAA
    Jul 2016
  • IEEE
    Modelling human body motion using lagrange interpolation
    IEEE
    Jun 2016
    This study presents research on modelling the movement of human limbs using dynamic movement primitives and recovering the motion from these models. The displacements data for the body joints were used to create minimum error Lagrange polynomials for the time input parameter. Considering the computational load when the complete dataset was used, interval and group parameters were required. These parameters were chosen as the values resulting in minimum mean square and mean absolute errors. Results show that positional information for a given time can be efficiently retrieved for a specific joint by choosing the polynomial group belonging to the joint.