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@eyyuptemlioglu
Turkey旗标 Istanbul, Turkey
会员,2016年9月1日加入
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eyyuptemlioglu

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I received B.S. and M.S degrees from the Department of Electronics and Communication Engineering, Istanbul Technical University, Istanbul, Turkey in 2013 and 2015, respectively. Now, I'm PhD student in Istanbul Technical University. I worked more than 3 years in R&D projects. My research areas : *** Signal processing *** Image processing *** Computer vision *** Pattern recognition *** Machine learning *** Deep learning Programming languages : *** MATLAB *** C/C++ *** Python Libraries : *** OpenCV *** Dlib Framework : *** Keras *** Caffe GUI design : *** Qt *** MATLAB Integrated development environments (IDE) : *** Qt *** Visual Studio *** MATLAB *** IPython Operating systems : *** Windows *** Linux (Ubuntu)
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经验

R&D engineer

Feb 2013 - Sep 2016 (3 years)

R&D engineer

教育

BS

2008 - 2013 (5 years)

MS

2013 - 2015 (2 years)

PhD

2016 - 2017 (1 year)

刊物

Comparison of feature extraction methods for landmine detection using Ground Penetrating Radar

HOG, SIFT, SURF, BRIEF, EHD Receiver Operating Characteristic (ROC) curves are calculated for comparison of methods.

Comparative analysis of short and long GPR pulses for landmine detection

Comparative analysis of short and long GPR pulses for landmine detection

Sparse representations based clutter removal in GPR images

PCA, SVD, FAST-ICA, JADE-ICA, MCA

Clutter Removal in Ground-Penetrating Radar Images Using Morphological Component Analysis

Singular Value Decomposition (SVD), Principal Component Analysis (PCA) and Independent Component Analysis (ICA) are commonly used for clutter removal. They all aim to decompose the GPR images into subcomponents that represent the clutter and the target separately. In this letter, we propose a sparse model for differentiating the target and the clutter using appropriate dictionaries based on Morphological Component Analysis (MCA).

Histograms of Dominant Orientations for anti-personnel landmine detection using GPR

Histograms of Dominant Orientations (HDO) feature extraction method is implemented for landmine detection problem.

A least mean square approach to buried object detection in ground penetrating radar

In this study, Least Mean Square (LMS) approach is used to solve buried object detection problem. The proposed approach is tested with a realistic data set simulated by using a new version of gprMax electromagnetic modeling software. The data set consists of several different soil types, objects, different burial depths and surface types. Resulting Receiver Operating Characteristic (ROC) curves demonstrate the performance of the proposed method.