Nazlıbilek, Sedat

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https://hdl.handle.net/20.500.14411/8361
Name Variants
S.,Nazlibilek
N., Sedat
Nazlıbilek,S.
Sedat, Nazlıbilek
N.,Sedat
Nazlibilek,S.
S., Nazlibilek
S.,Nazlıbilek
Sedat, Nazlibilek
Nazlıbilek, Sedat
Nazlibilek, Sedat
Job Title
Doçent Doktor
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Main Affiliation
Department of Mechatronics Engineering
Status
Former Staff
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Scopus Author ID
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WoS Researcher ID

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NO POVERTY1
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GOOD HEALTH AND WELL-BEING3
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1
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QUALITY EDUCATION4
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CLEAN WATER AND SANITATION6
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AFFORDABLE AND CLEAN ENERGY7
AFFORDABLE AND CLEAN ENERGY
1
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This researcher does not have a Scopus ID.
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Scholarly Output

22

Articles

14

Views / Downloads

41/84

Supervised MSc Theses

3

Supervised PhD Theses

3

WoS Citation Count

245

Scopus Citation Count

303

Patents

0

Projects

0

WoS Citations per Publication

11.14

Scopus Citations per Publication

13.77

Open Access Source

1

Supervised Theses

6

JournalCount
Measurement7
Indian Journal of Pure and Applied Physics2
IEEE Transactions on Instrumentation and Measurement2
20th Annual International Conference on Mechatronics and Machine Vision in Practice, M2VIP 2013 -- 20th Annual International Conference on Mechatronics and Machine Vision in Practice, M2VIP 2013 -- 20 September 2013 through 20 September 2013 -- Ankara -- 1022761
International Conference of Control, Dynamic Systems, and Robotics -- 4th International Conference of Control, Dynamic Systems, and Robotics, CDSR 2017 -- 21 August 2017 through 23 August 2017 -- Toronto -- 1399181
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Has files: falseSubject: Binary robust invariant scalable keypoint (BRISK)

Scholarly Output Search Results

Now showing 1 - 1 of 1
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    Article
    Citation - WoS: 5
    Citation - Scopus: 5
    A Study on the Performance of Magnetic Material Identification System by Sift-Brisk and Neural Network Methods
    (Ieee-inst Electrical Electronics Engineers inc, 2015) Ege, Yavuz; Nazlibilek, Sedat; Kakilli, Adnan; Citak, Hakan; Kalender, Osman; Karacor, Deniz; Sengul, Gokhan
    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 Earth's 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.