Güneş, Ahmet

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https://hdl.handle.net/20.500.14411/7663
Name Variants
A.,Gunes
Gunes, Ahmet
G., Ahmet
A., Gunes
Ahmet, Gunes
G.,Ahmet
Gunes,A.
Güneş, Ahmet
Güneş,A.
A.,Güneş
Ahmet, Güneş
Job Title
Doktor Öğretim Üyesi
Email Address
ahmet.gunes@atilim.edu.tr
Main Affiliation
Department of Mechatronics Engineering
Status
Former Staff
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14

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Scholarly Output

12

Articles

3

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35/78

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1

WoS Citation Count

40

Scopus Citation Count

53

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WoS Citations per Publication

3.33

Scopus Citations per Publication

4.42

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JournalCount
Applied Sciences2
26th IEEE Signal Processing and Communications Applications Conference (SIU) -- MAY 02-05, 2018 -- Izmir, TURKEY2
26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 -- 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 -- 2 May 2018 through 5 May 2018 -- Izmir -- 1377802
IEEE Microwave and Wireless Components Letters1
International Geoscience and Remote Sensing Symposium (IGARSS) -- 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 -- 28 July 2019 through 2 August 2019 -- Yokohama -- 1547921
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Now showing 1 - 10 of 12
  • Thumbnail Image
    Conference Object
    Multiple Underwater Target Bearing Tracking Using Member Filter
    (Ieee, 2018) Gunes, Ahmet; Guldogan, Mehmet B.
    Underwater acoustic vector sensors (AVS) are devices which can measure scalar pressure and three dimensional acceleration or particle velocity with only one sensor. By using these four measurements, target detection and tracking is possible. In case multiple targets exist, multi-target detection and tracking methods must be applied. Because these methods are more general, the algorithms are more involved and complex. In this framework, multi-target multi-Bernoulli (MeMBer) is a promising filter based on random finite sets (RFS) for multi-target tracking problems. In this work, for the first time in the literature, MeMBer filter is analyzed using a single underwater acoustic vector sensor in a scenario including two targets. Simulation results indicate that MeMBer filter can successfully track the targets.
  • Thumbnail Image
    Article
    Citation - WoS: 25
    Citation - Scopus: 34
    Tobset: a New Tobacco Crop and Weeds Image Dataset and Its Utilization for Vision-Based Spraying by Agricultural Robots
    (Mdpi, 2022) Alam, Muhammad Shahab; Khan, Muhammad Umer; Alam, Mansoor; Tufail, Muhammad; Güneş, Ahmet; Khan, Muhammad Umer; Gunes, Ahmet; Salah, Bashir; Khan, Muhammad Tahir; Khan, Muhammad Umer; Güneş, Ahmet; Mechatronics Engineering; Department of Mechatronics Engineering; Mechatronics Engineering; Department of Mechatronics Engineering
    Selective agrochemical spraying is a highly intricate task in precision agriculture. It requires spraying equipment to distinguish between crop (plants) and weeds and perform spray operations in real-time accordingly. The study presented in this paper entails the development of two convolutional neural networks (CNNs)-based vision frameworks, i.e., Faster R-CNN and YOLOv5, for the detection and classification of tobacco crops/weeds in real time. An essential requirement for CNN is to pre-train it well on a large dataset to distinguish crops from weeds, lately the same trained network can be utilized in real fields. We present an open access image dataset (TobSet) of tobacco plants and weeds acquired from local fields at different growth stages and varying lighting conditions. The TobSet comprises 7000 images of tobacco plants and 1000 images of weeds and bare soil, taken manually with digital cameras periodically over two months. Both vision frameworks are trained and then tested using this dataset. The Faster R-CNN-based vision framework manifested supremacy over the YOLOv5-based vision framework in terms of accuracy and robustness, whereas the YOLOv5-based vision framework demonstrated faster inference. Experimental evaluation of the system is performed in tobacco fields via a four-wheeled mobile robot sprayer controlled using a computer equipped with NVIDIA GTX 1650 GPU. The results demonstrate that Faster R-CNN and YOLOv5-based vision systems can analyze plants at 10 and 16 frames per second (fps) with a classification accuracy of 98% and 94%, respectively. Moreover, the precise smart application of pesticides with the proposed system offered a 52% reduction in pesticide usage by spotting the targets only, i.e., tobacco plants.
  • Thumbnail Image
    Conference Object
    Shape Recognition With Low Cost Sensors
    (Ieee, 2018) Saloglu, Keziban; Hosafci, Arda; Birbilen, Merve; Bulut, Yigit A.; Gunes, Ahmet
    This paper proposes a method to recognize the shape of some objects that have different geometrical properties using an infra-red sensor.To that end, a mechanism that has two degrees of freedom is designed. Scanning of the different objects are obtained. Noise on scanning output is removed. Finally, all the outputs for different objects are discussed to obtain the specifications to do shape recognition.
  • Thumbnail Image
    Article
    Citation - WoS: 3
    Citation - Scopus: 3
    Pseudospectral Time Domain Method Implementation Using Finite Difference Time Stepping
    (Ieee-inst Electrical Electronics Engineers inc, 2018) Gunes, Ahmet; Aksoy, Serkan
    Lagrange interpolation polynomials-based Cheby-shev pseudospectral time domain (CPSTD) method is an efficient time domain solver for Maxwell equations. Although it has the lowest interpolation error among pseudospectral time domain methods, time derivatives must be calculated using higher order time derivative schemes, such as the Runge-Kutta method. The higher order time derivative methods slow down the computation speed at each step by several folds. In this letter, we show that central finite differences can be used for implementation of time derivatives in CPSTD method. Results are verified by a resonator problem.
  • Thumbnail Image
    Conference Object
    Citation - Scopus: 1
    Comparison of Target Detection Performance for Radiance and Reflectance Domain in Vnir Hyperspectral Images
    (Institute of Electrical and Electronics Engineers Inc., 2019) Ozdil,O.; Gunes,A.; Esin,Y.E.; Demirel,B.; Ozturk,S.
    In this paper, the hyperspectral detection of targets in visible-near infrared (VNIR) images is studied. The change of radiance domain signatures in images taken in different locations, time and altitudes are analyzed. A new radiance domain detection scheme for VNIR images under 1000 m altitude is proposed. The analysis shows that the radiance domain signatures of each target, that are collected from an image taken at 10 m altitude, can be effectively used for pure pixel target detection in other VNIR images taken at altitudes between 10 - 1000 m. The proposed approach is tested using several target types and on images taken at different altitudes and environmental conditions. Our results show that target detection in radiance domain provides a cheaper, easier and effective alternative to reflectance domain, in VNIR images. © 2019 IEEE.
  • Thumbnail Image
    Conference Object
    Shape Recognition With Low Cost Sensors;
    (Institute of Electrical and Electronics Engineers Inc., 2018) Saloglu,K.; Hosafci,A.; Birbilen,M.; Bulut,Y.A.; Gunes,A.
    This paper proposes a method to recognize the shape of some objects that have different geometrical properties using an infra-red sensor. To that end, a mechanism that has two degrees of freedom is designed. Scanning of the different objects are obtained. Noise on scanning output is removed. Finally, all the outputs for different objects are discussed to obtain the specifications to do shape recognition. © 2018 IEEE.
  • Thumbnail Image
    Conference Object
    Citation - WoS: 3
    Comparison of Target Detection Performance for Radiance and Reflectance Domain in Vnir Hyperspectral Images
    (Ieee, 2019) Ozdil, Omer; Gunes, Ahmet; Esin, Yunus Emre; Demirel, Berkan; Ozturk, Safak
    In this paper, the hyperspectral detection of targets in visible-near infrared (VNIR) images is studied. The change of radiance domain signatures in images taken in different locations, time and altitudes are analyzed. A new radiance domain detection scheme for VNIR images under 1000 m altitude is proposed. The analysis shows that the radiance domain signatures of each target, that are collected from an image taken at 10 m altitude, can be effectively used for pure pixel target detection in other VNIR images taken at altitudes between 10 - 1000 m. The proposed approach is tested using several target types and on images taken at different altitudes and environmental conditions. Our results show that target detection in radiance domain provides a cheaper, easier and effective alternative to reflectance domain, in VNIR images.
  • Thumbnail Image
    Conference Object
    Citation - Scopus: 1
    Multiple Underwater Target Bearing Tracking Using Member Filter;
    (Institute of Electrical and Electronics Engineers Inc., 2018) Gunes,A.; Guldogan,M.B.
    Underwater acoustic vector sensors (AVS) are devices which can measure scalar pressure and three dimensional acceleration or particle velocity with only one sensor. By using these four measurements, target detection and tracking is possible. In case multiple targets exist, multi-target detection and tracking methods must be applied. Because these methods are more general, the algorithms are more involved and complex. In this framework, multi-target multi-Bernoulli (MeMBer) is a promising filter based on random finite sets (RFS) for multi-target tracking problems. In this work, for the first time in the literature, MeMBer filter is analyzed using a single underwater acoustic vector sensor in a scenario including two targets. Simulation results indicate that MeMBer filter can successfully track the targets. © 2018 IEEE.
  • Thumbnail Image
    Doctoral Thesis
    Kameralarin Özdevimli Kalibrasyonu için Yeni Bir Yaklaşim
    (2019) Gürel, Cahit; Tora, Hakan; Güneş, Ahmet
    Kamera, robotik uygulamalardaki en önemli sensörlerden birisidir. Kalibre edilmiş bir kamera, kalibre edilmemişine göre daha fazla bilgi sunabilir. Ayrıca, kalibre edilmiş kameranın kalibrasyon değerleri mekanik ve termal değişikler nedeni ile zamanla bozulabilir. Dolayısıyla mobil robotik platformalar için özdevimli kamera kalibrasyonu ihtiyaç oluşturmaktadır. Özdevimli kamera kalibrasyonu için ölçüleri bilinen bir objeye ihtiyaç duyulmaması nedeni ile hem daha esnektir hem de görüntü üzerindeki az sayıda nokta eşleştirmesi kalibrasyon hessaplamaları için yeterlidir. Bu kapsamda, kameranın pozisyonu ve yönelim açısı bilgilerini katarak daha basit ve daha yüksek doğruluk değerine sahip yeni bir kalibrasyon metodu öneriyoruz. Önerilen metot sentetik veriler üzerinde, simülasyon ortamında ve gerçek bir donanım üzerinde test edilmiştir. Sonuçlar diğer özdevimli kamera kalibrasyonu metoduyla karşılaştırılmıştır. Düzlemsel hareket eden kameralar için sonuçlarımız diğer özdevimli kamera kalibrasyonu uygulamaları karşısında etkili olduğunu göstermektedir.
  • Thumbnail Image
    Conference Object
    Citation - Scopus: 2
    4-Stage Target Detection Approach in Hyperspectral Images
    (IEEE Computer Society, 2018) Ozdil,O.; Gunes,A.; Esin,Y.E.; Ozturk,S.; Demirel,B.
    Practical target detection systems require an automatic way to detect targets with high accuracy. Detection errors is not tolerable and they should be reduced as much as possible. In classical detection systems, generally single target detection algorithm is performed and the result will be evaluated according to the thresholding techniques. However, in these uncontrolled systems, false alarm rate strongly depends on the thresholding technique success. It is very hard to find a general and constant threshold value for images taken at different conditions and practical detection systems needs reliable threshold value. In this paper, we propose a new multi-stage target detection system which is the combination of different detection algorithms and thresholding technique. This system compose of 4-stages, i.e. namely 1-initial target detection (ACE, GLRT), 2-adaptive Constant False Alarm Rate (CFAR) thresholding, 3-spatially grouping, 4-statistical confidence operation. This system configuration removes the need for interactive user and it automatically implements confirmation and rejection steps. Moreover, this system can be used both for pure pixel and subpixel target detection purposes and it reduces computational processing time considerably with the implementation of consequtive processing stages. © 2018 IEEE.