Browsing by Author "Sari, Filiz"

Now showing 1 - 3 of 3

Citation - WoS: 2
Citation - Scopus: 2
Ber Anaylsis for Super Lorentz-Gaussian Laser Beams Propagating in Turbulent Media
(Gazi Univ, Fac Engineering Architecture, 2013) Gokce, Muhsin C.; Gökçe, Muhsin Caner; Sari, Filiz; Ozek, Faruk; Elektronik ve Haberleşme Mühendisliği
In this work, BER-SNR variation of super Lorentz-Gaussian laser beam that propagating horizontally path in turbulent media is analyzed. Using scintillation index which is obtained by extented Huygens-Fresnel principle, BER - SNR value is calculated. Matlab program is used to show BER - SNR variation depending on link distance and beamwidth. To conclude, in short link range, super Lorentz-Gaussian beam is proven to be better link performance superior to commercially available Gaussian beam.
Citation - WoS: 2
Citation - Scopus: 4
An Intelligent System for Detecting Mediterranean Fruit Fly [Medfly; Ceratitis Capitata (Wiedemann)]
(Pagepress Publ, 2022) Eyyuboglu, Halil Tanyer; Sari, Filiz; Uzun, Yusuf; Tolun, Mehmet Resit
Nowadays, the most critical agriculture-related problem is the harm caused to fruit, vegetable, nut, and flower crops by harmful pests, particularly the Mediterranean fruit fly, Ceratitis capitata, named Medfly. Medfly's existence in agricultural fields must be monitored systematically for effective combat against it. Special traps are utilised in the field to catch Medflies which will reveal their presence and applying pesticides at the right time will help reduce their population. A technologically supported automated remote monitoring system should eliminate frequent site visits as a more economical solution. This paper develops a deep learning system that can detect Medfly images on a picture and count their numbers. A particular trap equipped with an integrated camera that can take photos of the sticky band where Medflies are caught daily is utilised. Obtained pictures are then transmitted by an electronic circuit containing a SIM card to the central server where the object detection algorithm runs. This study employs a faster region-based convolutional neural network (Faster R-CNN) model in identifying trapped Medflies. When Medflies or other insects stick on the trap's sticky band, they spend extraordinary effort trying to release themselves in a panic until they die. Therefore, their shape is badly distorted as their bodies, wings, and legs are buckled. The challenge is that the deep learning system should detect these Medflies of distorted shape with high accuracy. Therefore, it is crucial to utilise pictures containing trapped Medfly images with distorted shapes for training and validation. In this paper, the success rate in identifying Medflies when other insects are also present is approximately 94%, achieved by the deep learning system training process, owing to the considerable amount of purpose-specific photographic data. This rate may be seen as quite favourable when compared to the success rates provided in the literature.
Türbülanslı Ortamda Yayılan Super Lorentzgauss Laser Demetleri İçin Ber Analizi
(2013) Sari, Filiz; Gökçe, Muhsin C.; Özek, Faruk
Çalışmada, türbülanslı ortamda yayılan super Lorentz-Gauss laser demetlerinin yatay iletim uzaklıklarındaki BER - SNR değişimi hesaplandı. BER hesaplamalarında kullanılan pırıldama indisleri, genişletilmiş HuygensFresnel prensibi ile elde edilmiştir. Matlab kullanılarak BERSNR değerinin bağlantı uzaklığına ve laser kaynak boyutuna bağımlılığı gösterildi. Kısa iletim mesafesinde super Lorentz-Gauss laser demetinin ticari olarak kullanılan Gauss laser demetine göre daha iyi bağlantı performansı sağladığı sonucuna varıldı.