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  • Publication
    Enhanced photoresponse of a self-powered gallium nitride photodetector via sequentially-deposited gold nanoparticles for sustainable optoelectronics
    ( 2023-01-01) TEKER, KAŞİF ; Teker T. U., TEKER K.
    © 2023, The Minerals, Metals & Materials Society.It is becoming crucial to design/fabricate eco-friendly, sustainable electronic and photonic devices to minimize the carbon footprint for future systems. In this study, we have demonstrated a steady photoresponse enhancement of the self-powered GaN ultraviolet photodetector (GaN-UVPD) via sequentially deposited gold nanoparticles (Au NPs) under 254, 302, and 365 nm UV light exposure. The AuNP-deposited GaN-UVPD exhibited excellent responsivity of 0.65 A/W and detectivity of 6.51 × 1012 cm.Hz1/2 W−1 under 302 nm UV light without any external power. Moreover, the sensitivity of the device increased from 1.98 × 106% to 3.32 × 106% following Au nanoparticle deposition. Additionally, the plausible mechanisms for the self-powered and Au nanoparticle-induced photoresponse enhancement have been discussed. In brief, the high-performance photoresponsivity of our self-powered GaN-UVPD could find many useful applications in sustainable energy and eco-friendly optoelectronic devices.
  • Publication
    Analysis of interpersonal relations: Film, green book
    ( 2023-01-01) YAZGAN, AYŞE MÜGE ; YAZGAN A. M.
  • Publication
  • Publication
    An evaluation on procedure of ıssuing the sea protest
    ( 2022-01-01) ÇAKAN ÇAVUŞ, CANSU ; ÇAKAN ÇAVUŞ C.
  • Publication
    Land classification in satellite images by injecting traditional features to CNN models
    ( 2023-01-01) ÜNSALAN, CEM ; Aksoy M. Ç., Sirmacek B., ÜNSALAN C.
    © 2023 Informa UK Limited, trading as Taylor & Francis Group.Deep learning methods have been successfully applied to remote-sensing problems for several years. Among these methods, CNN-based models have high accuracy in solving the land classification problem using satellite or aerial images. Although these models have high accuracy, this generally comes with large memory size requirements. However, it is desirable to have small-sized models for applications, such as the ones implemented on unmanned aerial vehicles, with low memory space. Unfortunately, small-sized CNN models do not provide as high accuracy as with their large-sized versions. In this study, we propose a novel method to improve the accuracy of CNN models, especially the ones with small size, by injecting traditional features into them. To test the effectiveness of the proposed method, we applied it to the CNN models SqueezeNet, MobileNetV2, ShuffleNetV2, VGG16 and ResNet50V2 having size 0.5 MB to 528 MB. We used the sample mean, grey-level co-occurrence matrix features, Hu moments, local binary patterns, histogram of oriented gradients and colour invariants as traditional features for injection. We tested the proposed method on the EuroSAT dataset to perform land classification. Our experimental results show that the proposed method significantly improves the land classification accuracy especially when applied to small-sized CNN models.