Yazar "Aydin, Ahmet" seçeneğine göre listele

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    AERIS-ED: A Novel Efficient Attention Riser for Multi-Scale Object Detection in Remote Sensing
    (MDPI, 2025) Aydin, Ahmet; Avaroglu, Erdinc
    Object detection in remote sensing images is still recognized as a demanding task, largely because of significant scale differences among objects and the complexity of background scenes. Detecting small and medium-sized targets within cluttered environments, in particular, continues to challenge many existing algorithms. To address these issues, this study presents a new model named AERIS-ED (Attention-Enhanced Real-time Intelligence System for Efficient Detection). The framework adopts a C3 (Cross Stage Partial with three convolutions) based backbone and incorporates Efficient Attention (EA) units, but unlike conventional designs, these modules are inserted only at the P3 and P4 levels of the feature pyramid. This focused integration enables richer feature interaction across scales and enhances the recognition of small and medium objects. Comprehensive experiments on the MAR20 and VEDAI datasets highlight the benefits of the proposed approach. On MAR20, the model achieves a mean Average Precision at an Intersection over Union threshold of 0.5 ([email protected]) of 95.1% with an inference latency of only 3.8 ms/img. On VEDAI, it secures 83.0% [email protected] while maintaining the same efficiency, thereby confirming its suitability for real-time applications. Overall, the results indicate that AERIS-ED strengthens detection accuracy for small objects without compromising computational speed. These improvements suggest that the architecture is not only promising for multi-scale detection research but also has strong potential in practical remote sensing tasks.
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    Designs of three different octagonal-shaped antennas for microwave-based breast cancer detection
    (Iop Publishing Ltd, 2023) Aydin, Emine Avsar; Aydin, Ahmet; Saribas, Gozde
    Today, there are numerous studies on Microwave Imaging Methods. Thanks to these studies, the success and accuracy rate, especially in breast cancer diagnosis, increases. Microstrip antennas are used in the microwave imaging method. In this study, three different antenna models aimed to be used in microwave imaging methods were designed on the CST Studio Suite program. The designed antennas have been tested on the phantom model in the simulation environment. The test results were examined, and the designed antennas were produced using 3D printing technology. The built antennas were tested in the laboratory using the phantom and tumor-containing phantom models and obtained results were compared.
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    Öğe
    EVALUATION OF LIMESTONE LAYER'S EFFECT FOR UWB MICROWAVE IMAGING OF BREAST MODELS USING NEURAL NETWORK
    (Univ North, 2017) Aydin, Ahmet; Avsar Aydin, Emine
    X-ray mammography is widely used for detection of breast cancer. Besides its popularity, this method did not have the potential of discriminating a tumor covered with limestone from a pure limestone mass. This might cause misdetection of some tumors covered with limestone or unnecessary surgery for a pure limestone mass. In this study, Ultra-Wide Band (UWB) signals are used for the imaging. A feed-forward artificial neural network (FF-ANN) is used to classify the mass in the breast whether it is a tumor or not by using the transmission coefficients obtained from UWB signals. A spherical tumor covered with limestone and pure limestone masses were designed and placed into the fibro-glandular layer of breast model using CST Microwave Studio Software. The radius of the masses for both cases is changed from 1 mm to 10 mm with 1 mm steps. Horn antennas were chosen to send and receive Ultra-Wide Band (UWB) signals between 2 and 18 GHz frequency range. The obtained results show that the proposed method, on the contrary of the mammogram, has the potential of discriminating the tumor covered with limestone from the pure limestone, for the mass sizes of 7, 8 and 10 mm. Consequently, the UWB microwave imaging can be used to distinguish these cases from each other.