BLU 596E Seminer Dersi Sunum Listesi

BLU 596E kapsamında gerçekleştirilecek olan sunumların detaylı bilgileri aşağıdaki gibidir:

No	1
Öğrenci No	708191019
Ad Soyad	Muhammet Bolat
Başlık	Super Resolution with Deep Learning(Derin Öğrenme ile Süper Çözünürlük)
Özet / Abstract	Image super-resolution (SR) aims to reconstruct a high-resolution image from a single low-resolution image. Aim of the presentation is to mention how to implement deep learning techniques for this problem and available state-of-art methods. Finally, we are going to discuss the results.süper çözünürlük yöntemleriyle birleştirilmesini ve alınan sonuçları sunmayı planlamaktayım.
Danışman Ad Soyad	Lütfiye Durak Ata, Nurullah Çalık (Eş Danışman)
Sunum Tarihi	30.11.2021
Sunum Saati	9:30
Bağlantı Linki	https://itu-edu-tr.zoom.us/j/95777231231?pwd=WXV1bGRNUG1Ibnh0YlhXYlpBMWVHdz09

No	2
Öğrenci No	708201004
Ad Soyad	Mehmet Mert Tuncer
Başlık	Physical Layer Security
Özet / Abstract	Physical layer security originated from the pioneering work of Shannon, who laid the foundation for secrecy systems and then, the well-known wiretap channel was introduced by Wyner in 1975. Specifically, to define secrecy in terms of the ability to transmit a secret information, in this work Wyner provided a guarantee that the eavesdropping channel was a degraded, but much more noisy form of the legitimate connection, allowing the secrecy capacity to be the highest rate of data transmission that could be transmitted securely while avoiding the risk of being decrypted by an eavesdropper. Because of the wireless medium’s overt character, the transmitted signal will be received by legitimate and illegitimate receivers alike. The transmitted information may be potentially exposed to various security attacks, thus providing security in wireless networks is a major issue that must be investigated on a massive scale the wireless network
Danışman Ad Soyad	Lütfiye Durak Ata
Sunum Tarihi	7.12.2021
Sunum Saati	9:30
Bağlantı Linki	https://itu-edu-tr.zoom.us/j/92110972222?pwd=U2wramZ0UmpZWTNxVzdVQ211WE1TUT09

No	3
Öğrenci No	704202002
Ad Soyad	Esra Ergun
Başlık	Joint Detection and Identification Feature Learning for Person Search
Özet / Abstract	Existing Re-Identification networks focus on retrieving manually cropped query images from manually cropped gallery images. Real-world applications include finding criminals, cross-camera target tracking, person activity analysis, etc. Processing with manually cropped images is not a fit for real-world applications. Person search focuses on retrieving a query image from whole gallery images. This study handles pedestrian detection and person search jointly in a single convolutional neural network. In the original work, an Online Instance Matching loss is proposed to train the network effectively. To validate the approach a largescale benchmark dataset is collected (CHUK-SYSU) which contains 18,184 images, 8,432 identities, and 96,143 pedestrian bounding boxes. The results showed the superiority of Online Instance Matching Loss over conventional Softmax loss.
Danışman Ad Soyad	Ertugrul Karacuha
Sunum Tarihi	14.12.2021
Sunum Saati	9:30
Bağlantı Linki	https://itu-edu-tr.zoom.us/j/95862337582?pwd=c3dEdXhhT1A2aU0xaWc3c00zUjlUdz09

Tarih 15 Kas 2021

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