Working as a TÜBİTAK fellow and a PhD researcher in Telecommunications at Yıldız Technical University, I am actively contributing to create the future of wireless systems, artificial intelligence-powered UAV networks, and mobile invention. Strong foundation in computer networking, embedded systems, and Android programming, I concentrate in developing end-to-end solutions linking, empowering, and transforming. From national sales leadership in the IT sector to practical engineering of AI-driven systems for disaster response, smart health, and 6G network optimisation, my career covers Along with publishing peer-reviewed research on deep learning, terabit processors, and mobile health in top-tier publications including IEEE and Springer, I have produced over thirty digital products including award-nominated platforms for education, logistics, fitness, and crisis intervention. Leading developer for several TEKNOFEST and academic projects, I reconstruct connection in disaster zones using UAV-based ad hoc networks (FANETs), therefore tying innovation with useful applications. Although I still speak five languages and participate regularly in cross-cultural initiatives, my technology stack consists of Python, Kotlin, Flutter, Flask, MySQL, and CCNA systems. In essence, I see technology as way of building systems that matter, scale, and serve. Currently focused on pioneering AI-integrated 6G designs, edge-intelligent UAV systems, and mobile-first tools for emerging markets, I have an eye on driving the next wave of worldwide connectivity and digital resilience.
Aiming to move from conventional throughput-centric paradigms to intelligent, context-aware systems able of perception and autonomous decision-making, sixth-generation (6G) wireless networks is seeking. Driven by recent developments in deep learning and edge artificial intelligence, computer vision (CV) proves to be a key enabler for such perceptive 6G systems. This paper offers a thorough overview bringing together the scattered terrain of CV-enabled 6G technologies. It benchmarks current models against major 6G performance criteria, evaluates architectural paradigms including federated and split learning, and presents a disciplined taxonomy of use cases. This study also notes the possibili... More >
Graphical Abstract
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