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Deep Learning

Deep CNN-Based Channel Estimation Using 3D Channel Correlation

Peihao Dong

Millimeter wave (mmWave) communications provide a promising solution to meet the proliferating demand for high data rate because of large bandwidth. The current “boomingly” deployed fifth generation communication system (5G) has not actually touched the dominant frequency band of mmWave and thus can hardly enjoy its merit on dramatically boosting transmission rate, which motivates us to conduct research on the ultimate implementation of mmWave communications.

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Coarse-to-Fine CNN for Image Super-Resolution

Chunwei Tian, Yong Xu, Wangmeng Zuo, Bob Zhang, Lunke Fei, Chia-Wen Lin

A coarse-to-fine SR CNN (CFSRCNN) consisting of a stack of feature extraction blocks (FEBs), an enhancement block (EB), a construction block (CB) and, a feature refinement block (FRB) is proposed to learn a robust SR model.

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Collaborative Cloud and Edge Mobile Computing in C-RAN Systems

Seok-Hwan Park, Seongah Jeong, Jinyeop Na, Osvaldo Simeone, Shlomo Shamai

To handle the various types of tasks in the upcoming cellular services, we can design the system with both cloud and edge computing capabilities, where the computational tasks can be partially offloaded to the ENs and the CP.

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Revitalizing Underwater Image Enhancement in the Deep Learning Era

Dr. Chongyi Li

Underwater image enhancement has drawn considerable attention in both image processing and underwater vision. Due to the complicated underwater environment and lighting conditions, enhancing underwater image is a challenging problem. 

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Recent Advances of Deep Learning within X-ray Security Imaging

Dr. Samet Akcay

This blog explores modern deep learning applications as well as traditional machine learning techniques for automated X-ray security imaging.

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