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20112023
most citedWeakly Supervised Attention Pyramid Convolutional Neural Network for Fine-Grained Visual Classification

209 citations · 332 across the 17 of their papers we have counts for

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12 papers · 1 filter

cs.IT2023

Semantics-Division Duplexing: A Novel Full-Duplex Paradigm

Kai Niu, Zijian Liang, Chao Dong +3

In-band full-duplex (IBFD) is a theoretically effective solution to increase the overall throughput for the future wireless communications system by enabling transmission and recep…

cs.IT2023★ 27 cited

A Golden Decade of Polar Codes: From Basic Principle to 5G Applications

Kai Niu, Ping Zhang, Jincheng Dai +2

After the pursuit of seventy years, the invention of polar codes indicates that we have found the first capacity-achieving coding with low complexity construction and decoding, whi…

cs.IT2022★ 3 cited

Toward Adaptive Semantic Communications: Efficient Data Transmission via Online Learned Nonlinear Transform Source-Channel Coding

Jincheng Dai, Sixian Wang, Ke Yang +5

The emerging field semantic communication is driving the research of end-to-end data transmission. By utilizing the powerful representation ability of deep learning models, learned…

cs.IT2022★ 4 cited

Communication Beyond Transmitting Bits: Semantics-Guided Source and Channel Coding

Jincheng Dai, Ping Zhang, Kai Niu +3

Classical communication paradigms focus on accurately transmitting bits over a noisy channel, and Shannon theory provides a fundamental theoretical limit on the rate of reliable co…

cs.IT2022★ 8 cited

Towards Semantic Communications: A Paradigm Shift

Kai Niu, Jincheng Dai, Shengshi Yao +4

The last seventy years have witnessed the transition of communication from Shannon's theoretical concept to current high-efficient practical systems. Classical communication system…

cs.IT2021

Nonlinear Transform Source-Channel Coding for Semantic Communications

Jincheng Dai, Sixian Wang, Kailin Tan +4

In this paper, we propose a class of high-efficiency deep joint source-channel coding methods that can closely adapt to the source distribution under the nonlinear transform, it ca…