Can we explain the advantage natural languages enjoy over ideographies in a way that enables us to attempt the design of an ideography that "works"? I deploy an adapted version of Shannon's source- and channel-coding partitioning of a communication system to explain the communicative dynamics and shortfalls of ideographies, and reveal ways in which entrenchable, generalist ideographies could be designed.
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http://dx.doi.org/10.1017/S0140525X23000705 | DOI Listing |
Entropy (Basel)
November 2024
The School of Ocean Information Engineering, Jimei University, Xiamen 361021, China.
Joint source channel anytime coding (JSCAC) is a kind of joint source channel coding (JSCC) systems based on the causal spatially coupled coding and joint expanding window decoding (JEWD) techniques. JSCAC demonstrates greatly improved error correction performance, as well as higher decoding complexity. This work proposes a joint hybrid window decoding (JHWD) algorithm for JSCAC systems, aiming to reduce the decoding complexity while maintaining comparable error correction performance with the state of the art.
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August 2024
School of Physics and Electronic Science, Hunan University of Science and Technology, Xiangtan, 411201, China.
Effectively compressing transmitted images and reducing the distortion of reconstructed images are challenges in image semantic communication. This paper proposes a novel image semantic communication model that integrates a dynamic decision generation network and a generative adversarial network to address these challenges as efficiently as possible. At the transmitter, features are extracted and selected based on the channel's signal-to-noise ratio (SNR) using semantic encoding and a dynamic decision generation network.
View Article and Find Full Text PDFSensors (Basel)
June 2024
State Key Laboratory of Media Convergence & Communication, Communication University of China, Beijing 100024, China.
Joint source-channel coding (JSCC) based on deep learning has shown significant advancements in image transmission tasks. However, previous channel-adaptive JSCC methods often rely on the signal-to-noise ratio (SNR) of the current channel for encoding, which overlooks the neural network's self-adaptive capability across varying SNRs. This paper investigates the self-adaptive capability of deep learning-based JSCC models to dynamically changing channels and introduces a novel method named Channel-Blind JSCC (CBJSCC).
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May 2024
Key Laboratory of Universal Wireless Communications, Beijing University of Posts and Telecommunications, Beijing 100876, China.
We consider the problem of learned speech transmission. Existing methods have exploited joint source-channel coding (JSCC) to encode speech directly to transmitted symbols to improve the robustness over noisy channels. However, the fundamental limit of these methods is the failure of identification of content diversity across speech frames, leading to inefficient transmission.
View Article and Find Full Text PDFEntropy (Basel)
January 2024
Department of Electrical and Computer Engineering, Hong Kong University of Science and Technology (HKUST), Hong Kong 999077.
In recent years, semantic communication has received significant attention from both academia and industry, driven by the growing demands for ultra-low latency and high-throughput capabilities in emerging intelligent services. Nonetheless, a comprehensive and effective theoretical framework for semantic communication has yet to be established. In particular, finding the fundamental limits of semantic communication, exploring the capabilities of semantic-aware networks, or utilizing theoretical guidance for deep learning in semantic communication are very important yet still unresolved issues.
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