SemAI: Semantic Artificial Intelligence-enhanced DNA storage for Internet-of-Things
<|reference_start|>SemAI: Semantic Artificial Intelligence-enhanced DNA storage for Internet-of-Things: In the wake of the swift evolution of technologies such as the Internet of Things (IoT), the global data landscape undergoes an exponential surge, propelling DNA storage into the spotlight as a prospective medium for contemporary cloud storage applications. This paper introduces a Semantic Artificial Intelligence-enhanced DNA storage (SemAI-DNA) paradigm, distinguishing itself from prevalent deep learning-based methodologies through two key modifications: 1) embedding a semantic extraction module at the encoding terminus, facilitating the meticulous encoding and storage of nuanced semantic information; 2) conceiving a forethoughtful multi-reads filtering model at the decoding terminus, leveraging the inherent multi-copy propensity of DNA molecules to bolster system fault tolerance, coupled with a strategically optimized decoder's architectural framework. Numerical results demonstrate the SemAI-DNA's efficacy, attaining 2.61 dB Peak Signal-to-Noise Ratio (PSNR) gain and 0.13 improvement in Structural Similarity Index (SSIM) over conventional deep learning-based approaches.<|reference_end|>
@article{wu2024semai:,
    title={SemAI: Semantic Artificial Intelligence-enhanced DNA storage for
  Internet-of-Things},
    author={Wenfeng Wu, Luping Xiang, Qiang Liu, and Kun Yang},
    journal={arXiv preprint arXiv:2409.12213},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.12213},
    primaryClass={cs.LG cs.AI}
}