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Molecular Arithmetic Coding (MAC) for Internet of Bio-Nano Things (IoBNT)
Melih \c{S}ahin, Beyza E. Ortlek, Ozgur B. Akan
https://arxiv.org/abs/2403.04672 …

Molecular Arithmetic Coding (MAC) for Internet of Bio-Nano Things (IoBNT)
Molecular Communication (MC) has emerged as a promising paradigm employing molecules to transfer information at the nano-scale. Unlike MC channel coding, MC source coding has remained mostly an unexplored area of research. In a recent paper, prefix source coding was introduced into the field, through an MC-adapted version of the Huffman Coding. In the context of MC source coding, this paper proposes the Molecular Arithmetic Coding (MAC) whose algorithmic implementation and code-structure is non…

Learning on JPEG-LDPC Compressed Images: Classifying with Syndromes
Ahcen Aliouat, Elsa Dupraz
https://arxiv.org/abs/2403.10202 https://

Learning on JPEG-LDPC Compressed Images: Classifying with Syndromes
In goal-oriented communications, the objective of the receiver is often to apply a Deep-Learning model, rather than reconstructing the original data. In this context, direct learning over compressed data, without any prior decoding, holds promise for enhancing the time-efficient execution of inference models at the receiver. However, conventional entropic-coding methods like Huffman and Arithmetic break data structure, rendering them unsuitable for learning without decoding. In this paper, we p…