Efficient Data Compression based on Run-length Coding for Electronic Skins
Year: 2024
Type: conference-paper
Abstract: Electronic skin (E-skin) is an advanced wearable technique with great potential in tactile feedback applications. E-skins with a large number of sensors may generate a great amount of data that usually incurs serious difficulties in data processing and transmission. Current research on E-skins mainly focuses on hardware design and sensor development, without considering the issue of data compression. To address the issue, this paper reports efficient compression of data from neuromorphic E-skin using algorithms based on run-length coding. Two run-of-zero-based coding methods, including Golomb coding and frequency-directed run-length (FDR) coding, are exploited to achieve data compression. Experimental results show that both the Golomb coding and FDR coding based algorithms can compress the E-skin data efficiently with compression ratios over 79%.
Authors: Wei Yao, Shengliang Peng, Libo Chen, Zhi-Bin Zhang
Institutions: Huaqiao University, Uppsala University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funders: China Scholarship Council, Nature
OpenAlex ID: W4409248262