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实时压缩触觉互联网数据流。

Real-Time Compression for Tactile Internet Data Streams.

机构信息

Department of Computer Science, Central Michigan University, Mount Pleasant, MI 48859, USA.

School of Electrical, Computer, and Energy Engineering, Arizona State University, Tempe, AZ 85287-5706, USA.

出版信息

Sensors (Basel). 2021 Mar 9;21(5):1924. doi: 10.3390/s21051924.

Abstract

The Tactile Internet will require ultra-low latencies for combining machines and humans in systems where humans are in the control loop. Real-time and perceptual coding in these systems commonly require content-specific approaches. We present a generic approach based on deliberately reduced number accuracy and evaluate the trade-off between savings achieved and errors introduced with real-world data for kinesthetic movement and tele-surgery. Our combination of bitplane-level accuracy adaptability with perceptual threshold-based limits allows for great flexibility in broad application scenarios. Combining the attainable savings with the relatively small introduced errors enables the optimal selection of a working point for the method in actual implementations.

摘要

触觉互联网将需要超低延迟,以便在人类处于控制回路的机器和人类组合的系统中实现这一点。这些系统中的实时和感知编码通常需要特定于内容的方法。我们提出了一种基于故意减少位数精度的通用方法,并使用运动觉和远程手术的真实数据评估了所实现的节省和引入的误差之间的权衡。我们在基于位平面级精度适应性和基于感知阈值的限制方面的结合,使得在广泛的应用场景中具有很大的灵活性。将可实现的节省与相对较小的引入误差相结合,使得在实际实现中为该方法选择最佳工作点成为可能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d742/7967243/4ed847eddb8a/sensors-21-01924-g001.jpg

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