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Open AccessArticle

Finding Patterns in Signals Using Lossy Text Compression

1
Robotics & Big Data Lab, Computer Science Department, University of Haifa, Haifa 3498838, Israel
2
School of Information and Communication Technology, Griffith University, Brisbane 4111, Australia
*
Author to whom correspondence should be addressed.
Algorithms 2019, 12(12), 267; https://doi.org/10.3390/a12120267
Received: 30 June 2019 / Revised: 28 November 2019 / Accepted: 29 November 2019 / Published: 11 December 2019
(This article belongs to the Special Issue Data Compression Algorithms and their Applications)
Whether the source is autonomous car, robotic vacuum cleaner, or a quadcopter, signals from sensors tend to have some hidden patterns that repeat themselves. For example, typical GPS traces from a smartphone contain periodic trajectories such as “home, work, home, work, ⋯”. Our goal in this study was to automatically reverse engineer such signals, identify their periodicity, and then use it to compress and de-noise these signals. To do so, we present a novel method of using algorithms from the field of pattern matching and text compression to represent the “language” in such signals. Common text compression algorithms are less tailored to handle such strings. Moreover, they are lossless, and cannot be used to recover noisy signals. To this end, we define the recursive run-length encoding (RRLE) method, which is a generalization of the well known run-length encoding (RLE) method. Then, we suggest lossy and lossless algorithms to compress and de-noise such signals. Unlike previous results, running time and optimality guarantees are proved for each algorithm. Experimental results on synthetic and real data sets are provided. We demonstrate our system by showing how it can be used to turn commercial micro air-vehicles into autonomous robots. This is by reverse engineering their unpublished communication protocols and using a laptop or on-board micro-computer to control them. Our open source code may be useful for both the community of millions of toy robots users, as well as for researchers that may extend it for further protocols. View Full-Text
Keywords: data compression; run-length; RRLE; periods; robotics; signals data compression; run-length; RRLE; periods; robotics; signals
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MDPI and ACS Style

Rozenberg, L.; Lotan, S.; Feldman, D. Finding Patterns in Signals Using Lossy Text Compression. Algorithms 2019, 12, 267.

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