Finding Patterns in Signals Using Lossy Text Compression

Liat Rozenberg,Dan Feldman,Sagi Lotan

doi:10.3390/a12120267

Abstract

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 This is by reverse engineering their unpublished communication protocols and using a laptop or on-board micro-computer to control them.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.

Highlights

The compression scheme we present in this paper is called recursive run-length encoding (RRLE), and is a natural generalization of run-length-encoding (RLE), but is more suited for semi-periodic strings that are produced by sensors on robots
In this subsection we suggest a novel generalization of the classic run-length encoding compression, called recursive run-length-encoding (RRLE) which is more suitable to our applications
We show experimental results on real data strings. These strings were obtained by recording communication signals from the remote controllers (RC) of a pair of toy robots: (i) The

Summary

Introduction

While this paper deals with a natural open problem in string compression and representation (“stringology”), its origin was in our robotics lab. Traditional labs have relatively expensive, potentially dangerous robots, such as heavy quadcopters, crawlers, and humanoids that cost thousands of dollars. Robots that cost a few dozen dollars. We have seen dozens types of robots in toy stores and malls, including helicopters, quadcopters, cars, small humanoids, and even combinations such as quadcopters with wheels. Due to their price, size, and plastic material, such robots can be used safely indoors (e.g., home, school, or university), are more resistant to crashes, and it is easy to fix or replace their parts

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Conclusion