First stage identification of syntactic elements in an extra-terrestrial signal |
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| Authors: | John Elliott |
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| Institution: | 1. Space Technology Ireland, Ltd., NUI Maynooth, Co. Kildare, Ireland;2. Planetary Science Branch of the Space Physics Laboratory, Vikram Sarabhai Space Centre, ISRO, Trivandrum, India;3. Institute of Physics, Poland;4. SINP MSU, Russia;5. Sci/Eng ESMG, India;6. Astronaut Research and Training Centre of China /ACC, Beijing, China;7. JAXA, Japan;8. Russian Academy of Sciences, IMBP, Moscow, Russia;9. DLR, Germany;10. University of Houston, USA;11. Universiti Kebangsaan, Malaysia;12. Physical Research Laboratory, India;13. Cologne, Germany;14. Canadian Space Agency, Canada;15. University of Tennessee, USA;1. State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China;2. University of the Chinese Academy of Sciences, Beijing 100049, China;3. Northeastern University, Shenyang 110819, China;4. Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong Special Administrative Region;1. Shizuoka University, Hamamatsu, Shizuoka 432-8561, Japan;2. Nihon University, Funabashi, Chiba 274-8501, Japan;1. School of Automation, Nanjing University of Science and Technology, Nanjing, 210094, China;2. Department of Aeronautics and Astronautics, Kyushu University, Fukuoka, 819-0395, Japan |
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| Abstract: | By investigating the generic attributes of a representative set of terrestrial languages at varying levels of abstraction, it is our endeavour to try and isolate elements of the signal universe, which are computationally tractable for its detection and structural decipherment. Ultimately, our aim is to contribute in some way to the understanding of what ‘languageness’ actually is. This paper describes algorithms and software developed to characterise and detect generic intelligent language-like features in an input signal, using natural language learning techniques: looking for characteristic statistical “language-signatures” in test corpora. As a first step towards such species-independent language-detection, we present a suite of programs to analyse digital representations of a range of data, and use the results to extrapolate whether or not there are language-like structures which distinguish this data from other sources, such as music, images, and white noise. |
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