CALCULATION OF COMPLEXITY OF A PULSE TRANSFORMATION IN TIME-VARYING MEDIUM Ruzhytska N.N., Nerukh A.G. Kharkiv National University of RadioElectronics, 14 Lenin Avenue, Kharkov, 61166, Ukraine. E-mail: [email protected] Nerukh D.A. Unilever Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK. E-mail: [email protected] Abstract: - A complexity of electromagnetic signals transformed by a short sequence of cycles of medium parameters time changing is investigated. Dependence of the complexity on the modulation parameters is considered and a difference between forward and backward waves that are inevitable result of the medium time changing is shown. INTRODUCTION Interactions between electromagnetic pulses and semiconductor active media in waveguides are of significant importance in optical communication technology and time-domain techniques for solving such electromagnetic problems have received increased attention in the literature recently. Parametric phenomena in active media are especially important in optoelectronic systems as they allow controlling of electromagnetic signals by the temporal adjustment of the medium parameters. It is known that a single abrupt change of medium parameters leads to changing of a pulse shape [1-3]. The complexity of the phenomenon arises when only a few cycles of the modulation are considered that happens in ultrafast optics. In this paper a change of the complexity of the initial electromagnetic pulse E0 (t , x) with a number of modulation cycles is considered. In the modulation cycles the permittivity receives constant 1 magnitude on the disturbance intervals of the cycles (n 1)T t T1 (n 1)T , n 1,..., N and the permittivity has constant magnitude on the quiescence intervals of the cycles T1 (n 1)T t nT , n 1,..., N . Here, T is the duration of the cycle of the parameters change, T1 is the duration of the disturbance interval of each cycle. TRANSFORMATION OF THE PULSE AND ITS COMPLEXITY Investigation of the pulse transformation is based on the Volterra integral equation method, which allows considering problems with arbitrary primary signal . If the initial field has the form E0 (t , x) f ( x vt ) then the transformed field is described by the following expressions: on the disturbance interval of the n-th cycle, n 1 En (t , x) En ( ) (t x / v1 ) En ( ) (t x / v1 ) , where En ( ) (t x / v1 ) t x / v1 t x / v1 a (a 1) Fn(1) ( (a 1)(n 1)T ) ( a 1) Fn(1) ( ( a 1)( n 1)T ) 2 a a (1) En ( ) (t x / v1 ) t x / v1 t x / v1 a (a 1) Fn(1) ( (a 1)(n 1)T ) ( a 1) Fn(1) ( ( a 1)( n 1)T ) 2 a a () () on the quiescent interval of the n-th cycle Fn (t , x) Fn (t x / v) Fn (t x / v) , where Fn ( ) (t x / v) F0 (t x / v) 1 2a 2 n 1 (a 1) E () k 1 k 0 ( t x / v a 1 t x / v a 1 (kT T1 )) Ek( 1) ( kT ) a a a a t x / v a 1 t x / v a 1 (a 1) Ek( 1) ( (kT T1 )) Ek( 1) ( kT ) , a a a a (2) Fn ( ) (t x / v) 1 2a 2 n 1 (a 1) E () k 1 k 0 ( t x / v a 1 t x / v a 1 (kT T1 )) Ek( 1) ( kT ) a a a a t x / v a 1 t x / v a 1 (a 1) Ek( 1) ( (kT T1 )) Ek( 1) ( kT ) a a a a In order to estimate how complex the transformed signals are we calculated the ‘finite statistical complexity’ measure of the signals [5, 6]. This approach of estimating the complexity of dynamical process rests on such well-known theories as KolmogorovChaitin algorithmic complexity and Shannon entropy. This measure of complexity 5 0.8 0.6 E, backward quiescence Complexity E, forward quiescence Complexity 0.3 5 1.0 4 3 0.4 2 0.2 0 10 4 3 0.2 2 1 0.1 0 5 10 15 20 25 partition 0.0 20 partition 0.0 -0.1 -0.2 -5 0 5 10 15 20 25 -20 t-x/v a=1.5 N=5 NT=8 T1=0.5T quiescence 0 -10 0 10 20 t+x/v 1 2 3 4 5 a=1.5 N=5 NT=8 T1=0.5T quiescence 1 2 3 4 5 Fig. 1. Transformation of the pulse on the quiescent interval when the medium change beginning coincides with the beginning of the pulse, a / 1 . shows how much information is stored in the signal. It also indicates how much information is needed to predict the next value of the signal if we know all the values up to some moment in time. The algorithm of computing the finite statistical complexity follows the method described in . Dependence a signal shape and its complexity on the number of modulation cycled is shown in Fig. 1, 2. The complexity increases with the number of partitions and asymptotical behaviour at infinite number of partitions has a binary logarithm character . 6 6 4 3 4 2 E backward Complexity 20 E forward 1 0 10 complexity 6 5 0 5 10 15 20 partition 4 2 0 0 2 2 4 6 8 10 partition 0 0 -2 -10 0 0.0 4.0 8.0 0 1 a=1.5 T=8 T1=0.5 back 2 3 40 60 80 t+x/v2 t-x/v2 a=1.5 T=8 T1=0.5T 20 12.0 4 1 2 3 4 5 5 Fig. 2. Transformation of the pulse on the disturbed interval when the medium change beginning coincides with the pulse maximum. CONCLUSION Investigations show a strong transformation of the electromagnetic signal by only a short sequence of modulation cycles. Overall the complexity of the signals increases with the number of cycles. However, in those cases when the signals get narrower keeping their shape the complexity may decrease. 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