**Looking at Remote Sensing the Timing of an Organisation's Point of View and the Anticipation of Today's Problems**

Y. A. Polkanov *Private Belarus* 

### **1. Introduction**

16 Will-be-set-by-IN-TECH

216 Remote Sensing of Planet Earth

Takaki, T.; Omasa, Y. & Ishii, I. (2008). Force Visualization Mechanism using Moiré Fringe for

Takaki, T.; Omasa, Y.; Ishii, I.; Kawahara, T.; & Okajima, M. (2010). Force Visualization

Umemoto, S.; Fujii, M.; Miyamoto, N.; Okamoto, T.; Hara, T.; Ito, H. & Fujino, Y.

Yun, C. -B.; Soho, H.; Jung, H. J.; Spencer, B. F. & Nagayama, T. (2010). Wireless

Reid, G. T. (1984). Moiré firnges in metrology, *Optics and Lasers in Engineering*, Vol. 5, pp. 63-93,

Basehore, M. L.; & Post, D. (1981). Moiré method for in-plane and out-of-plane displacement

Meadows, D. M.; Johnson, W. O.; & Allen, J. B. (1970). Generation of Surface Contours by

Masanao, M. (1986). *Sensing Techniques of Mechanical Quantities*, Corona Publishing Co., Ltd.,

Holman, J. P. (2001). *Experimental Method for Engineers*, Thomes Casson, ISBN 0-07-366055-8. Kawahara, K.; Sakuma, N.; & Nishizaki, Y. (1993). Effect of Third Elements on Damping

Kawahara, K.; Sakuma, N.; & Nishizaki, Y. (1993). Effect of Fourth Elements on Damping

for Remote Sensing, *Transactions of the Japan Society of Mechanical Engineers, Series C*,

Capacity of Mn-20Cu Alloy, *Journal of the Japan Institute of metals*, Vol. 57, No. 9, pp.

Capacity of Mn-20Cu-5Ni Alloy, *Journal of the Japan Institute of metals*, Vol. 57, No. 9,

Takasaki, H. (1970). Moiré Topography, *Applied Optics*, Vol. 9, No. 6, pp. 1467-1472, 1970. Takaki, T.; Omasa, Y.; & Ishii, I. (2010). Acceleration Visualization Marker using Moiré Fringe

measurements *Applied Optics*, Vol. 21, No. 9, pp. 321-328, 1981.

Moiré Patterns, *Applied Optics*, Vol. 9, No. 4, pp. 942-947, 1970.

Vol. 76, No. 770, pp. 2592-2597, 2010. (in Japanese)

ISBN 4-627-61161-7. (in Japanese)

1089-1096, 1993. (in Japanese)

pp. 1097-1100, 1993. (in Japanese)

Robot Grippers, *Proceedings of the 28th Annual Conference of the Robotics Society of Japan*,

Mechanism Using a Moiré Fringe Applied to Endoscopic Surgical Instruments, *Proceedings of the 2010 IEEE International Conference on Robotics and Automation*, pp.

(2010). Deflection measurement for bridges with frequency-shifted feedback laser, *Proceedings of the Bridge Maintenance, Safety, Management and Life-cycle Optimization*,

sensing technologies for bridge monitoring and assessment, *Proceedings of the Bridge Maintenance, Safety, Management and Life-cycle Optimization*, pp. 113-132, 2010. Kim, C. W.; Kawatani, M.; Ozaki, R.; Makihata, N.; & Kano, M. (2010). Low-cost wireless

sensor node for vibration monitoring of infrastructures, *Proceedings of the Bridge Maintenance, Safety, Management and Life-cycle Optimization*, pp. 780-786, 2010. Kobayashi, A. S. (1987). *Handbook on Experimental mechanics*, Prentech-hall, ISBN

**7. References**

1K3-07, 2008. (in Japanese)

3648-3653, 2010.

pp. 2570-2574, 2010.

0-13-377706-5.

1984.

Any remote measurement involves recording a signal from some sort of continuous medium of atmosphere in general. This is a signal which possesses a certain temporary structure, and in turn, this temporary structure bears some information on the spatial inhomogeneities of the continuous medium's structure and the arrangement of its specific properties (e.g. optical, microphysical, etc.). The nature of these structures depends upon the thermodynamic processes in the environment and the sustainability of these processes. Thermodynamics is the inevitable factor for their participation and it demands an account of the processes having obviously extended character. The classical approach assumes some property of the environment at a certain point in time and at a certain point in the medium. In accordance with this, today's remote measurements use the digitisation of a received signal with certain stable time step of digitisation. All efforts have been consolidated so as to receive the medium-sized digital signal samples which have been reduced to an acceptable size. Such an approach has at its core a logical contradiction – information of the properties of an extended environment trying to get at the point where it actually is not. There is something that is subject to consideration absolutely from other positions and the use of other tools. Measurements should be conducted in a certain 'visible' volume which provides the effect of the 'presence' of the medium and which has a specific thermodynamic 'meaning'; that is, that it has some of the 'thermodynamic memory'. These volumes should be comparable (in length) to the length of all zones' (lines') measurements. However, this generates a new contradiction which arises when the discretisation signal is read out. How should one get a spatial resolution close to the size of the inhomogeneity with the signal time's discretization using intervals commensurate with the length of the track measurements? This contradiction can be resolved only indirectly, using a principle that can be called a kind of 'principle of relativity'. Here, we use a pair of discrete samples which have a common border and a second boundary which is different to the desired step of discretization. This approach provides for the possibility of studying the environment and its irregularities while maintaining the required signal/noise ratio.

The internal logic of this approach abstracts the properties of the medium at the point and then moves on to the study of the environment as a self-organising system. The 'test body' of such research is the structure of the inhomogeneities of the medium. The nature of this

Looking at Remote Sensing the Timing of an

systems I and II (Polkanov, Y. A. and Ashkinadze, D. A., 1988):

L /l 1,2 s i l /l T s

=

the expressions (Polkanov, Y. A. et al., 1985; Polkanov, Y. A. et al., 2004):

used in subsequent calculations for the system II (2).

**2.2 Processing** 

the supervised space.

organising of the environment.

Organisation's Point of View and the Anticipation of Today's Problems 219

All estimates are carried out based on an expression derived from the lidar equation for

ni = AW1 (e-2σli (1 - e-2σlc))/L2 (1)

2 li 2 lc <sup>2</sup> n AW e 1 e / l l / 2 i1,i2 2 i s σ

Where ni, ni1,i2 - obtained discrete values from the scattering signal (number of photon counts); A – a coefficient which brings together the supporting equipment characteristics; W1,2 – the power of the laser radiation; L – the distance from the centre section of the route, by which the signal is recorded; li - the distance from the system to this site; ls – the length of the section; lT the length of the shadow zone of the lidar where the signal is not recorded (600 m). We assume for the system that II L2 > L1, (ni2 - ni1) = ni. An advanced assessment of the relative measurement error of the signal (δi, δix for System I and System II) was conducted on the basis

Where tβ – the coefficient equal to the probability of the matching error computed to its actual value (if tβ = 2, the probability is equal to 0.95). The necessity of this evaluation is due to the appearance depending δix (t) for system II (signal/noise = const). This is due to the progressive rise in the value of the time intervals recording the scattering signal (with the digitisation step – ts). The level of background illumination takes into account the introduction of the coefficient B = f (t) in (4). The measurement error for individuals counts the signal and background-level measurement errors, becoming comparable for large intervals of TS. They are significantly higher than the level of internal noise (in. ns.) receiving system (in this case, n in.ns ~ 0.1, ts = 0,4 ms). Moreover, the summed value of the signal increases to a certain point in time, reaching a maximum level of accumulated signal (Kovalev, V. A., 1973; Ablavskij, L. M. and Kruglov, P. A., 1974). However, the level of background illumination increases linearly with time. The calculations used the results of the actual measurement system I (ni, nb, σ). The coefficient A in (1) is also evaluated and

The following processing scheme was assumed: the initial signal (as a time function) the generalised structure of a signal an elementary cell of the signal structure. The multiplication of such cells allows the complete restoration of the characteristic structures in

The indicator of the time stability of the signal structure was the dispersion of the components of the elementary cell of a signal structure. If the dispersion exceeds an interval between elements of the revealed cell then the structure is unstable. The correlation of the generalised frequency structure of a horizontal signal and the generalised parameter which fixes the thermodynamic stability of the environment is a characteristic sign of the self-

( ) ( ) ( )

− − = −+ (2)

δi = tβ((ni – nn)1/2)/ ni (3)

δix = tβ((nix – 2Bx nn)1/2)/ nix (4)

 σ

structure is directly dependent upon the thermodynamic stability of the environment. Changes within the structure of the inhomogeneities are more mobile and are preceded by changes in the thermodynamic state of the environment as a whole. We take this as an axiom. As such, the structure of the inhomogeneities is central to the prediction of processes within the environment. This becomes especially important during the development process of a catastrophic scenario. Their nonlinear nature makes standard methods for the analysis of irregularities ineffective because of the number of initial assumptions, which often only apply to the environment in the classical sense. Therefore, I propose a structural-statistical method for analysing the structure of inhomogeneities.
