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Proceedings of Czech and Slovak Technical Universities

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September 2002, Volume 11, Number 3

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P. Pechac [references] [full-text] [Download Citations]
Electromagnetic Wave Propagation Modeling Using the Ant Colony Optimization Algorithm

The Ant Colony Optimization algorithm - a multi-agent approach to combinatorial optimization problems - is introduced for a simple ray tracing performed on only an ordinary bitmap describing a two-dimensional scenario. This bitmap can be obtained as a simple scan where different colors represent different mediums or obstacles. It is shown that using the presented algorithm a path minimizing the wave traveling time can be found according to the Fermat's principle. An example of practical application is a simple ray tracing performed on only an ordinary scanned bitmap of the city map. Together with the Berg's recursive model a non-line-of-sight path loss could be calculated without any need of building database. In this way the coverage predictions for urban microcells could become extremely easy and fast to apply.

  1. DORIGO, M., MANIEZZO, V., COLORNI, A. The Ant System: Optimization by a Colony of Cooperating Agents. IEEE Transactions on Systems, Man, and Cybernetics Part B. 1996, vol. 26, no. 1, pp. 1 - 13.
  2. MIETTINEN, K., MAKELA, M., NEITTAANMAKI, P., PERI-AUX, J., editors. Evolutionary Algorithms in Engineering and Computer Science. Wiley. 1999.
  3. DORIGO, M., GAMBARDELLA, L. M. Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem. IEEE Trans. on Evolutionary Computation. 1997, vol. 1, pp. 53-66.
  4. PECHAC, P. Application of Ant Colony Optimisation Algorithm on the Recursive Propagation Model for Urban Microcells. URSI XXVIIth General Assembly, August 2002, Maastricht, in press.
  5. BERG, J-E. A Recursive Method for Street Microcell Path Loss Calculation. In Proceedings IEEE International Symposium on Personal, Indoor and Mobile Radio Communications PIMRC'95. 1995, vol. 1, pp. 140 - 143.
  6. ETSI technical report TR 101 112, Selection procedures for the choice of radio transmission technologies of the UMTS. ETSI, 1998.
  7. KOLAR, J. Theoretical Computer Science. Praha: Vydavatel-stvi CVUT, 1998.

S. Gona, Z. Raida [references] [full-text] [Download Citations]
Design of Planar Reflector Antennas

A design of single and folded reflector antennas is presented. We focus in the use of an optimization during the design process. In comparison to existing papers, possibility of realization of folded antenna at X band frequencies is demonstrated. Further more, an original procedure of phase center extraction of antennas and scatters is described. The procedure is applied in the design of simple reflector antenna incorporating a non-hyperbolic, electrically small reflector.

  1. POZAR, D. M., TARGONSKI, S. D., SYRIGOS, H. D. Design of Millimeter Wave Microstrip Reflectarrays. IEEE Transac-tions on Antennas and Propagation. 1997, vol. 45, no. 2, p. 287 - 295.
  2. MENZEL, W., PILZ, D. A 77-GHz FM/CW Radar Front-End with a Low-Profile Low Loss Printed Antenna. IEEE Trans. on Microwave Theory and Technique. 1999, vol.47, no. 12, p. 2237-2241.
  3. ENCINAR, J. A. Design of Two-Layer Printed Reflectarrays Using Patches of Variable Size. IEEE Transactions on Anten-nas and Propagation. 2001, vol. 49, no. 10, p. 1403 - 1410.
  4. FRANCHOIS, A., PICHOT, C. Microwave Imaging - Complex Permittivity Reconstruction with a Levenberg-Marquardt Met-hod. IEEE Transact. on Antennas and Propagation. 1997, vol. 45, no. 2, p. 203 - 215.
  5. SCOTT, G. Spectral Domain Method in Electromagnetics. London: Artech House. 1989.
  6. GONA, S. A study of influence of dielectric cover on FSS pro-perties. In Proceedings of the International Conference Radio-elektronika 2002. Bratislava (Slovakia). 2002.

Z. Tobes, Z. Raida [references] [full-text] [Download Citations]
Use of the Analog Neural Networks in the Adaptive Antenna Control Systems

In the paper, original control system of adaptive antennas, which is based on Kalman filter, is presented and compared with earlier approaches to this problem. The designed control circuit eliminates some disadvantages of the control circuits based on the classical Kalman neural network and the Wang one, and enables a real time processing of quickly changing signals processed by adaptive antennas. Especially, the dependence of the convergence rate on ratio of eigenvalues and the risk of instability are significantly reduced.

  1. CERNOHORSKY, D., NOVACEK, Z. Antennas and Propaga-tion of EM Waves. Brno: Brno University of Technology, 1992.
  2. LUENBERGER, D. E. Linear and Nonlinear Programming. Ad-dison Wesley Reading, MA, 1984.
  3. COMPTON, R. T., Jr. Improved Feedback Loop for Adaptive Arrays. IEEE Transactions on Aerospace Electronic Systems. 1980, vol. 16, no. 2, p. 128 - 136.
  4. CHANG, P. R., YANG, W. H., CHAN, K. K. A Neural Network Approach to MVDR Beamforming Problem. IEEE Trans. on Antennas and Propagation. 1992, vol. 40, no. 3, p. 313 - 322.
  5. WANG, J. Recurrent Neural Network for Solving Quadratic Programming Problems with Equality Constraints. Electronics Letters. 1992, vol. 28, no. 14, p. 1345 - 1347.
  6. KENNEDY, M. P., CHUA, L. O. Neural Networks for Nonlinear Programming. IEEE Transactions on Circuits and Systems. 1988, vol. 35, no. 5, p. 554 - 562.
  7. CHEN, Y. H., CHIANG, C. T. Adaptive Beam-Forming Using the Constrained Kalman Filter. IEEE Trans. on Antennas and Propagation. 1993, vol. 41, no. 11, p. 1576 - 1580.
  8. WIDROW, B., MANTEY, P. E., GRIFFITHS, L. J., GOODE, B. B. Adaptive Antenna Systems. Proc. IEEE. 1967, vol. 55, no. 12, p. 2143 - 2159.
  9. TOBES, Z., RAIDA, Z. Analog Neural Networks for the Control of Adaptive Antennas. In Proceedings of the International Symposium on Antennas JINA'96. 1996, Nice (France): France Telecom, p. 601 - 604.
  10. GODARA, L. C. Applications of Antenna Arrays to Mobile Communications, Part I: Performance Improvement, Feasibi-lity, and System Considerations. Proceedings of the IEEE. 1997, vol. 85, no. 7, p. 1031 - 1060.
  11. GODARA, L. C. Applications of Antenna Arrays to Mobile Communications, Part II: Beam-Forming and Direction-of-Arrival Considerations. Proceedings of the IEEE. 1997, vol. 85, no. 8, p. 1195 - 1245.
  12. RAIDA, Z. Improvement of Convergence Properties of Wang's Neural Network. Electronics Letters. 1994, vol. 30, no. 22, p. 1864 - 1866.
  13. RAIDA, Z. Stability of digital adaptive antennas. Ph.D. thesis. Brno: Brno University of Technology, 1994 (in Czech).
  14. KLEMES, M. A. Practical Method of Obtaining Constant Con-vergence Rates in LMS Adaptive Arrays. IEEE Trans. on An-tennas and Propagation. 1986, vol. 34, no. 3, p. 440 - 446.
  15. TOBES, Z., RAIDA, Z. Stability Problems of Wang's Neural Networks. In Proc. of the Conference Radioelektronika '96. Brno (Czech Republic), 1996, p. 366 - 369.
  16. BARTSCH, H.J. Mathematical formulas. Praha: SNTL Praha, 1987 (in Czech).
  17. WANG,J. Electronic Realization of Recurrent Neural Network for Solving Simultaneous Linear Equations. Electronics Let-ters. 1992, vol. 28, no. 5, p. 493 - 495.
  18. TOBES, Z., RAIDA, Z. Improvements of Analog Neural Networks Based on Kalman Filter. Radioengineering. 2002, vol. 11, no. 1, p. 6 - 13.

P. Kvarda [references] [full-text] [Download Citations]
Investigating the Rossler Attractor Using Lorentz Plot and Lyapunov Exponents

To investigate the Rossler attractor, introduced in 1976 by O.E. Rossler [3], we used Lorenz plot to show deterministic character and designated the Lyapunov exponent to show the chaotic character of the system.

  1. McCAULEY, J. L. Chaos, Dynamics and Fractals an algorithmic approach to deterministic chaos. Cambridge University Press, 1993.
  2. LORENZ, E. N. J. Atmos. Sci. 20, 1963, p. 130.
  3. ROSSLER O. E. An Equation for Continuous Chaos. Physics Letters, 1976, 57A(5).
  4. WEBSTER, J. (ed.), Wiley Encyclopedia of Electrical and Electronics Engineering Online, John Wiley & Sons, (1999), (
  5. KENNEDY, P. Chaos in the Colpitts Oscillator. IEEE Trans. on Circuits and Systems I: Fundam. Theory and Applic. 1994, vol. 41, no. 11, p.771-774.
  6. KVARDA, P. Master Thesis. Bratislava: Faculty of Electrical Engineering and Informatics, Slovak Technical University, 2000.
  7. KVARDA, P. Identifying the Deterministic Chaos by Using the Lorenz Maps. Radioengineering, 2000, vol. 9, no. 4, p. 32-33.
  8. KVARDA, P.: Identifying the Deterministic Chaos by Using the Lyapunov Exp. Radioengineering, 2001, vol. 10, no. 2, p. 38-40.

A. Prokes [references] [full-text] [Download Citations]
Correctness of Velocity Evaluation of System Using Spatial Filter

In this paper, a velocity measurement method using the spatial filter is presented. Luminous emitance of the surface passing through the moving spatial filter and optical set is projected to the active area of photo-detector. The velocity determination is based on the frequency spectrum evaluation of the photo-detector output signal. The formula for velocity computing is derived first. Then, correctness of velocity evaluation in dependence on the surface and measuring system properties is discussed.

  1. DUSEK, M., PANTOFLICEK, J., VACEK, R. Opticke mereni rychlosti - metoda prostorove filtrace. Jemna mechanika a optika. 1991, c. 9, s. 231 - 236.
  2. YFUSUMI, A. M. Velocity Sense Detection Based on the Spa-tial Filter Method. IEEE Transactions on Instrumentation and Measurement. 1990, vol. 39, no. 4.
  3. RICNY, V., PROKES, A. The Velocity Meter with the Spatial Filter. In Proceedings of the Int. Electrotechnical and Computer. Science Conf. ERK 99. Portoroz (Slovenia), 1999, p. 501 - 503.

J. Turan, L. Ovsenik, M. Benca, E. F. Carome [references] [full-text] [Download Citations]
Laboratory Equipment Type Fiber Optic Refractometer

Using fiber optics and micro optics technologies we designed an innovative fiber optic index of refraction transducer that has unique properties. On the base of this transducer a laboratory equipment type fiber optic refractometer was developed for liquid index of refraction measurements. Such refractometer may be used for medical, pharmaceutical, industrial fluid, petrochemical, plastic, food, and beverage industry applications. For example, it may be used for measuring the concentrations of aqueous solutions: as the concentration or density of a solute increase, the refractive index increases proportionately. The paper describes development work related to design of laboratory type fiber optic refractometer and describes experiments to evaluation of its basic properties.

  1. DAVIS, Ch. M., CAROME, E. F., WEIK, M. H., EZEKIEL, S., EINZING, R. E. Fiber Optic Sensor Technology Handbook. Optical Technologies, Herndon, 1986.
  2. TURAN, J., PETRIK, S. Fiber Optic Sensors. Alfa, Bratislava, 1990.
  3. TURAN, J., CAROME, E. F., OVSENIK, L. Fiber Optic Refracto-meter for Liquid Index of Refraction Measurements. In. Proc. of TELSIKS2001. Nis (Yugoslavia), 2001, p. 489-491.
  4. JASENEK, J., CERMAK, O. Optical Refractometry with Synthesized Coherence Function. In: Photonics, Devices and Systems. In Proc. of SPIE, 2000, vol. 4016, p. 204-210.
  5. TRAVICA, S. et al. Optically Powered Fiber-Optic Temperature thick Film NTC Sensor. In Proc. of LASER'98, Tucson, Arizona (USA), 1998, p. 562-567.

R. Lukac [references] [full-text] [Download Citations]
3-D Center-Weighted Vector Directional Filters for Noisy Color Sequences

This paper focuses on a noise filtering in color image sequences, where a new class of center-weighted vector directional filters is provided. According to high dimensionality of color image sequences, where besides the spatial frequencies in the frames it is necessary to consider the temporal correlation of an image sequence and the correlation between color channels too, the processing of color image sequences represents very important and interesting problem. Clearly, the color image sequences represent three-dimensional (3-D) vector-valued image signals and thus, the 3-D vector filters provide optimal approach, only. Novelty of this paper lies in the impulse noise suppression by a new class of center-weighted vector directional filters, where the influence of the filter parameter to filter performance is analyzed. The interesting behavior of a new filter class is illustrated by a number of experimental results and comparisons with the well-known filtering algorithms for color image sequences.

  1. ARCE, G.R. Multistage order statistic filters for image sequence processing. IEEE Transactions on Signal Processing. 1991, vol. 39, no. 5, p. 1146 - 1163.
  2. ASTOLA, J., HAAVISTO, P., NEUVO, Y. Vector median filters. Proceedings of the IEEE. 1990, vol. 78, no. 4, p. 678-689.
  3. GABBOUJ, M., CHEICKH, F.A. Vector median-vector directional hybrid filter for color image restoration. In Proceedings of EUSIPCO-96, 1996, p. 879 - 881.
  4. GRGIC, M., GHANBARI, M., GRGIC, S. Texture-based image retrieval in MPEG-7 multimedia system. In Proceedings of the IEEE Region 8 EUROCON'2001, vol. 2, p. 365 - 368.
  5. KLEIHORST, R.P., LAGENDIJK, R.L., BIEMOND, J. Noise reduction of image sequences using motion compensation and signal decomposition. IEEE Transactions on Image Processing. 1995, vol. 4, no. 3, p. 274 - 284.
  6. LUKAC, R. Vector LUM smoothers as impulse detector for color images. In Proceedings of European Conference on Circuit Theory and Design ECCTD '01 "Circuit Paradigm in the 21st Century". Espoo (Finland), 2001, vol. III, p. 137 - 140.
  7. LUKAC, R. Adaptive impulse noise filtering by using center-weighted directional information. In Proceedings of the 1st European Conference on Color in Graphics, Image and Vision CGIV'2002. Poitiers (France), 2002, p. 86 - 89.
  8. LUKAC, R., MARCHEVSKY, S. LUM smoother with smooth control for noisy image sequences. EURASIP Journal on Applied Signal Processing. 2001, vol. 01, no. 2, p. 110 - 120.
  9. LUKAC, R., MARCHEVSKY, S. Boolean expression of LUM smoothers. IEEE Signal Processing Letters. 2001, vol. 8, no. 11, p. 292 - 294.
  10. LUKAC, R., MARCHEVSKY, S. The use of threshold LUM smoothers in noised color sequences. In Proceedings of the International Conference on Trends in Communications IEEE REGION 8 EUROCON'2001. Bratislava (Slovakia), 2001, vol. 2, p. 373 - 376.
  11. LUKAC, R., MARCHEVSKY, S. Adaptive vector LUM smoother. In Proceedings of the 2001 IEEE International Conference on Image Processing ICIP 2001. Thessaloniki (Greece), 2001, vol. 1, p. 878 - 881.
  12. PLATANOITIS, K. N., ANDROUTSOS, D., VENETSANO-POULOS, A. N. Color image processing using adaptive vector directional filters. IEEE Transactions on Circuits and Systems II. 1998, vol. 45, no. 10, p. 1414 - 1419.
  13. SHARMA, G. Digital color imaging. IEEE Transactions on Image Processing. 1997, vol. 6, no. 7, p. 901 - 932.
  14. TRAHANIAS, P. E., KARAKOS, D., VENETSANOPOULOS, A. N. Directional processing of color images: theory and experimental results. IEEE Transactions on Image Processing. 1996, vol. 5, no. 6, p. 868 - 881.
  15. VIERO, T., OISTAMO, K., NEUVO, Y. Three-dimensional median related filters for color image sequence filtering. IEEE Transactions on Circuits and Systems for Video Technology. 1994, vol. 4, no. 2, p. 129 - 142.

A. Usakova, J. Kotuliakova, M. Zajac [references] [full-text] [Download Citations]
Walsh - Hadamard Transformation of a Convolution

A convolution is mathematical operation used in signal processing, in the homomorphous signal processing and digital image processing (e.g. image interpolation). In regard of computational complexity of the convolution in the time domain, it used to calculate in the other domain. Exp. x(n) * h(n) R X(W) × H(W), resp. X(W) × H(W), shows that a convolution in the time domain corresponds to multiplication in the Z domain, respectively frequency domain. This paper shows utilization of Walsh-Hadamard orthogonal transformations for convolution.

  1. AHMED, N., RAO, K. R. Orthogonal Transforms for Digital Signal Processing. Berlin: Springer-Verlag, 1975.
  2. ELLIOT, D. F., RAO, K. R. Fast Transforms, Algorithms, Analyses, Applications. Orlando: Academic Press, 1982.
  3. Grant project No. 1/7625/20 of the Ministry of Education of the Slovak Republic

P. Alexova, P. Kosut, J. Polec, K., Kotuliakova [references] [full-text] [Download Citations]
A Comparison of Selected GBN ARQ Schemes for Variable-Error-Rate Channel Using QAM

In non-stationary channels, error rates vary considerably. The paper compares Yao's Adaptive Go-back-N (GBN) Automatic-Repeat-Request (ARQ) scheme with Adaptive go-back-N with sliding window mechanism which both estimate the channel state in a simple manner, and adaptively switch their operation mode. The throughput of these schemes is compared in conditions of Additive White Gauss Noise (AWGN) channel with independent errors using 16-QAM modulation.

  1. LIN, S., COSTELLO, D., MILLER, M. J. Automatic-Repeat-Request Error-Control Schemes. IEEE Communication Magazine. 1994, vol. 22, no. 12, pp. 5-16.
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  4. YAO, Y.D. An Effective Go-Back-N ARQ Scheme for Variable-Error-Rate Channels. IEEE Trans. Commun. 1995, vol. 43, no. 1, p. 20-23.
  5. BRUNNEL, H., MOENACLAEY, M. Efficient ARQ Scheme for High Error Rate Channels. Electron. Lett. 1984, vol. 20, p. 986-987.
  6. CORAZZA, G. E., VATALARO, V. A Statistical Model for Land Mobile Satellite Channels and its Application to Nongeostationary Orbit Systems. IEEE Transaction on Vehicular Technology. 1994, vol. 43, no. 3, p. 738-741.
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  9. OKRAH, P. Digital Radio Modulation: A Wireless Reference Guide, 2001,