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Proceedings of Czech and Slovak Technical Universities

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September 2001, Volume 10, Number 3

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A. I. Rybin, T. Dostal [references] [full-text]
Nodal Analysis of Circuits Containing Current Conveyors

A special method of the nodal analysis of the circuits containing several types of the multiport current conveyors is presented in this paper. The method is based on the given regular and homogeneous models of the irregular current conveyors by the gyrators. Then a diakoptic solving and modification of the inversion of the admittance matrix is applied

  1. TOUMAZOU, C., LIDGEY, F.J., HAIGH, D.G. Analogue IC design: The current-mode approach. London: Peter Peregrinus Ltd., 1990.
  2. VLACH, J. Basic network theory with computer applications. New York: Van Nostrand Reinhold, 1992.
  3. ELVAN, H.O., SOLIMAN, A.M. Novel CMOS differential voltage current conveyor and its applications. IEE Proc. Circuits Devices Systems. 1997, no. 3, p. 195-200.
  4. RYBIN, A.I., DOSTAL, T. Diakoptic modeling of VLS nonstandard networks. In Proc. of international conference Analysis, control & design SYS'95. Brno: AMSE, 1995, pp. 73-78.
  5. MANJUK, I. J. Software for analysis of linear networks based on diakoptic modification. Kiev (Ukraine):Technical University of Kiev - KPI, 2001.

Z. Kolka [references] [full-text]
Synthesis of Optimized Piecewise-Linear Systems Using Similarity Transformation, Part I: Basic Principles

Practical realization of nonlinear dynamic systems based on their state models raises an issue of finding such a form with low sensitivity to changes of network parameters. Present paper deals with a method for sensitivity optimization that keeps qualitative character of the system dynamics and increases the robustness of practical realization.

  1. CHUA, L.O. Global Unfolding of Chua's Circuit. IEICE Trans. Fundamentals. 1993, vol. E76-A, no. 5, p. 704-734.
  2. CHUA, L.O., WU, C.W. On Linear Topological Conjugacy of Lure's Systems. IEEE Trans. CAS. 1996, vol. 43, p. 158-161.
  3. POSPISIL, J., BRZOBOHATY, J., KOLKA, Z., HORSKA, J. Simplest ODE Equivalents of Chua's Equations. Intern. Jour-nal of Bifurcation and Chaos. 2000, vol. 10, no. 1, p. 1-23.
  4. SCHAUMANN, M. S. et al. Design of Analog Filters. Passive, Active RC, and Switched Capacitor. Engelwood Cliffs, NJ: Prentice-Hall, 1990.

J. Pospisil, Z. Kolka, J. Horska [references] [full-text]
Synthesis of Optimized Piecewise-Linear Systems Using Similarity Transformation, Part II: Second-Order Systems

State models of dynamical systems can be used as prototypes in practical realization of electronic chaotic oscillators. Experimental verification shows that namely eigenvalue sensitivities of these prototypes are very important for such a purpose. In the paper the optimization design procedure for the second-order linear and piecewise-linear (PWL) autonomous dynamical systems is suggested. This gives the possibility to obtain minimum eigenvalue sensitivities with respect to the change of the individual state model parameters.

  1. HANUS, S. Realization of Third-Order Chaotic Systems Using Their Elementary Canonical State Models. In Proc. of the con-ference Radioelektronika'97. Bratislava, 1997, p. 44-45.
  2. POSPISIL, J., BRZOBOHATY, J. Elementary Canonical State Models of Chua's Circuit Family. IEEE Trans. Circ. Syst.-I: Fundamentals. 1996, vol. 43, no. 8, p. 702-705.
  3. POSPISIL, J., BRZOBOHATY, J., KOLKA, Z., HORSKA, J. Decomposed Canonical State Models of the Third-Order Piecewise-Linear Dynamical Systems. In Proc. ECCTD'99, Stresa, 1999, p. 181-184.
  4. POSPISIL, J., BRZOBOHATY, J., KOLKA, Z., HORSKA, J. Simplest ODE Equivalents of Chua's Equations. International Journal of Bifurcation and Chaos. 2000, vol. 10, no. 1, p. 1-23.
  5. CHUA, L.O., WU, C.W. On Linear Topological Conjugacy of Lure's Systems. IEEE Trans. CAS, 1996, vol. 43, p. 158-161.
  6. KOLKA, Z. Using Similarity Transformation for Nonlinear Sys-tem Synthesis. In: Proc. Radioelektronika '2001. Brno, 2001, p. 5-7.

M. Antosova, V. Davidek [references] [full-text]
Design and Implementation of Wave Digital Filters

One of possibilities of the Wave Digital Filters (WDF) design is using the classical LC-filters theory. The aim of this paper is to demonstrate the design of WDF from the LC filter and the implementation of WDF on the fixed-point digital signal processor. The theory of wave digital filter has been developed by using the classical scattering parameter theory. The theory of ladder filters is well-known, and so our present problem can thus be reduced to a problem how to replace the L and C elements of the filters by adaptors and delay elements, adders and multipliers.

  1. FETWEIS, A: Digital Filter Structures Related to Classical Filter Networks. AEU, Band 25, 1971.
  2. FETWEIS, A., MEERKOETTER, K. On Adaptors for Wave Digital Filters. IEEE Trans. ASSP-23, 1975.
  3. HASLER, M., NEJRYNK, J. Electric Filters. Artech House, Inc., 1986
  4. FETTWEIS, A. Wave Digital Filters: Theory and Practice, Fellow. Proceedings of IEEE, vol. CT-19, Feb. 1986.
  5. UNBEHAUEN, R., CICHOCKI, A. MOS SC and Continuous-Time Integrated Circuits and Systems, Springer-Verlag, 1989.
  6. ANTOSOVA, M. Wave Digital Filters. Ph.D. equiv. thesis, CTU Prague, 1992 (in Czech).
  7. PSENICKA, B., UGALDE, F. G., SAVAGE, J., DAVIDEK, V.: Design of State Digital Filters. IEEE Trans. on Signal Processing, vol. 46, No. 9, September 1998.
  8. CHUNG, J., PARHI, K. Pipelined Lattice and Wave Digital Recursive Filters. Kluwer Academic Publishers, Boston, 1996.
  9. DAVIDEK, V., LAIPERT, M., VLCEK, M. Analog and Digital Filters. Monografie CTU, Prague, 2000 (in Czech).
  10. WINDER, S. Filter Design. Newnes-Butford Techn. Publishing, 1998.

Z. Matousek, J. Kurty [references] [full-text]
Radar Target Classification Using Neural Network and Median Filter

The paper deals with Radar Target Classification based on the use of a neural network. A radar signal was acquired from the output of a J frequency band noncoherent radar. We applied the three layer feed forward neural network using the backpropagation learning algorithm. We defined classes of radar targets and designated each of them by its number. Our classification process resulted in the number of a radar target class, which the radar target belongs to.

  1. KVASNICKA, V. et al. Uvod do teorie neuronovych sieti. [In Slovak]. Bratislava: IRIS, 1997.
  2. TUMA, M. Moznosti redukce neuronovych siti. [In Czech]. In Proceedings of the International Conference CATEA99, Brno, 1997, p. 119 - 122.
  3. BOTHA, E. C., BARNARD, E., BARNARD, CH. J. Feature-based Classification of Aerospace Radar Targets Using Neural Networks. Neural Networks. 1996, vol. 9, no. 1, p. 129-142.
  4. INGGS, M. R., ROBINSON, A. D. Ship Target Recognition Using Low Resolution Radar and Neural Networks. IEEE Transactions on Aerospace and Electronic Systems. 1999, vol. 35, no. 2, p. 386-392.

J. Kaiser, E. Kostal [references] [full-text]
The Color Splitting System for TV Cameras - XYZ Prism

One of the dominant aspects, which prejudices the quality of color image reproduction, is the first operation in TV chain - scanning. Up to this day, the color splitting system, working in RGB colorimetric system, is still entirely used. The existence of negative parts of the color matching functions r(l), g(l), b(l) causes complications by optical separation of partial pictures R, G, B in classic scanning system. It leads to distortion of reproduction of color images. However, the specific technical and scientific applications, where the color carries the substantial part of information (cosmic development, medicine), demand high fidelity of color reproduction. This article submits the results of the design of the color splitting system working in XYZ colorimetric system (next only XYZ prism). Shortly the way to obtain theoretical spectral reflectances of partial filters of XYZ prism is described. Further, these filters are approximated by real optical interference filters and the geometry of XYZ prism is established. Finally, the results of the colorimetric distortion test of proposed scanning system are stated.

  1. KOSTAL, E. Obrazova a televizni technika II: Televize (Image and television technique II: TV). Praha: CVUT, 1998.
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  4. SLAVIK, J. Navrh svetlodelici soustavy pro kameru pracujici v kolorimetrickem systemu X,Y,Z (Design of colour splitting system for TV camera working in colorimetric system X,Y,Z). Unpublished manuscript.
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  9. PAZDERAK, J. Kolorimetrie snimacich soustav barevne tele-vize a elektronicke kolorimetricke korekce (Colorimetry of scanning systems of colour TV and electronic colorimetric corrections). Edice CS. TELEVIZE. 1974, issue II, vol. 16.
  10. SVOBODA, V. Kolorimetrie a zdokonalene televizni soustavy (Colorimetry and improved TV systems), In: Televize 94 c.1, IVP CT Praha 1994, s. 65-114
  11. KAISER, J. Kolorimetrie zdokonalenych TV soustav (Colori-metry of improved TV systems). Diploma thesis. Praha: CVUT 2001
  12. KOSTAL, E., KAISER, J., SLAVIK, J.: Hranolova svetlodelici soustava pro televizni kamery (The colour splitting system for TV cameras). Prihlaska vynalezu (patent application) c. PV 2000-1167, 30.3.2000.

C. Stupak [references] [full-text]
Filtering of the Color Images Distorted by Impulse Noise

The paper deals with color image filtering distorted by impulse noise. The component, transformation, and vector filtering are analyzed. The filters are evaluated besides the classical criteria (mean absolute error and mean square error), and by the color difference criterion. Moreover, use of the impulse detectors in the color image filtration is analyzed.

  1. DRUTAROVSKY, M., MARCHEVSKY, S. Vector Neural Stack Filters. In Proceedings of the International Conference on Digital Signal Processing '93. Kosice: TU Kosice, 1993.
  2. FORD, A., ROBERTS, A. Color Space Conversions. In 1998, p. 1-31.
  3. GONZALEZ, R. C., WOODS, R. E. Digital Image Processing. London: Addison Wesley, 1992.
  4. LUKAC, R., STUPAK, Cs. A Class of Impulse Detectors Controlled By a Threshold. In Proceedings of 3rd International Scientific Conference Information and Algorithms '99. Presov: University of Presov, 1999, p. 178-181.
  5. MARCHEVSKY, S. Neural Stack Filters in Image Processing. Habilitation thesis. Kosice: University of Kosice, 1994.
  6. MARCHEVSKY, S. The Filtering Methods for Color Image Restoration. In Proceedings of Tempus Telecomnet 2nd International Workshop. Smolenice Castle, 1997, p. 106-114.
  7. MARCHEVSKY, S., DRUTAROVSKY, M., CHOMAT, O. Iterative Filtering of Noisy Images by Adaptive Neural Network Filter. In Proceedings of the Conference New Trends in Signal Processing I. Liptovsky Mikulas: TU of Liptovsky Mikulas, 1996, p. 118-121.
  8. MOUCHA, V. Median digital filtering of two-dimensional sig-nals distorted by impulsive noise used in air correlation-extremal navigation systems. Habilitation Thesis. Kosice: Vysoka vojenska letecka skola Kosice, 1992, (in Slovak).
  9. POYNTON, C.A. Frequently Asked Questions about Color. In, 1997. p. 1-24.
  10. ROZINAJ, G., POLEC, J., KOTULIAKOVA, J., PODHRAD-SKY, P., MARCEK, A., MARCHEVSKY, S. at all Digital Signal Processing II. Bratislava: Faber, 1997.
  11. SHARMA, G., TRUSSELL, H. J. Digital Color Imaging. IEEE Transactions on Image Processing. 1997, vol. 6, no. 7, p. 901-932.
  12. STUPAK, Cs. Digital Image Filtration Based on Local Statistics. In Proceedings of 3rd International Scientific Conference Electro '99. Zilina: University of Zilina, 1999, p. 106-111.
  13. STUPAK, Cs. Digital Image Filtering by Help of Classificators. In Proceedings of 24th Student Scientific Conference Joined with Competition. Budapest: TU Budapest, 1999, p. 82.
  14. STUPAK, Cs., LUKAC, R. Impulse Detection in Grayscale Images. In Proceedings of the 4th International Conference on Digital Signal Processing DSP '99. Herzany, 1999, p. 96-99.
  15. VERTAN, C., MALCIU, M., BUZULOIU, V., POPESCU, V. Median Filtering Techniques for Vector Valued Signals. In Proceedings of the IEEE International Conference on Image Proces-sing ICIP' 96. Lansanne (Switzerland), 1996, vol. 1. p. 977-980.
  16. LUKAC, R. Impulse Detection by Entropy Detector (H-Detector). Journal of Electrical Engineering. 1999, vol. 50, no. 9-10, p. 310-312.
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R. Jurik [references] [full-text]
Acceleration of Area Shift Calculation

The goal of the area shift measurement is to find the coordinates (location) of the searched pattern of one two-dimensional sequence in the second two-dimensional sequence. It is supposed that searched pattern is included in the searched sequence in the unknown location. As it was published in [3], it is possible to determine the area shift using either the correlation or difference method. The calculations using the definition equations of the correlation or difference method are very severe. This contribution shows how it is possible to significantly decrease needed amount of the calculation operations when appropriate characteristics of the compared two-dimensional sequences are used. The method, which uses histogram comparison, allows eliminating up to 90 % of the searched patterns from the calculations that use the definition equations. The method that uses the comparison of the mean values of the searched pattern segments reaches even better results. It can eliminate up to 99.9 % of the searched patterns. Both new eliminating methods significantly contribute to acceleration of the area shift calculation.

  1. JURIK, R.: Correlation Measurement of the Aircraft Track Velocity Vector. In: Proceedings of the Fifth Electrotechnical and Computer Science Conference EKR '96, volume B, Portoroz, Slovenija, 1996, p. 275-276.
  2. JURIK, R.: Segment Shift Measurement Using the Difference Method. In: Proceedings of the Digital Signal Processing '97. FEI TU in Kosice, Dept. of Electronics and Multimedia Communications, Kosice, 1997, p. 189-190.
  3. JURIK, R.: Optical Measurement of Linear and Area Shift Using Correlation and Difference Method. [PhD Thesis.] FEECS TU of Brno, Institute of Radioelectronics, Brno, 1999.

V. Sebesta, J. Kolouch [references] [full-text]
Pseudo-Chaotic Sequences Generated Using Integer-Number Iterations

A proposed system generates a pseudo-chaotic sequence using integer-number look-up-table (LUT). The current output of LUT is used as LUT input address in the next step. The generated sequence approximates or simulates a chaotic sequence. The main facility of the method is high speed of the generating; the main drawback is bounded length of the non-periodic part of the pseudo-chaotic sequence.

  1. SANG, T., WANG. R., YAN, Y. Constructing Chaotic Discrete Sequences for Digital Communications Based on Correlation Analysis. IEEE Transactions on Signal Processing. 2000, vol. 48, no. 9, p. 2557-2565.
  2. WANG, S., YIP, P. C., LEUNG, H. Estimating Initial Conditions of Noisy Chaotic signals Generated by Piece-Wise Linear Markov Maps Using Itineraties. IEEE Transactions on Signal Processing. 1999, vol. 47, no. 12, p. 3289-3302.
  3. ISABELLE, S., WORNEL, G. Statistical analysis and spectral estimation techniques for 1D chaotic maps. IEEE Trans. on Signal Processing. 1994, vol. 45, p. 1524-1527.
  4. ZHOU, L. H., FENG, Z. J. A New Idea of Using One-Dimensional PWL Map in Digital Secure Communications - Dual-Resolution Approach. IEEE Transactions on Circuits and Systems - II: Analog and Digital Signal Processing. 2000, vol. 47, p. 1107-1111.

M. Kollar [references] [full-text]
Flip-Flop Sensor Controlled by Slow-Rise Control Pulse

In this paper we deal with dynamic properties of the flip-flop sensor. Special attention will be paid to the condition of control by slow-rise segment of the control pulse and the derivation of the equivalent voltage. The results of the theoretical considerations are verified by simulations using SPICE, VERILOG, and a laboratory experiment.

  1. LIAN, W., Integrated silicon flip-flop sensor. Doctoral Thesis. Delft: Technise Universitet Delft, 1990.
  2. KOLLAR, M., Autocompensative system with analog feedback. Diploma Thesis. Kosice: Tech. Univer. of Kosice, 2000.
  3. KALAKAJ, P.; SPANY, V., SOLTYS, R., Flip-flop sensors with feedback. International Conference Belegrade, Tesla III Mille-nium, October, p. 145-149, 1996.
  4. SPANY, V., PIVKA, L., Dynamic properties of flip-flop sensors, Electrical Engineering, vol. 47, No.7-8, p.169-178, 1996.
  5. LEVICKY, D., MICHAELI, L., SPANY, V., PIVKA, L., KALAKAJ, P., Autocompensative system with flip-flop sensor. International Conference Napoli, p.185-189. , 1996.

P. Mikulik, J. Saliga [references] [full-text]
Volterra Filtering for ADC Error Correction

Dynamic non-linearity of analog-to-digital converters (ADC) contributes significantly to the distortion of digitized signals. This paper introduces a new effective method for compensation such a distortion based on application of Volterra filtering. Considering an a-priori error model of ADC allows finding an efficient inverse Volterra model for error correction. Efficiency of proposed method is demonstrated on experimental results.

  1. ARPAIA, P., DAPONTE, P., MICHAELI, L. A dynamic error model for integrating analog-to-digital converters. Measure-ment. 1999, vol. 25, p. 225 - 264.
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  3. REBOLD, T. A., IRONS, F. H. A phase plane approach to the compensation of high speed analog-to-digital converters. Proceedings of the IEEE International Symposium on Circuits and Systems. Philadelphia: IEEE, 1987, p. 455-458.
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  5. TSIMBINOS, J., LEVER, K. V. Applications of higher order statistics to modeling, identification and cancellation of non-linear distortion in high-speed samplers and analogue-to-digital converters using the Volterra and Wiener models. IEEE, 1993.
  6. TSIMBINOS, J. Identification and compensation of nonlinear distortion, A thesis submitted in accordance with the require-ments for the degree of Doctor of Philosophy, Institute for Telecommunications Research School of Electronic Engineering, University of South Australia.
  7. MIRRI, D., IUCULANO, G., FILICORI, F., PASINI, G., VANNINI, G. Modeling of non-ideal dynamic characteristics in S/H-ADC devices. Proceedings of IMTC'95. Waltham. 1995, p. 27-32.
  8. KOCUR, D. Algoritmy adaptacie adaptivnych volterrovych cislicovych filtrov (in Slovak language). Thesis for habilitation. Technical University Kosice. 1994, p. 9-22.
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  10. KOCUR D. Adaptive Volterra filters: a tutorial review. Digital signal processing and communications, Proceedings of workshop, Oradea, Romania, 1995, p. 1-32
  11. ARPAIA, P., MIKULIK, P. Dynamic error correction of in-tegrating analog-to-digital converters by using Volterra filtra-tion. Proceedings of 5th. International Workshop on ADC Modelling and Testing. Vienna: TU Vienna. 2000, p. 39-44.
  12. EVANS, C., REES, D., JONES, L., WEISS, M. Probing signals for measuring nonlinear Volterra kernels, Proceedings of IMTC'95. Waltham. 1995, p. 10-15.
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  14. ARPAIA, P., DAPONTE, P., MICHAELI, L. An a-priori ap-proach to phase-plane modelling of SAR A/D converters. IEEE Transactions on Instrumentation and Measurement. 1998, vol. 47, no. 4, p. 849-857.
  15. MICHAELI, L., SALIGA, J., SEDLAK, V. An approach to diagnostic of the AD converter embedded on ATMEL microcontrollers. Proceedings of 4th Workshop on ADC Modeling and Testing. Bordeaux. 1999, p. 247-252.
  16. IEEE Std. 1057-1994, "Standard for Digitizing Waveform Recorders".