ISSN 1210-2512 (Print)

ISSN 1805-9600 (Online)

Radioengineering

Radioeng

Proceedings of Czech and Slovak Technical Universities

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September 2003, Volume 12, Number 3

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J. Dobes, J. Michal [references] [full-text] [Download Citations]
Using the Variable-Length Arithmetic for an Accurate Poles-Zeros Analysis

In the paper, a reduction algorithm for transforming the general eigenvalue problem to the standard one is presented for both classical full-matrix methods and a sparse-matrix technique appropriate for large-scale circuits. An optimal pivoting strategy for the two methods is proposed to increase the precision of the computations. The accuracy of the algorithms is furthermore increased using longer numerical data. First, a ORQJ.GRXEOH precision sparse algorithm is compared with the GRXEOH precision sparse and full-matrix ones. Finally, the application of a suitable multiple-precision arithmetic library is evaluated.

  1. RUBNER-PETERSEN, T. On sparse matrix reduction for computingthe poles and zeros of linear systems, In Proceedings of the 4thInternational Symposium on Network Theory. Ljubljana (Slovenia),1979.
  2. RALSTON A. A first course in numerical analysis. New York:McGraw-Hill Inc., 1965.
  3. KNUTH, D. Seminumerical algorithms. 3rd ed., vol. 2 of The Art ofComputer Programming. Reading (MA): Addison-Wesley, 1997,sections 4.2.1 and 4.3.1.
  4. BIOLEK, D. Secondary root polishing techniques in computercircuit analysis. In Proceedings of the Radioelektronika'99. Brno(Czech Republic), 1999, p. 42 - 45.
  5. DOBES, J. An accurate poles-zeros analysis for large-scale analogand digital circuits. In Proceedings of the IEEE InternationalConference on Electronics, Circuits and Systems. St.Julians (Malta),2001, p. 1027 - 1030.
  6. BIOLEK, D., BIOLKOVA, V., DOBES, J. (Semi)symbolicmodeling of large linear systems: pending issues. In Proceedings ofthe ISSSE'01 URSI International Symposium on Signals, Systems,and Electronics. Tokyo (Japan), 2001, p. 397 - 399.

T. Dostal [references] [full-text] [Download Citations]
Filters with Multi-Loop Feedback Structure in Current Mode

Universal multifunctional (low-pass, high-pass, band-pass, band-reject and all-pass) nth-order active RC filters in current mode are presented in this paper. The filters are based on several multi-loop feedback and state-variable structures. Their modification and implementation using multi-output transconductors (OTA) and current followers are given.

  1. CHEN, W.K. The circuits and filters handbook. Florida: CRC Press,1995.
  2. BOWRON, P., STEPHENSON, F. W. Active filters for communicationsand instrumentation. London: McGraw-Hill, 1979.
  3. TOUMAZOU, C., LIDGEY, F. J., HAIGH, D. G. Analogue IC design:The current-mode approach. London: Peter Peregrinus Ltd.,1990.
  4. MATSUMOTO, F., MIYAKE, NOGUCHI, Z. A high precision lowvoltagebipolar current mirror circuit and its compensation for stability.Internat. Journal of Electronics. 2000, vol. 87, no. 1, p. 71 - 78.
  5. HAJEK, K., SEDLACEK, J. NAFID program as powerful tool infilter education area. In Proceedings of the Conference CIBLIS'97.Leicester (UK), 1997, p. PK-4 1-10.
  6. BIOLEK, D., KOLKA, Z., SVIEZENY, B. Teaching of electricalcircuits using symbolic and semisymbolic programs. In Proceedingsof the 11th Conference EAEEIE. Ulm (Germany), 2000, p. 26 - 30.
  7. SUN, Y., FIDLER, J. K. Current-mode OTA-C realization of arbitraryfilter characteristics. Electronics Letters. 1996, vol. 32, no. 13,p. 1181 - 1182.
  8. SUN, Y., FIDLER, J. K. Current-mode multiple-loop filters usingdual-output OTA's and grounded capacitors. International Journalof Circuit Theory and Application. 1997, vol. 25, no. 1, p. 69 - 80.
  9. ACAR, C., ANDAY, F., KUNTMAN, H. On the realization of OTACfilters. International Journal of Circuit Theory and Application.1993, vol. 21, no. 3, p. 331 - 341.
  10. DOSTAL, T. Realisation of Arbitrary Filter Characteristics UsingCurrent Conveyors. (will be published).

V. Sokol, K. Hoffmann, J. Vajtr [references] [full-text] [Download Citations]
Noise Figure Measurement of Highly Mismatched DUT

A device mismatch seriously degrades the accuracy of its noise figure characterization. A new second stage correction technique for highly mismatched device under test is proposed and compared to the standard technique. The presented method is based on additional vector measurement. It takes into account measuring receiver noise figure dependence on the DUT output mismatch besides an available gain correction. Significant accuracy improvement of measured data and decreased error variation is demonstrated. The suggested method is in principle able to eliminate all systematic errors in noise figure measurement.

  1. VODRAN, D. Noise figure measurement: correction related to matchand gain. Microwave Journal. 1999, p. 22 - 38.
  2. COLLANTES, J. M., POLLARD, R. D, SAYED, M. Effects of DUTmismatch on the noise figure characterization: a comparative analysisof two Y-factor techniques. IEEE Transactions on Instrumentationand Measurement. 2002, vol. 51, no. 12, p. 1150 - 1156.
  3. BRYANT, G. H. Principles of microwave measurements. London:IEE, 1993.

B. Taha-Ahmed, M. Calvo-Ramon, L. de Haro-Ariet [references] [full-text] [Download Citations]
On the UMTS Downlink Capacity in (Open Rural and Countryside) Zone Near Deep Space Network (DSN) Installations

The UMTS macrocell downlink capacity is evaluated for macrocells that operate at the same frequency as the Deep Space Network (DSN) and that are nearby the DSN installations. It has been found that the cell capacity is not affected when the distance between the DSN installations and the macrocell is more than 21 km. For lower distance, the effect is high and the downlink vanishes at a distance less than 2 km when the microcell radius is 1 km. Near the DSN installation, the macrocell radius has to be 1 km or less.

  1. HO, C., SUE, M., PENG, T., KINMAN, P., TAN, H. Interferenceeffects of deep space network transmitters on IMT-2000/UMTS receiverat S-Band. TMO Progress Report 42-142. 2000.
  2. International Telecommunication Union, Radiocommunications Sector:Propagation by Diffraction. Recommendation ITU-R P.526-5,1997.
  3. HERNANDO RABANOS, J. M, Radio transmission, Editorial. CERA,1998, (in Spanish).
  4. CALVO-RAMON, M. Third generation IMT-2000 (UMTS) mobilecommunication systems. Fundacion Airtel Vodafone, 2002, (in Spanish).
  5. HOLMA, H., TOSKLA, A. WCDMA for UMTS. J. Wiley, 2000.
  6. LEE, J. S., MILLER, L. E. CDMA Systems engineering handbook.London: Artech House, 1998.

B. Taha-Ahmed, M. Calvo-Ramon, L. de Haro-Ariet [references] [full-text] [Download Citations]
W-CDMA Uplink Capacity and Interference Statistics of a LongGroove-Shaped Road Microcells Using A Hybrid Propagation Model

The uplink capacity and the interference statistics of the sectors of a long groove-shaped road W-CDMA microcell are studied. A model of 9 microcells in a groove-shaped road is used to analyze the uplink. A hybrid model for the propagation is used in the analysis. The capacity and the interference statistics of the cell are studied for different sector ranges, different specific attenuation factors, different antenna side lobe levels and different bend losses.

  1. CHO, H. S., CHUNG, M. Y., KANG, S. H., SUNG, D. K. Performance analysis of cross- and cigar shaped urban microcells considering user mobility characteristics. IEEE Transactions on Vehicular Technology. 2000, vol. 49, no. 1, p 105 - 115.
  2. MIN, S., BERTONI, H. L. Effect of path loss model on CDMA system design for highway microcells. In Proceedings of the 48th Vehi-cular Technology Congress. Ottawa (Canada), 1998, p 1009 - 1013.
  3. HASHEM, B., SOUSA, E. S. Reverse link capacity and interference statistics of a fixed-step power-controlled DS/CDMA system under slow multipath fading. IEEE Transactions on Communication. 1999, vol. 47, no. 12, p. 1905 - 1912.
  4. AHMED, B. T., RAMON, M. C., ARIET, L. H. Shaped W-CDMA cells performance in highways. IJECE. 2002, vol. 1, no. 2, p. 80 to 84.
  5. AHMED, B. T., RAMON, M. C., ARIET, L. H. W-CDMA uplink capacity and interference statistics of a long tunnel cigar-shaped mic-rocells. JCN, submitted.
  6. OHTAKI, Y., SENGOKU, M., SAKURI, K., YAMAGUCHI, Y., ABE, T. Propagation characteristics in open-groove waveguides surrounded by rough sidewalls. IEEE Transactions on Electromag-netic Compatibility. 1990, vol. 32, no. 3, p 177 - 184.
  7. ZHANG, Y. P., HWANG, Y., PARSONS, J. D. UHF radio propagation characteristics in straight open-groove structure. IEEE Transactions on Vehicular Technology. 1999, vol. 48, no. 1, p. 249 - 254.
  8. MELIS, B., ROMANO, G. UMTS W-CDMA: Evaluation of radio performance by means of link level simulations. IEEE Transactions on Personal Communications. 2000, vol. 7, no. 3, p. 42 - 49.

D. Kula [references] [full-text] [Download Citations]
Compaction Filter as an Optimum Solution for Multirate Subband Coder of Cyclostationary Signals

A consistent theory of optimum subband coding of zero mean wide-sense cyclostationary signals with N-periodic statistics is presented in this paper. Blocked polyphase representation of the analysis and synthesis filter banks is introduced as an effective way of multirate subband coder description. Optimum energy compaction using Nyquist-M process is presented as a solution for maximizing the coding gain of the coder. In two definitions and four theorems the author proves that Nyquist-M filters fulfill necessary and sufficient conditions imposed on subband signals. Results from Matlab simulations are presented to support theoretical conclusions.

  1. KULA, D. Optimum subband coding of cyclostationary signals.Ph.D. thesis proposals. Brno: Brno University of Technology, 1999.
  2. DASGUPTA, S., SCHWARTZ, C., ANDERSON, B. Optimum subbandcoding of cyclostationary signals. In Proceedings of the InternationalConference ICASSP99. Phoenix (Arizona), 1999.
  3. HARDY, G. M., LITTLEWOOD, J. E., POLYA, G. Inequalities.Cambridge: Cambridge University Press, 1934.
  4. VAIDYANATHAN, P. P. Theory of optimal orthonormal subbandcoders. IEEE Transactions on Signal Processing. 1998, vol. 43, no.6, p 1528 - 1543.
  5. KULA, D. Necessity of decorrelation of subband signals in WSCSmultirate coders. In Proceedings of the Conference Radioelektronika1999. Brno: Brno University of Technology, 1999.
  6. MARSHAL, A.W., OLKIN, I. Inequalities: theory of majorizationand its applications. New York: Academic Press, 1979.
  7. SCHWARTZ, C. Linear time varying all pass systems in digital signalprocessing. Ph.D. thesis. Iowa: University of Iowa, 1998.
  8. VAYDIANATHAN, P.P. Multirate systems and filter banks. EnglewoodCliffs: Prentice Hall, 1992.
  9. KULA, D. Fundamentals of an optimal multirate subband coding ofcyclostationary signals. Radioengineering. 2000, vol. 9, no. 2, p. 5 to9.

A. Pribilova [references] [full-text] [Download Citations]
Preemphasis Influence on Harmonic Speech Model with Autoregressive Parameterization

Autoregressive speech parameterization with and without preemphasis is discussed for the source-filter model and the harmonic model. Quality of synthetic speech is compared for the harmonic speech model using autoregressive parameterization without preemphasis, with constant and adaptive preemphasis. Experimental results are evaluated by the RMS log spectral measure between the smoothed spectra of original and synthesized male, female, and childish speech sampled at 8 kHz and 16 kHz. Although the harmonic model is used, the benefit of the adaptive preemphasis could be valid for the source-filter model, as well.

  1. MARKEL, J. D., GRAY, A. H. A linear prediction vocoder simulationbased upon the autocorrelation method. IEEE Transactions onAcoustics, Speech, and Signal Processing, 1974, vol. ASSP-22, no.2, p. 124 - 134.
  2. GRAY, A. H., MARKEL, J. D. A spectral-flatness measure for studyingthe autocorrelation method of linear prediction of speech signals.IEEE Transactions on Acoustics, Speech, and Signal Processing,1974, vol. ASSP-22, no. 3, p. 207 - 217.
  3. McAULAY, R. J., QUATIERI, T. F. Sinusoidal coding. In SpeechCoding and Synthesis (Kleijn, W. B., Paliwal, K. K., Eds.). Elsevier,1995, p. 121 - 173.
  4. QUATIERI, T., F., McAULAY, R., J. Audio signal processing basedon sinusoidal analysis/synthesis. In Applications of Digital SignalProcessing to Audio and Acoustics (Kahrs, M., Brandenburg, K.,Eds.). Kluwer Academic Publishers, 2001, p. 343 - 416.
  5. AHMADI, S., SPANIAS, A., S. A new phase model for sinusoidaltransform coding of speech. IEEE Transactions on Speech and AudioProcessing, 1998, vol. 6, no. 5, p. 495 - 501.
  6. STYLIANOU, Y. Concatenative speech synthesis using a harmonicplus noise model. Third ESCA/COCOSDA Workshop on Speech Synthesis,Jenolan Caves, B. Mountains (Australia), 1998, p. 261 - 266.
  7. CRESPO, M., A., R., et al. On the use of a sinusoidal model forspeech synthesis in text-to-speech. In: Progress in Speech Synthesis(van Santen, J., P., H. et al., Eds.). New York: Springer-Verlag,1997, p. 57 - 70.
  8. LI, C. Analysis-by-synthesis multimode harmonic speech coding atlow bit rate. PhD Thesis. Santa Barbara (USA): University of California,2000.
  9. AHMADI, S., SPANIAS, A. S. Low bit-rate speech coding based onan improved sinusoidal model. Speech Communication, 2001, vol.34, no. 4, p. 369 - 390.
  10. SKOGLUND, J. Analysis and quantization of glottal pulse shapes.Speech Communication. 1998, vol. 24, no. 2, p. 133 - 152.
  11. GRAY, A. H., MARKEL, J. D. Distance measures for speech processing.IEEE Transactions on Acoustics, Speech, and Signal Processing.1976, vol. ASSP-24, no. 5, p. 380 - 391.
  12. MADLOVA, A. Comparison of spectral measure and listening testsresults. Measurement of Speech and Audio Quality in Networks.International On-line Workshop. 2002, p. 37 - 40.(http://wireless.feld.cvut.cz/mesaqin2002/full07.pdf).
  13. MADLOVA, A. Autoregressive and cepstral parametrization in harmonicspeech modelling. Journal of Electrical Engineering. 2002,vol. 53, no. 1-2, p. 46 - 49.
  14. MADLOVA, A. Some parametric methods of speech processing.PhD Thesis. Bratislava (Slovakia): Slovak University of Technology,2001.
  15. HESS, W. Pitch determination of speech signals. Algorithms anddevices. Berlin: Springer-Verlag, 1983.
  16. GARDNER, W. R., RAO, B. D. Noncausal all-pole modeling ofvoiced speech. IEEE Transactions on Speech and Audio Processing.1997, vol. 5, no. 1, p. 1 - 10.

J. Stastny, P. Sovka [references] [full-text] [Download Citations]
Cross-Language Experiment

The contribution addresses the cross-language experiment. The aim was to test the possibility of the conversion French phoneme models into Czech ones. This model conversion uses the Hidden Markov Models (HMM) classification procedure. The first step consists of the iterative mapping of French models to Czech ones. The mapping is given by the analysis the confusion matrix. The second step is the Baum-Welch re-estimation resulting in the final models for Czech language. Despite of the differences between French and Czech languages the final recognition score reaches 64% for the phoneme recognition and 74% for digit recognition. Relatively low recognition accuracy is caused by the inadequate noise model. The experiences gained with the cross-language experiment were utilized for the classification of simple human body movements. The solution of this problem and results are described in the second part of this contribution under the title EEG Signals Classification-Introduction to the Problem.

  1. NOUZA, J. InfoCity. http://itakura.kes.vslib.cz/kes/infocitye.html
  2. HOLADA, M., NOUZA, J. Tools for building, maintaining and evaluating voice operated telephone information system. 13th COST Meeting. Budapest, 1999 (in the frame of COST 249 - Continuous speech recognition over the telephone line, . http://www.elis.rug.ac.be/ELISgroups/speech/ cost249/index.html).
  3. DOBLER,S. Speech recognition technology for mobile phones. Ericsson Review. 2000, no. 3.
  4. ABE, N., WARMUTH, M. K. On the computational complexity of approximating distributions by probabilistic automata. In Proc. of the 3rd Workshop on Computat. Learning Theory. 1990, p. 52 - 66.
  5. YOUNG, S. J. A review of large-vocabulary continuous-speech recognition. IEEE Signal Processing Magazine. 1996, p. 45 - 57.
  6. RABINER, L. R. A tutorial on hidden Markov models and selected applications in speech recognition. IEEE Signal Processing Magazine. 1989, p 267 - 296.
  7. PICONE, J. Continuous speech recognition using hidden Markov models. IEEE ASSP Magazine. 1990, p. 26 - 41.
  8. YOUNG, S. J. HTK reference manual. Cambridge University Engineering Department. 1993.
  9. PICONE, J. Signal modelling techniques in speech recognition. Pro-ceedings of the IEEE. 1993.
  10. CONSTANTINESCU, A., CHOLLET, G. Swiss polyphone and polyvar: Building databases for speech recognition and speaker verification. 3rd Slovenian-German and 2nd SDRV workshop, Speech and Image Understanding. Ljubljana (Slovenia), 1996.
  11. SOVKA, P. Cross language experiment verifying approach converting French flexible vocabulary to Czech vocabulary. Research report of sabbatical stay. Paris: ENST, 1997.
  12. CONSTANTINESCU, A. et al. Bootstrapping a Czech vocabulary through cross-language use of French phonemes. Presentation on COST 249 Committee. Roma (Italy), 1997.
  13. SCHULTZ, T., WAIBEL, A. Experiments on cross-language acoustic modeling. Interactive Systems Laboratories. Carnegie Mellon Univ. (USA), Univ. of Karlsruhe (Germany), EUROSPEECH 2001. http://www.is.cs.cmu.edu/papers/speech/EUROSPEECH01/.
  14. VOPICKA, J. French - Czech cross - language experiment. In Poster 1998. Prague: CTU, 1998, p. 46.
  15. VOPICKA, J., POLLAK, P., SOVKA, P. UHLIR, J. ASR with noisy speech pre-processing and phoneme model re-estimation. In Proceedings of Robust Methods for Speech Recognition in Adverse Con-ditions. Brussels: COST Office, 1999, p. 151 - 154.
  16. VOPICKA, J., POLLAK, P., SOVKA, P. UHLIR, J. Phoneme model based ASR of words in car environment. In Polish-Hungarian-Czech Workshop on Circuit Theory, Signal Processing, and Application. Prague: CTU, 1999, p. 89 - 92.
  17. JONES, R. J. SpeechDat cymru: A large-scale Welsh telephony data-base. In 1st International Conference on Language Resources and Evaluation. Granada (Spain), 1998.
  18. NOVOTNY J. Systematic analysis of triphone-based command recogniser in additive noises. Internal research report. Prague: FEE CTU, #Z03- 1, 2003 (in Czech)
  19. POLLAK, P., CERNOCKY, J. Final recruitement metodology and documentation of speakers typology for the final Czech database. Research report. Prague: CTU, 2000, ED2.12.2.b. 8 p.
  20. POLLAK, P., CERNOCKY, J. Installation of recording device including recording, annotation and documentation on 10 1st speakers - strategy for recruitment of speakers. Research report. Prague: CTU, 2000, ED.2.12.a. 20 p.
  21. CERNOCKY, J., POLLAK, P., HANZL, V. Czech recordings and annotations on CD's - documentation on the Czech database and database access. Research rep. Prague: CTU, 2000. ED2.3.2. 37 p.

M. Partyk, J. Polec, I. Kolingerova [references] [full-text] [Download Citations]
Hybrid Scheme with Triangulations for Transform Coding

Our approach to image region approximation offers a complete scheme consisting of several steps. We separate the encoding of region boundaries from the texture description. The original image is first segmented using an unsupervised segmentation method for color-texture regions. Following polygonal approximation of created regions causes the degradation of region boundaries. The triangulation is then applied to polygons and either all short edges, or all small triangles are filtered out from the triangular mesh. This results in new smaller regions. The encoding and decoding of polygons and triangles is very efficient, because we need to store only the vertices. For texture approximation we use 2D shape independent orthogonal transforms (e.g. DCT II). The texture is encoded with a code similar to JPEG arithmetic code. The encoding scheme proposed in this paper is much faster than latest approaches with polygonal approximation. We present the two triangulation algorithms - constrained greedy (CGT) and constrained Delaunay triangulation (CDT). Both CGT and CDT provide similar results.

  1. GILGE, M., ENGELHARDT, T., MEHLAN, R. Coding of arbitrary shaped image segments based on a generalized orthogonal transform. In Signal Processing: Image Communication. 1989, no. 1, p. 153 to 180.
  2. KAUP, A., AACH, T. Coding of segmented images using shape-independent basis functions. In IEEE Transactions on Image Processing. 1998, vol. 7, no. 7, p. 937 - 947.
  3. POLEC, J., et al. New scheme for region approximation and coding with shape independent transform. In Proceedings of Symposium Photogrammetric Computer Vision. Graz (Austria), 2002, p. B214 to 217.
  4. DENG, Y., MANJUNATH, B. S. Unsupervised segmentation method for color-texture regions in images and video. In IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI '01). 2001, August.
  5. POLEC, J., KOLINGEROVA, I., PARTYK, M. Hybrid approach with polygonization and triangulation for transform coding. In Proceedings of the East-West-Vision Conference. Graz (Austria), 2002, p. 219 - 220.
  6. PREPARATA, F. P., SHAMOS, M. Computational geometry: An introduction. New York: Springer-Verlag, 1985.
  7. SHEWCHUK, J. R. Triangle: Engineering a 2D quality mesh generator and Delaunay triangulator. In First Workshop on Applied Com-putational Geometry. Philadelphia (Pennsylvania), 1996, p. 124 to 133.

R. Hudec [references] [full-text] [Download Citations]
Mixed Noise Suppression in Color Images by Signal-Dependent LMS L-Filters

The paper is devoted to the signal-dependent (SD) design of adaptive LMS L-filters with marginal data ordering for color images. The same stem of SD processing of noised grayscale images was applied on noisy color images. Component-wise and multichannel modifications of SD LMS L-filter in R'G'B' (gamma corrected RGB signals) color space were developed. Both modifications for filtering two-dimensional static color images degraded by mixed noise consisting of additive Gaussian white noise and impulsive noise were used. Moreover, single-channel spatial impulse detectors as detectors of impulses and details were used, too. Considering experimental results, SD modifications of L-filters for noisy color images can be concluded to yield the best results.

  1. KOTROPOULOS, C., PITAS, I. Adaptive LMS L-filters for noisesuppression in image. IEEE Transactions on Image Processing.1996, vol. 5, no. 12, p. 1596-1609.
  2. KOCUR, D., HUDEC, R., MARCHEVSKY, S. Suppression ofmixed noise in the similar images by using adaptive LMS L-filters.Radioengineering. 2000, vol. 9, no. 4, p. 15-21.
  3. KOTROPOULOS, C., PITAS, I. Adaptive multichannel marginal Lfilters.SPIE Optical Engineering. 1999, vol. 38, no. 4, p. 688-704.
  4. HUDEC, R., MARCHEVSKY, S. Extension of impulse detectors tospatial dimension and their utilisation as switch in the LMS L-SDfilter. Radioengineering. 2001, vol. 10, no. 1, p. 11-15.
  5. FORD, A., ROBERTS, A. Colour space conversions.http://www.wmin.ac.uk/ITRG. 1998, p. 1-31.
  6. ASTOLA, J., HAAVISTO, P., NEUVO, Y. Vector median filters.Proceedings of the IEEE. 1990, vol. 78, no. 4, p. 678-689.
  7. KOTROPOULOS, C., PITAS, I., GABRANI, M. Multichannel adaptiveL-filters in color image processing. In Proceedings of the IEEEInternational Conference on Image Processing ICIP '96. 1996, Lausanne(Switzerland), p. 989-992.
  8. LUKAC R., STUPAK Cs. A Class of impulse detectors controlled bya threshold. In Proceedings of the International Conference Informationand Algorithms 99. 99, Presov: University of Presov, p.178-181.
  9. MOUCHA, V., MARCHEVSKY, S., LUKAC, R., STUPAK, Cs.Cislicova filtracia obrazovych signalov. Kosice: Vydavatelstvo Vojenskejleteckej akademie M.R. Stefanika v Kosiciach, 2000.

J. Stastny, P. Sovka, A. Stancak [references] [full-text] [Download Citations]
EEG Signal Classification: Introduction to the Problem

The contribution describes the design, optimization and verification of the off-line single-trial movement classification system. Four types of movements are used for the classification: the right index finger extension vs. flexion as well as the right shoulder (proximal) vs. right index finger (distal) movement. The classification system utilizes hidden information stored in the characteristic shapes of human brain activity (EEG signal). The great variability of EEG potentials requires using of context information and hence the classifier based on Hidden Markov Models (HMM). The suitable parameterization, model structure as well as training and classification process are suggested on the base of spectral analysis results and experience with the speech recognition. The training and the classification are performed with the disjoint sets of EEG realizations. Classification experiments are performed with 10 randomly chosen sets of EEG realizations. The final average score of the distal/proximal movement classification is 80%; the standard deviation of classification results is 9%. The classification of the extension / flexion gives comparable results.

  1. EBRAHINI, T., VESIN, J-M., GARCIA, G. Brain-computer interface.in multimedia communication. IEEE Signal Processing Magazine.2003, no. 1, p. 14 - 24.
  2. OBERMAIER, B., GUGER, C., PFURTSCHELLER, G. HiddenMarkov models used for off line classification of EEG data. BiomedizinischeTechnik. 1999, vol. 44, no. 6, p. 158-162.
  3. STASTNY, J. Analysis of states in EEG signals. M. Sc. Thesis. Prague:Czech Technical University, 2001. (in Czech)
  4. YOUNG, S. J. HTK reference manual. Cambridge University EngineeringDepartment, 1993.
  5. PELTORANTA, M., PFURTSCHELLER, G. Neural network basedclassification of non-averaged event-related EEG responses. Institutesfor Information Processing Graz: Report Series, 1992.
  6. KUBAT, M., FLOTZINGER, D., PFURTSCHELLER, G. Discoveringpatterns in EEG signals: comparative study of pattern recognitionand machine learning methods. Institutes for Information ProcessingGraz: Report Series, 1992.
  7. STASTNY, J., STANCAK, A., SOVKA, P. EEG signal classification.In Abstract Book, 23rd Annual Int. Conference of IEEE - Engineeringin Medicine and Biology. Istanbul (Turkey), 2001, p. 132.
  8. STANCAK, A., PFURTSCHELLER, G. The effects of handednessand type of movement on the contralateral preponderance of µ -rhythm desynchronisation. Electroencephalography and ClinicalNeurophysiology, 1996, vol. 99, p. 174 - 182.
  9. STASTNY, J., SOVKA, P., STANCAK, A. Extension and flexionmovements: analysis and classification. Research report #Z99-2,FEL CVUT, Department of Circuit Theory, 1999.
  10. MCFARLAND, D. J., MCCANE, L. M., DAVID, S. V., WOLPAW,J. R. Spatial filter selection for EEG-based communication. Electroencephalographyand Clinical Neurophysiology, 1997, vol. 103, p.386 - 394.
  11. PENNY, W. D., ROBERTS, S. J. Experiments with an EEG-basedcomputer interface. Technical Report, Department of Electrical Engineering,Imperial College, London, 1999.
  12. PENNY, W. D., ROBERTS, S. J., STOKES, M. J. EEG-based communication:a pattern recognition approach. Brain-Computer InterfaceTechnology: Theory and Practice. First International Meeting,Rensselaerville, New York, 1999.
  13. RAMESH, S. Methods to improve the spatial resolution of EEG. Int.Journal of Bioelectromagnetism, 1999, vol. 1, p. 102 - 111.
  14. WOLPAW, J. R., BIRBAUMER, N., MCFARLAND, D. J., PFURTSCHELLER,G., VAUGHAN, T. M. Brain-computer interfaces forcommunication and control. Clinical Neurophysiology, 2002, vol.113, p. 767 - 791.
  15. WOLPAW, J. R., MCFARLAND,D. J., VAUGHAN, T. M. Braincomputerinterface research at the Wadsworth Centre. IEEE Trans.on Rehabilitation Engineering. 2000, vol. 8, no. 2, p. 222 - 226.
  16. RAMOSER, H., JOHANNES MULLER GERKING, J., PFURTSCHELLER,G. Optimal spatial filtering of single trial EEG duringimagined hand movement. IEEE Transactions on RehabilitationEngineering, 2000, vol. 8, no. 12, p. 441 - 446.
  17. SCHLOEGL, A., LUGGER, K., PFURTSCHELLER, G. Usingadaptive autoregressive parameters for a brain-computer interfaceequipment. Proceedings of the 19th International Conference, IEEE/EMBS. Illinois (USA), 1997, p. 1533 - 1535.
  18. STASTNY, J., "State of the art" of BCI systems. Research report#Z2003-3, FEL CVUT, Department of Circuit Theory, 2003.

Z. Dostal, I. Mokris [references] [full-text] [Download Citations]
Exploitation of Higher Order Moments Increase the Tracking Aircraft by the Extended Alpha-Beta Filter

The paper analyzes the possibility of exploitation of higher order moments for increasing the precision of tracking of a flying aircraft by the α-β filter. For tracking of a flying aircraft by the α-β filter the 3rd and 4th order moments in 3D space are used.

  1. BARTSCH, H. J. Mathematics formulas. Praha: SNTL, 1983.
  2. FAROOQ, M., BRUDER, S. Information type filters for tracking a maneuvering target. IEEE Transactions on Aerospace and Electronic Systems. 1990, vol. 26, no. 3, p. 441 - 454.
  3. KALKATA, P. R. The tracking index: A generalized parameter for α-β and α-β-γ target tracks. IEEE Transaction on Aerospace and Electronic Systems. 1984, vol. AES-20, no. 2, p. 174 - 182.
  4. KAWASE, T., TSURUNOSONO, H., EHARA, N., SASASE, I. An alpha-beta tracking filter combined with maneuver-driven circular prediction. Electronics and Communications in Japan, Part I - Communications 80, 1997, no. 10, p. 1-9.
  5. KAWASE, T., TSURUNOSONO, H., EHARA, N., SASASE, I. An alpha-beta tracking filter combined with ellipsoidal prediction using the generalized Hough transform. Electronics and Communications in Japan, Part I - Communications 81, 1998, no. 8, p. 9 - 18.
  6. KOSUGE, Y., KAMEDA, H., MANO, S., KONDOU, M. A Cartesian coordinate conversion algorithm for radar tracking with range rate measurement. Electronics and Communications in Japan, Part I - Communications 80, 1997, no. 4, p. 51-61.
  7. KOSUGE, Y., KAMEDA, H., MANE, S. Kalman filter and alpha-beta filters for radar tracking. Electronics and Communications in Japan, Part I - Communications 80, 1997, no. 3, p. 67-77.
  8. MOKRIS, I., DOSTAL, Z. Tracking filters for tracking of moving air objects. [Scientific Monograph]. Military Academy, Liptovsky Mikulas, 1999, (in Slovak).