Digital Signal Processing

Russian
Scientific & Technical
Journal


“Digital Signal Processing” No. 4-2016

In the issue:

- digital algorithm based on sign signal processing
- estimate of a power spectral density
- novel approach to the construction of the orthogonal system
- offset estimation for OFDM systems in MIMO channels
- decoding of LDPC codes
- detecting a correlated signals in noise
- recognition of scanned handwritten texts
- interfaces of multiprocessor system



Digital algorithm to compute estimate of a power spectral density based on sign signal processing using time-weighting functions
V.N. Yakimov, e-mail: yvnr@hotmail.com
A.V. Mashkov,
e
-mail: mavstu@list.ru

Samara State Technical University (SSTU), Russia, Samara

Keywords: random process, power spectral density, time-weighting function, stochastic quantization, sign signal, time readout.

Abstract
The developed Digital algorithm for estimating of a power spectral density random process based on sign signal processing. These signals are the result of the transformation of the investigated random process using the sign-function analog stochastic quantization. This type of quantization is a very rough binary conversion, which is based on a comparison of the investigated random process with uniformly distributed auxiliary random signal acting as a threshold quantization. Such quantization allows very rough two-level quantization of random process without systematic error. Using of a sign-analog stochastic quantization has given analytical calculation of Fourier cosine transformation of time-weighting functions in going from the continuous represent spectral estimates to implement it in digital form. That approach eliminates the systematic error caused by the implementation of the operations of integration in digital form. As a result of analytical calculations of the cosine-Fourier transform operation of multiplication is converted to procedures that require performing logical and simple arithmetic operations of addition and subtraction. As a result, developed a digital algorithm increases the computational efficiency and reduces the complexity of the digital estimation of power spectral density. The results of numerical experiment estimating the power spectral density using the algorithm developed for some of the most used time-weighting function. Model of the investigated realization of a random process represented statistically independent harmonic components in the presence of additive noise.


References

1. Bendat, J., Peirsol, A. Application analysis of random data. Moscow: Publishing house Mir, 1989. 540 p. ISBN 5-03-001071-8

2. Denisenko A.N. Signaly. Teoreticheskaya radiotekhnika. Spravochnoe posobie. – M.: Goryachaya liniya-Telekom, 2005 – 704 s.

3. Marpl S.L. Tsifrovoy spektralnyy analiz i ego prilozheniya. – M.: Mir, 1990. – 584 s.

4. Maks J. Metody i tekhnika obrabotki signalov pri fizicheskikh izmereniyakh. – T.1. – M.: Mir. 1983 – 312 s.

5. Mirsky G.Ya. Kharakteristiki stokhasticheskoy vzaimosvyazi i ikh izmereniya [Characteristics of stochastic relations and their measurement]. M.: Energoizdat [Moscow: Energoizdat], 1982. 320 p.

6. Yakimov V.N. Obobshchennaya matematicheskaya model dvukhurovnevogo znakovogo preobrazovaniya // Tekhnika mashinostroeniya. – 2000. – ¹ 4. – S. 72–74.

7. Yakimov V.N. Tsifrovoy korrelyatsionnyy analiz na osnove intervalnogo predstavleniya rezultata znakovogo preobrazovaniya sluchaynykh protsessov [Digital correlation analysis based on interval representation of the result of symbolic transformations of random processes]. Pribory i sistemy. Upravlenie, kontrol, diagnostika [Instruments and systems. Management, control, diagnostics]. 2001. ¹ 11. P. 61-66.

8. Yakimov V.N. Strukturnoe proektirovanie tsifrovykh korrelometrov dlya operativnogo korrelyatsionnogo analiza na osnove znakovogo analogo-stokhasticheskogo kvantovaniya [The structural design of digital correlators for operative correlation analysis based on the sign-analog stochastic quantization]. Izmeritelnaya tekhnika [Measuring equipment]. 2007. ¹ 4. P. 6-11.

9. Yakimov V.N. The structural design of digital correlometers for operational correlation analysis based on sign-function analog-stochastic quantization // Measurement Techniques. – Publisher: Springer New York. 2007. Vol. 50, Nî. 4. Pp. 356-363.

10. Yakimov V.N. Tsifrovoy spektralnyy analiz na osnove znakovogo dvukhurovnevogo preobrazovaniya nepreryvnykh sluchaynykh protsessov i asimptoticheski nesmeshchennoy otsenki korrelyatsionnoy funktsii // Izmeritelnaya tekhnika. – 2005. – ¹ 12. – S. 18-23.

11. Yakimov V.N. Digital spectral analysis based on sign two-level transformation of continuous random processes and asymptotically unbiased estimation of the correlation function // Measurement Techniques. – Publisher: Springer New York. 2005. Vol. 48, Nî 12. Pp. 1171-1178.

12. Yakimov V.N. Digital spectral analysis based on signed two-level quantization of continuous random processes // In Proceedings of the 13th International Metrology Congress (On CD-ROM); 18-21 June 2007, Lille (France).

13. Dvorkovich V.P., Dvorkovich A.V. Okonnye funktsii dlya garmonicheskogo analiza signalov. – M.: Tekhnosfera, – 2014. – 112 s.

14. Prabhu K. M. M. Window Functions and Their Applications in Signal Processing. – CRC Press, Taylor & Francis Group, 2014. – XXII, 382 p.

Pulse random processes spectral density estimation using selected characteristic functions
V.S. Parshin, e-mail: vsparshin@gmail.com
The Ryazan State Radio Engineering University (RSREU), Russia, Ryazan

Keywords: : spectral analysis, pulse random process, characteristic function, statistic characteristics.

Abstract

Many objectives arising in the processing of the signals in radar, radio-navigation, related systems are reduced to the analysis of pulse random processes. Pulse sequence can act both as a carrier of useful information, and be a hindrance. Considering, that the signal processing is often performed in the spectral domain, relevant is the assessment of the spectral power densities of pulse sequence, at that in practice often have to evaluate the spectral power for one implementation of the process. It is know, that the evaluation of the spectral density of the power pulse sequences, obtained using fast or discrete Fourier in one implementation of the process is manque. To obtain well-founded estimates commonly used smoothing selective spectra by means spectral windows. For the pulse sequences occurring at random times, it is possible to obtain well-founded and nonadjacent spectral density estimation for one implementation without the smoothing procedure. This are explained by the fact that for many types of pulsed random processes common property is the dependence of the form of the spectral density from the square modulus of the characteristic function of the parameters of the pulse train. In work it was carried out assessment of statistical analysis of a square of the module of characteristic function of accidental stationary processes. Were received ratios for mathematical expectation and dispersion of assessment of a square of the module of characteristic function, the accuracy of which is confirmed by the simulation results. It is shown that this estimate is asymptotically unbiased and well-founded (in the mean square sense). Therefore, knowing the model of the pulse sequence in the time domain always possible to estimate the characteristic function of parameters of pulse sequences and on its basis to calculate the spectral density. The simulation results of two types of stationary pulse sequences are given – pulse sequences with identical durations and amplitudes, arising in accidental random time and the sequences, consisting of pulses with the accidental amplitudes arising in accidental random time. Results of simulation completely confirm theoretical calculations.

References
1. Levin B. R. Teoreticheskie osnovyi statisticheskoy radiotehniki (The theoretical basic of statistic radio engineering). M.: Sov. radio, 1974, - 552 p.

2. Cox D.R., Lewis P.A. The Statistical Analysis of Series of Events. London: Methuen, Nev York: John Willey, 1966, - 310 p.

3. Konovalov G. V., Tarasenko E. M. Impulsnyie sluchaynyie protsessyi v electrosvyazi (Pulsed random processes in telecommunication). Ì.: Communication, 1973, - 304 p.

4. Parshin V. S. Statisticheskie harakteristiki otesenki spektra posledovatelnosti impulsov , modulirovannyih po polozheniyu (Statistic evaluation of the spectrum characteristics of the pulse sequence, modulated on the position) // Herald RSREA – Ryazan’, 2005. – Release 16. – pp. 61-65.

5. Parshin V. S., Lavrov A. M. Vliyanie korrelirovannosti amplituda i vremeni poyavleniya impulsov na formu spektra moschnosti impulsnyiy posledovatelnosti (Influence of correlation amplitude and time of appearance of pulses to the shape of the power spectrum of the pulse sequence) // Scientific session, dedicated to the Radio Day. The works Russian NTO radio engineering, electronics and communication in honor of A. S. Popov: thesis, report, conference. – M., 2006. – Release 61. – T. 1. – pp. 107-109.

6. Marple S.L. Digital Spectral Analysis with applications. Martin Marietta Aerospace, Baltimor, Maryland, Prentice-Hall, Inc, Englewood Cliffs, New Jersey, 1987, - 584 p.

7. Veshkurtsev Yu. M. Prikladnoy analiz harakteristicheskoy funktsii sluchaynyih protsessov (Applied analysis of the characteristic function of random processes). – M.: Radio and communication, 2003, - 201 p.

8. Parshin V. S. Otsenivaine harakteristicheskih funktsiy parametrov impulsnyih sluchaynyih protsessov (Evaluation of characteristic functions parameters of pulses random processes) // News of higher schools. Radioelectronic – 1989.- T. 32. ¹3 – pp. 54-55.


A metod of constructing compactly supported ortogonal wavelets

R. A. Rafikov, e-mail: rafikov.rust0@gmail.com
The Ryazan State Radio Engineering University (RSREU), Russia, Ryazan

Keywords: wavelet, scaling function, symmetry, high-order moments.

Abstract
The novel approach to the construction of the orthogonal system of compactly supported scaling and wavelet functions is suggested. This approach allows construction of functions without the requirement on vanishing high-order moments for functions.

Construction of wavelets with compactly supported functions includes determination of the coefficients hn of the refinement equation:

There are fundamental requirements on scaling φ(x) and wavelet ψ(x) functions lead to the main equations for the coefficients hn of the refinement equation:

The fundamental requirements allow N+1 equations for the coefficients hn, while the total number of coefficients equals 2N. N+1 coincides with 2N only if N=1. If N>1, to generate N-1 equation, the fundamental requirements should be supplemented with additional demands on functions φ(x) and ψ(x). These demands are referred to as secondary. Ingrid Daubechies has suggested secondary demand on wavelet moments [1]:

In paper instead of demands on moments for functions introduced symmetry of the coefficients hn relative to the coefficients hN-1 (or hN) as a secondary demand. This condition links the coefficients of the refinement equation ( hi= h2(n-1)-i).



References
1. I. Daubechies, Ten lectures on wavelets, SIAM, Philadelphia, PA, 1992.


Method for increasing the accuracy of timing and frequency offset estimation for OFDM systems in MIMO channels
A.V. Bakke, e-mail bakke.a.v@tor.rsreu.ru
I.V. Lukashin, e-mail:
lukashin.iv@yandex.ru
The Ryazan State Radio Engineering University (RSREU), Russia, Ryazan


Keywords: OFDM, time and frequency synchronization.

Abstract
For most communication system MIMO-OFDM synchronization algorithms’ is unsolved of the quality of functioning problem in non-stationary multipath channel at low signal-to-noise ratios. The degradation of the quality is evident in significant decrease of accuracy timing and frequency offset estimation.

The paper is proposed a method to improve the accuracy of timing and frequency offset estimation synchronization algorithm Schmidl&Cox. The algorithm Schmidl&Cox is based on the use of two preamble symbols. The first symbol is used for detection the preamble and estimation the fractional frequency offset, the second symbol – to estimate the integer frequency offset. Detection and symbol timing estimation is based on finding the likelihood function. The presence of the second symbol provides the possibility of implementing additional components in the likelihood function. This will reduce the risk of making false decisions in the presence of inter-symbol interference at the stage of symbol timing estimation.

In the proposed method, an additional component of the likelihood function is defined in the frequency domain based on calculations of cross-correlation functions of first and second symbol preamble reference spectrum’s and analyzed block spectrum’s. The resulting likelihood function is based on a weighted application of likelihood function Schmidl & Cox algorithm and additional components.

The research of proposed method effect’s on the accuracy timing and frequency offset estimation for various values of weight coefficient in non-stationary multipath channel is produced. As model of multipath the COST 259 (Typical Urban) was used. The simulation showed that the proposed method for different configurations spatial diversity MIMO can significantly improve the accuracy of timing and frequency offset estimation in non-stationary multipath channel at low signal-to-noise ratios.

References
1. Pollet T., Van Bladel M., Moeneclaey M. Ber sensitivity of OFDM systems to carrier frequency offsetand wiener phase noise // IEEE Trans. Commun. 1995. vol. 43, pp. 191–193.

2. P.H. Moose. A Technique for Orthogonal Frequency Division Multiplexing Frequency Offset Correction // IEEE Trans. on Communications. 1994. vol. 42, no. 10, pp. 2908-2914.

3. M. Speth, F. Classen, H. Meyr. Frame synchronization of ofdm systems in frequency selective fading channels // IEEE 47th Vehicular Technology Conference. 1997. vol. 3. pp. 1807– 1811

4. F. Classen, H. Meyr. Frequency synchronization algorithm for OFDM systems suitable for communication over frequency selective fading channels // IEEE VTC’94, pp. 1655–1659, 1994.

5. Schmidl T.M., Cox D.C. Robust Frequency and Timing Synchronization for OFDM // IEEE Trans. Communications. 1997. vol. 45. no 12. pp. 1613-1621.

6. Minn H, Bhargava V.K., Ben Letaief K. A Robust Timing and Frequency Synchronization for OFDM Systems // IEEE Transactions on Wireless communications. 2003. vol. 2. no. 4. P. 822-838.

7. Park B., Cheon H., Kang C.G., Hong D.S. A Novel Timing Estimation Method for OFDM systems // IEEE Commun. Lett. 2003. vol. 7. pp. 239 – 241.

8. Choi S. D., Choi J. M., Lee J. H. An initial timing offset estimation method for OFDM systems in Rayleigh fading channel // IEEE 64th Vehicular Technology Conference. 2006. pp. 1–5.

9. A.N. Mody, G.L. Stuber, Synchronization for MIMO OFDM Systems // IEEE Global Communications Conference. 2001. vol. 1, pp.509-513.

10. A. van Zelst, T.C.W. Schenk. Implementation of a MIMO OFDM Based Wireless LAN System // IEEE Transactions on Signal Processing. 2004. vol.52, no. 2, pp.483-494..

11. G. L. Stuber, J. R. Barry, S. W. McLaughlin, Y. Li, M. A. Ingram, T. G. Pratt. Broadband MIMO-OFDM wireless communications // Proceedings of the IEEE. 2004 vol. 92, pp. 271 – 294.

12. Y. Wen, F. Danilo-Lemoine. A novel postfix synchronization method for OFDM systems // Personal, Indoor and Mobile Radio Communications (PIMRC 2007). 2007. pp. 1 – 5.

13. D. C. Chu. Polyphase codes with good periodic correlation properties // IEEE Trans. Inf. Theory. 1972. vol. 18. no. 4. pp. 531-532.

14. Bakke A.V. Algoritm chastotnoj i vremennoj sinhronizacii dlja priema OFDM signalov po mnogoluchevym kanalam svjazi (Time and frequency synchronization algorithm for receiving of OFDM signals in multipath communication channels) // Cifrovaja obrabotka signalov. 2015. no. 4. pp. 3-8.

15. Bakke A.V., Lukashin I.V. Usovershenstvovannyj algoritm vremennoj sinhronizacii s ispol'zovaniem drobnogo preobrazovanija Fourier (An improved time synchronization algorithm by using fractional Fourier transform) // Vestnik RGRTU. 2015. no. 54, part 1, pp. 20-24.

16. C. Iskander. A MATLAB-based Object-Oriented Approach to Multipath Fading Channel Simulation. http://www.mathworks.com/matlabcentral/fx_files/18869/1/ ChannelModelingWhitePaper .pdf

17. ETSI TR 125 943. Universal Mobile Telecommunications System (UMTS). Version 7.0.0. Release 7. 2007.


Images of cycles of cyclic liftings in the base graph of protograph LDPC codes
A.A. Ovinnikov, e-mail:
ovinnikov.a.a@tor.rsreu.ru
The Ryazan State Engineering University (RSREU), Russia, Ryazan

Keywords:
error-correction coding, LDPC, protograph, cyclic liftings, base graph, girth.

Abstract
This report describes the problem of identifying the possible intersections of cycles in the protographs of quasi-cyclic LDPC codes. The performance of an LDPC code is related to the short cycle structure of the Tanner graph. Numerous publications have used the cycle structure of the Tanner graph as an important measure of the efficiency of LDPC codes, with the general belief that for good performance, short cycles should be avoided in the Tanner graph of the code. It was shown that in addition to the girth, the number and statistics of short cycles are also important performance metric of the code. The close relationship between the performance of message passing decoders and the cycle structure of the graph, motivates the search for efficient algorithms that can enumerate cycles of different length in the graph, as well as finding their relationships to the other structures which affect the performance of such coding schemes. For structured LDPC codes, resulted from a lifting process, it is also important to know the relationship between the girth of the LDPC code and other code parameters such as lifting degree and the size of base matrix. It is worth noting that all the LDPC codes currently adopted in standards are structured QC protograph LDPC codes. In this paper we analyze the relationship between short cycles in the base graph and make a list of cycle identification rules, which can be used as a part of algorithms of synthesis of quasi-cyclic codes with low density parity checks. We closely examine the relationships among the subgraphs of the base graph that rise to short cycles in the lifted graph. As a result of this study, we derive relevant rules on the required cycle metric of the protograph. The list of such rules can be used as a part of algorithms of synthesis of quasi-cyclic codes with low density parity checks.

References
1. A.A. Ovinnikov, Issledovanie vlijanija spectra svjazanosti ciklov v grafe Tannera na enengeticheskij vyigryh ot codirovanija izvestnyh LDPC codov (ACE spectrum influence on channel coding gain of known LDPC codes) // Cifrovaija obrabotka signalov. M., 2015, no. 4, pp. 24 29.

2. W. E. Ryan and S. Lin. “Channel Codes. Classical and Modern”, Cambridge University Press, 2009.

3. D. Declercq, M. Fossorier, E. Biglieri, Channel Coding. Theory, Algorithms, and Applications. Academic Press Library in Mobile and Wireless Communications, 2014.

4. X.-Y. Hu, E. Eleftheriou, and D.-M. Arnold, “Progressive edge-growth Tanner graphs,” in Proc. IEEE GlobeCom, Nov. 2001, vol. 2, pp. 995-1001.

5. Fossorier M. P. C. Quasi-Cyclic Low-Density Parity-Check Codes From Circulant Permutation Matrices / M. P. C. Fossorier // IEEE Transactions on information theory, vol. 50, no. 8, aug. 2004, p. 1788-1793.


UMP-APP decoding of LDPC Codes with self-correction modifie
Volkov I.Y., Dryakhlov A.A., Likhobabin E.A., Mirokhin E.I., Terekhov K.G.

Keywords: forward error correction, LDPC codes, UMP-APP algorithm, self-correction.

Abstract
In this paper we propose self-correction modifier for the UMP-APP decoding of LDPC codes. Like self-corrected “min-sum” decoding our method modifies the variable nodes processing by erasing unreliable messages. As shown by Monte-Carlo simulations, self-corrected UMP-APP decoding performs better then common UMP-APP decoding.


References
1. R.G. Gallager, “Low-density parity-check codes,” Cambridge, MA: M.I.T. Press, 1963. – 60 p.

2. D.J.C. MacKay, R.M. Neal, “Near Shannon limit performance of low density parity check codes,” Electron. Lett., vol. 32, no. 18, pp. 1645–1646, Aug. 1996.

3. T. Richardson, A. Shokrollahi, R. Urbanke, “Design of capacity-approaching irregular low-density parity check codes,” IEEE Trans. Inform. Theory, vol. 47, pp. 619–637, Feb. 2001.

4. W.E. Ryan, S. Lin, “Channel codes. Classical and modern,” Cambridge, University Press, 2009. – 692 p.

5. S.J. Johnson, “Iterative Error Correction,” Cambridge, University Press, 2010. – 335 p.

6. M. Franceschini, G. Ferrari, R. Raheli, “LDPC Coded Modulation,” Springer 2009, 196 p.

7. E.H. Lu, T.C. Chen, P.Y. Lu “Theoretic approach to BP-based WBF decoding algorithm of LDPC codes,” Wireless and Pervasive Computing (ISWPC), 2013 International Symposium on 20-22 Nov. 2013. Taipei.

8. V. Savin, “Self-corrected min-sum decoding of LDPC codes,” IEEE International symposium on Information Theory, 2008, pp. 146-150.

9. V.Savin, D.Declercq, “Min-Sum-based decoders running on noisy hardware,” IEEE Global Communication Conference, 2013, pp. 1879-1884.

10. J.Andrade, G. Falcao, V. Silva, J.P. Baretto, N. Goncalves, V.Savin, “Near-LSPA Performance at MSA Complexity,” IEEE International Conference on Communications, 2013, pp.3281-3285.

11. J. Andrade, G. Falcao, V. Silva, “Accelerating and decelerating min-sum-based gear-shift LDPC decoders”, Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference, South Brisbane, 19-24 April 2015, pp. 3004-3008.

12. V.V. Vityazev, E.A. Likhobabin, “Using self-correction for min-sum based decoding algorithms of LDPC codes,” 2015 Mediterranean Conference on Embedded Computing (MECO), June 2015, pp.93-95.

13. M. Fossorier, M. Mihaljevich, H. Imai, “Reduced complexity iterative decoding of low density parity check codes based on belief propagation,” IEEE Trans. on Comm. – 1999, May, vol. 47. ¹ 5, pp. 673-680.

14. J.Chen, M. Fossorier, “Decoding Low-Density Parity Check Codes with Normalized APP-Based Algorithm,” GLOBECOM’01, San Antonio. – 2001, Nov., vol.2, pp. 1026-1030.

15. http://www.inference.phy.cam.ac.uk/mackay/codes/data.html: Encyclopedia of sparse graph codes.


Analysis of correlated signals in noise detection performance for small observation samples
Bartenev V.G., e-mail: syntaltechno@mail.ru

Moscow Technological University (MIREA)

Keywords: signal detection, correlated signals, maximum likelihood correlation estimate, probability of false alarm and probability of detection.

Abstract
The problem of detecting a correlated signals in noise is considered. The detector optimal for this problem is the maximum likelihood type. Unfortunately, the amount of computations required to design this optimal detector can be quite substantial. For this reason, two suboptimal detectors are introduced, which much easier to design than the optimal detector. One suboptimal detector may be realized only using multiplication and coherent addition. Another suboptimal detector very simple using after the multiplication a binary decision device. Numerical study is presented which indicates that suboptimal detector with multiplication and coherent addition more effective, than optimal detector in detection probability but optimal detector not sensitive to changes in noise power. Simple detector with multiplication and binary decision device shows much worse results.

References
1. Bartenev V.G. Selecting an amplitude and coherent regimes in programmable radar// Radio engineering. 2007. ¹9, p.57-61.

2. Bartenev V.G. Application of the Wishart distribution for the analysis of the effectiveness of adaptive systems MTI // Radiotechnology and Electronics. 1981, ¹2, p.356-361.

3. Bartenev V.G. Bartenev M.V. The process of finding the probability characteristics on the output of non-linear systems // Digital Signal Processing. 2013. ¹4. p. 42-44.


Evaluation of processor word length required for solving stochastic signals processing problems
M.V. Ratynsky, e-mail: m3v5r7@inbox.ru
A.K. Kiryakmasov, e-mail: brain.nutro2012@gmail.com
JSC "Russian Scientific-Research Institute of Radiotechnology" (VNIIRT), Russia, Moscow


Keywords: processor word length, stochastic signals, signal processing.

Abstract
The required processor word length (the number of mantissa bits for floating-point calculations) is evaluated for four stochastic signal processing problems: detection, enumeration and directions of arrival estimation and adaptive space filtering. The evaluation is carried out by computer simulation, with mantissa of each intermediate result (for each quadrature component when the numbers are complex) being cut off to the desired number of bits, while the number of bits is changed over sufficiently wide limits. For each of the problems the algorithm of solution is outlined, and criterion of required word length evaluation is determined.

The results of evaluation are as follows. Detection problem requires 16...18 bit word length, the latter being limited by false alarm probability increase. Enumeration problem requires 19...21 bit word length, the latter being limited by arising of enumeration errors. Directions of arrival estimation problem requires 10...12 bit word length, the latter being limited by false directions of arrival arising. Adaptive space filtering problem requires 14...16 bit word length, the latter being limited by signals suppression decrease.

Three problems of the four considered (namely detection, enumeration and adaptive space filtering problems) are solved without explicit forming and inverting covariance matrix of input signals, those steps being substituted by essentially equivalent finding filter that orthogonalizes the rows of input signal matrix. Such a solution has higher numerical stability, i.e. it needs smaller processor word length against explicit forming and inverting covariance matrix, which is demonstrated by direct verification for adaptive space filtering problem as an example.

References

1. Van Trees H.L. Detection, estimation and modulation theory. Part IV. Optimum array pro-cessing. New York: Wiley, 2002.

2. Smith S.W. Digital signal processing. San Diego: California Technical Publishing, 1999.

3. Ratynsky M.V., Petrov S.V. Implementation of stochastic signals processing algorithms in real arithmetic // Digital signal processing, 2013, no. 4, pp. 22 – 24 (in Russian).

4. Petrov S.V. Synthesis and analysis of stochastic signal detection algorithms in the multielement array systems // Antennas, 2015, no.7 (218), pp. 29 – 36 (in Russian).

5. Ratynsky M.V., Petrov S.V. Effective algorithm of finding maximal singular value for solving the problem of stochastic signal detection // Digital signal processing, 2013, no. 2, pp. 35 – 38 (in Russian).

6. Ratynsky M.V. Adaptation and superresution in antenna arrays. Ì.: Radio and communications, 2003 (in Russian).

7. Golub G.H., Van Loan C.F. Matrix computations. The John Hopkins University Press, 1989.

8. Wilkinson J.H., Reinsch C. Handbook for automatic computation. Linear algebra. New York: Heidelberg; Berlin: Springer-Verlag, 1972.

9. Ratynsky M.V. Choice of diagonal loading value in adaptive space processing problem // Advances of modern radioelectronics, 2016, no. 7, pp. 53 – 63 (in Russian).


The decrease in the number of recognition errors of scanned handwritten texts
I.Ya. Lvovich1, e-mail: komkovvivt@yandex.ru
Ya.E. Lvovich2, e-mail: komkovvivt@yandex.ru
A.A.Mozgovoy2, e-mail: komkovvivt@yandex.ru
A.P.Preobrazhenskiy2, e-mail: app@vivt.ru
O.N.Choporov2, e-mail: komkovvivt@yandex.ru
1Paneuropean university, Slovakia, Bratislava
2
Voronezh institute of high technologies (ANOO VO VIVT), Russia, Voronezh

Keywords: OCR, optical recognition, handwriting, HMM.

Abstract
One of the popular approaches used for handwriting recognition, is the representation of images of full words as in a sequence of symbols of the Markov chain. A set of symbols derived from images is analyzed for compliance with the pre-arranged word models (models-templates). The word, the model of which has the maximum probability of the analyzed pattern generation, is recognized as the desired. Variations of handwritten words writing necessitate analyzing the sequence of symbols derived from images by means of models, developed for the words consisting of a different number of symbols. In case when the analyzed word differs from the word used for the model - template only in the ending, the model - template of a longer word obtains mathematical precedence over the model of the shorter word, resulting in recognition errors. The solution to the described problem based on normalization of horizontal dimensions of handwritten words images is provided in the article.

Normalization is necessary not only for the images for recognition but also for the images for model development. Equaliy of number of states in the analyzed image with the model dimension ensures the non-existence of the above-noted problem.

The analysis of 800 graphic images of handwritten words written in twenty different handwritings was performed for the purpose of estimation of normalization parameters.

The analysis of graphic images proved a linear dependence of the mean square deviation of the word length from the average value of its length. Thus, the necessity of the increase of the overlap area of linear dimensions of recognizable words by several word model dimensions. This will ensure that the largest number of word models developed from the handwritten words written in different handwritings is involved in determination of the best model.

The conducted experiment showed a significant effect of normalization. The average increase in the percentage of recognition was 7.7 percent in comparison with the algorithms (neural networks), where skeletonization was not used.

References

1. CognitiveForms Paperless excellence [Electronic resource] - http://cognitiveforms.com/ru/products_and_services/Cuneiform

2. Yakovlev S.S. The recognition system of moving objects based on artificial neural networks // ITK NANB. - Minsk, 2004. - pp. 230-234.

3. Hyuvenen E., Seppyanen I. The world of LispÌèð Ëèñïà // in 2 vol. - Ì.: Ìèð, - 1990. – 318 p.

4. Bahlmann C., Haasdonk B., Burkhardt H. Online handwriting recognition with support vector machines - a kernel approach // IEEE Transactions on Pattern Analysis and Machine Intelligence. - Vol. 26. - No. 3. - 2004. - P. 299-310.

5. Bentounsi H., Batouche M. Incremental support vector machines for handwritten Arabic character recognition // Proceedings of the International Conference on Information and Communication Technologies. - 2004. - P. 1764-1767.

6. Sanguansat P., Asdornwised W., Jitapunkul S. Online Thai handwritten character recognition using hidden Markov models and support vector machines // Symposium on Communications and Information Technologies. - 2004. - Japan. - October 26-29. - 2004. - P. 492-497.

7. Bin Z., Yong L., Shao-Wei X. Support vector machine and its application in handwritten numeral recognition // Proceedings of the 15th International Conference on Pattern Recognition. - 2000. - P. 720-723.

8. Shu H. On-Line Handwriting Recognition Using Hidden Markov Models // Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science. - February 1. - 1997.

9. Biadsy F. Jihad El-Sana, Nizar Habash Online Arabic Handwriting Recognition Using Hidden Markov Models // The 10th international workshop on frontiers of handwriting recognition. - 2006.

10. Microsoft Windows 7 [Electronic resource] - Ðåæèì äîñòóïà: http://www.microsoft.com/rus/dino7/index.html.

11. Paragon software Ìíîãîÿçû÷íûé PenReader 9.0 [Electronic resource] - Ðåæèì äîñòóïà: http://www.penreader.com/.

12. Handwriting on the Go [Electronic resource] - Ðåæèì äîñòóïà: http://myscript.com/solutions/#mobility-section.

13. Horst Bunke Recognition of Cursive Roman Handwriting - Past, Present and Future // Proceedings of the Seventh International Conference on Document Analysis and Recognition (ICDAR 2003), 2003, Volume 1, pp. 448–459.

14. Norris D. Shortlist B: A Bayesian Model of Continuous Speech Recognition // Psychological Review, Vol. 115, No. 2, 2008 pp. 357–395.

15. Mozgovoi A.A. The problems of application of hidden Markov models in handwriting recognition // In the world of scientific discovery. 2013. ¹6. pp.186-198.

16. Sangeetha Devi S., Dr. T. Amitha Invariant and Zernike Based Offline Handwritten Character Recognition // International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 3 Issue 5, May 2014, pp. 1950-1954.

17. Mozgovoi A.A. The technique of synthesis dictionary for the task of automatic recognition of handwritten words // Telecommunications. 2014. ¹5. pp.3-4.

18. Mozgovoi A.A. Preliminary processing of images of characters with the aim of improving the quality of subsequent carcass (thinning) // Vestnik of Voronezh Institute of high technologies. - 2013. - ¹ 10. - pp. 156-160.

19. Mozgovoi A.A. System handwriting recognition using the mathematical apparatus of hidden Markov models // Artificial intelligence. Intelligent systems AI-2013, proceedings of the International scientific-technical conference (vil. Katsively, Krym, 23 - 27 september 2013). - Donetsk: . - 2013. - pp.109-111.

20. Louloudis G., Gatos B., Halatsis C. Text Line Detection in Unconstrained Handwritten Documents Using a BlockBased Hough Transform Approach / G. Louloudis, // Document Analysis and Recognition, 2007. ICDAR 2007. Ninth International Conference on, Volume 2. pp. 599-603.

21. Vijay Laxmi Sahu, Babita Kubde Offline Handwritten Character Recognition Techniques using Neural Network // A Review IJSR Volume 2 Issue 1, January 2013 pp. 87-94.



If you have any question please write: info@dspa.ru