|Digital Signal Processing||
Digital algorithm to compute estimate of a power spectral density based on sign signal processing using time-weighting functions
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
Keywords: : spectral analysis, pulse random process, characteristic function, statistic characteristics.
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.
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 :
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).
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.
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.
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.
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.
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.
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.
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).
Keywords: OCR, optical recognition, handwriting, HMM.
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.
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:
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: email@example.com