Digital Signal Processing 
Russian 
Efficiency of productcodes turbodecoding algorithms with different methods of information exchange between iterations Abstract To describe standard algorithm consider product code concatenated serially from two block codes with lengths n_{1} and n_{2}. Then code word of the product code may be considered as an n_{1} × n_{2} matrix, rows of which are code words of the first code and columns are code words of the second code. Turbo decoding is an iterative process consisting of carrying out a decoding of the rows followed by a decoding of the columns using softinput softoutput (SISO) decoder of the corresponding component codes and reiterating this procedure several times. At each stage the decoder recalculates reliability estimation of each symbol, which form together n_{1} × n_{2} reliability matrix. Let R_{k}^{in} and R_{k}^{out} be input and output reliability matrixes at SISO stage number k. Then standard algorithm calculates the so called extrinsic information The assessment of the significance of acoustic features in the task of detecting voice activity Keywords: : speech signal, feature importance, voice activity detector, noise. References 2. Kinnunen T., Rajan P. A practical, selfadaptive voice activity detector for speaker verification with noisy telephone and microphone data // ICASSP. 2013. P. 7229–7233. 3. Tazi E.B., Benabbou A., Harti M. Voice activity detection for robust speaker identification system // Spec. Issue Int. J. Comput. Appl. Softw. Eng. Databases Expert Syst. – SEDEXS. 2012. ¹ 9. P. 35–39. 4. Matveev Y.U. Tekhnologii biometricheskoj identifikatsii lichnosti po golosu i drugim modal'nostyam // Vestnik MGTU im. N.E. Baumana. Ser. «Priborostroenie». 2012. ¹ 3. P. 46–61. 5. Pasanen A. Voice activity detection in noise robust speech recognition // Theses, Tampere Univ. Technol. 2002. 6. Johnston J. Transform coding of audio signals using perceptual noise criteria // Sel. Areas Commun. IEEE. 1988. Vol. 6, ¹ 2. P. 314–323. 7. Kinnunen T., Chernenko E. Voice activity detection using MFCC features and support vector machine // Int. Conf. Speech. 2007. Vol. 2. P. 555–561. 8. Kravtsov S.A., Topnikov A.I., Priorov A.L. Detektor rechevoy aktivnosti na osnove golosuyushchih modeley gaussovskih smesey // Elektromagnitnye volny i elektronnye sistemy. 2015. Vol. 20, ¹ 8. P. 29–34. 9. Kravtsov S.A., Topnikov A.I. Analiz raboty lineynyh klassifikatorov v zadache detektirovaniya rechevoy aktivnosti // Tsifrovaya obrabotka signalov i ee primenenie. Dokl. 18y mezhdunar. konf. 2016. T. 1. P. 403–409. 10. Cooper D. Speech detection using gammatone features and oneclass support vector machine // Dis. University of Central Florida Orlando. 2013. 11. Gudnason J., Brookes M. Voice source cepstrum coefficients for speaker identification // Acoust. Speech Signal Process. 2008. ICASSP 2008. IEEE. 2008. P. 4821–4824. 12. Tazi E., Benabbou A., Harti M. Efficient text independent speaker identification based on GFCC and CMN methods // Multimed. Comput. Syst. (ICMCS). 2012 Int. Conf. on. IEEE. 2012. P. 90–95. 13. Wong E., Sridharan S. Comparison of linear prediction cepstrum coefficients and melfrequency cepstrum coefficients for language identification // Intell. Multimedia, Video Speech Process. 2001. P. 95–98. 14. Moattar M., Homayounpour M. A simple but efficient realtime voice activity detection algorithm // Signal Process. Conf. Eur. IEEE. 2009. P. 2549–2553. 15. Sokolov E. Seminary po reshayushchim derev'yam // machinelearning.ru.
Abstract 2. Faludi R. Building wireless sensor networks. O’Really Media, USA, 2011. – 301 p. 3. Dargie W., Poellabauer C. Fundamentals of wireless sensor network: theory and practice. John Wiley And Sons Ltd., United Kingdom, 2010. – 311 p. 4. Eldar C., Kutyniok G. Compressed sensing: theory and applications. Cambridge University Press, 2012. – 555 p. 5. Foucart S., Rauhut H. A mathematical introduction to compressive sensing. Berlin: Springer, 2013. – 625 p. 6. Candes E., Wakin M. An introduction to compressive sampling // IEEE Signal Processing Magazine. Vol. 25, ¹2, 2008. – P. 21 – 30. 7. Baraniuk R. Compressive sensing // IEEE Signal Processing Magazine. Vol. 24, ¹4, 2007. – P. 118 – 121. 8. Donoho D. Compressed sensing // IEEE Transaction on Information Theory. Vol. 52, ¹4, 2006. – P. 1289 – 1306. 9. Tropp J., Gilbert A. Signal recovery from random measurements via orthogonal matching pursuit // IEEE Transaction on Information Theory. Vol. 53, ¹12, 2007. – P. 4655 – 4666. 10. Cai T., Wang L. Orthogonal matching pursuit for sparse signal recovery with noise // IEEE Transaction on Information Theory. Vol. 57, ¹7, 2011. – P. 4680 – 4688. 11. Needell D., Vershynin R. Signal recovery from incomplete and inaccurate measurements via regularized orthogonal matching pursuit // IEEE Journal of Selected Topics in Signal Processing. Vol. 4, ¹2, 2010. – P. 310 – 316. 12. Needell D., Tropp J. CoSaMP: Iterative signal recovery from incomplete and inaccurate samples // Applied and Computational Harmonic Analysis. Vol. 26, ¹3, 2009. – P. 301 – 321. 13. Parfenov V.I., Golovanov D.Y. Opredelenie nezanyatih chastotnih polliapazonov po sjatim izmereniyam // Infokommunikachionnie tehnologii. V.13, ¹3, 2015. – P. 305 – 312. 14. Parfenov V.I., Golovanov D.Y. Effectivnoste ochenki vremennogo polojeniya sverhcorotkogo signala s ispolzovaniem algoritma, osnovannogo na teorie Compressive Sensing // Vestnic Voroneg. Gos. Universitita.Ser.: Fizika, matematika, ¹1, 2015. – P. 27 – 36. 15. Xiong W., Cao J., Li S. Sparse signal recovery with unknown signal sparsity // EURASIP Journal on Advances in Signal Processing. Vol. 2014, ¹1, 2014.
Abstract As a nonlinear frequency modulation signal, signal with relatively low level of side lobes synthesized by author is used. Losses on weighting intime domain are absent for such signal. In the offered detectors to stabilize of false detection probability, normalization of signal power to estimation mean signal power in running by length window, is used. Due to great dependence of compressed nonlinear frequency modulation signal form on Doppler translation, it is offered to use a channel bank matched filter. Comparison of detectors efficiency is provided in wide range of Doppler frequencies. It is obtained that using of detector of nonlinear frequency modulation signal and with a channel bank matched filter in all range of Doppler frequencies, is not efficient due to value of additional loss by detection of lowspeed objects and also instrument cost, low efficiency when signals with different Doppler frequency collide. It is offered to divide concerned Doppler frequency band (140…140 KHz) on subbands and use different signal types and corresponding detectors in each subband. To detect aerodynamic objects for Doppler frequencies 12,5…12,5 KHz in the absence powerful signals from local objects it is proposed to use the detector with nonlinear frequency modulation signal and singlechannel matched filter. It is possible to obtain the gain of threshold signal value of 1,5…3,5 dB with respect to a non weighted linear frequency modulation signal detector (with respect to a linear frequency modulation signal detector and weighting by Hamming – to 2 dB). A dualchannel linear frequency modulation signal detector is recommended in case of powerful signals from local objects. In the case if Doppler frequencies of detected and/or overlapping in time band signals from objects lying in bands 140…12,5 KHz and 12,5…140 KHz, dualchannel linear frequency modulation signal detector is recommended. It is obtained that gain of nonlinear frequency modulation signal detector and with a singlechannel matched filter might be 2…10 dB (power of filter input noise signal is 20 dB), and gain of nonlinear frequency modulation signal detector and with a multichannel matched filter is 1…2 dB and more. References 2. Kobzarev YU.B. The modern radiolocation.  M.: Sovetskoe radio, 1969. – p.235. 3. Handbook. Radio electronics systems: foundation of construction and theory./ Pod red. YA.D.SHirmana.  M.: Radiotekhnika, 2007. – p.306. 4. Tel'minov O.A. The longterm methods of probing signal frequency modulation for problem of radarimages synthesis // Materialy 5j Mezhdunarodnoj konferencii «Cifrovaya obrabotka signalov i eyo primenenie» DSPA2003. – C.14. 5. Bessonova E.V., Irhin V.I. Decrease of side lobe level of anautocorrelation function of compound signals // Trudy XV nauchnoj konferencii po radiofizike. – NNGU, 2011. – pp.131–133. 6. Anan'ev A.V., Bezuglov D.A., YUhnov V.I. Increasing of noise immunity of narrowband channels in terms of using signals with chirp modulation // Sovremennye problemy nauki i obrazovaniya. – 2013. – no.1. – pp. 1–9. 7. Eyung W.Kang Radar system, analysis, design, and simulation.  2008. – pp.281–289. ISBN13: 9781596933477. 8. Patent 2518052 Russia, MPK G01S 13/00. METHOD OF STABILISING FALSE ALARM PROBABILITY (VERSIONS) AND DEVICE FOR REALISING SAID METHOD/ Belyaev B.G., ZHibinov V.A., Prudnikov S.A. (Russia)  ¹ 2012139914/07; zayavl.18.09.12; opubl.10.06.2014. 9. Patent 2585257 Russia, MPK G01S 7/36. // METHOD FOR DOUBLECHANNEL DETECTION OF RADAR SIGNALS WITH FALSE ALARM PROBABILITY STABILISATION/ Elagina K.A., Aksel'rod G.Z. (Russia)  ¹ 2015117986/07; zayavl.13.05.15; opubl. 27.05.2016. 10. Foundation of construction of radio engineering troops radar / Pod red. V.N.Tyapkina.  Krasnoyarsk,Sib.feder unt, 2011. – pp.466, 471473. 11. Lozovskij I.F. Algorithm of signals censoration in case of power heterogeneous noise.  Voprosy radioehlektroniki, no.3, 2002. – pp.97106. 12. Aksel'rod G.Z., Elagina K.A. Use of nonlinear frequency signal for losses of the detection increase.  Izvestiya vysshih uchebnyh zavedenij Rossii. Radioehlektronika, no.2, 2015. – pp.4043. 13. Nitzberg R. Analysis of the arithmetic mean CFAR normaliser for fluctuating targets / Nitzberg R. // IEEE Trans. vol.AES14. –1978, Jan.
Abstract The output discrete harmonic signal is calculated as the sum of the expansion coefficients of the harmonic function over the Walsh basis weighted by the values of the orthonormal basis of the discrete Walsh functions. The Walsh basis is easily generated from the current phase value produced by phase accumulator, with minor hardware cost by binary logical exclusive OR (XOR operation). Algorithm based on Gray code can be used to define the relationship between phase and any Walsh function. Formed Walsh functions are used as control signals for the signed inverters. The set of signed expansion coefficients is precalculated according to the direct discrete Walsh transform. The outputs of inverters are summed, forming the recovered cosine. An example of cosine decomposition over Walsh basis, calculation and analysis of Walsh expansion coefficients are also given. Only a small part of the coefficients has considerable weight in the basis. The best criterion for selection of coefficients is the choice of the first N maximum coefficients by magnitude. The coefficient distribution by bit width demonstrates a way of the following simplification of phasecosine converter. The proposed Walsh and classical memory synthesizers were described by Verilog language and compiled for the FPGA xc7vx690tffg17612 of Virtex7 family. The proposed Walsh phasecosine converter requires about five times less resources compared to the classic memory phasecosine converter at the same level of spurious free dynamic range more than 100 dB. The proposed Walsh direct digital synthesizer is recommended for implementation on programmable logic or application specific integrated circuits. References 2. Tierney J., Rader C.M., Gold B. A digital frequency synthesizer // IEEE Transactions on Audio and Electroacoustics. 1971. Vol. 19. No. 3. P. 48–57. 3. Smekalov A.I. Metod pryamogo fazovogo sinteza siganala. Analiz i matematicheskoe modelirovanie // Radiotekhnika. 2011. No. 1. P. 1629. 4. Smekalov A.I., Djigan V.I. Primenenie lineynoy interpolyatsii signaka v pryamom tsifrovom sinteze chastot // Telekommunikatsii. 2015. No.9. P. 27. 5. Djigan V.I., Smekalov A.I. Tsifrovoy sintezator s pryamim vichisleniem garmonicheskogo signala // Izvestia visshih uxhebnih zavedeniy. Elektronica. 2015. Vol. 20. No. 6. P. 625633. 6. Kampik M., Popek G. Lowspur numerically controlled oscillator using Taylor series approximation // XI International PhD Workshop OWD 2009. 7. Wheatley C.E., Phillips D.E. Spurious suppression in direct digital synthesizers // Proceedings of the 35th Annual Frequency Control Symposium. 1981. P. 428. 8. Sunderland D.A. CMOS/SOS frequency synthesizer LSI circuit for spread spectrum communications // IEEE Journal of SolidState Circuits. 1984. Vol. SC19. No. 4. P. 497506. 9. Golubov B.I., Efimov A.V., Skvortsov V.A. Walsh series and transformations: theory and applications. Moscow: Nauka, 1987. 352 p. 10. Smekalov A.I. Primenenie bazisa funktsiy Uolsha v tsifrovom sinteze chastot // 18ya Mezhdunarodnaya konferentsia “Tsifrovaya obrabotka siganlov i ee primenenia  DSPA2016”. Moscow, 2016. Vol. 2. P. 793798. 11. Smekalov A.I. Realizatsia tsifrovogo sintezatora chastor na osnove bazisa funktsiy Uolsha // Mezdunarodnaya konferentsia “Radioelektronnie ustroystva I sistemi dlya infokommunikatsionnih tehnologiy  REDS2016”. Moscow, 2016. Vol. 1. P. 127132. 12. Ugryumov E.P. Digital circuit design: Texbook. 2nd ed. St. Petersburg: BHVSt Petersburg, 2007. 800 p. 13. URL: http://www.xilinx.com (access date: 29.05.2016).
Abstract The possibility of direct synthesis of digital IIR filters with complex selective requirements directly in an integer state space can be provided by the methodology integer nonlinear programming. This ideology allows for the efficient design of the integer recursive filters with a given number of bits represent data at the highest performance requirements of aggregate frequency characteristics of the filter at any form of their assignment. This article discusses issues integral simulation and synthesis of recursive (IIR) digital filters taking into account the capacity to implement them on digital platforms with integer arithmetic calculations. The problem statement and solution of multifunctional synthesis of digital filters such a problem on the basis of the numerical methods of integer nonlinear mathematical programming are given. As an several typical examples, the problem solution of synthesis of IIRfilters with difficult selective requirements has been given. The analysis of their characteristics is resulted. 2. Shkelev E.I., Bugrov V.N., Proidakov V.I., Artemev V.V. Integer digital filters  effective solution for 8bits digital platforms. Moscow, Components and technologies, ¹ 10, 2013, p. 104 – 110. 3. Bugrov V.N., Proidakov V.I., Artemev V.V. Search design technology integer digital filters. Part 1, Moscow, Components and technologies, ¹ 10, 2013, p. 104 – 110. 4. Bugrov V.N., Proidakov V.I., Artemev V.V. Search design technology integer digital filters. Part 2, Moscow, Components and technologies, ¹ 8, 2014, p. 43 – 49. 5. Bugrov V.N., Proidakov V.I., Artemev V.V. Search design technology integer digital filters. Part 3, Moscow, Components and technologies, ¹ 1, 2015, p. 154 – 159. 6. Bugrov V.N., Proidakov V.I., Artemev V.V. Synthesis of digital filters by methods of integer nonlinear programming. 17th international conference "Digital signal processing and its applications – DSPA2915", Abstracts. M: NTO RES them. A. S. Popov, 2015, p. 200 – 204. 7. Demðster A.G., Macleod M.D. IIR digital filter design using minimum adder multiplier blocks.//IEEE Trans.on Circuits and SystemsII, 1998, v. 45, N 6. 8. Voinov B.S., Bugrov V.N., Voinov B.B. Informacionnie tekhnologii i sistemi: poisk optimalnih, originalnih i racionalnih resheniy. Moscow.: Science, 2007, 730 p. 9. Artemev V.V., Bugrov V.N. IIR filter design with phase linearity. Moscow, Components and technologies, ¹ 7, 2013, p. 132134. 10. Bugrov V.N. Integer design of the Gaussian digital filters. // Vestnik Newsletter NNSU, 2012, ¹ 3. p. 49 – 54/ 11. Mingazin A.T. Synthesis of IIRfilters of low complexity with characteristics similar to those of the Gaussian curve. Moscow, Components and technologies, ¹ 11, 2013, p. 144 – 148. 12. Jaconia B.E. TV. Moscow, Hot line – Telecom, 2007,618 p. 13. Briliantov D.P., Kulikov B.N., Roxman M.A. Portable color TVs. Moscow, Radio and communication, 1989, 306 p. 14. Rabiner R, Gold B. Theory and application of digital signal processing. Moscow, Mir, 1978, 848 p 15. Ifeachor E., Jervis B. Digital signal processing. A practical approach. Moscow.: "Wiliame", 2004, 992 p.
16. Mingazin A.T. Synthesis of IIRfilters of low complexity with characteristics similar to those of the Gaussian curve. Moscow, DSPA, 2002. Vol.1. p. 9093.
In this paper a particular example is considered to illustrate the efficiency of various methods of digital bandpass filter (DBF) bank design. 64channel filter system is designed with squareness ratio of filter amplitudefrequency characteristic α = 10 and the permissible deviations from the desired frequency response ε_{1perm}=10^{2} and ε_{2perm}=10^{3}. The sampling frequency of the input complex signal ƒ_{S} = 10 kHz. R_{T} – the number of multiplication operations per time unit, S – the number of the data memory cells, τ – the delay time. The analysis of the presented results of the costs calculation for the implementation of the DBF set with the frequency selection desired properties allows to draw the following conclusions.
2. Lin L. and FarhangBoroujeny B. Cosine modulated multitone modulation for very high speed digital subscriber lines // EURASIP J. Appl. Signal Processing, 2006, Aprticle ID 19329. 3. Vityazev V.V., Ovinnikov A.A. Metody analiza/sinteza v sistemah besprovodnoi svyazi so mnogimi nesushimi // Jelekrosvjaz. 2013. ¹ 9 . – S. 2832. 4. Vityazev V.V. Digital frequency selection of signals. Ì.: Radio and telecommunication, 1993. 240 p. 5. Vityazev V.V., Muraviev S.I., Stepashkin A.I. Metod sinteza cifrovyh uzkopolosnyh KIHfiletrov // Izvestya vuzov. Radioelektronika. – 1981. – Ò. 24, ¹ 7. – S.. 5559. 6. Emmanaual C. Ifeachor, Barrie W. Jervis. Digital Signal Processing: A Practical Approach : Prentice Hall, 2002. 992 p. 7. Vityazev V.V., Muraviev S.I . Sintez cifrovoy sistemy chastotnoy selekcii signalov na osnove polupolosneh grebenchatyh filtrov // Jelekrosvjaz. – 1988.  ¹ 3. – S.5761.
Abstract Adders of the digital system structure can create some quantization errors known as errors of overflow of adders. Cascade IIR filter structures with second order sections (SOS) are the most sensitive to these errors. Unlike FIR filter structures, overflow mode (Saturate or Wrap) for each sum in IIR filter structure will distort a result. The scaling allows preventing or minimizing errors of overflow. Theoretical basis of scaling is known. However this basis does not consider many problems related to modeling the procedures by MATLAB. The report contains some additional interpretations of the existing theory allowing the user to realize the above mentioned modeling. General principles of scaling procedures are described and main steps of scaling are substantiated. It is considered implementation of these principles for cascade structures with typical SOS: Directform I, Directform II, Directform I transposed, Directform II transposed. Each of scaling stages is illustrated by a specific SOS example with two sections. Calculation formulas for the Gains and Numerators after the scaling are derived. The calculation of scaling factors is produced on the basis of the norm Linf for Magnitude Response. The general algorithm and his application are examined for cascade structures with typical SOS. The paper illustrates the theoretical approach by results of IIR filter scaling using GUI FDATool. General principles of normalizing Numerators and Gains are described and main steps of normalizing are substantiated. The paper illustrates the theoretical approach by results of IIR filter normalizing. Simple MATLAB algorithm for calculation of the own noise variance of the cascade structure is proposed after the scaling. Calculation formulas for the own noise variance after the scaling are derived. The cascade structure of IIR filters with known SOS structures and new Numerators is described as the dfilt object with the corresponding coefficient matrix. Coefficient matrixes of equivalent system functions are formed within the cycle by consecutive nullification of initial matrix elements. Equivalent impulse responses of cascade structure parts are calculated within the cycle with automatic limitations up to equal length. However coefficient matrices will differ before and after the scaling. As a result the own noise variance will be less after the scaling. The paper illustrates this effect for IIR filters with different SOS structures. 2. E. Ifeachor, B. Jervis Digital Signal Processing // Moscow –: Saint Petersburg – Kiev: "Wiljams", 2004/ 3. A.V. Oppenheim, R.V. Schafer Digital Signal Processing // Moscow: "Technosfera", 2006. 4. A.I. Solonina, S.M/ Arbusov Digital Signal Processing. Modeling in MATLAB // Saint Petersburg: "BHV Petersburg", 2008. 5. A.I. Solonina Own noise estimates of recursive digital filter structures and their MATLAB calculation // Digital signal processing, 2015, ¹ 2. pp.3946.
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