Digital Signal Processing

Russian
Scientific & Technical
Journal


“Digital Signal Processing” No. 1-2016

In the issue:

- multichannel detection systems

- adaptive notch filters
- decomposition on empirical modes
- reduction of the Peak to Average Power Ratio
- distortion detection of BPSK
- assessment of time parameters of electromyograms
- simulation training neurochips
- simulation in MATLAB



The comparative analysis of two methods combining signals in multichannel systems
Bartenev V.G., Bartenev M.V.
The Moscow  Technological  University (MIREA), Russia, Moscow, e
-mail:
syntaltechno@mail.ru, syntaltechno@gmail.com

Keywords: multichannel detection systems, combining signals with minimum and maximum selection, probability characteristic of detection.

Abstract
The article devoted to the comparative analysis of two methods combining signals in multichannel systems.

In many radio systems are widely used multi-channel processing. In particular, when constructing a radar moving target detectors to improve their efficiency widely used the multichannel Doppler filters and notch multichannel filters. Thus, if in multi-channel Doppler filters the target signal can appear in one of the Doppler channels, in the multichannel notch filters the target may be in all the channels, since the velocity transparency zone of channels coincide. In this context, in multi-channel Doppler filter usually used the maximum selection in combining of channels. In the multi-notch filters it is preferable to implement minimum selection in combining of channels. It is interesting to compare the characteristics of these two methods of channels combining in relation to a multi-channel processing. Without loss of generality in solving the problem of comparing the effectiveness of two methods of channels combining the investigation was performed when type and efficiency of Doppler or multi notch filters in this study have been not considered. The attention was focused on different types of signal processing such as post detector integration, CFAR problem. It was assumed that at the input of minimum or maximum selection circuits in each channel was used in the quadrature square law detectors. And in each quadrature Gaussian noise acted with zero mean and unit variance. Noises in the channels were independent. Useful signals in all channels have the same random fluctuating amplitude and were also independent. It was shown, using analytical approach and MATLAB model verification of probability characteristics, that in the simple case the maximum detection method preferable. Using non coherent integration in each channel make both maximum and minimum methods equivalent. If after non coherent integration was used the adaptive constant false alarm rate device the minimum detection method become more affective for low detection probabilities.

References

1. Bartenev V.G. Effective combining of quadrature channels // Modern electronics. 2010. Vol. 2, p. 78-79

2. Ryndyk, A.G. Ryabkov A.P. Multichannel notch filter with a minimum selection // News of Russian universities. Electronics. 2012. Vol. 4, p. 81-85 .

The research of the adaptive notch filter cross-links’ impact on probing signals with intra-modulation
Gordeev A.U., e-mail: alexurgor2008@gmail.com

Keywords: : adaptive filter, spectral analysis, super resolution, the Steiglitz-McBride algorithm, chirp-signal, correlated interference, cross-links, notch filter, MTI system, pulse-compression filter, Doppler filter, filter weights generation.

Abstract

The potential of implementation of two-channel adaptive systemwith filtering weights cross-control for the passive correlated interference suppression and using probing chirp-signal is presented in the article. The conditions under which considered system usage is theoretically acceptable and also certain spectral analysis super resolution techniques implementation is reasonable for the filter weights generation are adduced.The Steiglitz-McBride algorithm implementation yielded the best results in terms of the most efficient interference supðpression under certain conditions are presented. The adaptive MTI, which usage minimizes contained in input unclassified sample of observations wanted chirp-signalis proposed.

References
1. Bartenev V.G. Sposob adaptivnoj filtracii diskretnyx pomex. Patent ¹ 2341015 po zayavke ¹ 2007101649 ot 17.01.07. Publikaciya FMPS v Byul. ¹21 ot 27.07.08.

2. Gordeev A.Yu., Bartenev V.G. Sposob adaptivnoj filtracii diskretnyx pomex. Zayavka na patent ¹ 201314267208 ot 19.09.13. Publikaciya FIPS v Byul. ¹9 ot 27.03.15.

3. Bartenev V.G. Adaptivnyj reshetchatyj filtr dlya podavleniya diskretnyx korrelirovannyx pomex. Doklad na 10-j Mezhdunarodnoj konferencii «Cifrovaya obrabotka signalov i ee primenenie» DSPA-2008. Moskva, 26-28 marta, 2008,S.164-168.

4. Bartenev V.G., Gordeev A.Yu. Primenenie metodov Proni i Shtejglica-MakBrajda dlya formirovaniya vesovyx koefficientov pri adaptivnoj filtracii neklassificirovannyx vyborok nablyudeniya. Trudy 14-oj Mezhdunarodnoj konferencii «Cifrovaya obrabotka signalov i ee primenenie» DSPA-2012. Moskva, 2012, S. 257-260.

5. Bartenev V.G., Gordeev A.Yu. Novyj sposob formirovaniya vesovyx koefficientov pri adaptivnoj filtracii neklassificirovannyx vyborok nablyudenij // Cifrovaya obrabotka signalov. 2012, ¹2,S. 65-67.

6. Bartenev V.G., Gordeev A.Yu. Sravnitelnyj analiz effektivnosti adaptivnoj filtracii po metodu Proni i Shtejglica-MakBrajda. // Sbornik trudov 61 NTK MIREA. 2012. Ch. 3. S. 55-60.

7. Bartenev V.G., Gordeev A.Yu. Novyj sposob razrabotki dvuxchastotnogo klassifikatora diskretnyx korrelirovannyx pomex. // Sbornik dokladov Vserossijskoj konferencii RSPOVI 2013. Smolensk, 2013, S. 196-199.

8. Gordeev A.Yu. Sravnitelnyj analiz effektivnosti razlichnyx metodov spektralnogo analiza sverxvysokogo razresheniya dlya filtracii neklassificirovannyx vyborok nablyudeniya. // Materialy 69-j Mezhdunarodnoj konferencii «Radioelektronnye ustrojstva i sistemy dlya infokommunikacionnyx texnologij» REDS-2014. Moskva, 2014. S. 37-41.

9. Gordeev A.Yu., Yacyshen V.V. Perspektivnye metody povysheniya effektivnosti podavleniya passivnyx pomex sistemami selekcii dvizhushhixsya celej // Elektromagnitnye volny i elektronnye sistemy. 2015, T.20, ¹3. S. 40-52.

10. Marple S. L. Digital Spectral Analysis with Applications: 1990. – 584 p.

11.Sergienko A. B. Digital Signal Processing. SPb.: BXV-Peterburg, 2011. – 768 p.

12. Burg I. P. Maximum Entropy Spectral Analysis. Proc. 37th Meeting of the Society of Exploration Geophysicists. Oklahoma City, Okla., Îctober 1967.

13. Parks, Thomas W., and C. Sidney Burrus. Digital Filter Design. New York: John Wiley & Sons, 1987, pp 226–228.

14. Steiglitz, K., and L. E. McBride. "A Technique for the Identification of Linear Systems." IEEE® Transactions on Automatic Control. Vol. AC-10, 1965, pp. 461–464.

15. Ljung, Lennart. System Identification: Theory for the User. 2nd Edition. Upper Saddle River, NJ: Prentice Hall, 1999, p. 354. .


Integer design of FIR Filters with linear phase

V.N. Bugrov, e-mail: bug@rf.unn.ru
N.S. Morozov, e-mail: nsmorozov@rf.unn.ru
N.I. Lobachevsky state university of Nizhni Novgorod (NNSU) , Russia, N.Novgorod

Keywords: the digital FIR-filter, multifunctional synthesis, integer nonlinear programming, digital filter with linear phase, integer design.

Abstract
The phase response linearity is one of the key requirements for the digital filters performing the selection of the useful signal in a given frequency range. Current requirements for phase linearity of the transmission coefficient of the digital filter are very tough - phase distortion Δφ(ω) for most of DSP-applications should not exceed 1–3 degrees within the passband. The classic way of ensuring the linearity of the phase characteristic of the FIR filter, as is well known, the condition of symmetry (or anti-symmetry) of its impulse response. However, classical methods do not allow us to design linear phase FIR filter only in the desired bandwidth. Classic design can be obtained with real or even complex (for structures with frequency selection, for example) multidimensional state space only. This feature makes the solution complicate or even makes the implementation of this solution impossible on digital platforms with integer arithmetic, primarily in the FPGA and microcontrollers. This paper presents the statement and the solution of the problem of synthesis of digital FIR-filter with linear phase only in the specified passband by integer nonlinear mathematical programming. The article considers solutions of the problem of synthesizing an FIR-filter with symmetrical and with free integer coefficients. Comparative evaluation of selective ability according to the results of software implementation of those integer filters on a digital platform is presented.

References
1. Ifeachor E., Jervis B. Digital signal processing. A practical approach. Moscow.: "Wiliame", 2004, 992 p.

2. Antoniou A. Digital Filters: Analysis and Design. New York: McGraw Hill Higher Education, 1979. 524 p.

3. Cappellini V., Constantinides A. G., Emiliani P. Digital filters and their applications. London: Academic Press, 1978. 393 p.

4. Lim Y. C., Parker S. R. A discrete coefficient FIR digital filter design based upon an LMS criteria.- Proc. IEEE ISCAS.-1982.-p. 796-799.

5. Lim Y. C., Parker S. R., Constantinides A. G. Finite word length FIR filter design using integer programming over a discrete coefficient space //IEEE Trans.-1982.-Vol. ASSP-30, ¹ 4. -p. 661-664.

6. Siohan P., Benslimane A. Design of optimal finite wordlength linear phase FIR filters: New applications//Proc. IEEE ICASSP. -1984.-p. 30.1.1-30.1.4.

7. Artemev V.V., Bugrov V.N. IIR filter design with phase linearity. Moscow, Components and technologies, ¹ 7, 2013, p. 132-134.

8. Bugrov V.N. Development of digital filters by methods of integer nonlinear programming. // Vestnik Newsletter NNSU, 2009, ¹ 6. p. 61 – 70.

9. Shkelev E.I., Bugrov V.N., Proidakov V.I., Artemev V.V. Integer digital filters - effective solution for 8-bits digital platforms. Moscow, Components and technologies, ¹ 10, 2013, p. 104 – 110.

10. Voinov B.S., Bugrov V.N., Voinov B.B. Informacionnie tekhnologii i sistemi: poisk optimalnih, originalnih i racionalnih resheniy. Moscow.: Science, 2007, 730 p.

11. Semenov B.Y. MSP430 MCU. the first acquaintance., Ì.: «Solon-press», 2006, 120 p.

12. Lemm G. Analog and digital filters. . Moscow.: Mir, 1990, 590 p.


Decomposition on empirical mode based differentiation and integration

Myasnikova N.V., e-mail: genok123@mail.ru
Beresten M.P., e-mail: beresten@sura.ru
Penza State University, Russia, Penza, e-mail: avitel@pnzgu.ru

Keywords: alternating components, empirical mode decomposition
.

Abstract
The method of empirical mode decomposition, both in ascending and descending order of their frequencies, is substantiated. The method is based on suppression of high-frequency components at integration and on accentuation of high-frequency components at differentiation.

Recently the interest to empirical modes decomposition has been growing. The authors apply such decomposition as a signal preliminary processing, which allows increasing the signal/interference ratio, simplifying the algorithm of parametrical analysis due to transformation of a complex task of evaluation of the parameters of p model order into simple tasks of evaluation of components of first and second order, significantly reducing the period of analysis.

The method proposed is based on suppression of high-frequency components at integration and on their accentuation at differentiation:

- to extract the modes in ascending order of their frequencies multiple integration of signal is executed in order to suppress high-frequency components, till the termination of altering of number of extrema, i.e. only one (of the lowest frequency) component remains; the modes are extracted from integrated sequences by differentiation, subtraction of extracted component from integrated sequences of lower order, repetition of the same actions with an already withdrawn low-frequency component with the sequences integrated, starting with the previous one; the components extracted from the integrated sequences are to be differentiated in accordance with Lanczos scheme as many times as the sequence has been integrated;

- to extract the modes in the descending order of their frequencies multiple differentiation is executed to accentuate high-frequency components, till the sequence with alternating extrema is extracted; the modes are extracted from differentiated sequences by integration, subtraction of the extracted component from differentiated sequences of lower-order, repetition of the same actions with an already withdrawn high-frequency component with differentiated sequences, starting with the previous one; the components extracted from the differentiated sequences are to be integrated with application of weighting as many times as the sequence has been differentiated.

References
1. Myasnikova, N. V. Empirical modes decomposition application as the problem of signals digital processing / N. V. Myasnikova, L.A.Dolgikh, M.G.Myasnikova. // Sensors and systems. – 2011. – ¹ 5. – page 8–10.

2. Myasnikova, N. V. , Time-frequency distributions on the basis of extreme filtering/ N. V. Myasnikova, M.P. Beresten // Sensors and systems. – 2013. – ¹ 10. – page 9–12.

3. Myasnikova N.V. Empirical modes decomposition on the basis of extreme filtration // Myasnikova N.V., Beresten Ì.P. Digital signal processing. 2014. ¹ 4. Pages 13-17.

4. Marple, S.L., Jr., Digital Spectral Analysis with Applications, 584 p., Mir, Moscow, 1990.

5. Myasnikova, N.V. The algorithm of extraction of low-frequency modes / N.V. Myasnikova, Ì.P. Beresten // The 17th International conference ‘Digital signals processing and its application». Moscow. March 25–27, 2015. The works of the Russian scientific and technical society of radio engineering, electronics and communication named after A.S.Popov. Digital signals processing and its application. Series. – Moscow.: Russian scientific and technical society of radio engineering, electronics and communication named after A.S.Popov, 2015. – Pages. 78-82.

6. Myasnikova, N.V. Empirical modes decomposition on the basis of differentiation and integration / N.V. Myasnikova, Ì.P. Beresten / Collected articles: Perspective information technologies (PIT 2015) The works of International scientific and technical conference. Samara State Aerospace University. Samara, 2015. – Pages 101-105.

7. Myasnikova, N.V. The combined method of Empirical modes decomposition on the basis of differentiation and integration / N. V. Myasnikova, M.P. Beresten, A.A. Primak // Modern society, science and education: collection of scientific papers on the materials of the International scientific - practical conference in 16 parts . 31 March 2015. Part 8 Publisher: OOO "Consulting company Ucom " ( Tambov ) p. 76–77.


Optimization detection systems of coherent signals
D.I. Popov., e-mail: adop@mail.ru
The Ryazan State Radio Engineering University (RSREU), Russia, Ryazan

Keywords:
auto-compensation, adaptation, adaptive rejecter filters, probabilistic criterion, optimization, clutter, energy criterion.

Abstract
The object of the study are coherent signal detection of moving targets at the background clutter, performing coherent rejection followed by a multi-channel coherent accumulation rejection residues. As the rejection filter (RF) is considered an adaptive nonrecursive filter, and as coherent accumulator - multichannel filter (MF). The subject of research are the methods of parametric optimization of systems for detection of coherent signals on energy and probabilistic criterion. Objective - to compare the effectiveness of signal detection systems optimized for energy and probabilistic criterion.

Optimize energy criterion of coherent signal detection system is based on the extreme properties of the characteristic (own) matrices of numbers, and probabilistic criteria - on numerical methods of nonlinear programming.

A two-stage procedure to optimize systems for detection of coherent signals based on the RF-MF combination. In the first stage on the energy or the probability criterion is optimized RF. The second step is optimized MF. In the case of a different weighting in the channels used analytical procedure optimization energy criterion of maximum ratio Rayleigh, which is an approximate version of optimization on probabilistic criterion, and in the case of the same weighting in the channels of the methods of nonlinear programming is the numerical solution of probabilistic criterion. Analysis of processing systems may also be carried out on the energy and probabilistic criterion.

For a small dynamic range for clutter detection systems fixed coherent structure signals a preference should be given to the method of optimization on probabilistic criterion. By increasing the dynamic range of a convergence of parameters and efficiency of systems in comparable cases, that in view of the facilities of the analytical solutions of the optimization problem, as well as more opportunities for implementation of adaptive algorithms indicates the usefulness of the method of optimization on energy criterion.

References
1. Radio Electronic Systems: Fundamentals of Construction and Theory. Hand-book / Ya.D. Shirman, S. T. Bagdasarian et al; under edition of Ya.D. Shirman. – 2nd Edition. – Moscow: Radiotekhnika Publ., 2007. - 512 p.

2. Radar Handbook / Ed. by M. I. Skolnik. - 3rd ed. - McGraw–Hill, 2008. - 1352 p.

3. Popov, D.I. Optimalnaya obrabotka mnogochastotnjch signalov // Izvestiya vuzov Russia. Radioelektronika. - 2013. - Vjup. 1. - S. 32-39.

4. Popov, D.I. Optimizaciys system kogerentno-vesovoi obrabotki mnogochastotnjch signalov // Cifrovaya obrabotka signalov. – 2013. - ¹ 4. - S. 17-21.

5. Popov, D.I. Adaptaciya nerekuesivnjch reghectornjch filtrov // Izvestiya vuzov. Radioelektronika. - 2009. - T. 52. ¹ 4. - S. 46-55.

6. Popov, D.I. Sintez adapnjch reghectornjch filtrov visokich porjadkov // Izvestiya vuzov. Radioelektronika. - 1999. - T. 42. ¹ 6. - S. 46-51.

7. A. s. 934816 SSSR, MPK6 G 01 S 7/36, G 01 S 13/52. Reghectornij filtr / D.I. Popov; zayavl. 30.10.1980; opubl. 27.11.1998, Byul. ¹ 33. - 20 s.

8. Popov, D.I. Ocenivanie parametrov passivnych pomeh // Izvestiya vuzov. Radioelektronika. - 2003. - T. 46. ¹ 3. - S. 71-80.

9. Popov, D.I. Avtokompensaciya doplerovskoi fazy passivnyh pomeh // Cifrovaya obrabotka signalov. – 2009. - ¹ 2. - S. 30-33.

10. A. s. 875960 SSSR, MPK6 G 01 S 7/36, G 01 S 13/52. Ustroistvo dlya podavleniya passivnyh pomeh / D.I. Popov; zayavl. 07.01.1980; opubl. 27.11.1998, Byul. ¹ 33. - 11 s.

11. A. s. 1015757 SSSR, MPK6 G 01 S 7/36. Ustroistvo podavleniya passivnyh pomeh / D.I. Popov; zayavl. 05.12.1977; opubl. 27.11.1998, Byul. ¹ 33.-12 s.

12. A. s. 1098399 SSSR, MPK6 G 01 S 7/36. Ustroistvo adaptivnoi rezhekcii passivnyh pomeh / D.I. Popov; zayavl. 12.06.1981; opubl. 20.12.1998, Byul. ¹ 35. - 16 s.

13. Popov, D.I. Adaptivnoe podavlenie passivnyh pomeh // Cifrovaya obrabotka signalov. – 2014. - ¹ 4. - S. 32-37.

14. Popov, D.I. Adaptivnye porogovye ustroistva // Izvestiya vuzov. Radioelektronika. - 2006. - T. 49. ¹ 3. - S. 30-35.


Research signal processing algorithms with OFDM modulation and the development of recommendations to reduce the crest factor
Le Van Ki., e-mail: levanky@phystech.edu
Moscow Institute of Physics and Technology (MIPT), Russia, Moscow

Keywords: crest factor reduction, active constellation extension algorithm, adaptive active constellation extension algorithm, tone reservation, peak to average power ratio (PAPR), Orthogonal Frequency-division multiplexing (OFDM).

Abstract
OFDM (Orthogonal Frequency Division Multiplexing) technique has been widely adopted in many wireless communication systems due to its high data-rate transmission ability and robustness to the multipath fading channel. One major drawback of the OFDM signal is the high peak-to-average power ratio (PAPR) problem. The high PAPR results in the in-band distortion and out-of-band radiation when the OFDM signal is fed into a nonlinear power amplifier (PA). Large fluctuations of OFDM signal amplitude represents a major drawback for amplification in mobile communication systems.

In this paper we analyze the structure of a radio signal with OFDM modulation and conduct a study (research) to reduce the Peak to Average Power Ratio (PAPR) by the use of some of the carrier frequencies (Tone reservation), by extending some modulation constellation points toward the outside and around of the constellation (fixed and adaptive - Active Constellation Extension and Adaptive Active Constellation Extension). As a result of joint research and such methods of processing, PAPR of OFDM signals is reduced significantly (about 5 dB) and the efficiency of using output amplifiers of the transmitting means is improved.

References
1. Dvorkovich V. P., Dvorkovich À. V., Measurement in video information systems (theory and practice)// Ì.: Technosphere, 2015, 890 p.

2. Korzhihin E.O, Vlasyuk I.V, Methods for reducing the crest factor in the terrestrial digital television broadcast system standard DVB-T2 //T- Comm -Mobile communications systems and digital broadcasting. Release on the results of the 6th industry conference MTUCI "Information Society Technologies", Ì.: « Media Publisher » – 2012 . – ¹ 9–p.83-86.

3. ETSI EN 302 755 V1.3.1 (2012-04) Digital Video Broadcasting (DVB); Frame structure channel coding and modulation for a second generation digital terrestrial television broadcasting system (DVBBT2).

4. Tellado, J., Cioffi, J.M., PAR Reduction in Multicarrier Transmission Systems.

5. Dae-Woon Lim, Hyung-Suk Noh, Jong-Seon No, Near Optimal PRT Set Selection Algorithm for Tone Reservation in OFDM Systems // IEEE Transactions On Broadcasting, Vol. 54, No. 3, September 2008.

6. Grace R. Woo, Douglas L. Jones, Peak Power Reduction in MIMO OFDM via Active Channel Extension // IEEE 2636 - 2639 Vol. 4,16-20 May 2005

7. Madhuri P., Dr Malleswari B. L., Peak-To-Average Power Ratio Reduction by CB-ACE and Adaptive Ace Algorithms // ISSN 2250-2459, Volume 2, Issue 2, February 2012.

8. G.Karthikeyan, Dr.G.Indumathi, S.Kannadhasan, PAPR Reduction in OFDM Systems using Adaptive Active Constellation Extension Algorithm // ISSN 2320 – 9798, Vol. 1, Issue 4, June 2013.


Distortion Detection Algorithm Development for BPSK Signal Constellation
A.E. Kiselnikov, e-mail: a.kiselnikov@uniyar.ac.ru
M.A. Dubov, e-mail: michaeldubov@gmail.com
A.L. Priorov, e-mail: andcat@yandex.ru
The Yaroslavl State University n.a. P.G. Demidov (YaSU n.a. P.G. Demidov), Russia, Yaroslavl

Keywords: EVM, BPSK, noise immunity, signal constellation, distortion compensation, quadrature receiver, non-reference metric.

Abstract
The scope of this work is a distortion identification of BPSK signal constellation. The cause of signal distortion can be both multipath signal propagation in dense urban areas and different time constant and phase response of quadrature analog paths with quadrature demodulation. These phenomena lead to a distortion of the signal constellation and decreased immunity of communication system.

An algorithm for the received BPSK signal quality assessing is developed. It allows to distinguish the signal constellation distortion effects and AWGN acting on the signal. Since the power of the received signal does not depend on signal constellation rotation, the distortion identification using the reference metric is difficult. The power control system does not effective in this case. It is necessary to use the non-reference metrics such as EVM. The main feature of the algorithm is the use of a received signal quality mixed metric, which includes the elements of the standard metric (BER) and the non-reference metric (EVM).

The proposed algorithm makes possible to distinguish the effect of AWGN signal constellation and compensate it by analyzing the error vector. Another important feature of this algorithm is its ability to compensate the distortions introduced by the analog RF path that now is an actual task in mind of reducing the quality of the electronic components in the domestic market.

References
1. Okunev Yu.B. Digital transmission of PSK modulated signals. Moscow. Radio i Svyas, 1991. 296 p.

2. Kenington P.B. RF and Baseband Techniques for Software Defined Radio / Artech House, 2005. 352 p.

3. Software Defined Radio. Edited by Walter Tuttlebee. John Wiley & Sons, Ltd 2002. 402 p.1

4. Lam G. Digital and analog filters. Theory and design. Moscow. Mir, 1982. 586 p.

5. Mathuranathan V. Simulation of Digital Communication Systems Using Matlab. Second edition. / Mathuranathan V. E-book, Mathuranathan V. at Smashwords, Published at 2013.

6. Umar H. Rizvi, Gerard J. M. Janssen and Jos H. Weber. BER Analysis of BPSK and QPSK Constellations in the Presence of ADC Quantization Noise // Proceedings of APCC2008, Kioto, Japan 2008 IEICE 08 SB 0083.

7. Amin A. Computation of Bit-Error Rate of Coherent and Non-Coherent Detection M-Ary PSK With Gray Code in BFWA Systems // International Journal of Advancements in Computing Technology, Vol. 3, Nu 1, February 2011.

8. Martirosov V.Å. Optimal receive in digital communication systems. Moscow. Radiotehnika, 2010. 208 p.

9. Chile C.M. Bounds and Approximations for Rapid Evaluation of Coherent MPSK Error Probabilities // IEEE Trans. Commun., Vol/ COM-33, pp. 271–273, March 1985.

10. Sklyar B. Digital communications. Williams, 2007. 1104 p.

11. McKinley M.D. EVM Calculation for Broadband Modulated Signals // 64th ARFTG Conf. Dig., Orlando, Florida. 2004. pp. 45–52.

12. Hassun R., Flaherty M., Matreci R., and Taylor M. Effective evaluation of link quality using error vector magnitude techniques. In Wireless Communications Conference, 1997.

13. Jensen T.L., & Larsen T. (2013). Robust Computation of Error Vector Magnitude for Wireless Standards // IEEE Trans. Commun., 61(2), 648–657. 10.1109/TCOMM.2012.022513.120093.

14. Dubov Ì.À., Priorov À.L. Metodika neetalonnoi otsenki sootnoshenia signal/shum i veroyatnosti bitovoi oshibki // DSPA. 2012. ¹ 4. P. 37–43.

15. Zivkovic M. and Mathar R. Preamble-based SNR estimation in frequency selective channels for wireless OFDM systems. In IEEE VTC 2009, 2009.

16. Georgiadis A. Gain, phase imbalance, and phase noise effects on error vector magnitude // IEEE Transactions on Vehicular Technology, 53(2):443–449, 2004.

17. Schmogrow R., Nebendahl B., Winter M., Josten A., Hillerkuss D., Koenig S., Meyer J., Dreschmann M., Huebner M., Koos C., Becker J., Freude W., and Leuthold J. Error vector magnitude as a performance measure for advanced modulation formats // Photonics Technology Letters, IEEE, 24(1): 61–63, Jan 2012.

18. Rao C.R. Handbook of statistics. Vol. 24. Data mining and data visualization. ELSEVIER B.V., 2005. 644 p.

19. Martushev Yu. Yu. Digital modeling of radio devices. Practical experience. Moscow. Goryachaya liniya – Telekom, 2012. 188 p.

20. Goldsmith A. Wireless communications. Stanford University, 2004. 419 p.


Algorithms for apparatus function estimation in tasks of images recovery
V.K. Klochko, e-mail: klochkovk@mail.ru
V.P. Kuznetsov

The Ryazan State Radio Engineering University (RSREU), Russia, Ryazan


Keywords: radiometer, passive location, apparatus function, recovery of images, matrix method
.

Abstract
In practice of the radiometric researches there is problem reducing efficiency of use of radiometers in case of supervision over objects.

The problem is connected with a priori unknown apparatus function (AF) of the radiometer. Such function (AF) characterizes influence of the directional pattern of the antenna, path of preprocessing of the radiometer and external factors on the required image of objects. In actual practice operation of the radiometer the value of AF will differ from its value measured in laboratory conditions owing to blurring of the AF form that also reduces efficiency of the radiometer.

The purpose of work is development of algorithms for estimation of the unknown AF in the conditions of aprioristic uncertainty.

The objective is achieved by the solution of the following task: development of new algorithms of AF estimation allowing to increase the accuracy of AF estimates in real practice of supervision due to application of optimum methods of estimation on the basis of radio thermal image standard.

The largest accuracy of estimation of AF was shown by modification of the algorithm based on a matrix method and answering to criterion of an optimality. At the same time application of this algorithm demands knowledge of the standard image that in actual practice it isn't always feasible. Another algorithm in which the initial description of AF is given on the basis of the antenna pattern which characteristic is usually known is represented to more realistic. However automation of selection of the AF parameters in the course of recovery of the image is compli-cated at the small relation a signal-noise and demands participation of the person operator.

The offered algorithms can find application in the existing radiometric systems of microwave range, and also in the optical systems of the Infrared range intended for detection and recognition of objects according to their restored image.

References

1. Sharkov E.A. Radio thermal remote sensing of Earth: physical bases: in 2 t. / T. 1. M.: IKI Russian Academy of Sciences, 2014. 544 pp.

2. Vasilenko G. I., Taratorin A.M. Recovery of images. M.: Radio and communication, 1986. 304 pp.

3. Klochko V. K. Recovery of object’s mages in the conditions of atmospheric distortions // the Bulletin of the Ryazan state radio engineering university, no. 33. 2010, pp. 24 – 28.

4. Klochko V. K., Kuznetsov V.P. Recovery of object’s images on the thinned-out matrix of observations // the Bulletin of the Ryazan state radio engineering university. 2016, no. 55, pp. 111 – 117.

5. Gonsalez R., Woods R., Eddins S. Digital image processing in the environment of MATLAB. M.: Technosphere, 2006. 616 pp.

6. Konewhov A.L., Kostevich A.G., Kuryachy M. I. Determination of function of dispersion of a point by characteristic fragments of images // Log "Reports of Tomsk State University of Management Systems and Radiotronics" No. 26, part 1, 2012, pp. 116 – 120.

7. Pirogov Yu.A., Timanovsky A.L. Superpermission in systems of passive radiovision of the millimetric range / Radio technician, 2006, no. 3, pp. 14 – 19.

8. Voskoboynikov Yu.E. Combined non-linear algorithm of recovery of contrasting images in case of inaccurately given hardware function // Avtometriya. 2007, no. 6, pp. 3 – 16.

9. Passive radiolocation: methods of object’s detection / Under the editorship of R.P. Bystrov and A.V. Sokolov. M.: Radio engineering. 2008. 320 pp.


Algorithm for an assessment of the main time parameters of small intestinal electromyograms by the moving average method
Zherebtsov A.V., assistant researcher of the experimental pathology laboratory, N.V. Sklifosovsky Research Institute for Emergency Medicine, Moscow, e-mail: alexey1235@mail.ru
Kamenetskaya M.M., student of Moscow institute of physics and technology
Tropskaya N.S., Ph.D., D.Sc., leading researcher of the experimental pathology laboratory, N.V. Sklifosovsky Research Institute for Emergency Medicine, Moscow, e-mail: ntropskaya@mail.ru

Keywords: small intestinal electromyogram, migrating myoelectric complex, automated processing signal, moving average, threshold method
.

Abstract
This work is about analysis and automated processing electromyography signals of rats intestinal smooth muscles in chronical experiments. A duration of phase III migrating myoelectric complex (MMC) and a period of cycle MMC were selected as sufficient main time parameters of MMC. Calculations root mean square of moving windows is the main method of the developed algorithm quantitative analysis main parameters of small intestine MMC. The proposed algorithm shows high accuracy (The percentage differences between the calculation and the visual expert analysis of the duration phase III and the period of cycle MMC is 6,5% and 6,7% respectively).


References

1. Bogach P.G., Reshodko L.V. Algorithmic and automatic models of smooth muscle activities. – Kiev: Naukova Dumka, 1979.-348p.

2. Vasilev V. A., Popova T.S., Tropskaya N.S. Ocenka dvigatelnoj aktivnosti organov zheludochno-kishechnogo trakta // Rossijskij zhurnal gastroenterologii, gepatologii, koloproktologii. 1995, ¹ 4, s. 48-54.

3. Lebedev N.N. Biorhythms of digestive system. – M.: Medicine, 1987.-256p.

4. Tropskaya N.S., Popova T.S. Nekotorye aspekty regulyacii motornoj funkcii zheludka i tonkoj kishki // Klinicheskie perspektivy gastroenterologii, gepatologii, koloproktologii.- 2008.- ¹4.- 12-16.

5. Groh W. J. et al. Computerized analysis of spike-burst activity of the upper gastrointestinal tract //Digestive diseases and sciences. – 1984. – Ò. 29. – ¹. 5. – Ñ. 422-426.

6. Husebye E., Hellstrom P.M., Sundler F., Chen J., Midtvedt T. Influence of microbial species on small intestinal myoelectric activity and transit in germ-free rats. Am. J. Physiol. Gastrointest. Liver Physiol. 2001; 280 (3): G368–G380.

7. Sarna S. Myoelectrical and Contractile Activities of the Gastrointestinal Tract / In: Schuster M.M., Crowell M.D., Kenneth L.K. Schuster Atlas of Gastrointestinal Motility in Health and Desease. - London: BC Decker Inc., Hamilton, 2002.- P. 1-18.

8. Stam R. et al. Computer analysis of the migrating motility complex of the small intestine recorded in freely moving rats //Journal of pharmacological and toxicological methods. – 1995. – Ò. 33. – ¹. 3. – Ñ. 129-136.

9. Van Schelven L. J., Nieuwenhuijs V. B., Akkermans L. M. A. Automated, quantitative analysis of interdigestive small intestinal myoelectric activity in rats //Neurogastroenterology & Motility. – 2002. – Ò. 14. – ¹. 1. – Ñ. 15-23.

10. Yakovlev V. G. The algorithm for detection of peaks in physiological curves //Avtomatika i Telemekhanika. – 1977. – ¹. 12. – Ñ. 94-105.


Simulation training neurochip integrated into the nervous tissue

Turovski Y.A., Voronezh State University (VSU), Russia, Voronezh, e-mail: yaroslav_turovsk@mail.ru
Kurgalin S.D., Voronezh State University (VSU), Russia, Voronezh, e-mail: kurgalin@bk.ru
Adamenko A.A., Voronezh State University of Engineering Technology (VSUET), Russia, Voronezh,
e-mail: adamenko.artem@gmail.com

Keywords: artificial neural network, neurochip, modeling, algorithm, software system, the nervous tissue.

Abstract
A software system for simulation training neurochips to repair damaged nerve tissue. This software uses an artificial neural network (ANN) in a model of nerve tissue. To simulate the recovery of neural tissue, developed modules to first create the ANN, then damage it, in order to simulate the learning process neurochip for repair of nerve tissue. At the stage of modeling the functioning of the nervous tissue is not damaged software package used for training ANN using algorithms such as backpropagation algorithm, the improved algorithm of back propagation, genetic algorithm, evolutionary algorithm, sorting algorithm weights. To simulate the damage phase ANN designed manual and automated change the weights of ANN. For the simulation phase restoration of damaged nerve tissue, software package allows you to simulate the restoration of the damaged network with a network correction by the latest evolutionary selection. Numerical experiments confirm the possibility of software for creating, damage and restoration of ANN to simulate repair damaged tissue in the nervous microscopic regions of the brain.


References

1. Analysis of the incidence of stroke with the use of information technology. Starodubtseva O.S., Begichev S.V. Basic research. 2012. No. 8–2. pp. 424-427.

2. The neurochip: a new multielectrode device for stimulating and recording from cultured neurons. Michael P. Maher, J. Pine, J. Wright, Y. Tai. Journal of Neuroscience Methods. 1999. no. 87 pp. 45–56.

3. http://humanenhancementusingbrainchips.weebly.com/neurochips.html

4. http://neuroproof.com/en/MEA-Neurochip-Recordings.html

5. http://www.ucalgary.ca/news/utoday/august10-2010/neurochip

6. Neurogenesis and neuronal communication on micropatterned neurochips. Bani-Yaghoub M., Tremblay R., Voicu R., Mealing G., Monette R., Py C., Faid K., Sikorska M. Biotechnol Bioeng. 2005. no. 92-3. pp. 336-45.

7. http://www.sinapseinstitute.org/projects/neurochip/

8. Neurochips functionalised with cell adhesion protein. H. Sorribas, C. Padeste, P. Sonderegger, C. Stricker, L. Tiefenauer. European Cells and Materia. 2001. no. 2. pp. 37-38.

9. Artificial Neural Networks in Medical Diagnosis. Q. K. Al-Shayea. International Journal of Computer Science Issues. 2011. no. 8. pp. 150-154.

10. A Comprehensive Study of Artificial Neural Networks. V. Sharma, S. Rai, A. Dev. International Journal of Advanced Research in Computer Science and Software Engineering. 2012. no. 2. pp. 278-284.

11. Improved backpropagation learning in neural networks with windowe momentum. E. Istook, T. Martinez. International Journal Of Neural Systems. no. 12. pp. 303-318.

12. Learning representations by back-propagating errors. D. E. Rumelhart, G. E. Hinton, R. J. Williams. Nature. 1986. no. 323. pp. 533-536.

13. A Direct Adaptive Method for Faster Backpropagation Learning: The RPROP Algorithm. M. Riedmiller, H. Braun. Institut fur Logik, Komplexitat und Deduktionssyteme, University of Karlsruhe pp. 586-591.

14. A Constructive Algorithm for the Training of a Multilayer Perceptron Based on the Genetic Algorithm. H. C. Andersen, A. C. Tsoi. Complex Systems. 1993. no. 7. pp. 249-268.

15. https://www4.rgu.ac.uk/files/chapter3%20-%20bp.pdf

16. On the momentum term in gradient descent learning algorithms. N. Qian. Neural Networks. 1999. no. 12. pp. 145-151.

17. http://www.aiportal.ru/articles/neural-networks/activation-function.html

18. Practical Mathematical Optimization: An Introduction to Basic Optimization Theory and Classical and New Gradient-Based Algorithms. Jan A. Snyman. Applied Optimization. 2005. no. 97. pp. 43-48.

19. Neural networks and their applications. Chris M.Bishop. Review of scientific instruments. 1994. no. 6. pp. 1803-1832.

20. Modification of the age classification algorithm of people face image on the basis of the ranking method. A. Rybintsev, T. Lukin V. Konushin, A. Konushin. Proceedings RCDL-2012. 2012. pp. 133-137.

21. Achieving Privacy in Data Mining Using Normalization. G. Manikandan, N. Sairam, S. Sharmili, S. Venkatakrishnan. Indian Journal of Science & Technology. 2013. no. 6. pp. 4268.

22. Improving the Learning Speed of 2-Layer Neural Networks by Choosing Initial Values of the Adaptive Weights. D.Nguyen, B.Widrow. Stanford University, Information Systems Laboratory. Stanford CA 94305. pp. 21-26.

23. Neural network approximation of attainable aircraft. Kozlova OG Science and education: e-science and technology publication. 2009. no. 7.

24. Economic statistics. T. Chernova. Tutorial. 1999. pp. 22-31.




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