Digital Signal Processing |
Russian |
Theory, methods and algorithms for determining envelopes of discrete finite real signals on the basis of parametric Fourier transformations Abstract 2. Ponomarev V.A, Ponomareva O.V., Ponomareva N.V. [The method of fast calculation of the discrete Hilbert transform in the frequency domain]. Modern information and electronic technologies, 2014, no.15, pp. 183-184 (in Russ.). 3. Ponomareva N.V., Ponomareva O.V., Hvorenkov V.V. [Determination of anharmonic discrete signal envelope based on the Hilbert transform in the frequency domain]. Intelligent systems in production, 2018, vol.16, no.1, pp.33-40 (in Russ.). 4. Ponomareva Olga, Ponomarev Alexey, Smirnova Natalia. Hilbert envelope extraction from real discrete finite signals considering the nonlocality of Hilbert transform. 22th International Conference on Digital Signal Processing and its Applications, DSPA 2020. 22. 2020. Ñ. 9213286. 5. Marpl-ml. S.L. Cifrovoj spektral'nyj analiz i ego prilozheniya: Perevod s angl. [Digital Spectral Analysis and its Applications]. Moscow, World., 1990, 584 p.(in Russ.) 6. Dudgeon D.E. Multidimensional Digital Signal Processing Prentice Hall, 1995. — 406 p. 7. Prehtt U. Cifrovaya obrabotka izobrazhenij. V 2-h knigah. Perevod s angl. [Digital image processing]. Moscow, World., 1982, 790 p.(in Russ.) 8. Vakman D.E. [On the definition of the concepts of amplitude, phase and instantaneous signal frequency]. Radiotekhnika i elektronika. 1972, ¹5, pp. 973-978 (in Russ.) 9. Vanshtejn L.A., Vakman D.E. Razdelenie chastot v torii kolebanij i voln [Separation of frequencies in thorium oscillations and waves]. Moscow, Nauka, 1983. 288 p. (in Russ.) 10. Fink L.M. Signaly. Pomekhi. Oshibki [Signals. Interference. Mistakes] Moscow, Radio and communications, 1984. 256 p. (in Russ.) 11. Lajons R. Cifrovaya obrabotka signalov / 2-e izd., per. s angl [Digital signal processing / 2nd ed., Trans. from English] Moscow, Binom-Press, 2006.636 p. (in Russ.) 12. Rabiner L., Gold B. Theory and Application of digital signal processing, New Jersey, Prentice-hall, 1975, 772 p. 13. Trahtman A.M. Vvedenie v obobshchennuyu spektral'nuyu teoriyu. [Introduction to Generalized Spectral Theory]. Moscow, Soviet radio, 1982, 352 p. (in Russ.) 14. Alexey V. Ponomarev Systems Analysis of Discrete Two-Dimensional Signal Processingin Fourier Bases. Springer Nature Switzerland AG 2020 M. Favorskaya and L. C. Jain (eds.), Advances in Signal Processing, Intelligent Systems Reference Library 184, https://doi.org/10.1007/978-3-030-40312-6_7 15. Olga V. Ponomareva, Alexey V. Ponomarev and Natalya V. Smirnova Sliding Spatial Frequency Processing of Discrete Signals. Springer Nature.Switzerland AG 2020. M. Favorskaya and L. C. Jain (eds.), Advances in Signal Processing, Intelligent Systems Reference Library 184, https://doi.org/10.1007/978-3-030-40312-6_8. 16. Olga V. Ponomareva, Alexey V. Ponomarev and Natalya V. Smirnova Interpolation of Real and Complex Discrete Signals in the Spatial Domain. Springer Nature Switzerland AG 2020.M. Favorskaya and L. C. Jain (eds.), Advances in Signal Processing, Intelligent Systems Reference Library 184, https://doi.org/10.1007/978-3-030-40312-6_9 17. Ponomareva O., Ponomarev A., Smirnova N. Complex-Conjugate Symmetry of Coefficients of Two-Dimensional Discrete Fourier Transform with Variable Parameters of Real Signals 2022 24th International Conference on Digital Signal Processing and its Applications, DSPA 2022, 2022. 18. Ponomarev A., Ponomareva O., Smirnova N. 2D Discrete Fast Fourier Transform with variable parameters. 2022 24th International Conference on Digital Signal Processing and its Applications, DSPA 2022, 2022. 19. Ponomarev A., Ponomareva O., Smirnova N. Evolution of One-Dimensional and Two-Dimensional Discrete Fourier Transform 2022 24th International Conference on Digital Signal Processing and its Applications, DSPA 2022, 2022. 20. Ponomarev A., Ponomareva O., Smirnova N. Fast Algorithms for Two-Dimensional Discrete Fourier Transform of Vibroacoustic Signals in Solving Problems of Control and Technical Condition of Machines and Mechanisms 2022 International Conference on Dynamics and Vibroacoustics of Machines, DVM 2022, 2022. 21. Ponomarev A., Ponomareva O., Smirnova N. Two-Dimensional Discrete Fourier Transform with Variable Parameters in Solving Fundamental Problems of Dynamics and Vibrodiagnostics of Machines. 2022 International Conference on Dynamics and Vibroacoustics of Machines, DVM 2022, 2022. 22. Ponomareva O.V., Ponomarev A.V. Theoretical Foundations of digital Vector Fourier Analysis of two-dimensional Signals Padded with Zero Samples|// Information and Control Systems. 2021. ¹ 1 (110). Ñ. 55-64. 23. Ponomareva O.V., Ponomarev A.V, Smirnova N.V. [Algoritmy pryamogo i obratnogo parametricheskogo bystrogo preobrazovaniya Fur'e]. Information Technology. 2022, no. 1, pp. 9-19. (in Russ.) 24. Ponomareva O.V., Ponomarev A.V, Smirnova N.V. [Dvumernye bystrye preobrazovaniya Fur'e s var'iruemymi parametrami]. Digital signal processing. 2022, no 3, pp.3-13. (in Russ.) 25. Ponomareva O.V., Ponomarev A.V, Smirnova N.V. [Perekrestnaya kompleksno-sopryazhennaya simmetriya koefficientov dvumernogo diskretnogo preobrazovaniya Fur'e c var'iruemymi parametrami dejstvitel'nyh signalov]. Digital signal processing. 2022, no 4, pp.3-12. (in Russ.)
Keywords: identification of linear system; adaptive filter; impulse response; misalignment; Echo Return Loss Enhancement (ERLE). 2. Farhang-Boroujeny B. Adaptive filters theory and applications, 2-nd ed. John Wiley & Sons, 2013, 778 p. 3. Djigan V.I. Adaptivnaya fil'traciya signalov: teoriya i algoritmy (Adaptive signal filtering: theory and algorithms). M: Tekhnosfera, 2013. 528 s. (In Russian). 4. Haykin S. Adaptive filter theory, 5th ed., Pearson Education Inc., 2014, 889 p. 5. Diniz P. S. R. Adaptive filtering algorithms and practical implementation, 5-th ed. Springer, 2020, 495 p. 6. Benesty J., Huang Y., Eds. Adaptive signal processing: applications to real-world problems. Springer-Verlag, 2003, 356 p. 7. Digital network echo cancellers, ITU-T Recommendation G.168. Series G: Transmission systems and media, digital systems and networks. International telephone connections and circuits – Apparatus associated with long-distance telephone circuits, Geneva, 2001, 116 p. 8. Allen J. B., Berkley D. A. Image method for efficiently simulation small-room acoustics, Journal of Acoustical Society of America, 1979, vol. 64, no 4, pp. 943–950. 9. Lehmann E. A., Johansson A. M. Diffuse reverberation model for efficient image-source simulation of room impulse responses, IEEE Trans. Audio, Speech, and Language Processing, 2010, vol. 18, no 6, pp. 1429–1439. 10. Chen W. Y. Simulation techniques and standards development for digital subscriber line systems, Macmillan Technical Publishing, 1998, 644 p. 11. Starr T., Cioffi J. M., Silverman P. J. Understanding digital subscriber line technology, Prentice Hall, 1999, 474 p.
Cylindric Adaptive antenna Arrays
Abstract 2. Aktivnye fazirovannye antennye reshetki (Active phased arrays) / Pod red. D. I. Voskresenskogo i A. I. Kanashchenkova. M.: Radiotekhnika, 2004. 488 s. (In Russian) 3. Brown A. D., Boeringer D., Cooke T. Electronically scanned arrays. MATLAB® modelling and simulation. CRC Press, 2012. 214 p. 4. Balanis C. A. Antenna theory: analysis and design. 4-th ed. John Wiley & Sons, Inc., 2016. 1095 p. 5. Maillou R. J. Phased array antenna handbook, 3-rd ed. Artech House, Inc., 2017. 506 p. 6. Zhuravlev A. K., Lukoshkin A. P., Poddubnij S. S. Obrabotka signalov v adaptivnyh antennyh reshetkah (Signal processing in adaptive antenna arrays). L.: Izdatel'svo Leningradskogo universiteta, 1983. 240 s. (In Russian). 7. Compton R. T. Adaptive antennas. Concepts and performance. Prentice Hall, 1988. 448 p. 8. Pistol'kors A. A., Litvinov O. S. Vvedenie v teoriyu adaptivnyh antenn (Introduction in adaptive arrays theory). M.: Nauka, 1991. 200 s. (In Russian). 9. Hudson J. E. Adaptive array principles. The Institution of Engineering and Technology, 2007. 253 p. 10. Monzingo R. A., Haupt R. L., Miller T. W. Introduction to adaptive arrays, 2nd ed. SciTech Publishing, 2011. 510 p. 11. Widrow B., Stearns D. D. Adaptive signal processing. Pearson. 1985. 528 p. 12. Farhang-Boroujeny B. Adaptive filters theory and applications. 2-nd ed. John Wiley & Sons, 2013. 778 p. 13. Djigan V. I. Adaptivnaya fil'traciya signalov: teoriya i algoritmy (Adaptive signal filtering: theory and algorithms). M: Tekhnosfera, 2013. 528 s. (In Russian). 14. Haykin S. Adaptive filter theory. 5-th ed. Pearson Education Inc., 2014. 889 p. 15. Diniz P. S. R. Adaptive filtering algorithms and practical implementation. 5-th ed. Springer, 2020. 495 p. 16. Giordano A. A., Hsu F. M. Least square estimation with application to digital signal processing. John Willey & Sons, Inc., 1985. 412 p. 17. Djigan V. I. Mnogokanal'nye RLS- i bystrye RLS-algoritmy adaptivnoj fil'tracii (Multichannel and fast RLS adaptive algorithms). Uspekhi sovremennoj radioelektroniki (Journal Achievements of Modern Radioelectronics). 2004. ¹ 11. S. 48-77. (In Russian). 18. Djigan V. I. Recursive least squares – an idea whose time has come. Proceedings of the 7-th International Workshop on Spectral Methods and Multirate Signal Processing. Moscow, Russia, September 1 – 2, 2007. 4 p. 19. Kuo S. M., Gan W.-S. Digital signal processors: architectures, implementations and applications. Prentice Hal, 2004. 624 p. 20. Woods R., McAllister J., Lightbody G., Ying Yi. FPGA-based implementation of signal processing systems. 2-nd ed. Willey, 2017. 360 p. 21. Welch T. B., Wright H. G., Morrow M. G. Real-time digital signal processing from MATLAB to C with the TMS320C6x DSPs. 3-rd ed. CRC Press, 2017. 480 p. 22. Vityazev S. V. Cifrovye processory obrabotki signalov (Digital signal processors). M.: Goryachaya liniya-Telkom, 2017. 100 s. (In Russian). 23. Steyskal H. Digital beamforming antennasþ Microwave Journal. 1987. ¹ 1. P. 107-124. 24. Litva J., Lo T. K.-Y. Digital beamforming in wireless communications. Artech House., 1996. 301 p. 25. Grigor'ev L. N. Cifrovoe formirovanie diagrammy napravlennosti v fazirovannyh antennyh reshetkah (Digital beamforming in phased arrays). M.: Radiotekhnika, 2010. 144 p. (In Russian). 26. Slyusar V. I. Razvitie skhemotekhniki CAR: nekotorye itogi. Chast' 1 (Solutions in antenna arrays with digital beamforming: some results. Part 1) // Pervaya milya. Last mile (First Mile. Last Mile). 2018. ¹ 1. C. 72-77. (In Russian). 27. Slyusar V. I. Razvitie skhemotekhniki CAR: nekotorye itogi. Chast' 2 (Solutions in antenna arrays with digital beamforming: some results. Part 2) // Pervaya milya. Last mile (First Mile. Last Mile). 2018. ¹ 2. C. 76-80. (In Russian). 28. Djigan V. I. Circular adaptive antenna array. Proceedings of the 19-th IEEE East-West Design & Test Symposium (EWDTS). Batumi, Georgia, September 10 – 13, 2021. P. 21–24. 29. King R. W. P., Fikioris G. J., Mack R. B. Cylindrical antennas and arrays. Cambridge University Press, 2005. 652 p. 30. Frost O. L. An algorithm for linearly constrained adaptive array processing. Proceedings of the IEEE. 1972. Vol. 60. ¹ 8. P. 926-935. 31. Djigan V. I. Some tricks of calculations in MIL RLS algorithm. Proceedings of the 23-th International Conference on Digital Signal Processing and its Applications (DSPA-2021). Moscow, Russia, March 24 – 26, 2021. 4 p. 32. Pletneva I. D., Djigan V. I. Modelirovanie obrabotki signalov v cifrovyh antennyh reshetkah (Signal processing simulation in antenna arrays with digital beamforming). Issledovaniya v oblasti cifrovyh sistem svyazi (Research in Modern Digital Telecommunication Systems). M.: Izd.MIET, 2007. P. 36-43. (In Russian). 33. Makarov S. N., Iyer V., Kulkami S., Best S. R. Antenna and EM modelling with MATLAB® Antenna Toolbox. John Wiley and Sons, Inc., 2021. 319 p.
Abstract The use of the matrix eigenvalue method led to the formation of the characteristic function of the output (decisive) statistics to a form convenient for integration and obtaining calculation expressions for the probabilities of false alarm and correct detection, with the help of which the desired detection characteristics are determined. A comparative analysis of the characteristics of the appearance of single-channel systems for processing multi-frequency signals against a back-ground of white noise with a different nature of interperiod processing (coherent or incoherent) in frequency channels and an analysis of the accuracy of measuring the radial velocity of the target depending on the parameters of the multi-frequency signal. Analysis of the efficiency of detection and measurement of multi-frequency signals shows that the proposed processing systems based on combining the results of single-channel coherent accumulation of products of complex conjugate samples in each frequency channel allow, with an optimal number of channels, to obtain energy gains compared with single-frequency systems and multi-frequency systems based on incoherent accumulation, as well as to increase the accuracy of unambiguous measurements of the radial velocity of the target. In particular, the gains in the signal-to-noise threshold ratio of a multi-frequency signal detection system, invariant in each frequency channel to Doppler phase shifts, compared with a multi-channel Doppler frequency system, are established. References 2. Richards M.A., Scheer J.A., Holm W.A. (Eds.). Principles of Modern Radar: Basic Principles. New York: SciTech Publishing, IET, Edison. 2010. – 924 p. 3. Melvin W. L., Scheer J.A. (Eds.). Principles of Modern Radar: Advanced Techniques. New York: SciTech Publishing, IET, Edison, 2013. – 846 p. 4. Radar Handbook / Ed. by M.I. Skolnik. 3rd ed. McGraw–Hill, 2008. 1352 p. 5. Popov D.I. Adaptacija nerekursivnyh rezhektornyh fil'trov // Izvestija vuzov. Ra-diojelektronika. 2009. vol. 52. no. 4. P. 46-55. (in Russian). 6. Popov D.I. Autocompensation of the Doppler phase of clutter // Cifrovaja obrabotka signalov. 2009. no 2. pp. 30–33. (in Russian). 7. Popov D.I. Avtokompensacija doplerovskoj fazy mnogochastotnyh passivnyh pomeh // Vestnik Rjazanskogo gosudarstvennogo radiotehnicheskogo universiteta. 2018. no. 65. pp. 32–37. 8. Popov D.I. Adaptive suppression of clutter // Cifrovaja obrabotka signalov. 2014. no. 4. pp. 32-37. (in Russian). 9. Popov D.I. Adaptivnije regektornjie filtrij kaskadnogo tipa // Cifrovaya obrabotka signalov. 2016. no. 2. pp. 53-56. (in Russian). 10. Popov D.I. Adaptive notch filter with real weights // Cifrovaya obrabotka signalov. 2017. no. 1. pp. 22-26. (in Russian). 11. Popov D.I. Optimizacja nerekursivnjih regektornjie filtrov s chastichnoj adaptaciej // Cifrovaya obrabotka signalov. 2018. no. 1. pp. 28-32. (in Russian). 12. Popov D.I. Optimizacija rezhektornyh fil'trov po verojatnostnomu kriteriju // Cifrovaja obrabotka signalov. 2021. no. 1. P. 55-58. (in Russian). 13. Kuz'min S.Z. Cifrovaja radiolokacija. Vvedenie v teoriju (Digital radar. Introduction to Theory). Kiev: KViC, 2000. 428 p. (in Russian). 14. Cifrovaja obrabotka signalov v mnogofunkcional'nyh radiolokatorah. Metody. Algoritmy. Apparatura: monografija (Digital signal processing in multifunctional radars. Methods. Algorithms. Equipment: monograph) / pod red. G.V. Zajceva. M.: Radiotehnika, 2015. 376 p. (in Russian). 15. Klochko V.K., Kuznecov V.P., Levitin A.V. i dr. Algoritmy opredelenija koordi-nat dvizhushhihsja celej na baze mnogokanal'noj doplerovskoj RLS // Vestnik Rjazanskogo gosudarstvennogo radiotehnicheskogo universiteta. 2015. no. 53. pp. 3-10. (in Russian). 16. Klochko V.K., Kuznecov V.P., Vu Ba Hung. Ocenivanie parametrov radiosignalov ot podvizhnyh malovysotnyh ob#ektov // Vestnik Rjazanskogo gosudarstvennogo radioteh-nicheskogo universiteta. 2022. no. ¹ 80. pp. 12-23. (in Russian). 17. Popov D.I., Belokrylov A.G. Sintez obnaruzhitelej-izmeritelej mnogochastotnyh signalov // Izvestija vuzov. Radiojelektronika. 2001. v. 44. no. 11. pp. 33-40. (in Russian). 18. Popov D.I. Analiz mnogokanal'nyh obnaruzhitelej mnogochastotnyh signalov // Cifrovaja obrabotka signalov. 2023. no. 2. pp. 49-53. (in Russian).
Intelligent Multipath Routing in Software Defined Networks Based on Firefly Swarm Behavior Model
Abstract References 2. Solozobov A.S. Masshtabiruyemost' i nadozhnost' programmno-konfiguriruyemykh setey // Informatsionnyye tekhnologii i telekommunikatsii. 2014. T. 2. ¹ 3. pp. 111-115 (in Russian). 3. Kolyadenko YU.YU., Belousova Ye.E. Programmno-konfiguriruyemyye seti na baze protokola openflow i ikh kharakteristiki // ScienceRise. 2016. T. 3. ¹ 2 (20). pp. 11-16 (in Russian). 4. Volkov A.S., Baskakov A.Ye. Razrabotka protsedury dvunapravlennogo poiska dlya resheniya zadachi marshrutizatsii v transportnykh programmno-konfiguriruyemykh setey // Trudy MAI. 2021. ¹ 118 (in Russian). 5. Polezhayev P.N., Bakhareva N.F., Shukhman A.Ye. Razrabotka effektivnogo geneticheskogo algoritma marshrutizatsii i obespecheniya kachestva obsluzhivaniya dlya programmno-konfiguriruyemoy seti // Vestnik Orenburgskogo gosudarstvennogo universiteta. 2015. ¹ 1 (176). pp. 229-233 (in Russian). 6. Polezhayev P.N., Ushakov YU.A., Polyak R.I., Mironov A.P. Primeneniye metodov murav'inoy kolonii v razrabotke effektivnykh algoritmov marshrutizatsii i obespecheniya QoS dlya korporativnykh programmno-konfiguriruyemykh setey // Intellekt. Innovatsii. Investitsii. 2014. ¹ 4. pp. 106-113 (in Russian). 7. Koryachko V.P., Perepelkin D.A., Ivanchikova M.A., Byshov V.S., Tsyganov I.Yu. Analysis QoS Metrics in Software Defined Networks // Proceedings MECO 2017 – IEEE 6th Mediterranean Conference on Embedded Computing (MECO-2017), 2017, pp. 374–378. DOI: 10.1109/MECO.2017.7977240. 8. Manov I.A. Effektivnaya mnogoparametricheskaya marshrutizatsiya trafika v programmno-konfiguriruyemykh setyakh // V sbornike: Tsifrovaya obrabotka signalov i yeyo primeneniye – DSPA-2019. Doklady 21-y Mezhdunarodnoy konferentsii. 2019. pp. 215-220 (in Russian). 9. Venkatesh K, Srinivas L, Krishnan MM, Shanthini A. QoS improvisation of delay sensitive communication using SDN based multipath routing for medical applications // Future Generation Computer Systems 2019; 93: 56–65. 10. Sahhaf S., Tavernier W., Colle D., Pickavet M. Adaptive and reliable multipath provisioning for media transfer in SDN-based overlay networks // Computer Communications 2017; 106: 107–16. 11. Ejaz S., Iqbal Z., Shah P. A., Bukhari B. H., Ali A. Traffic load balancing using software defined networking (SDN) controller as virtualized network function (2019). 12. K. Rajasekaran, Kannan Balasubramanian. Energy Conscious based Multipath Routing Algorithm in WSN, International Journal of Computer Network and Information Security (IJCNIS), Vol.8, No.1, pp.27-34, 2016.DOI: 10.5815/ijcnis.2016.01.04 13. Kulakov Y., Kohan A. Traffic orchestration in data center network based on software-defined networking technology. In: International Conference on Computer Science, Engineering and Education Applications ICCSEEA 2019: Advances in Computer Science for Engineering and Education II, pp. 228-237 (2019). 14. Perepelkin D. A. Konceptual'nyj podhod dinamicheskogo formirovanija trafika programmno-konfiguriruemyh telekommunikacionnyh setej s balansirovkoj nagruzki // Informacionnye tehnologii. 2015. T. 21. ¹ 8. C. 602-610 (in Russian). 15. Korjachko V. P., Perepelkin D. A., Ivanchikova M. A., Byshov V. S., Cyganov I. Ju. Programmnaja infrastruktura i vizual'naja sreda raspredelennoj obrabotki potokov dannyh v programmno-konfiguriruemyh setjah // Vestnik Rjazanskogo gosudarstvennogo radiotehnicheskogo universiteta. 2018. ¹ 65. pp. 44-54. DOI: 10.21667/1995-4565-2018-65-3-44-54 (in Russian). 16. Nikul'chev E. V., Pajain S. V., Pluzhnik E. V. Dinamicheskoe upravlenie trafikom programmno-konfiguriruemyh setej v oblachnoj infrastrukture // Vestnik Rjazanskogo gosudarstvennogo radiotehnicheskogo universiteta. 2013. ¹ 3 (45), pp. 54-57 (in Russian). 17. Leohin Ju. L., Fathulin T. D. Ocenka vozmozhnosti predostavlenija garantirovannoj skorosti peredachi dannyh v programmno-konfiguriruemoj opticheskoj seti // Vestnik Rjazanskogo gosudarstvennogo radiotehnicheskogo universiteta. 2020. ¹ 71. pp. 45-59. DOI: 10.21667/1995-4565-2020-71-45-59 (in Russian). 18. Ushakova M. V., Ushakov Ju. A. Issledovanie seti virtual'noj infrastruktury centra obrabotki dannyh s gibridnoj programmno-konfiguriruemoj kommutaciej // Vestnik Rjazanskogo gosudarstvennogo radiotehnicheskogo universiteta. 2021. ¹ 75. pp. 34-43. DOI: 10.21667/1995-4565-2021-75-34-43 (in Russian). 19. Perepelkin D. A., Nguen V. T. Issledovanie i analiz processov mnogoputevoj marshrutizacii i balansirovki potokov dannyh v programmno-konfiguriruemyh setjah na osnove geneticheskogo algoritma // Vestnik Rjazanskogo gosudarstvennogo radiotehnicheskogo universiteta. 2022. ¹ 79. pp. 31-48. DOI: 10.21667/1995-4565-2022-79-31-48 (in Russian). 20. Perepelkin D. A., Nguen V. T. Intellektual'naja mnogoputevaja marshrutizacija v programmno-konfiguriruemyh setjah na osnove algoritma iskusstvennoj pchelinoj kolonii // Informacionnye tehnologii. 2022. T. 28. ¹ 8. pp. 395-404. DOI: 10.17587/it.28.395-404 (in Russian). 21. Perepelkin D. A., Ivanchikova M. A., Nguen V. T. Intellektual'naja mnogoputevaja marshrutizacija v programmno-konfiguriruemyh setjah na osnove algoritmov optimizacii murav'inoj kolonii // Informacionnye tehnologii. 2022. T. 28. ¹ 10. pp. 520-528. DOI: 10.17587/it.28.520-528 (in Russian). 22. Perepelkin D. A., Ivanchikova M. A., Nguen V. T. Intellektual'naja mnogoputevaja marshrutizacija v programmno-konfiguriruemyh setjah na osnove algoritma migracii stai ptic // Vestnik Rjazanskogo gosudarstvennogo radiotehnicheskogo universiteta. 2022. ¹ 82. S. 44-59. DOI: 10.21667/1995-4565-2022-82-44-59 (in Russian).
Model development and classification of MIMO communication channels in 3D coordinates Abstract The simulation results show that the coefficients of the correlation matrix change little when the sphere of the scatterers is rotated according to scenario A and B of the COST259 model. The distribution of scatterers along an ellipsoid between the transmitting and receiving device in accordance with scenarios C and D leads to the formation of a maximum correlation coefficient in the direction of the transmitting device. The rotation of the coordinates of the scatterers along the ellipsoid around the Ou axis in accordance with scenario E leads to a significant change in the coefficient of the correlation matrix with a maximum when the scatterers are located on the surface of the ellipsoid near the receiving or transmitting devices. The simulation is consistent with the scenarios of the location of the diffusers according to the COST259 standard. With a fixed location of the receiving and transmitting device, the obtained dependences of the dispersion of the channel coefficients on the angle of rotation of the coordinates of the scatterers can be interpreted as the angular spectrum of the antenna system. In further research, it is proposed to consider the influence of the antenna system parameters on the correlation matrix of channel coefficients at different scenes of the scatterers, as well as to take into account signal losses in the environment and when passing obstacles. References 2. Parshin, Yu.N. Analiz propusknoy sposobnosti kanala peredachi informacii ot bespilotnogo letatelnogo apparata pri netochnoy kanalnoy matrice / Yu.N. Parshin, V.I. Kudryashov // Vestnik Ryazanskogo gosudarstvennogo radiotehticheskogo universiteta. – 2015. – ¹ 52. – p. 19–24. 3. Nawaz, S.J. 3-D Gaussian scatter density propagation model employing a directional antenna at BS / S.J. Nawaz, M.N. Patwary, N.M. Khan, H.Yu. // 2010 5th Advanced Satellite Multimedia Systems Conference and the 11th Signal Processing for Space Communications Workshop. 2010. Pp 395-400. 4. Alsehaili, M. Angle of arrival statistics of a three-dimensional geometrical scattering channel model for indoor and outdoor propagation environments / M. Alsehaili, S. Noghanian, D. A. Buchanan, A. R. Sebak. // IEEE Progress in Electromagnetic Research. 2010. Vol. 109. Pp 191-209. 5. Tennakoon, P. Three-dimensional geometrical channel modeling with different scatterer distributions / P. Tennakoon, C.B. Wavegedara // The International Conference on Advances in Computing, Communications and Informatics (ICACCI). 2015. Pp 154-160.. Eason, B. Noble, and I. N. Sneddon, “On certain integrals of Lipschitz-Hankel type involving products of Bessel functions,” Phil. Trans. Roy. Soc. London, vol. A247, pp. 529–551, April 1955. 6. Liberti, J. C. A geometrically based model for line of sight multipath radio channels / J.C. Liberti, T. S. Rappaport // IEEE Vehicular Technology Conf., 844–848, Apr. 1996. 7. Aslam, M.I. Joint and marginal probabilities for time of arrival and angle of arrival using ellipsoidal model / M.I. Aslam, A.Z. Shaikh // 2013 3rd IEEE International Conference on Computer, Control and Communication (IC4). 8. Patzold, M. A space-time channel simulator for MIMO channels based on the geometrical one-ring scattering model / M. Patzold, B. O. Hogstad. // Wireless Communications and Mobile Computing. Special Issue on Multiple-Input Multiple-Output (MIMO) Communications. – Nov. 2004. – V4. – ¹. 7. – P. 727–737. 9. 3GPP TR 25.943 version 9.0.0 Release 9 Deployment aspects (Release 17) ETSI TR 125 943 V9.0.0. – 2022. 10. Parshin, Yu.N. Programmno-apparatnij kompleks testirovaniya kanalnoy matrici MIMO sistemy peredachi informacii ot podvizhnogo objekta / Yu.N. Parshin, P.V. Zharikov, P.À. Kaznacheev // Vestnik Ryazanskogo gosudarstvennogo radiotehticheskogo universiteta. – Ryazan: RSREU. – 2015. – ¹4. – v. 54, part 1. – p. 3-8. 11. Parshin, Yu.N. Issledovanie vliyaniya shiriny spectra sluchajnogo testovogo signala na tochnost ocenivania kompleksnogo koefficienta peredachi kanalov radiotrakta / Yu.N. Parshin, P.V. Zharikov // Vestnik Ryazanskogo gosudarstvennogo radiotehticheskogo universiteta. – Ryazan: RSREU. – 2010. – ¹1, v. 31. – p. 16-19. 12. Parshin, Yu.N. Vliyanie prostranstvennoj korrelyacii na effektivnost optimizacii prostranstvennoj strukturi mnogoantennoj sistemi pri raznesennom prieme / Yu.N. Parshin, A.V. Xendzov // Vestnik Ryazanskogo gosudarstvennogo radiotehticheskogo universiteta. – Ryazan: RSREU, 2006, v. 19. – p. 54-62.
Research of compliance the integrated-multiplicative quality index of digital im-ages and quality mean opinion scores
The previously known integrated quality index (IQI) [1] can be calculated based on the values of normalized average brightness, standard deviation, global contrast, number of histogram bins, and entropy. It is shown [2] that an image without any content has an IQI value tending to the maximum possible. This fundamentally does not correspond to subjective assessment. The integrated-multiplicative quality index (IMQI) [2] has been proposed to take into account the signal-to-noise ratio and local image contrast during quality estimating. The IMQI measure has no described disadvantage. To score the pertinence of quality estimates, it is necessary to compare their values with the values of subjective expert assessments of image quality. This study has been performed using TID2013 dataset images. TID2013 [4] is the set of 3000 images generated from 25 reference images by applying 24 distortions with 5 corruption intensity levels. Each image is accompanied by the expert quality assessment mean opinion score (MOS), obtained by averaging through 985 experiments, and the assessment standard deviation. The usefulness of an image quality index is defined by how close it's values correlate with actual expert assessments. To evaluate the correlation stability between values and its statistical significance in the sample the Spearman rank correlation coefficient [6] is used, as in [5, 7, 8]. The more stable and more statistically significant the correlation in the sample the higher the Spearmen rank correlation coefficient. Spearman rank correlation coefficient for IQI and MOS is 0.12, but for IMQI and MOS it is 0.26. It is shown that the IMQI numerical values correlate with expert assessments 2.17 times better than the values of the IQI. This fact means that using IMQI to range images in MEVS is preferable to using IQI. 2. Sychev A.S., Kholopov I.S. Bezetalonnyj integral'no-mul'tiplikativnyj pokazatel' kachestva cifrovyh polutonovyh izobrazhenij (No-reference integrated-multiplicative quality in-dex for digital grayscale images) // Cifrovaya obrabotka signalov (Digital Signal Processing). 2018. ¹3. Pp. 51-57. 3. Laboratory for Image & Video Engineering - The University of Texas at Austin. URL: https://live.ece.utexas.edu/research/quality/subjective.htm. 4. Nikolay Ponomarenko homepage - TID2013. URL: https://www.ponomarenko.info/tid2013.htm. 5. Ponomarenko N., Jin L. Image database TID2013: Peculiarities, results and perspectives // Signal Processing: Image Communication. 2015. Pp. 57 77. 6. Gmurman V.E. Teoriya veroyatnostej i matematicheskaya statistika (Probability theory and mathematical statistics) // M.: YUrajt, 2015. 479 p. 7. Wang Z., Bovik A.C. Image Quality Assessment: From Error Visibility to Structural Similarity // IEEE transactions on image processing. 2004. ¹4. Pp. 600 612. 8. Sheikh H.R., Sabir M.F. A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms // IEEE Transactions in Image Processing. 2006. ¹11. Pp. 3441 3452. 9. Sychev A.S., Kholopov I.S. Sravnitel'nyj analiz metodik ocenki moshchnosti shuma na cifrovom izobrazhenii (Comparative analysis of image noise estimation methods) // Infor-macionnye tekhnologii i nanotekhnologii (ITNT-2021) (International Conference on Information Technology and Nanotechnology). 2021. Pp. 20392.
Influence of multitone continuous wave interference on the pseudo-random signal searching and increasing the efficiency of the notch by weighting with Dolph-Chebyshev functions modern modifications
2. Smirnov N.I., Gorgadze S.F. Pomekhoustoichivost' asinkhronnykh sistem peredachi s shumopodobnymi signalami pri deistvii uzkopolosnykh pomekh (Noise immunity of asynchronous transmission systems with noise-like signals under the action of narrow-band interference) // Radiotehnika (Journal Radioengineering). 1993. no 7. pp. 27–36. 3. GLONASS. Printsipy postroeniya i funktsionirovaniya (GLONASS. Design principles and functioning) / ed. by A.I. Perov, V.N. Kharisov. Ì.: Radiotekhnika. 2010. 800 p. 4. Avdeev V.A., Koshkarov A.S., Konnov E.V. Obnaruzhenie pomekh v chastotnykh diapazonakh kosmicheskikh navigatsionnykh system (Detection of interference in the frequency ranges of space navigation systems) // Zhurnal radiojelektroniki [jelektronnyj zhurnal] (Journal of Radio electronics). 2015. no 10. URL: http://jre.cplire.ru/jre/oct15/12/text.pdf. 5. Pomekhozashchishchennost' sistem radiosvyazi s rasshireniem spektra signalov modulyatsiei nesushchei psevdosluchainoi posledovatel'nost'yu (Noise immunity of radio communication systems with the expansion of the spectrum of signals by modulation of the carrier pseudo-random sequence) / V.I. Borisov, V.M. Zinchuk, A.E. Limarev, N.P. Mukhin, G.S. Nakhmanson. M.: Radio i svyaz', 2003. 640 p. 6. Borio D. GNSS acquisition in the presence of continuous wave interference // IEEE Transactions on aerospace and electronic systems. 2010. vol. 46. no 1. pp. 47–60. 7. Kuzmin E.V. O vlijanii kvantovanija po urovnju na jeffektivnost' procedury poiska shumopodobnogo signala po zaderzhke na fone shuma i garmonicheskoj pomehi (Efficiency of the spread spectrum signal searching procedure in case of continuous wave interference and quantization effect) // Tsifrovaya obrabotka signalov (Digital signal processing). 2020. no 2. pp. 41–45. 8. Kuzmin E.V., Zograf F.G. Vliyanie garmonicheskoi pomekhi na effektivnost' protsedury besporogovogo poiska shumopodobnogo signala po vremeni zapazdyvaniya s perekhodom v chastotnuyu oblast' opredeleniya (Influence of continuous wave interference on the efficiency of the non-threshold search procedure for a noise-like signal by delay time with transition to the frequency domain) // Radiotekhnika i elektronika (Radioengineering & Electronics). 2022. vol. 67. no 8. pp. 774–781. 9. Bakit'ko R.V., Pol'shhikov V.P., Shilov A.I., Hackelevich Ja.D., Boldenkov E.N. Ispol'zovanie vesovyh funkcij dlja predvaritel'noj obrabotki shumopodobnyh signalov pri nalichii sil'nyh interferencionnyh pomeh (Using weighting functions for preprocessing spread spectrum signals in the presence of strong interference) // Radiotehnika. 2006. no 6. pp. 13–17. 10. Kuzmin E.V. Povyshenie effektivnosti obrabotki signalov na fone garmonicheskoi pomekhi za schet vybora funktsii predvaritel'nogo vzveshivaniya dlya chastotnogo rezhektora (Increasing the efficiency of the signals processing in case of continuous wave interference by choosing the function of the preliminary weighting for frequency notch) // Tsifrovaya obrabotka signalov (Digital signal processing). 2021. no 4. pp. 16–20. 11. Kuzmin E.V. Pokazateli kachestva algoritma DPF-rezhekcii uzkopolosnoj pomehi pri razlichnyh funkcijah predvaritel'nogo vzveshivanija (Quality indicators of the DFT-based algorithm for narrow-band interference rejection under various functions of the preliminary weighing) // Tsifrovaya obrabotka signalov (Digital signal processing). 2023. no 1. pp. 48–53. 12. Kravchenko V.F., Pustovoit V.I. Novyj klass vesovyh funkcij i ih spektral'nye svojstva (A new class of weight functions and their spectral properties) // Doklady akademii nayk (Reports of the academy of sciences). 2002. vol. 386. no 1. pp. 38–42. 13. Okonnye funkcii dlja garmonicheskogo analiza signalov (Window functions for harmonic analysis of signal) / V.P. Dvorkovich, A.V. Dvorkovich. M.: Tehnosfera, 2016. 208 p. 14. Kuzmin E.V. Analiz chastotnyh harakteristik procedur kvadraturnoj korreljacionnoj obrabotki kompleksnyh signalov (Analysis of the frequency responses of the quadrature correlation processing of complex signals) // Tsifrovaya obrabotka signalov (Digital signal processing). 2020. no 4. pp. 13–20. 15. Matematicheskie metody statistiki (Mathematical methods of statistics) / H. Cramer. M.: Mir. 1975. 648 p. 16. Radiotehnicheskie cepi i signaly: ucheb. dlja vuzov. 3-e izd. pererab. i dop. (Radio engineering circuits and signals: textbook for universities. 3d ed. rev. and add.) / S.I. Baskakov. M.: Vysshaja shkola. 2000. 462 p. 17. Teorija slozhnyh signalov (Complex signal theory) / L.E. Varakin. M.: Sovetskoe radio. 1970. 376 p.
Simulation modeling of test and training datasets of a neural network sonar detector The method developed by the authors is aimed at forming training datasets presented in the form of sonar images for intelligent sonar. Hydroacoustic images are formed as a result of calculating the spectrogram of an additive signal obtained by adding discrete elementary hydroacoustic signals obtained as a result of simulation over time. As a result of preprocessing in the spectrogram, the target is separated from the noise and echoes of other objects. The spectrogram is a matrix of numbers corresponding to the intensity of the reflected signal, the rows of the matrix correspond to the distance to the object, the columns correspond to the frequency of the signal. Reference versions of spectrograms can be obtained by one of the resource-intensive methods (full-scale experiment or signal simulation), and variations of spectrograms for a neural network associated with changes, for example, in a number of target parameters can be obtained directly by modifying the reference image with its preliminary division into bit planes. As a result of the study, it was concluded that the spectrogram and its binary representation are suitable for use as training kits for intelligent sonar, as well as the possibility of significantly reducing the time of formation of training images due to the direct synthesis of spectrograms based on their reference versions, taking into account changes in individual parameters of the sonar environment. 2. P. Etter, Underwater Acoustic Modelling and Simulation. CRC Press, 2013, ZSCC: NoCitationData[s0], ISBN: 978-1-4822-9514-6. DOI: 10.1201/b13906. [Online]. Available: https://doi.org/10.1201/b13906. 3. Kim T.A., Areshhenkov D.A., Sotnikov A.A. Issledovanie sredstv imitacionnogo modelirovaniya mnogomernyx signalov v sistemax iskusstvennogo intellekta realnogo vremeni (Research of simulation tools for multidimensional signals in real-time artificial intelligence systems) // Sovremennye naukoemkie texnologii. 2022. ¹ 10-2. pp. 218-225 4. Olshevskij V.V. Statisticheskie metody v gidrolokacii (Statistical methods in sonar). L.: Sudostroenie, 1983. 280 p. 5. Bykov V.V. Cifrovoe modelirovanie v statisticheskoj radiotexnike (Digital modeling in statistical radio engineering). M.: Sov. radio, 1971. 275 p. 6. Rozanov I.A., Sotnikov A.A. Metod adaptivnogo imitacionnogo modelirovaniya mnogomernyx signalov v informacionno-upravlyayushhix sistemax realnogo vremeni (The method of adaptive simulation of multidimensional signals in real-time information and control systems). Sovremennaya nauka: aktualnye problemy teorii i praktiki. Seriya «Estestvennye i texnicheskie nauki», 2022, No 10-2, pp.87-92 7. Syuzev V.V., Kim T.A., Askerova N.A. Sotnikov A.A. Obobshhennyj mnogokriterialnyj metod modelirovaniya mnogomernyx signalov v informacionno-upravlyayushhix sistemax realnogo vremeni (Generalized multi-criteria method for modeling multidimensional signals in real-time information and control systems). Fundamentalnye, poiskovye, prikladnye issledovaniya i innovacionnye proekty. Nacionalnaya nauchno-prakticheskaya konferenciya. RTU MIREA. 2022 8. Kim T.A., Sotnikov A.A. Imitacionnoe modelirovanie radiolokacionnyx pomex spektralno-korrelyacionnymi metodami sredstvami modulnoj platformy PXI (Simulation of radar interference by spectral correlation methods using the PXI modular platform). Cifrovaya obrabotka signalov i eyo primenenie (DSPA-2022) 24-ya Mezhdunarodnaya konferenciya. Vypusk: XXIV. M.: Rossijskoe nauchno-texnicheskoe obshhestvo radiotexniki, e`lektroniki i svyazi im. A.S. Popova, 2022. pp. 225-230. 9. Rozanov I.A., Sotnikov A.A. Metod adaptivnogo imitacionnogo modelirovaniya mnogomernyx signalov v informacionno-upravlyayushhix sistemax realnogo vremeni (The method of adaptive simulation of multidimensional signals in real-time information and control systems) // Sovremennaya nauka: aktualnye problemy teorii i praktiki. Seriya: Estestvennye i texnicheskie nauki. 2022. ¹ 10-2. pp. 87-92. 10. Krutyakov M.A., Rozanov I.A., Sotnikov A.A. Imitacionnoe modelirovanie signala gidroakusticheskoj granichnoj reverberacii v bazise Xartli (Simulation of the signal of hydroacoustic boundary reverberation in the Hartley basis). Sovremennaya nauka: aktualnye problemy teorii i praktiki. Seriya: Estestvennye i Texnicheskie Nauki. 2019. No1. pp. 60-66. 11. Sungatullin E.N., Ustimenko V.M. Formirovanie shirokopolosnyx pomex (Formation of broadband interference). Aktualnye problemy infotelekommunikacij v nauke i obrazovanii. 2016. pp. 210-213. 12. H. Peyvandi, M. Farrokhrooz, H. Roufarshbaf, and S.-J. Park, ‘SONAR Systems and Underwater Signal Processing: Classic and Modern Approaches’, Sonar Systems. InTech, Sep. 12, 2011. doi: 10.5772/17505. 13. J.E. Thorner. «Approaches to sonar beamforming», IEEE Technical Conference on Southern Tier, Binghamton, NY, USA, 1990, pp. 69-78, doi: 10.1109/STIER.1990.324633. 14. Kim T.A., Rozanov I.A., Sotnikov A.A. Metod korrelyacionnoj obrabotki gidrolokacionnyx chastotno-manipulirovannyx signalov. Sovremennaya nauka: aktualnye problemy teorii i praktiki (The method of correlation processing of sonar frequency-manipulated signals). Seriya: Estestvennye i Texnicheskie Nauki. 2022. No 9. pp. 60-66. 15. Ponomareva O.V., Ponomarev A.V., Ponomareva N.V. Dvumernye bystrye preobrazovaniya Fure s variruemymi parametrami (Two-dimensional fast Fourier transforms with variable parameters). Cifrovaya obrabotka signalov. 2022. No 3. pp. 3-13. 16. Ortogonalnoe kodirovanie binarnyx izobrazhenij (Orthogonal encoding of binary images) / B.V. Kostrov, N.N. Grinchenko, S.N. Baranova, E.A. Trushina, A.A. Vyugina. Vestnik YaVVU PVO. 2023. No 2. pp. 82-87. 17. Kostrov B.V., Grinchenko N.N., Vyugina A.A., Baranova S.N. Parallelnye vychisleniya v zadachax vosstanovleniya iskazhennyx izobrazhenij v prostranstvenno-spektralnoj forme (Parallel computations in problems of reconstruction of distorted images in spatio-spectral form). Trudy Instituta sistemnogo programmirovaniya RAN, tom 35, vyp. 2, 2023, 157-168 DOI: 10.15514/ISPRAS-2023-35(2)-11. 18. Matematicheskaya model processa peredachi izobrazhenij na osnove bitovyx ploskostej (A mathematical model of the image transmission process based on bit planes) / N.N. Grinchenko, S.N. Baranova, M.A. Lobachev [i dr.]. Vestnik Koncerna VKO «Almaz-Antej». 2023. No 1. pp. 82-89. URL: https://doi org/10.38013/2542-0542-2023-1-82-89 19. Binarizaciya tekstovyx izobrazhenij na osnove texnologii bitovyx ploskostej (Binarization of text images based on bit-plane technology) / B.V. Kostrov, N.N. Grinchenko, S.N. Baranova, E.A. Trushina, A.A. Vyugina. Vestnik YaVVU PVO 2023. No 2. pp.75-81.
Probabilistic assessment of recognition of radar signals recorded when observing human movements The proposed method for processing the input radar signal is based on correlation processing. The signal template obtained by observing recognizable gestures is used as a standard. The complexity of processing such signals lies in the fact that the recorded signals may differ from the standard under the influence of the speed of gestures, viewing angle, and distance of the object. The solution to this problem is the use of several standards for recognition (multi-scale analysis), this approach is close to the principle of wavelet transform. To confirm the effectiveness of the method, the results of experiments in which the input signal differs in duration within ±25% of the standard are demonstrated. The experiment involved observing ten repetitions of gestures in one of six recognition classes. The result of the experiment is an increase in the probability of recognition in comparison with classical analysis using a single template. The recognition probability assessment was carried out according to the empirical law of normal distribution. 2. Alekseev I.V., Mitrokhin M.A. Modern methods of speech recognition for building a voice interface for controlling special-purpose systems. News from universities. Volga region. Technical science. 2019. No. 2 (50). pp. 3-10. 3. Immoreev I.Ya., Fesenko M.V. Pulse ultra-wideband sensor. Patent for invention RU 2369323 C1, 10.10.2009. Application No. 2008106039/14 dated 02/20/2008. [Patent]. 4. Immoreev I.Ya., Samkov S.V., Pavlov S.N. Pulse ultra-wideband sensor. Patent for invention RU 2321341 C1, 04/10/2008. Application No. 2006135225/14 dated 10/06/2006. [Patent]. 5. Immoreev I.Ya. Ultra-wideband short-range radars for detecting and determining the parameters of living objects. Bulletin of the Moscow Aviation Institute. 2011. T. 18. No. 1. P. 18. 6. Ashryapov M.I. Radar recognition of Doppler signals using wavelet-correlation analysis. Proceedings of VNIIEM. 2022. T. 188. No. 3. pp. 18-24. 7. Ashryapov M.I. Methods for recognizing micro-Doppler portraits based on correlation wavelet analysis. Journal of Siberian Federal University. Engineering & Technologies. Journal of the Siberian Federal University. Technics and techology. 2022. 15(6), pp. 759-767. DOI:10.17516/1999-494X-0434. 8. Mitsel A.A. Applied mathematical statistics. Practical work. Tomsk: TUSUR. 2015. 81 p. 9. Kobzar A.I. Applied mathematical statistics (Modern methods in mathematics), 2006. 10. Iglin S.P. Probability theory and mathematical statistics based on MATLAB. Kharkov, 2006. 612 p.
Permutation decoding with a system of adapted alternative solutions 2. W.W. Peterson, E.J. Weldon, Jr. Error-correction codes. MIT Press, Cambridge, Mass., 1972. (in Russian). 3. Gladkikh A.A., Klimov R.V., Chilihin N. Y., Methods of efficient decoding of redundant codes and their modern applications. – Ulyanovsk: USTU, 2016. (in Russian) 4. R. H Morelos-Zaragosa. The Art of Error Correcting Coding, John Wiley & Sons, Ltd Baffins Lane, Chichester, England, 2002. (in Russian). 5. B. Sclar. Digital communications. Fundamentals and Applications. Prentice Hall, 2003 (in Russian). 6. Gladkikh A.A., Namestnikov S.M., Pchelin N.A. Efficient permutation decoding of binary block redundant codes / Automation of management processes. – no. 1 (47), 2017, pp. 67 – 74. (in Russian). 7. Properties of cyclic structures in the permutation decoding system of redundant codes / Babanov N.Y., Gladkikh A.A., Namestnikov S.M., Shakhtanov S.V. // Automation of management processes. 2020. no. 2 (60). pp. 82–89. (in Russian). 8. Gladkikh A.A., Namestnikov S.M., Novoselov A.V., Tolikina M.Y., Al-Merci A.S.A. Evaluation of the effectiveness of data protection against errors based on Bayesian inference in the system of iterative transformations // Automation of management processes. 2022. no. 4 (70). pp. 120–130. (in Russian). 9. Novoselov A.V., Shakhtanov S.V., Al-Merci A.S.A., Tolikina M.Y. Evaluation of criteria for the effectiveness of error protection based on permutation decoding // Automation of management processes. 2022. no. 3 (69). pp. 27–34. (in Russian). 10. Gladkikh A.A., Ganin D.V., Pchelin N.A., Shakhtanov S.V., Ochepovsky A.V. Coding methods and permutation decoding in the systems for network processing of data // International Journal of Control and Automation. 2020. Vol. 13. no 1. pp. 93–100. 11. Evaluation of the statistical characteristics of the permutation decoder by its software implementation / A.L.Kh. Attabi, À.À. Brynza, D.V. Ganin, À.À. Nichunaev, A.V. Novoselov // Automation of management processes. 2023. no. 2 (72). pp. 91–98.
Introduction to the design and analysis of digital filters in the SimInTech environment This application allows you to design filters with finite impulse response (FIR) and infinite impulse response (IIR), build all the necessary characteristics: magnitude response, phase response, group delay response, phase delay, impulse response, step response, pole/zero plot. It is possible to display from 1 to 6 characteristics on the screen at the same time. The article also provides a detailed description of the parameter settings panel for filter calculation. The following design methods are available for IIR filters: Butterworth, Chebyshev (type 1), Chebyshev (type 2), and elliptical. Methods for FIR filters: Equiripple and Window: Rectangular, Bartlett, Hamming, Hann, Blackman, Chebyshev, Kaiser. There is also an input form “Order value”, in which you can enter a fixed value of the order of the calculated filter or select the “Minimum” parameter, which will allow you to calculate a filter with a minimum order, based on the specified requirements. For IIR filters, it is also possible to use partitioning to move from a direct form to a cascade (sequential) form. 2. SimInTech dynamic modeling environment: [website]. URL: https://simintech.ru/. 3. Digital signal processing. Textbook for universities / V.V. Vityazev, V.A. Volchenkov, A.A. Ovinnikov, etc. – M.: Goryachaya Liniya-Telekom, 2023. – 188 p.
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