بررسی استخراج سیگنال های تضعیف شده مخابراتی در محیط های نویزی
محورهای موضوعی : مهندسی الکترونیک
الهام محمدزاده
1
(دانشجوی کارشناسی ارشد مهندسی برق، دانشکده فنی مهندسی، دانشگاه آزاد اسلامی، واحد شهر قدس، تهران، ایران)
مهدی زارع
2
(استادیار گروه برق، دانشکده فنی مهندسی ، دانشگاه آزاد اسلامی ، واحد شهر قدس ، تهران، ایران)
مژده مهدوی
3
(استادیار، گروه برق، دانشکده فنی مهندسی ، دانشگاه آزاد اسلامی ، واحد شهر قدس ، تهران، ایران)
کلید واژه: استخراج سیگنال, بهبود کیفیت سیگنال, سیستم های مخابراتی, پردازش غیرخطی,
چکیده مقاله :
روش‌های متداول استخراج سیگنال دریافتی در خروجی سیستم‌های مخابراتی عموماً مبتنی بر متد‌های خطی هستند. این روش‌ها دارای محدودیت‌های بسیاری بوده که از جمله آنها می‌‌توان به عدم توانایی در حذف نویز داخل باند اشاره کرد. هدف از این مقاله، بررسی بازسازی سیگنال اصلی از روی سیگنال ضعیف دریافت شده است، تا بتوان سیگنال اصلی از درون انواع نویزهای موجود بیرون کشیده. در این حالت از روش میانگین گیری جهت کاهش اعوجاج استفاده میشود تا سیگنال دریافتی بتواند قوی شده و در گیرنده استخراج شود. آزمایشهای این پژوهش در قالب چند سناریو طراحی و شبیه سازی شده است که نتایج آنها بیانگر آن است که در تعداد کانال مشخص، افزایش تعداد تکرارها، معمولاً باعث افزایش سیگنال به نویز می‌گردد. همچنین با کاهش دامنه در آزمایشهای مختلف و در تعداد تکرار ثابت، نتایج سیگنال به نویز کاهش می‌یابد. نتایج شبیه سازی بیانگر آن است که چنانچه سطح سیگنال دریافتی بیشتر از نصف دامنه نویز باشد، سیگنال اصلی قابل استخراج است. ولی در صورتی که سطح سیگنال دریافتی کمتر از نصف دامنه نویز باشد، استخراج سیگنال اصلی بسیار سخت و گاهاً امکان پذیر نمی‌باشد.
Common methods of reducing signal distortion received at the output of telecommunication systems are generally based on linear methods. These methods have many limitations, including the inability to remove the noise inside the band. The purpose of this paper is to reduce distortion and reconstruction of received weak signal and pull it out of the noise sea. In this paper, we employed averaging method to reduce distortion and improve the received signal. The analysis is performed and simulated in several scenarios and results show that in a certain number of channels, increasing the number of repetitions usually increases the signal to noise. Also, as the amplitude decreases in different experiments, the signal-to-noise decreases with constant repetitions. If the received signal amplitude is more than half of the noise amplitude, the main signal is extracted. However, if the signal amplitude is less than half of the noise amplitude, the main signal will not be extracted.
[1] J. D. Downie, and A. B. Ruffin, “Analysis of Signal Distortion and Crosstalk Penalties Induced by Optical Filters in Optical Networks,” Journal of Light wave Technology, vol.23, no.9, pp. 1876-1886, 2003.
[2] J.S. Blasco, E. Iáñez, A. Ubeda and J.M. Azorín. “Visual evoked potential-based brain–machine interface applications to assist disabled people”. Expert Systems with Applications. vol.39, no.9, pp. 7908-7918, 2012.
[3] R. Tobias and P. Henning, “Integrating recursive minimum tracking and codebook based noise estimation for improved reduction of non-stationary noise,” Signal Processing, vol.92, pp.767-779, 2012.
[4] K. M. Saroj, “Recursive and noise-exclusive fuzzy switching median filter for impulse noise reduction,” Engineering Applications of Artificial Intelligence, vol.57, pp.445-450, 2014.
[5] D. Fonseca, A. V. T. Cartaxo, and P. Monteiro, “Adaptive Opt electrical Filters for Improved Generation of Optical Single Sideband Signals with Different Pulse Shapes,” Journal of Light wave Technology, vol.25, No.8, 2007.
[6] M. A. Masry and S. S. Hemami, “A metric for continuous quality evaluation of compressed video with severe distortions,” Signal Processing: Image Communication, vol.19, pp.133-146, 2010.
[7] K. Sun, J. Zhang, W. Shi and Jingdie Gou, “Extraction of Partial Discharge Pulses from the Complex Noisy Signals of Power Cables Based on CEEMDAN and Wavelet Packet”, Energies, vol.12,no.17,pp.3242,2019.
[8] Z. Yongli, H. Tailin, L. Baihe, L. Yang and L. Fuwen, “Detection and extraction of shockwave signal in noisy environments”, Journal of Intelligent & Fuzzy Systems, vol. 37, no. 4, pp. 4499-4510, 2019.
[9] K. Ji, Y. Shen and F. Wang, “Signal Extraction from GNSS Position Time Series Using Weighted Wavelet Analysis”, Remote Sens, vol.12,no.6,pp.992, 2020.
[10] S. Giarnetti, F. Leccese and M. Caciotta, “Non recursive Nonlinear Least Squares for periodic signal fitting,” Elsevier, Measurement, vol.103, pp.208-216, 2017.
[11] S. Giarnetti, F. Leccese, M. Caciotta, “Non recursive multi-harmonic least squares fitting for grid frequency estimation,” Elsevier, Measurement, vol. 66, pp.229-237, 2015.
[12] S. Cuomo, G. De Pietro, R. Farina, A. Galletti and G. Sannino, “A revised scheme for real time ECG Signal denoising based on recursive filtering,” Biomedical Signal Processing and Control, vol.27, pp.134-144, 2016.
[13] A. Ahangi, M. Karamnejad, N. Mohammadi, R. Ebrahimpour and N. Bagheri “Multiple classifier system for EEG signal classification with application to brain–computer interfaces”. Neural Computing and Applications. Vol.23, no.5, pp.1319-1327, 2013;
[14] M. Bamdad, H. Zarshenas, H. Grailu “A survey on BCI application in rehabilitation to improve”. Journal of Research in Rehabilitation Sciences, vol.9, no.6, pp.1153-1166, 2014.
[15] G.F. Woodman “A brief introduction to the use of event-related potentials in studies of perception and attention”. Attention, Perception, & Psychophysics, vol.72, no.8, pp.2031-2046, 2010.
[16] M.M. Fouad, K.M. Amin, N. El-Bendary and A.E. Hassanien “Brain Computer Interface: A Review”. Brain-Computer Interfaces: Springer,vol.74, pp. 3-30, 2015.
[17] A. Roman-Gonzalez “EEG Signal Processing for BCI Applications. Human–Computer Systems Interaction: Backgrounds and Applications 2” Springer, vol.98, pp. 571-591, 2012.
[18] S. Deng, R. Srinivasan, T. Lappas and M. D'Zmura “EEG classification of imagined syllable rhythm using Hilbert spectrum methods”. Journal of neural engineering, vol.7, no.4, pp.6-7, 2010,
[19] J.D. Vega Arias, C. Hintermüller and C. Guger “Generic Brain-computer Interface for Social and Human-computer Interaction”. Procedeeng of the Fifth International Conference on Advances in Computer-Human Interactions Spain,2012, pp.145-9.
[20] Pérez-Marcos, J.A. Buitrago, F.D. Velásquez “Writing through a robot: A proof of concept for a brain–machine interface”. Medical engineering & physics, vol.33,no.10, pp.1314-1317, 2011.
[21] علائی محمد، امیری رضایی، "حذف نویز از سیگنال همدوس بازگشتی رادار با استفاده از تبدیل چیرپلت"، مجله علمی-پژوهشی علوم و فناوری های پدافند غیرعامل، پائیز 1389
[22] C.H. Wu, H.C. Chang, P.L. Lee, K.S.Li, J.J. Sie, C.W. Sun, et al. “Frequency recognition in an SSVEP-based brain computer interface using empirical mode decomposition and refined generalized zero-crossing”. Journal of neuroscience methods, vol.196, no.1, pp.170-181, 2011.
_||_[1] J. D. Downie, and A. B. Ruffin, “Analysis of Signal Distortion and Crosstalk Penalties Induced by Optical Filters in Optical Networks,” Journal of Light wave Technology, vol.23, no.9, pp. 1876-1886, 2003.
[2] J.S. Blasco, E. Iáñez, A. Ubeda and J.M. Azorín. “Visual evoked potential-based brain–machine interface applications to assist disabled people”. Expert Systems with Applications. vol.39, no.9, pp. 7908-7918, 2012.
[3] R. Tobias and P. Henning, “Integrating recursive minimum tracking and codebook based noise estimation for improved reduction of non-stationary noise,” Signal Processing, vol.92, pp.767-779, 2012.
[4] K. M. Saroj, “Recursive and noise-exclusive fuzzy switching median filter for impulse noise reduction,” Engineering Applications of Artificial Intelligence, vol.57, pp.445-450, 2014.
[5] D. Fonseca, A. V. T. Cartaxo, and P. Monteiro, “Adaptive Opt electrical Filters for Improved Generation of Optical Single Sideband Signals with Different Pulse Shapes,” Journal of Light wave Technology, vol.25, No.8, 2007.
[6] M. A. Masry and S. S. Hemami, “A metric for continuous quality evaluation of compressed video with severe distortions,” Signal Processing: Image Communication, vol.19, pp.133-146, 2010.
[7] K. Sun, J. Zhang, W. Shi and Jingdie Gou, “Extraction of Partial Discharge Pulses from the Complex Noisy Signals of Power Cables Based on CEEMDAN and Wavelet Packet”, Energies, vol.12,no.17,pp.3242,2019.
[8] Z. Yongli, H. Tailin, L. Baihe, L. Yang and L. Fuwen, “Detection and extraction of shockwave signal in noisy environments”, Journal of Intelligent & Fuzzy Systems, vol. 37, no. 4, pp. 4499-4510, 2019.
[9] K. Ji, Y. Shen and F. Wang, “Signal Extraction from GNSS Position Time Series Using Weighted Wavelet Analysis”, Remote Sens, vol.12,no.6,pp.992, 2020.
[10] S. Giarnetti, F. Leccese and M. Caciotta, “Non recursive Nonlinear Least Squares for periodic signal fitting,” Elsevier, Measurement, vol.103, pp.208-216, 2017.
[11] S. Giarnetti, F. Leccese, M. Caciotta, “Non recursive multi-harmonic least squares fitting for grid frequency estimation,” Elsevier, Measurement, vol. 66, pp.229-237, 2015.
[12] S. Cuomo, G. De Pietro, R. Farina, A. Galletti and G. Sannino, “A revised scheme for real time ECG Signal denoising based on recursive filtering,” Biomedical Signal Processing and Control, vol.27, pp.134-144, 2016.
[13] A. Ahangi, M. Karamnejad, N. Mohammadi, R. Ebrahimpour and N. Bagheri “Multiple classifier system for EEG signal classification with application to brain–computer interfaces”. Neural Computing and Applications. Vol.23, no.5, pp.1319-1327, 2013;
[14] M. Bamdad, H. Zarshenas, H. Grailu “A survey on BCI application in rehabilitation to improve”. Journal of Research in Rehabilitation Sciences, vol.9, no.6, pp.1153-1166, 2014.
[15] G.F. Woodman “A brief introduction to the use of event-related potentials in studies of perception and attention”. Attention, Perception, & Psychophysics, vol.72, no.8, pp.2031-2046, 2010.
[16] M.M. Fouad, K.M. Amin, N. El-Bendary and A.E. Hassanien “Brain Computer Interface: A Review”. Brain-Computer Interfaces: Springer,vol.74, pp. 3-30, 2015.
[17] A. Roman-Gonzalez “EEG Signal Processing for BCI Applications. Human–Computer Systems Interaction: Backgrounds and Applications 2” Springer, vol.98, pp. 571-591, 2012.
[18] S. Deng, R. Srinivasan, T. Lappas and M. D'Zmura “EEG classification of imagined syllable rhythm using Hilbert spectrum methods”. Journal of neural engineering, vol.7, no.4, pp.6-7, 2010,
[19] J.D. Vega Arias, C. Hintermüller and C. Guger “Generic Brain-computer Interface for Social and Human-computer Interaction”. Procedeeng of the Fifth International Conference on Advances in Computer-Human Interactions Spain,2012, pp.145-9.
[20] Pérez-Marcos, J.A. Buitrago, F.D. Velásquez “Writing through a robot: A proof of concept for a brain–machine interface”. Medical engineering & physics, vol.33,no.10, pp.1314-1317, 2011.
[21] علائی محمد، امیری رضایی، "حذف نویز از سیگنال همدوس بازگشتی رادار با استفاده از تبدیل چیرپلت"، مجله علمی-پژوهشی علوم و فناوری های پدافند غیرعامل، پائیز 1389
[22] C.H. Wu, H.C. Chang, P.L. Lee, K.S.Li, J.J. Sie, C.W. Sun, et al. “Frequency recognition in an SSVEP-based brain computer interface using empirical mode decomposition and refined generalized zero-crossing”. Journal of neuroscience methods, vol.196, no.1, pp.170-181, 2011.
