Εμφανίζονται 1 - 20 Αποτελέσματα από 59 για την αναζήτηση '"дискретне вейвлет-перетворення"', χρόνος αναζήτησης: 0,82δλ Περιορισμός αποτελεσμάτων
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    Academic Journal
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    Academic Journal

    Συγγραφείς: Sorokina, L., Goyko, A.

    Πηγή: Ways to Improve Construction Efficiency; № 44 (2020): Ways to Improve Construction Efficiency (Economics); 3-16
    Шляхи підвищення ефективності будівництва в умовах формування ринкових відносин; № 44 (2020): Шляхи підвищення ефективності будівництва в умовах формування ринкових відносин (Економічний); 3-16

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    Academic Journal

    Πηγή: New Materials and Technologies in Metallurgy and Mechanical Engineering; No. 2 (2021): New materials and technologies in metallurgy and mechanical engineering; 55-60 ; Новые материалы и технологии в металлургии и машиностроении; № 2 (2021): Нові матеріали і технології в металургії та машинобудуванні; 55-60 ; Нові матеріали і технології в металургії та машинобудуванні; № 2 (2021): Нові матеріали і технології в металургії та машинобудуванні; 55-60 ; 2786-7358 ; 1607-6885

    Διαθεσιμότητα: http://nmt.zntu.edu.ua/article/view/252951

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    Academic Journal

    Πηγή: New Materials and Technologies in Metallurgy and Mechanical Engineering; No. 1 (2021); 59-66 ; Новые материалы и технологии в металлургии и машиностроении; № 1 (2021); 59-66 ; Нові матеріали і технології в металургії та машинобудуванні; № 1 (2021); 59-66 ; 2786-7358 ; 1607-6885

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    Διαθεσιμότητα: http://nmt.zntu.edu.ua/article/view/240006

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    Academic Journal

    Πηγή: New Materials and Technologies in Metallurgy and Mechanical Engineering; No. 2 (2021): New materials and technologies in metallurgy and mechanical engineering; 55-60
    Новые материалы и технологии в металлургии и машиностроении; № 2 (2021): Нові матеріали і технології в металургії та машинобудуванні; 55-60
    Нові матеріали і технології в металургії та машинобудуванні; № 2 (2021): Нові матеріали і технології в металургії та машинобудуванні; 55-60

    Σύνδεσμος πρόσβασης: http://nmt.zntu.edu.ua/article/view/252951

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    Academic Journal

    Πηγή: Eastern-European Journal of Enterprise Technologies; Vol. 4 No. 9(112) (2021): Information and controlling system; 65-77
    Eastern-European Journal of Enterprise Technologies; Том 4 № 9(112) (2021): Информационно-управляющие системы; 65-77
    Eastern-European Journal of Enterprise Technologies; Том 4 № 9(112) (2021): Інформаційно-керуючі системи; 65-77

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    Σύνδεσμος πρόσβασης: http://journals.uran.ua/eejet/article/view/238601

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    Academic Journal

    Συνεισφορές: Івано-Франківський національний технічний університет нафти і газу, Івано-Франківськ, Україна, Ivano-Frankivsk National Technical University of Oil and Gas, Ivano-Frankivsk, Ukraine

    Θέμα γεωγραφικό: Тернопіль, Ternopil

    Περιγραφή αρχείου: 124-134

    Relation: Вісник Тернопільського національного технічного університету, 4 (116), 2024; Scientific Journal of the Ternopil National Technical University, 4 (116), 2024; https://doi.org/10.1088/1742-2140/aa7303; https://doi.org/10.1016/j.jesit.2018.03.003; https://doi.org/10.33108/visnyk_tntu2020.04.118; https://doi.org/10.1016/j.bspc.2015.12.002; https://doi.org/10.2139/ssrn.3356368; https://doi.org/10.3390/coatings11050496; https://doi.org/10.1155/2014/650891; https://doi.org/10.1109/pecon.2016.7951579; https://ww1.microchip.com/downloads/en/DeviceDoc/Atmel-7810-Automotive-Microcontrollers-ATmega328P_Datasheet.pdf.https://doi.org/10.1088/1742-6596/2414/1/012007; https://doi.org/10.23939/acps2024.01.061; https://doi.org/10.31649/1997-9266-2023-169-4-46-53; https://doi.org/10.3390/en10111694; https://doi.org/10.1109/access.2016.2587581; https://doi.org/10.1155/2015/528656; https://doi.org/10.3390/app10062162; https://doi.org/10.1016/j.epsr.2015.04.005; https://doi.org/10.1049/iet-smt.2013.0114; https://doi.org/10.1016/j.measurement.2015.07.050; 1. Javadi M., Ghasemzadeh H. (2017) Wavelet analysis for ground penetrating radar applications: a case study. Journal of Geophysics and Engineering, vol. 14, no. 5, pp. 1189–1202. https://doi.org/10.1088/1742-2140/aa7303; 2. Islam M. S., Pears R., Bacic B. (2018) A wavelet approach for precursor pattern detection in time series. Journal of Electrical Systems and Information Technology, vol. 5, no. 3, pp. 337–348. https://doi.org/10.1016/j.jesit.2018.03.003; 3. Y. Yavorska et al. (2020) Evaluation of methods for determining abnormalities in cardiovascular system by pulse signal under psycho-emotional stress in dental practice. Scientific journal of the Ternopil National Technical University, vol. 100, no. 4, pp. 118–126. https://doi.org/10.33108/visnyk_tntu2020.04.118; 4. Rodriguez-Hernandez M. A. (2016) Shift selection influence in partial cycle spinning denoising of biomedical signals. Biomedical Signal Processing and Control, vol. 26, pp. 64–68. Available at: https://doi.org/10.1016/j.bspc.2015.12.002. https://doi.org/10.1016/j.bspc.2015.12.002; 5. S. Shridhar et al. (2019) Denoising of ECG Signals Using Wavelet Transform and Principal Component Analysis. SSRN Electronic Journal. Available at: https://doi.org/10.2139/ssrn.3356368.; 6. X. Zeng et al. (2024) Study on Noise Reduction of Acoustic Emission Signals based on Improved Wavelet Thresholding. Scientific Journal of Technology, vol. 6, no. 3, pp. 1–9. https://doi.org/10.2139/ssrn.3356368; 7. P. Li et al. (2021) Denoising of LCR Wave Signal of Residual Stress for Rail Surface Based on Lifting Scheme Wavelet Packet Transform. Coatings, vol. 11, no. 5, pp. 496. Available at: https://doi.org/10.3390/coatings11050496.; 8. N. Liu et al. (2020) Research on Wavelet Threshold Denoising Method for UWB Tunnel Personnel Motion Location. Mathematical Problems in Engineering, vol. 2020, pp. 1–14. https://doi.org/10.3390/coatings11050496; 9. C. Tan et al. (2014) An Integrated Denoising Method for Sensor Mixed Noises Based on Wavelet Packet Transform and Energy-Correlation Analysis. Journal of Sensors, vol. 2014, pp. 1–11. Available at: https://doi.org/10.1155/2014/650891.; 11. R. Chu et al. (2022) An adaptive noise removal method for EEG signals. Journal of Physics: Conference Series, 2414, 012007.; 12. ATmega328P 8-bit AVR Microcontroller with 32K Bytes In-System Programmable Flash Datasheet [Electronic resource]. Available at: https://ww1.microchip.com/downloads/en/DeviceDoc/Atmel-7810-Automotive-Microcontrollers-ATmega328P_Datasheet.pdf.https://doi.org/10.1088/1742-6596/2414/1/012007; 13. Vanchak V., Melnychuk S. (2024) Influence Assessment of Distance to the Source of Pulse Signals With Harmonic Components on the Temporal Distortion of Their Forms. Advances in Cyber-Physical Systems, vol. 9, no. 1, pp. 61–67. Available at: https://doi.org/10.23939/acps2024.01.061.; 14. Vanchak V. S., Melnychuk S. I., Manuliak I. Z. (2023) Correlation Functions Application Effect on Periodic Pulse Signal with Harmonic Elements. Visnyk of Vinnytsia Politechnical Institute, vol. 169, no. 4. P. 46–53. https://doi.org/10.31649/1997-9266-2023-169-4-46-53; 15. Vanchak V., Melnychuk S., Manuliak I. Efficiency of low-pass filters based on FFT for SNR improvement of periodic impulse signals with harmonic components. materials of XII-th scientific and practical conference “Problems of informatics and computer technologies”: materials of scientific and practical conference, Chernivtsi, 10–12 November 2023. Chernivtsi, 2013. P. 71–73.; 16. Luo Y., Li Z., Wang H. (2017) A Review of Online Partial Discharge Measurement of Large Generators. Energies, vol. 10, no. 11, pp. 1694. Available at: https://doi.org/10.3390/en10111694.; 17. Singh A. (2016) Comparative Analysis of Gaussian Filter with Wavelet Denoising for Various Noises Present in Images. Indian Journal of Science and Technology, vol. 9, no. 1, pp. 1–8. https://doi.org/10.3390/en10111694; 18. Srivastava M., Anderson C. L., Freed J. H. (2016) A New Wavelet Denoising Method for Selecting Decomposition Levels and Noise Thresholds. IEEE Access, vol. 4, pp. 3862–3877. Available at: https://doi.org/10.1109/access.2016.2587581. https://doi.org/10.1109/ACCESS.2016.2587581; 19. C. He et al. (2015) A New Wavelet Thresholding Function Based on Hyperbolic Tangent Function. Mathematical Problems in Engineering, vol. 2015, pp. 1–10. Available at: https://doi.org/10.1155/2015/528656.; 20. Li Y., Li Z. (2020) Application of a Novel Wavelet Shrinkage Scheme to Partial Discharge Signal Denoising of Large Generators. Applied Sciences, vol. 10, no. 6, pp. 2162. https://doi.org/10.3390/app10062162; 21. C. F. F. C. Cunha et al. (2015) A new wavelet selection method for partial discharge denoising. Electric Power Systems Research, vol. 125, pp. 184–195. Available at: https://doi.org/10.1016/j.epsr.2015.04.005.; 22. Altay Ö., Kalenderli Ö. (2015) Wavelet base selection for de-noising and extraction of partial discharge pulses in noisy environment. IET Science, Measurement & Technology, vol. 9, no. 3, pp. 276–284. https://doi.org/10.1049/iet-smt.2013.0114; 23. A. T. Carvalho et al. (2015) Identification of partial discharges immersed in noise in large hydro-generators based on improved wavelet selection methods. Measurement, vol. 75, pp. 122–133. https://doi.org/10.1016/j.measurement.2015.07.050; 15. Vanchak V., Melnychuk S., Manuliak I. Efficiency of low-pass filters based on FFT for SNR improvement of periodic impulse signals with harmonic components. materials of XII-th scientific and practical conference "Problems of informatics and computer technologies": materials of scientific and practical conference, Chernivtsi, 10–12 November 2023. Chernivtsi, 2013. P. 71–73.; http://elartu.tntu.edu.ua/handle/lib/48179; https://doi.org/10.33108/visnyk_tntu2024.04.124

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    Academic Journal

    Πηγή: Системи обробки інформації. — 2019. — № 3(158). 54-64 ; Системы обработки информации. — 2019. — № 3(158). 54-64 ; Information Processing Systems. — 2019. — № 3(158). 54-64 ; 1681-7710

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