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Статті в журналах з теми "Signals’ analysis methods"
Ngui, Wai Keng, M. Salman Leong, Lim Meng Hee, and Ahmed M. Abdelrhman. "Wavelet Analysis: Mother Wavelet Selection Methods." Applied Mechanics and Materials 393 (September 2013): 953–58. http://dx.doi.org/10.4028/www.scientific.net/amm.393.953.
Повний текст джерелаQu, Yanhuai, Shuai Zhang, and Qingkai Han. "Comparison of Non-linear Signals Analysis Methods." MATEC Web of Conferences 232 (2018): 01014. http://dx.doi.org/10.1051/matecconf/201823201014.
Повний текст джерелаGarg, Malika. "Methods for the Analysis of EEG signals: A Review." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (September 30, 2021): 873–76. http://dx.doi.org/10.22214/ijraset.2021.38072.
Повний текст джерелаMuthuswamy, Jitendran, and Nitish V. Thakor. "Spectral analysis methods for neurological signals." Journal of Neuroscience Methods 83, no. 1 (August 1998): 1–14. http://dx.doi.org/10.1016/s0165-0270(98)00065-x.
Повний текст джерелаA, Mohammed. "Deconvolution methods for biomedical signals analysis." Indian Journal of Science and Technology 3, no. 2 (February 20, 2010): 105–9. http://dx.doi.org/10.17485/ijst/2010/v3i2.1.
Повний текст джерелаMusha, Takaaki, and Tatsuya Kumazawa. "Intensity analysis methods for transient signals." Applied Acoustics 69, no. 1 (January 2008): 60–67. http://dx.doi.org/10.1016/j.apacoust.2006.08.011.
Повний текст джерелаDebbal, S. M. "Pathological Electromyogram (EMG) Signal Analysis Parameters." Clinical Cardiology and Cardiovascular Interventions 4, no. 13 (August 9, 2021): 01–14. http://dx.doi.org/10.31579/2641-0419/185.
Повний текст джерелаIwaniec, Joanna, Marek Iwaniec, and Antoni Kalukiewicz. "Application of vectorcardiography and recurrence-based methods to analysis of ECG signals." MATEC Web of Conferences 241 (2018): 01015. http://dx.doi.org/10.1051/matecconf/201824101015.
Повний текст джерелаNam, Ki Woo, Seok Hwan Ahn, and Jin Wook Kim. "Nondestructive Evaluation in Materials Using Time-Frequency Analysis Methods." Key Engineering Materials 297-300 (November 2005): 2090–95. http://dx.doi.org/10.4028/www.scientific.net/kem.297-300.2090.
Повний текст джерелаGao, Yuan Sheng, Qiang Chen, Qiang Sun, Zhong Chen, and Wen Hai Zhang. "The Impedance Calculation Methods Using Damped Sinusoidal Signal." Advanced Materials Research 860-863 (December 2013): 2003–6. http://dx.doi.org/10.4028/www.scientific.net/amr.860-863.2003.
Повний текст джерелаДисертації з теми "Signals’ analysis methods"
Sava, Herkole P. "Spectral analysis of phonocardiographic signals using advanced parametric methods." Thesis, University of Edinburgh, 1995. http://hdl.handle.net/1842/12903.
Повний текст джерелаBalli, Tugce. "Nonlinear analysis methods for modelling of EEG and ECG signals." Thesis, University of Essex, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.528852.
Повний текст джерелаPham, Duong Hung. "Contributions to the analysis of multicomponent signals : synchrosqueezing and associated methods." Thesis, Université Grenoble Alpes (ComUE), 2018. http://www.theses.fr/2018GREAM044/document.
Повний текст джерелаMany physical signals including audio (music, speech), medical data (ECG, PCG), marine mammals or gravitational-waves can be accurately modeled as a superposition of amplitude and frequency-modulated waves (AM-FM modes), called multicomponent signals (MCSs). Time-frequency (TF) analysis plays a central role in characterizing such signals and in that framework, numerous methods have been proposed over the last decade. However, these methods suffer from an intrinsic limitation known as the uncertainty principle. In this regard, reassignment method (RM) was developed with the purpose of sharpening TF representations (TFRs) given respectively by the short-time Fourier transform (STFT) or the continuous wavelet transform (CWT). Unfortunately, it did not allow for mode reconstruction, in opposition to its recent variant known as synchrosqueezing transforms (SST). Nevertheless, many critical problems associated with the latter still remain to be addressed such as the weak frequency modulation condition, the mode retrieval of an MCS from its downsampled STFT or the TF signature estimation of irregular and discontinuous signals. This dissertation mainly deals with such problems in order to provide more powerful and accurate invertible TF methods for analyzing MCSs.This dissertation gives six valuable contributions. The first one introduces a second-order extension of wavelet-based SST along with a discussion on its theoretical analysis and practical implementation. The second one puts forward a generalization of existing STFT-based synchrosqueezing techniques known as the high-order STFT-based SST (FSSTn) that enables to better handle a wide range of MCSs. The third one proposes a new technique established on the second-order STFT-based SST (FSST2) and demodulation procedure, called demodulation-FSST2-based technique (DSST2), enabling a better performance of mode reconstruction. The fourth contribution is that of a novel approach allowing for the retrieval of modes of an MCS from its downsampled STFT. The fifth one presents an improved method developed in the reassignment framework, called adaptive contour representation computation (ACRC), for an efficient estimation of TF signatures of a larger class of MCSs. The last contribution is that of a joint analysis of ACRC with non-negative matrix factorization (NMF) to enable an effective denoising of phonocardiogram (PCG) signals
Lin, Chao. "P and T wave analysis in ECG signals using Bayesian methods." Phd thesis, Toulouse, INPT, 2012. http://oatao.univ-toulouse.fr/8990/1/lin.pdf.
Повний текст джерелаNagappa, Sharad. "Time-varying frequency analysis of bat echolocation signals using Monte Carlo methods." Thesis, University of Edinburgh, 2010. http://hdl.handle.net/1842/4622.
Повний текст джерелаRamnarain, Pallavi. "A Comparative Analysis of Methods for Baseline Drift Removal in Preterm Infant Respiration Signals." VCU Scholars Compass, 2010. http://scholarscompass.vcu.edu/etd/138.
Повний текст джерелаFuchs, Karen [Verfasser], and Gerhard [Akademischer Betreuer] Tutz. "Functional data analysis methods for the evaluation of sensor signals / Karen Fuchs ; Betreuer: Gerhard Tutz." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2017. http://d-nb.info/1156533767/34.
Повний текст джерелаVaerenbergh, Steven Van. "Kernel Methods for Nonlinear Identification, Equalization and Separation of Signals." Doctoral thesis, Universidad de Cantabria, 2010. http://hdl.handle.net/10803/10673.
Повний текст джерелаIn the last decade, kernel methods have become established techniques to perform nonlinear signal processing. Thanks to their foundation in the solid mathematical framework of reproducing kernel Hilbert spaces (RKHS), kernel methods yield convex optimization problems. In addition, they are universal nonlinear approximators and require only moderate computational complexity. These properties make them an attractive alternative to traditional nonlinear techniques such as Volterra series, polynomial filters and neural networks.This work aims to study the application of kernel methods to resolve nonlinear problems in signal processing and communications. Specifically, the problems treated in this thesis consist of the identification and equalization of nonlinear systems, both in supervised and blind scenarios, kernel adaptive filtering and nonlinear blind source separation.In a first contribution, a framework for identification and equalization of nonlinear Wiener and Hammerstein systems is designed, based on kernel canonical correlation analysis (KCCA). As a result of this study, various other related techniques are proposed, including two kernel recursive least squares (KRLS) algorithms with fixed memory size, and a KCCA-based blind equalization technique for Wiener systems that uses oversampling. The second part of this thesis treats two nonlinear blind decoding problems of sparse data, posed under conditions that do not permit the application of traditional clustering techniques. For these problems, which include the blind decoding of fast time-varying MIMO channels, a set of algorithms based on spectral clustering is designed. The effectiveness of the proposed techniques is demonstrated through various simulations.
Anand, K. "Methods for Blind Separation of Co-Channel BPSK Signals Arriving at an Antenna Array and Their Performance Analysis." Thesis, Indian Institute of Science, 1995. http://hdl.handle.net/2005/123.
Повний текст джерелаBaccherini, Simona. "Pattern recognition methods for EMG prosthetic control." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/12033/.
Повний текст джерелаКниги з теми "Signals’ analysis methods"
Signals and systems: Analysis using transform methods and MATLAB. Boston: McGraw-Hill, Higher Education, 2004.
Знайти повний текст джерелаSignals and systems: Analysis using transform methods and MATLAB. 2nd ed. New York: McGraw-Hill, 2012.
Знайти повний текст джерелаLeonowicz, Zbigniew. Parametric methods for time-frequency analysis of electric signals. Wrocław: Oficyna Wydawnicza Politechniki Wrocławskiej, 2006.
Знайти повний текст джерелаAn introduction to the digital analysis of stationary signals. Bristol, England: A. Hilger, 1989.
Знайти повний текст джерелаLi, Jian, Ph. D., 1965- and Stoica Petre, eds. Spectral analysis of signals: The missing data case. [San Rafael, Calif.]: Morgan & Claypool Publishers, 2005.
Знайти повний текст джерелаSignals and systems analysis in biomedical engineering. 2nd ed. Boca Raton: Taylor & Francis Group, 2010.
Знайти повний текст джерела1968-, Ling Tonghua, and Zhang Yiping 1970-, eds. Bao po zhen dong xin hao fen xi li lun yu ji shu: Analysis of blast vibration singals-theories and methods. Beijing: Ke xue chu ban she, 2009.
Знайти повний текст джерелаManfredi, Claudia, ed. Models and Analysis of Vocal Emissions for Biomedical Applications. Florence: Firenze University Press, 2013. http://dx.doi.org/10.36253/978-88-6655-470-7.
Повний текст джерелаManfredi, Claudia, ed. Models and Analysis of Vocal Emissions for Biomedical Applications. Florence: Firenze University Press, 2009. http://dx.doi.org/10.36253/978-88-6453-096-3.
Повний текст джерелаManfredi, Claudia, ed. Models and Analysis of Vocal Emissions for Biomedical Applications. Florence: Firenze University Press, 2011. http://dx.doi.org/10.36253/978-88-6655-011-2.
Повний текст джерелаЧастини книг з теми "Signals’ analysis methods"
Kiasaleh, Kamran. "Signal Processing Methods for Biological Signals." In Biological Signals Classification and Analysis, 175–275. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-54879-6_4.
Повний текст джерелаKiasaleh, Kamran. "Signal Decomposition Methods." In Biological Signals Classification and Analysis, 277–376. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-54879-6_5.
Повний текст джерелаWu, Xiaohui, Guoli Ji, and Qingshun Quinn Li. "Computational Analysis of Plant Polyadenylation Signals." In Methods in Molecular Biology, 3–11. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-2175-1_1.
Повний текст джерелаvon Heijne, Gunnar. "Cleavage-Sites in Protein Targeting Signals." In Methods in Protein Sequence Analysis, 231–38. Basel: Birkhäuser Basel, 1991. http://dx.doi.org/10.1007/978-3-0348-5678-2_23.
Повний текст джерелаSchmal, Christoph, Gregor Mönke, and Adrián E. Granada. "Analysis of Complex Circadian Time Series Data Using Wavelets." In Methods in Molecular Biology, 35–54. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2249-0_3.
Повний текст джерелаZhangy, L. Q., and A. Cichockiz. "Independent Residual Analysis for Temporally Correlated Signals." In Computational Methods in Neural Modeling, 158–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44868-3_21.
Повний текст джерелаSergienko, Vladimir P., and Sergey N. Bukharov. "Methods of Analysis of Noise and Vibration Signals." In Noise and Vibration in Friction Systems, 57–81. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11334-0_4.
Повний текст джерелаIsermann, Rolf, and Marco Münchhof. "Spectral Analysis Methods for Periodic and Non-Periodic Signals." In Identification of Dynamic Systems, 77–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-78879-9_3.
Повний текст джерелаYan, Tao, Ying Wang, Bo Qu, Xiao Liu, and Guoyong Wang. "Analysis Methods of Linear Distortion Characteristics for GNSS Signals." In Lecture Notes in Electrical Engineering, 183–95. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0029-5_17.
Повний текст джерелаAkiyama, Hiroki, and Hiroyuki Kamiguchi. "Analysis of Calcium Signals in Steering Neuronal Growth Cones In Vitro." In Methods in Molecular Biology, 17–27. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-0777-9_2.
Повний текст джерелаТези доповідей конференцій з теми "Signals’ analysis methods"
Luzhansky, Edward, Fow-Sen Choa, Scott Merritt, Anthony Yu, and Michael Krainak. "Comparative Analysis of QCL MWIR and SWIR Communication with PPM Signals." In Adaptive Optics: Analysis, Methods & Systems. Washington, D.C.: OSA, 2015. http://dx.doi.org/10.1364/aoms.2015.jt5a.7.
Повний текст джерелаGladysz, Szymon, and Erez N. Ribak. "Recovery of Exoplanetary Signals in Re-dispersed Speckle Clutter." In Adaptive Optics: Methods, Analysis and Applications. Washington, D.C.: OSA, 2011. http://dx.doi.org/10.1364/aopt.2011.jwa10.
Повний текст джерелаHaufe, Daniel, Nektarios Koukourakis, Lars Büttner, and Jürgen W. Czarske. "Transmission of Multiple Independent Signals Through a Multimode Fiber Using Optical Wavefront Shaping." In Adaptive Optics: Analysis, Methods & Systems. Washington, D.C.: OSA, 2016. http://dx.doi.org/10.1364/aoms.2016.aoth1c.3.
Повний текст джерелаMeiri, Amihai, Carl G. Ebeling, Jason Martineau, Zeev Zalevsky, Jordan M. Gerton, and Rajesh Menon. "Self-Interference of Coherent and Incoherent Signals for Sub-Nanometer Localization of Single Emitters." In Adaptive Optics: Analysis, Methods & Systems. Washington, D.C.: OSA, 2015. http://dx.doi.org/10.1364/aoms.2015.jw3a.3.
Повний текст джерелаOktem, Figen S., and Haldun M. Ozaktas. "Condition number in recovery of signals from partial fractional Fourier domain information." In Adaptive Optics: Methods, Analysis and Applications. Washington, D.C.: OSA, 2013. http://dx.doi.org/10.1364/aopt.2013.jtu4a.18.
Повний текст джерелаArdeenawatie Awang, Saidatul, M. P. Paulraj, and Sazali Yaacob. "Analysis of EEG signals by eigenvector methods." In 2012 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES 2012). IEEE, 2012. http://dx.doi.org/10.1109/iecbes.2012.6498164.
Повний текст джерелаKalbfleisch, Paul, Svenja Horn, and Monika Ivantysynova. "Cyclostationary Analysis of Measured Pump Acoustic and Vibration Signals." In BATH/ASME 2018 Symposium on Fluid Power and Motion Control. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/fpmc2018-8899.
Повний текст джерелаNam, Ki-Woo, Kun-Chan Lee, and Jeong-Hwan Oh. "Application of Joint-Time Frequency Analysis Methods for Nondestructive Evaluation." In ASME 1999 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/imece1999-0890.
Повний текст джерелаPeneva Gospodinova, Evgeniya. "Hurst Methods for Fractal Analysis of Electrocardiographical Signals." In International Conference on Research in Engineering and Technology. ACAVENT, 2019. http://dx.doi.org/10.33422/researchconf.2019.12.895.
Повний текст джерелаKoch, Olivier. "Comparison of Multipactor Analysis Methods for Galileo Signals." In 2018 IEEE Conference on Antenna Measurements & Applications (CAMA). IEEE, 2018. http://dx.doi.org/10.1109/cama.2018.8530632.
Повний текст джерелаЗвіти організацій з теми "Signals’ analysis methods"
Paulson, Albert S., and Gerald R. Swope. Signal Model Analysis Via Model-Critical Methods. Fort Belvoir, VA: Defense Technical Information Center, October 1988. http://dx.doi.org/10.21236/ada200685.
Повний текст джерелаKumaresan, R. Parametric Time-Scale Methods in Signal Analysis. Fort Belvoir, VA: Defense Technical Information Center, June 1993. http://dx.doi.org/10.21236/ada274212.
Повний текст джерелаBai, Z. D., and C. R. Rao. Spectral Analytic Methods for the Estimation of Number of Signals and Directions of Arrival. Fort Belvoir, VA: Defense Technical Information Center, November 1989. http://dx.doi.org/10.21236/ada217219.
Повний текст джерелаSimmons, Justin. Complete and Exact Small Signal Analysis of DC-to-DC Switched Power Converters Under Various Operating Modes and Control Methods. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.195.
Повний текст джерелаCorriveau, Elizabeth, Ashley Mossell, Holly VerMeulen, Samuel Beal, and Jay Clausen. The effectiveness of laser-induced breakdown spectroscopy (LIBS) as a quantitative tool for environmental characterization. Engineer Research and Development Center (U.S.), April 2021. http://dx.doi.org/10.21079/11681/40263.
Повний текст джерелаRipey, Mariya. NUMBERS IN THE NEWS TEXT (BASED ON MATERIAL OF ONE ISSUE OF NATIONWIDE NEWSPAPER “DAY”). Ivan Franko National University of Lviv, March 2021. http://dx.doi.org/10.30970/vjo.2021.50.11106.
Повний текст джерелаJury, William A., and David Russo. Characterization of Field-Scale Solute Transport in Spatially Variable Unsaturated Field Soils. United States Department of Agriculture, January 1994. http://dx.doi.org/10.32747/1994.7568772.bard.
Повний текст джерелаBaron, Lisa. Post-Dorian shoreline change at Cape Hatteras National Seashore: 2019 report. National Park Service, April 2021. http://dx.doi.org/10.36967/nrr-2282127.
Повний текст джерелаRon, Eliora, and Eugene Eugene Nester. Global functional genomics of plant cell transformation by agrobacterium. United States Department of Agriculture, March 2009. http://dx.doi.org/10.32747/2009.7695860.bard.
Повний текст джерелаFluhr, Robert, and Maor Bar-Peled. Novel Lectin Controls Wound-responses in Arabidopsis. United States Department of Agriculture, January 2012. http://dx.doi.org/10.32747/2012.7697123.bard.
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