Relevant bibliographies by topics / Extraction du signal

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Extraction du signal'

Author: Grafiati
Published: 4 June 2021
Last updated: 4 February 2022

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Journal articles on the topic "Extraction du signal"

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Pollard, Valerie, and Donald S. Prough. "Signal Extraction Technology." Anesthesia & Analgesia 83, no. 2 (1996): 213–14. http://dx.doi.org/10.1213/00000539-199608000-00002.

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Pollard, Valerie, and Donald S. Prough. "Signal Extraction Technology." Anesthesia & Analgesia 83, no. 2 (1996): 213–14. http://dx.doi.org/10.1097/00000539-199608000-00002.

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Barker, Steven J. "Signal Extraction Technology." Anesthesia & Analgesia 84, no. 4 (1997): 938. http://dx.doi.org/10.1097/00000539-199704000-00047.

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Barker, Steven J. "Signal Extraction Technology." Anesthesia & Analgesia 84, no. 4 (1997): 938. http://dx.doi.org/10.1213/00000539-199704000-00047.

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Short, Kevin M. "Signal Extraction from Chaotic Communications." International Journal of Bifurcation and Chaos 07, no. 07 (1997): 1579–97. http://dx.doi.org/10.1142/s0218127497001230.

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This paper will consider the use of nonlinear dynamic (NLD) forecasting to extract messages from chaotic communication systems. Earlier work has shown that one-step prediction methods have sometimes been able to reveal the presence of hidden messages as well as the frequency content of the hidden messages. However, recovery of the actual hidden message usually involved filtering in the frequency domain. In this paper we show that it may be possible to extract the hidden message signal without filtering in the frequency domain. The approaches which will be discussed involve either the use of mu
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Zhang, Shou Cheng. "An Improved Blind Source Extraction Algorithm." Advanced Materials Research 926-930 (May 2014): 2964–67. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.2964.

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One-unit independent component analysis with reference (ICA-R) is an efficient method capable of extracting a desired source signal by using reference signal. In this paper, a new fast one-unit ICA-R algorithm is derived by using kurtosis contrast function based on new constrained independent component analysis (cICA) theory. The proposed algorithm has lower computational complexity and accurate extraction. Experiments with synthetic signals demonstrate the efficacy and accuracy of the proposed algorithm.
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Hassani, Hossein, Emmanuel Sirimal Silva, and Zara Ghodsi. "Optimizing bicoid signal extraction." Mathematical Biosciences 294 (December 2017): 46–56. http://dx.doi.org/10.1016/j.mbs.2017.09.008.

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Wallace, Neil. "Lucas's signal-extraction model." Journal of Monetary Economics 30, no. 3 (1992): 433–47. http://dx.doi.org/10.1016/0304-3932(92)90005-m.

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Zhao, Yong Jian, and Hai Ning Jiang. "Extraction of Signals with Reference." Advanced Materials Research 989-994 (July 2014): 3613–16. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.3613.

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As a famous one-unit algorithm, FastICA can extract source signals one by one. In many applications, someone is only interested in a specific source signal. Through incorporating reference about the desired signal into a negentropy based contrast function, a constrained optimization problem is formed. Then an improved method is proposed which can extract the desired source signal exclusively. Computer simulations demonstrate its good performance.
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Chen, Ye Qu, Wen Zheng, and Xie Ben Wei. "Application of EMD to Integrated Signal Trend Extraction." Advanced Materials Research 591-593 (November 2012): 2072–76. http://dx.doi.org/10.4028/www.scientific.net/amr.591-593.2072.

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Huang’s data-driven technique of Empirical Mode Decomposition (EMD) is presented, and issues related to its effective implementation are discussed. Integrating signal directly will produce a trend, it will cause distortion and interfere with the calculation results. This paper discusses the reasons that cause the integrated signal trend, compares the different methods for extracting trend. The traditional steps use the linear fitting and a high-pass filter to remove low frequency signal to extract trend. This paper uses Empirical Mode Decomposition (EMD) method to extract integrated signals tr
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Dissertations / Theses on the topic "Extraction du signal"

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Arani, Amirali Shayan. "Noninvasive cardioneural signal extraction." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2008. http://wwwlib.umi.com/cr/ucsd/fullcit?p1457392.

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Thesis (M.S.)--University of California, San Diego, 2008.<br>Title from first page of PDF file (viewed Nov. 5, 2008). Available via ProQuest Digital Dissertations. Includes bibliographical references (p. 64-68).
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Fontanarava, Julien. "Signal Extraction from Scans of Electrocardiograms." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-248430.

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In this thesis, we propose a Deep Learning method for fully automated digitization of ECG (Electrocardiogram) sheets. We perform the digitization of ECG sheets in three steps: layout detection, column-wise signal segmentation, and finally signal retrieval - each of them performed by a Convolutional Neural Network. These steps leverage advances in the fields of object detection and pixel-wise segmentation due to the rise of CNNs in image processing. We train each network on synthetic images that reect the challenges of real-world data. The use of these realistic synthetic images aims at making
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Chilo, José. "Feature extraction for low-frequency signal classification /." Stockholm : Fysik, Physics, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4661.

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Bergemann, Tracy L. "Image analysis and signal extraction from cDNA microarrays /." Thesis, Connect to this title online; UW restricted, 2004. http://hdl.handle.net/1773/9603.

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Pennington, Jason R. "Radar Signal Characteristic Extraction with FFT-Based Techniques." Miami University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=miami1306201663.

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Vartiainen, J. (Johanna). "Concentrated signal extraction using consecutive mean excision algorithms." Doctoral thesis, Oulun yliopisto, 2010. http://urn.fi/urn:isbn:9789514263491.

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Abstract Spread spectrum communication systems may be affected by other types of signals called outliers. These coexisting signals are typically narrow (or concentrated) in the considered domain. This thesis considers two areas of outlier detection, namely the concentrated interference suppression (IS) and concentrated signal detection. The focus is on concentrated signal extraction using blind, iterative and low-complex consecutive mean excision (CME) -based algorithms that can be applied to both IS and detection. A summary of results obtained from studying the performance of the existing I
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Mason, Steven George. "A modification of OPM : a signal-independent methodology for single-trial signal extraction." Thesis, University of British Columbia, 1990. http://hdl.handle.net/2429/30024.

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Initial investigations of the Outlier Processing Method (OPM), first introduced by Birch [1][2][3] in 1988, have demonstrated a promising ability to extract a special class of signals, called highly variable events (HVEs), from coloured noise processes. The term HVE is introduced in this thesis to identify a finite-duration signal whose shape and latency vary dramatically from trial to trial and typically has a very low signal-to-noise ratio (SNR). This thesis presents a modified version of the original OPM algorithm, which can generate an estimate of the HVE with significantly less estimatio
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Forsling, Robin. "Decentralized Estimation Using Conservative Information Extraction." Licentiate thesis, Linköpings universitet, Reglerteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-171998.

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Sensor networks consist of sensors (e.g., radar and cameras) and processing units (e.g., estimators), where in the former information extraction occurs and in the latter estimates are formed. In decentralized estimation information extracted by sensors has been pre-processed at an intermediate processing unit prior to arriving at an estimator. Pre-processing of information allows for the complexity of large systems and systems-of-systems to be significantly reduced, and also makes the sensor network robust and flexible. One of the main disadvantages of pre-processing information is that inform
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Reindl, Klaus [Verfasser]. "Multichannel Acoustic Signal Extraction for Reverberant Environments / Klaus Reindl." München : Verlag Dr. Hut, 2015. http://d-nb.info/1074063287/34.

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Reindl, Klaus Josef [Verfasser]. "Multichannel Acoustic Signal Extraction for Reverberant Environments / Klaus Reindl." München : Verlag Dr. Hut, 2015. http://d-nb.info/1074063287/34.

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Books on the topic "Extraction du signal"

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Hu, Li, and Zhiguo Zhang, eds. EEG Signal Processing and Feature Extraction. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9113-2.

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Martin, Golz, Kuh Anthony, Obradovic Dragan, Tanaka Toshihisa, and SpringerLink (Online service), eds. Signal Processing Techniques for Knowledge Extraction and Information Fusion. Springer Science+Business Media, LLC, 2008.

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Mandic, Danilo, Martin Golz, Anthony Kuh, Dragan Obradovic, and Toshihisa Tanaka, eds. Signal Processing Techniques for Knowledge Extraction and Information Fusion. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-74367-7.

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Jones, Michael G. A comparison of signal enhancement methods for extracting tonal acoustic signals. National Aeronautics and Space Administration, Langley Research Center, 1998.

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Burridge, Peter. Forecasting and signal extraction in auto regressive-moving average models. University of Warwick,Department of Economics, 1986.

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Mokhlesabadifarahani, Bita, and Vinit Kumar Gunjan. EMG Signals Characterization in Three States of Contraction by Fuzzy Network and Feature Extraction. Springer Singapore, 2015. http://dx.doi.org/10.1007/978-981-287-320-0.

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Wildi, Marc. Signal Extraction. Springer, 2008.

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Signal Extraction. Springer-Verlag, 2005. http://dx.doi.org/10.1007/b138291.

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Hu, Li, and Zhiguo Zhang. EEG Signal Processing and Feature Extraction. Springer, 2019.

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Leong, Wai Yie. EEG Signal Processing: Feature extraction, selection and classification methods. The Institution of Engineering and Technology, 2019.

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Book chapters on the topic "Extraction du signal"

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Owens, F. J. "Feature Extraction." In Signal Processing of Speech. Macmillan Education UK, 1993. http://dx.doi.org/10.1007/978-1-349-22599-6_4.

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Elgendi, Mohamed. "PPG Feature Extraction." In PPG Signal Analysis. CRC Press, 2020. http://dx.doi.org/10.1201/9780429449581-6.

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Dong, Kejun, Li Zhao, and Chengyu Liu. "Respiratory Signal Extraction from ECG Signal." In Feature Engineering and Computational Intelligence in ECG Monitoring. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3824-7_13.

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Kazama, Shingo. "Extraction of Signal Yield." In Search for Charginos Nearly Mass-Degenerate with the Lightest Neutralino. Springer Japan, 2015. http://dx.doi.org/10.1007/978-4-431-55657-2_11.

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Shi, Xizhi. "Nonlinear PCA & Feature Extraction." In Blind Signal Processing. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-11347-5_4.

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Kayalvizhi, M. "EEG Signal Extraction Analysis Techniques." In Signal and Image Processing Techniques for the Development of Intelligent Healthcare Systems. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6141-2_12.

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Dunger, Jack. "The OXO Signal Extraction Framework." In Springer Theses. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31616-7_7.

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Kaiser, Regina, and Agustín Maravall. "ARIMA Models and Signal Extraction." In Measuring Business Cycles in Economic Time Series. Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4613-0129-5_3.

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Usha Sri, A., M. Malini, and G. Chandana. "Feature Extraction of Cardiotocography Signal." In Learning and Analytics in Intelligent Systems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-24322-7_10.

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Morgan, David P., and Christopher L. Scofield. "Signal Processing and Feature Extraction." In Neural Networks and Speech Processing. Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-3950-6_6.

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Conference papers on the topic "Extraction du signal"

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Changpeng, Ji, and Dai Wei. "Signal Extraction over Noise." In 2009 Fifth International Conference on Natural Computation. IEEE, 2009. http://dx.doi.org/10.1109/icnc.2009.31.

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Gualsaqui Miranda, Marco V., Ivan P. Vizcaino Espinosa, and Marco J. Flores Calero. "ECG signal features extraction." In 2016 IEEE Ecuador Technical Chapters Meeting (ETCM). IEEE, 2016. http://dx.doi.org/10.1109/etcm.2016.7750859.

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Li, Ming, Stella Batalama, Dimitris Pados, and John Matyjas. "Multiuser CDMA Signal Extraction." In MILCOM 2006. IEEE, 2006. http://dx.doi.org/10.1109/milcom.2006.302230.

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Li, J. X., G. Tao, K. Zhang, and H. Liu. "Acoustic Reflection Signals Extraction by Applying Blind Signal Separation." In 76th EAGE Conference and Exhibition 2014. EAGE Publications BV, 2014. http://dx.doi.org/10.3997/2214-4609.20140922.

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Mironovs, Deniss, Aleksey Mironov, and Andris Chate. "Harmonic components extraction influence on resulting modal parameters of vibrating structures." In The 13th international scientific conference “Modern Building Materials, Structures and Techniques”. Vilnius Gediminas Technical University, 2019. http://dx.doi.org/10.3846/mbmst.2019.012.

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It is possible to estimate condition of static structures, like bridges and buildings, by monitoring how their modal parameters (frequency, shape and damping) change in time, based on modal parameters relation to mechanical properties. Operational modal analysis (OMA) provides estimation of modal parameters. OMA basic assumption is that excitation forces are random and their amplitude and frequency nature is similar to white noise. For structures with periodic dynamic excitation, like wind turbines or helicopter blades, this assumption is violated, which makes OMA application complicated and u
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Fu, Jeffrey Shiang, Chengjie Cai, Yichang Cheng, and Wenkuan Su. "Target Signal Extraction by Adaptive Signal Decomposition Methods." In 2007 Asia-Pacific Microwave Conference - (APMC 2007). IEEE, 2007. http://dx.doi.org/10.1109/apmc.2007.4554781.

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Leveau, Pierre, Simon Maller, Juan Jose Burred, and Xabier Jaureguiberry. "Convolutive common audio signal extraction." In 2011 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA). IEEE, 2011. http://dx.doi.org/10.1109/aspaa.2011.6082287.

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Peshave, Juie D., and Rajveer Shastri. "Feature extraction of ECG signal." In 2014 International Conference on Communications and Signal Processing (ICCSP). IEEE, 2014. http://dx.doi.org/10.1109/iccsp.2014.6950168.

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Jaiswal, Rachana, and Srikant Satarkar. "Biometric Foetal Contour Extraction using Hybrid Level Set." In 6th International Conference on Signal and Image Processing (SIGI 2020). AIRCC Publishing Corporation, 2020. http://dx.doi.org/10.5121/csit.2020.102002.

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In medical imaging, accurate anatomical structure extraction is important for diagnosis and therapeutic interventional planning. So, for easier, quicker and accurate diagnosis of medical images, image processing technologies may be employed in analysis and feature extraction of medical images. In this paper, some modifications to level set algorithm are made and modified algorithm is used for extracting contour of foetal objects in an image. The proposed approach is applied on foetal ultrasound images. In traditional approach, foetal parameters are extracted manually from ultrasound images. Du
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Li, Hongkun, Changbo He, Daren Jiang, and Xuejun Wang. "Blade Incipient Crack Determination for Centrifugal Compressor Based on Pressure Pulsation Signal Feature Extraction." In ASME Turbo Expo 2016: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/gt2016-56273.

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Centrifugal compressor is a piece of key equipment for factories. Among the components of a centrifugal compressor, impeller is a pivotal part as it is used to transform kinetic energy to pressure energy. The blades are exposed to centrifugal forces, gas pressure, and the friction force which usually lead to cracks. Therefore, early crack feature extraction and pattern recognition are important to prevent it from failure. Although time series analysis for monitored signals can be used on feature extraction, it is not enough. So the incipient weak feature extraction method should be investigate
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Reports on the topic "Extraction du signal"

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Beder, Jay H. Sieves and Signal Extraction. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada177628.

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Cunningham, M., and F. Dowla. A Comparison of Digital Signal Extraction Techniques. Office of Scientific and Technical Information (OSTI), 2004. http://dx.doi.org/10.2172/15011424.

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Vaccaro, R. J., E. Maragakis, and R. L. Field. Transient Signal Extraction in a Multipath Environment. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada230738.

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Jagler, Karl B. Wavelet Signal Processing for Transient Feature Extraction. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada250519.

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Middleton, David. Scatter Channels and Target Modeling for Active Signal Extraction in Underwater Acoustics: 2. Summary and Synthesis of 1, [1]. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada637028.

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Seleznev, Daniel Maxim. Extraction of the Muon Revolution Frequency Distribution via the Fourier Analysis of the Fast Rotation Signal in the Muon g-2 Experiment. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1462062.

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Matzner, Shari. Model-Based Information Extraction From Synthetic Aperture Radar Signals. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.248.

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Smith, W. Clarke. Extracting bb Higgs Decay Signals using Multivariate Techniques. Office of Scientific and Technical Information (OSTI), 2012. http://dx.doi.org/10.2172/1049738.

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Hurd, Harry L. Workstation Tools for Feature Extraction and Classification for Nonstationary and Transient Signals. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada255389.

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Chen, Kun-Mu. Radar Target Discrimination and Identification Using Extinction-Pulses and Single-Mode Extraction Signals. Defense Technical Information Center, 1987. http://dx.doi.org/10.21236/ada211717.

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