Academic literature on the topic 'Reconstruction du signal'
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Journal articles on the topic "Reconstruction du signal"
Hua, Jing, Hua Zhang, Jizhong Liu, and Junlong Zhou. "Compressive Sensing of Multichannel Electrocardiogram Signals in Wireless Telehealth System." Journal of Circuits, Systems and Computers 25, no. 09 (June 21, 2016): 1650103. http://dx.doi.org/10.1142/s0218126616501036.
Full textMingjiang Shi, Xiaoyan Zhuang, and He Zhang. "Signal Reconstruction for Frequency Sparse Sampling Signals." Journal of Convergence Information Technology 8, no. 9 (May 15, 2013): 1197–203. http://dx.doi.org/10.4156/jcit.vol8.issue9.147.
Full textLiou, Ren Jean. "Ultrasonic Signal Reconstruction Using Compressed Sensing." Applied Mechanics and Materials 855 (October 2016): 165–70. http://dx.doi.org/10.4028/www.scientific.net/amm.855.165.
Full textAL-ASSAF, YOUSEF, and WAJDI M. AHMAD. "PARAMETER IDENTIFICATION OF CHAOTIC SYSTEMS USING WAVELETS AND NEURAL NETWORKS." International Journal of Bifurcation and Chaos 14, no. 04 (April 2004): 1467–76. http://dx.doi.org/10.1142/s0218127404009910.
Full textLu, Xinmiao, Cunfang Yang, Qiong Wu, Jiaxu Wang, Yuhan Wei, Liyu Zhang, Dongyuan Li, and Lanfei Zhao. "Improved Reconstruction Algorithm of Wireless Sensor Network Based on BFGS Quasi-Newton Method." Electronics 12, no. 6 (March 7, 2023): 1267. http://dx.doi.org/10.3390/electronics12061267.
Full textvan Bemmel, J. H., R. J. A. Schijvenaars, and J. A. Kors. "Reconstruction of Repetitive Signals." Methods of Information in Medicine 33, no. 01 (1994): 41–45. http://dx.doi.org/10.1055/s-0038-1634986.
Full textXuan Liu, Xuan Liu, and Jin U. Kang Jin U. Kang. "Iterative sparse reconstruction of spectral domain OCT signal." Chinese Optics Letters 12, no. 5 (2014): 051701–51704. http://dx.doi.org/10.3788/col201412.051701.
Full textZhang, Wenchao, Bo Zhang, Fei Xu, and Mohammad Asif. "Research on Numerical Simulation Method of Nonstationary Random Vibration Signal Sensor in Railway Transportation." Journal of Sensors 2022 (April 15, 2022): 1–7. http://dx.doi.org/10.1155/2022/7149477.
Full textKöse, Nesibe, H. Tuncay Güner, Grant L. Harley, and Joel Guiot. "Spring temperature variability over Turkey since 1800 CE reconstructed from a broad network of tree-ring data." Climate of the Past 13, no. 1 (January 4, 2017): 1–15. http://dx.doi.org/10.5194/cp-13-1-2017.
Full textLuo, Shan, Guoan Bi, Tong Wu, Yong Xiao, and Rongping Lin. "An Effective LFM Signal Reconstruction Method for Signal Denoising." Journal of Circuits, Systems and Computers 27, no. 09 (April 26, 2018): 1850140. http://dx.doi.org/10.1142/s0218126618501402.
Full textDissertations / Theses on the topic "Reconstruction du signal"
Serdaroglu, Bulent. "Signal Reconstruction From Nonuniform Samples." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12605850/index.pdf.
Full texts classical algorithms, a trade off algorithm, which claims to find an optimal balance between reconstruction accuracy and noise stability is analyzed and simulated for comparison between all discussed interpolators. At the end of the stability tests, Yen'
s third algorithm, known as the classical recurrent nonuniform sampling, is found to be superior over the remaining interpolators, from both an accuracy and stability point of view.
Neuman, Bartosz P. "Signal processing in diffusion MRI : high quality signal reconstruction." Thesis, University of Nottingham, 2014. http://eprints.nottingham.ac.uk/27691/.
Full textMoose, Phillip J. "Approximate signal reconstruction from partial information." Thesis, This resource online, 1994. http://scholar.lib.vt.edu/theses/available/etd-06102009-063326/.
Full textScoular, Spencer Charles. "Sampling and reconstruction of one-dimensional analogue signals." Thesis, University of Cambridge, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.283938.
Full textPillai, Anu Kalidas Muralidharan. "Signal Reconstruction Algorithms for Time-Interleaved ADCs." Doctoral thesis, Linköpings universitet, Kommunikationssystem, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-117826.
Full textFuller, Megan M. (Megan Marie). "Inverse filtering by signal reconstruction from phase." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/89858.
Full textThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
14
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 85-86).
A common problem that arises in image processing is that of performing inverse filtering on an image that has been blurred. Methods for doing this have been developed, but require fairly accurate knowledge of the magnitude of the Fourier transform of the blurring function and are sensitive to noise in the blurred image. It is known that a typical image is defined completely by its region of support and a sufficient number of samples of the phase of its Fourier transform. We will investigate a new method of deblurring images based only on phase data. It will be shown that this method is much more robust in the presence of noise than existing methods and that, because no magnitude information is required, it is also more robust to an incorrect guess of the blurring filter. Methods of finding the region of support of the image will also be explored.
by Megan M. Fuller.
S.M.
Cheng, Siuling. "Signal reconstruction from discrete-time Wigner distribution." Thesis, Virginia Tech, 1985. http://hdl.handle.net/10919/41550.
Full textWigner distribution is considered to be one of the most powerful tools for time-frequency analysis of rumvstationary signals. Wigner distribution is a bilinear signal transformation which provides two dimensional time-frequency characterization of one dimensional signals. Although much work has been done recently in signal analysis and applications using Wigner distribution, not many synthesis methods for Wigner distribution have been reported in the literature.
This thesis is concerned with signal synthesis from discrete-time Wigner distribution and from discrete-time pseudo-Wigner distribution and their applications in noise filtering and signal separation. Various algorithms are developed to reconstruct signals from the modified or specified Wigner distribution and pseudo-Wigner distribution which generally do not have a valid Wigner distributions or valid pseudo-Wigner distribution structures. These algorithms are successfully applied to the noise filtering and signal separation problems.
Master of Science
Santos, Dorabella Martins da Silva. "Signal reconstruction in structures with two channels." Doctoral thesis, Universidade de Aveiro, 2007. http://hdl.handle.net/10773/2211.
Full textEm sistemas ATM e transmissões em tempo real através de redes IP, os dados são transmitidos em pacotes de informação. Os pacotes perdidos ou muito atrasados levam à perda de informação em posições conhecidas (apagamentos). Contudo, em algumas situações as posições dos erros não são conhecidas e, portanto, a detecção dos erros tem que ser realizada usando um polinómio conhecido. A detecção e correcção de erros são estudadas para sinais digitais em códigos DFT em dois canais que apresentam muito melhor estabilidade que os respectivos códigos DFT num único canal. Para a estrutura de dois canais, um canal processa um código DFT normal, quanto que o outro canal inclui uma permutação, a razão principal para a melhoria na estabilidade. A permutação introduz aleatoriedade e é esta aleatoriedade que é responsável pela boa estabilidade destes códigos. O estudo dos códigos aleatórios vêm confirmar esta afirmação. Para sinais analógicos, foca-se a amostragem funcional e derivativa, onde um canal processa amostras do sinal e o outro processa amostras da derivada do sinal. A expansão sobreamostrada é apresentada e a recuperação de apagamentos é estudada. Neste caso, a estabilidade para a esturtura em dois canais quando a perda de amostras afecta ambos os canais é, em geral, muito pobre. Adicionalmente, a reconstrução de sinais tanto analógicos como digitais é tratada para o modelo do conversor integrate-and-fire. A reconstrução faz uso dos tempos de acção e de valores limites inerentes ao modelo e é viável por meio de um método iterativo baseado em projecções em conjuntos convexos (POCS).
In ATM as in real time transmissions over IP networks, the data are transmitted packet by packet. Lost or highly delayed packets lead to lost information in known locations (erasures). However, in some situations the error locations are not known and, therefore, error detection must be performed using a known polynomial. Error detection and correction are studied for digital signals in two-channel DFT codes which presents a much better stability than their single channel counterparts. For the two-channel structure, one channel processes an ordinary DFT code, while the other channel includes an interleaver, the main reason for the improvement in stability. The interleaver introduces randomness and it is this randomness that is responsible for the good stability of these codes. The study of random codes helps confirm this statement. For analogical signals, the focus is given to function and derivative sampling, where one channel processes samples of the signal and the other processes samples of the derivative of the signal. The oversampled expansion is presented and erasure recovery is studied. In this case, the stability of the twochannel structure when sample loss affects both channels is, in general, very poor. Additionally, the reconstruction of analogical as well as digital signals is dealt with for the integrate-and-fire converter model. The reconstruction makes use of the firing times and the threshold values inherent to the model and is viable by means of an iterative method based on projections onto convex sets (POCS).
Sastry, Challa, Gilles Hennenfent, and Felix J. Herrmann. "Signal reconstruction from incomplete and misplaced measurements." European Association of Geoscientists & Engineers, 2007. http://hdl.handle.net/2429/550.
Full textScrofani, James W. "Theory of multirate signal processing with applicatioin to signal and image reconstruction /." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2005. http://library.nps.navy.mil/uhtbin/hyperion/05Sep%5FScrofani%5FPhD.pdf.
Full textThesis Advisor(s): Charles W. Therrien. Includes bibliographical references (p. 125-132). Also available online.
Books on the topic "Reconstruction du signal"
Beaumont, A. J. Signal reconstruction techniques for improved measurement of transient emissions. Warrendale, Pa: SAE International, 1990.
Find full textPhase retrieval and zero crossings: Mathematical methods in image reconstruction. Dordrecht: Kluwer Academic Publishers, 1989.
Find full textPetrović, Predrag. Digital Processing and Reconstruction of Complex AC Signals. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2009.
Find full textSignal, Recovery and Synthesis Topical Meeting (4th 1992 New Orleans La ). Signal recovery and synthesis IV: Summaries of papers presented at the Signal Recovery and Synthesis Topical Meeting, April 14-15, 1992, New Orleans, Louisiana. Washington, DC: Optical Society of America, 1992.
Find full textSchultz, Gerrit. Magnetic Resonance Imaging with Nonlinear Gradient Fields: Signal Encoding and Image Reconstruction. Wiesbaden: Springer Fachmedien Wiesbaden, 2013.
Find full textFeng, Zhiqiang. A signal processing method for the acoustic image reconstruction of planar objects. Portsmouth: Portsmouth Polytechnic, Dept. of Electrical and Electronic Engineering, 1988.
Find full textKong, Tse Chi, ed. Reconstruction of chaotic signals with applications to chaos-based communications. [Beijing, China]: Tsinghua University Press, 2008.
Find full textPrabahan, Basu, ed. Information theoretic approaches to signal and image restoration. Bellingham, Wash: SPIE, 2011.
Find full textL, Jankovsky Amy, and Lewis Research Center, eds. Real-time sensor validation, signal reconstruction, and feature detection for an RLV propulsion testbed. [Cleveland, Ohio]: National Aeronautics and Space Administration, Lewis Research Center, 1998.
Find full textL, Jankovsky Amy, and Lewis Research Center, eds. Real-time sensor validation, signal reconstruction, and feature detection for an RLV propulsion testbed. [Cleveland, Ohio]: National Aeronautics and Space Administration, Lewis Research Center, 1998.
Find full textBook chapters on the topic "Reconstruction du signal"
Majumdar, Angshul. "Biomedical Signal Reconstruction." In Compressed Sensing for Engineers, 201–9. First edition. | Boca Raton, FL : CRC Press/Taylor & Francis, [2019] | Series: Devices, circuits, and systems: CRC Press, 2018. http://dx.doi.org/10.1201/9781351261364-11.
Full textMeister, Alexander. "Image and Signal Reconstruction." In Deconvolution Problems in Nonparametric Statistics, 151–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-87557-4_4.
Full textFeuer, Arie, and Graham C. Goodwin. "Sampling and Reconstruction." In Sampling in Digital Signal Processing and Control, 71–108. Boston, MA: Birkhäuser Boston, 1996. http://dx.doi.org/10.1007/978-1-4612-2460-0_2.
Full textGopi, E. S. "Sampling and Reconstruction of Signals." In Multi-Disciplinary Digital Signal Processing, 1–42. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57430-1_1.
Full textVaswani, Namrata, and Wei Lu. "Recursive Reconstruction of Sparse Signal Sequences." In Compressed Sensing & Sparse Filtering, 357–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38398-4_11.
Full textKrémé, A. Marina, Valentin Emiya, and Caroline Chaux. "Phase Reconstruction for Time-Frequency Inpainting." In Latent Variable Analysis and Signal Separation, 417–26. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93764-9_39.
Full textSun, Liqing, Xianbin Wen, Ming Lei, Haixia Xu, Junxue Zhu, and Yali Wei. "Signal Reconstruction Based on Block Compressed Sensing." In Artificial Intelligence and Computational Intelligence, 312–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23887-1_39.
Full textKhan, Nadia Masood, and Gul Muhammad Khan. "Signal Reconstruction Using Evolvable Recurrent Neural Networks." In Intelligent Data Engineering and Automated Learning – IDEAL 2018, 594–602. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03493-1_62.
Full textPizzolato, Marco, Aurobrata Ghosh, Timothé Boutelier, and Rachid Deriche. "Magnitude and Complex Based Diffusion Signal Reconstruction." In Computational Diffusion MRI, 127–40. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11182-7_12.
Full textBoyko, Nikita, Gulver Karamemis, Viktor Kuzmenko, and Stan Uryasev. "Sparse Signal Reconstruction: LASSO and Cardinality Approaches." In Springer Proceedings in Mathematics & Statistics, 77–90. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10046-3_4.
Full textConference papers on the topic "Reconstruction du signal"
Gooley, T. A., H. H. Barrett, M. Barth, and J. L. Denny. "Quantitative Comparisons of Choices of Prior Information in Image Reconstruction." In Signal Recovery and Synthesis. Washington, D.C.: Optica Publishing Group, 1989. http://dx.doi.org/10.1364/srs.1989.wa3.
Full textClarkson, Eric, Jack Denny, Harrison Barrett, Craig Abbey, and Brandon Gallas. "Night-sky reconstructions for linear digital imaging systems." In Signal Recovery and Synthesis. Washington, D.C.: Optica Publishing Group, 1998. http://dx.doi.org/10.1364/srs.1998.sthc.5.
Full textStankovic, Isidora, Milos Dakovic, and Cornel Ioana. "Time-frequency signal reconstruction of nonsparse audio signals." In 2017 22nd International Conference on Digital Signal Processing (DSP). IEEE, 2017. http://dx.doi.org/10.1109/icdsp.2017.8096044.
Full textChetty, V., D. Hayden, J. Goncalves, and S. Warnick. "Robust signal-structure reconstruction." In 2013 IEEE 52nd Annual Conference on Decision and Control (CDC). IEEE, 2013. http://dx.doi.org/10.1109/cdc.2013.6760369.
Full textTian, Jie, Xiaopu Zhang, Yong Chen, Peter Russhard, and Hua Ouyang. "Sparse Reconstruction Method of Non-Uniform Sampling and its Application in Blade Tip Timing System." In ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/gt2020-14753.
Full textHonglin Huang and Anamitra Makur. "A new iterative reconstruction scheme for signal reconstruction." In APCCAS 2008 - 2008 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS). IEEE, 2008. http://dx.doi.org/10.1109/apccas.2008.4746028.
Full textO'Hagan, Daniel W., Motlatsi Setsubi, and Stephen Paine. "Signal reconstruction of DVB-T2 signals in passive radar." In 2018 IEEE Radar Conference (RadarConf18). IEEE, 2018. http://dx.doi.org/10.1109/radar.2018.8378717.
Full textByrne, Charles L., and Michael A. Fiddy. "Signal Reconstruction as a Wiener Filter Approximation." In Photon Correlation Techniques and Applications. Washington, D.C.: Optica Publishing Group, 1988. http://dx.doi.org/10.1364/pcta.1988.pcmdr18.
Full textSheppard, CJR. "Microscope image reconstruction." In Signal Recovery and Synthesis. Washington, D.C.: Optica Publishing Group, 1998. http://dx.doi.org/10.1364/srs.1998.stue.2.
Full textShepard, Steven M., and Maria Frendberg Beemer. "Advances in thermographic signal reconstruction." In SPIE Sensing Technology + Applications, edited by Sheng-Jen (Tony) Hsieh and Joseph N. Zalameda. SPIE, 2015. http://dx.doi.org/10.1117/12.2176748.
Full textReports on the topic "Reconstruction du signal"
Nguyen, C. T., C. Ganesh, and S. E. Hammel. Advanced Techniques for Signal and Image Compression/Reconstruction with Wavelets. Fort Belvoir, VA: Defense Technical Information Center, May 1995. http://dx.doi.org/10.21236/ada297037.
Full textGanesh, C., C. T. Nguyen, M. Marafino, and S. E. Hammel. An Energy-Based Method for Signal Compression and Reconstruction with Wavelets. Fort Belvoir, VA: Defense Technical Information Center, September 1995. http://dx.doi.org/10.21236/ada305928.
Full textCasey, Stephen D. Signal Reconstruction and Analysis Via New Techniques in Harmonic and Complex Analysis. Fort Belvoir, VA: Defense Technical Information Center, August 2005. http://dx.doi.org/10.21236/ada440756.
Full textDehghani, Hamid. Three Dimensional Reconstruction Algorithm for Imaging Pathophysiological Signal within Breast Tissue Using Near Infrared Light. Fort Belvoir, VA: Defense Technical Information Center, July 2004. http://dx.doi.org/10.21236/ada428927.
Full textCastiglioni, Whitmaur, Alex Himmel, and Bryan Ramson. Simulation Studies Of Photon Signal Reconstruction In The DUNE Single Phase Far Detector With Xe Doping. Office of Scientific and Technical Information (OSTI), August 2019. http://dx.doi.org/10.2172/1614720.
Full textNguyen, Lam. Signal Processing Technique to Remove Signature Distortion in ARL Synchronous Impulse Reconstruction (SIRE) Ultra-Wideband (UWB) Radar. Fort Belvoir, VA: Defense Technical Information Center, March 2008. http://dx.doi.org/10.21236/ada478887.
Full textTan, Cheng-Yang. A boostrap algorithm for temporal signal reconstruction in the presence of noise from its fractional Fourier transformed intensity spectra. Office of Scientific and Technical Information (OSTI), February 2011. http://dx.doi.org/10.2172/1009591.
Full textNguyen, Lam. Signal and Image Processing Algorithms for the U.S. Army Research Laboratory Ultra-wideband (UWB) Synchronous Impulse Reconstruction (SIRE) Radar. Fort Belvoir, VA: Defense Technical Information Center, April 2009. http://dx.doi.org/10.21236/ada496571.
Full textGoodman, Joel, Keith Forsythe, and Benjamin Miller. Efficient Reconstruction of Block-Sparse Signals. Fort Belvoir, VA: Defense Technical Information Center, January 2011. http://dx.doi.org/10.21236/ada541046.
Full textAltes, R. A., P. W. Moore, and D. A. Helweg. Tomographic Image Reconstruction of MCM Targets Using Synthetic Dolphin Signals. Fort Belvoir, VA: Defense Technical Information Center, January 1998. http://dx.doi.org/10.21236/ada337008.
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