Literatura académica sobre el tema "Reconstruction du signal"
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Artículos de revistas sobre el tema "Reconstruction du signal"
Hua, Jing, Hua Zhang, Jizhong Liu y Junlong Zhou. "Compressive Sensing of Multichannel Electrocardiogram Signals in Wireless Telehealth System". Journal of Circuits, Systems and Computers 25, n.º 09 (21 de junio de 2016): 1650103. http://dx.doi.org/10.1142/s0218126616501036.
Texto completoMingjiang Shi, Xiaoyan Zhuang y He Zhang. "Signal Reconstruction for Frequency Sparse Sampling Signals". Journal of Convergence Information Technology 8, n.º 9 (15 de mayo de 2013): 1197–203. http://dx.doi.org/10.4156/jcit.vol8.issue9.147.
Texto completoLiou, Ren Jean. "Ultrasonic Signal Reconstruction Using Compressed Sensing". Applied Mechanics and Materials 855 (octubre de 2016): 165–70. http://dx.doi.org/10.4028/www.scientific.net/amm.855.165.
Texto completoAL-ASSAF, YOUSEF y WAJDI M. AHMAD. "PARAMETER IDENTIFICATION OF CHAOTIC SYSTEMS USING WAVELETS AND NEURAL NETWORKS". International Journal of Bifurcation and Chaos 14, n.º 04 (abril de 2004): 1467–76. http://dx.doi.org/10.1142/s0218127404009910.
Texto completoLu, Xinmiao, Cunfang Yang, Qiong Wu, Jiaxu Wang, Yuhan Wei, Liyu Zhang, Dongyuan Li y Lanfei Zhao. "Improved Reconstruction Algorithm of Wireless Sensor Network Based on BFGS Quasi-Newton Method". Electronics 12, n.º 6 (7 de marzo de 2023): 1267. http://dx.doi.org/10.3390/electronics12061267.
Texto completovan Bemmel, J. H., R. J. A. Schijvenaars y J. A. Kors. "Reconstruction of Repetitive Signals". Methods of Information in Medicine 33, n.º 01 (1994): 41–45. http://dx.doi.org/10.1055/s-0038-1634986.
Texto completoXuan Liu, Xuan Liu y Jin U. Kang Jin U. Kang. "Iterative sparse reconstruction of spectral domain OCT signal". Chinese Optics Letters 12, n.º 5 (2014): 051701–51704. http://dx.doi.org/10.3788/col201412.051701.
Texto completoZhang, Wenchao, Bo Zhang, Fei Xu y Mohammad Asif. "Research on Numerical Simulation Method of Nonstationary Random Vibration Signal Sensor in Railway Transportation". Journal of Sensors 2022 (15 de abril de 2022): 1–7. http://dx.doi.org/10.1155/2022/7149477.
Texto completoKöse, Nesibe, H. Tuncay Güner, Grant L. Harley y Joel Guiot. "Spring temperature variability over Turkey since 1800 CE reconstructed from a broad network of tree-ring data". Climate of the Past 13, n.º 1 (4 de enero de 2017): 1–15. http://dx.doi.org/10.5194/cp-13-1-2017.
Texto completoLuo, Shan, Guoan Bi, Tong Wu, Yong Xiao y Rongping Lin. "An Effective LFM Signal Reconstruction Method for Signal Denoising". Journal of Circuits, Systems and Computers 27, n.º 09 (26 de abril de 2018): 1850140. http://dx.doi.org/10.1142/s0218126618501402.
Texto completoTesis sobre el tema "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.
Texto completos 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/.
Texto completoMoose, Phillip J. "Approximate signal reconstruction from partial information". Thesis, This resource online, 1994. http://scholar.lib.vt.edu/theses/available/etd-06102009-063326/.
Texto completoScoular, 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.
Texto completoPillai, 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.
Texto completoFuller, Megan M. (Megan Marie). "Inverse filtering by signal reconstruction from phase". Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/89858.
Texto completoThis 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.
Texto completoWigner 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.
Texto completoEm 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 y Felix J. Herrmann. "Signal reconstruction from incomplete and misplaced measurements". European Association of Geoscientists & Engineers, 2007. http://hdl.handle.net/2429/550.
Texto completoScrofani, 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.
Texto completoThesis Advisor(s): Charles W. Therrien. Includes bibliographical references (p. 125-132). Also available online.
Libros sobre el tema "Reconstruction du signal"
Beaumont, A. J. Signal reconstruction techniques for improved measurement of transient emissions. Warrendale, Pa: SAE International, 1990.
Buscar texto completoPhase retrieval and zero crossings: Mathematical methods in image reconstruction. Dordrecht: Kluwer Academic Publishers, 1989.
Buscar texto completoPetrović, Predrag. Digital Processing and Reconstruction of Complex AC Signals. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2009.
Buscar texto completoSignal, 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.
Buscar texto completoSchultz, Gerrit. Magnetic Resonance Imaging with Nonlinear Gradient Fields: Signal Encoding and Image Reconstruction. Wiesbaden: Springer Fachmedien Wiesbaden, 2013.
Buscar texto completoFeng, Zhiqiang. A signal processing method for the acoustic image reconstruction of planar objects. Portsmouth: Portsmouth Polytechnic, Dept. of Electrical and Electronic Engineering, 1988.
Buscar texto completoKong, Tse Chi, ed. Reconstruction of chaotic signals with applications to chaos-based communications. [Beijing, China]: Tsinghua University Press, 2008.
Buscar texto completoPrabahan, Basu, ed. Information theoretic approaches to signal and image restoration. Bellingham, Wash: SPIE, 2011.
Buscar texto completoL, Jankovsky Amy y 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.
Buscar texto completoL, Jankovsky Amy y 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.
Buscar texto completoCapítulos de libros sobre el tema "Reconstruction du signal"
Majumdar, Angshul. "Biomedical Signal Reconstruction". En 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.
Texto completoMeister, Alexander. "Image and Signal Reconstruction". En 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.
Texto completoFeuer, Arie y Graham C. Goodwin. "Sampling and Reconstruction". En 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.
Texto completoGopi, E. S. "Sampling and Reconstruction of Signals". En Multi-Disciplinary Digital Signal Processing, 1–42. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57430-1_1.
Texto completoVaswani, Namrata y Wei Lu. "Recursive Reconstruction of Sparse Signal Sequences". En Compressed Sensing & Sparse Filtering, 357–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38398-4_11.
Texto completoKrémé, A. Marina, Valentin Emiya y Caroline Chaux. "Phase Reconstruction for Time-Frequency Inpainting". En 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.
Texto completoSun, Liqing, Xianbin Wen, Ming Lei, Haixia Xu, Junxue Zhu y Yali Wei. "Signal Reconstruction Based on Block Compressed Sensing". En 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.
Texto completoKhan, Nadia Masood y Gul Muhammad Khan. "Signal Reconstruction Using Evolvable Recurrent Neural Networks". En 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.
Texto completoPizzolato, Marco, Aurobrata Ghosh, Timothé Boutelier y Rachid Deriche. "Magnitude and Complex Based Diffusion Signal Reconstruction". En Computational Diffusion MRI, 127–40. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11182-7_12.
Texto completoBoyko, Nikita, Gulver Karamemis, Viktor Kuzmenko y Stan Uryasev. "Sparse Signal Reconstruction: LASSO and Cardinality Approaches". En Springer Proceedings in Mathematics & Statistics, 77–90. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10046-3_4.
Texto completoActas de conferencias sobre el tema "Reconstruction du signal"
Gooley, T. A., H. H. Barrett, M. Barth y J. L. Denny. "Quantitative Comparisons of Choices of Prior Information in Image Reconstruction". En Signal Recovery and Synthesis. Washington, D.C.: Optica Publishing Group, 1989. http://dx.doi.org/10.1364/srs.1989.wa3.
Texto completoClarkson, Eric, Jack Denny, Harrison Barrett, Craig Abbey y Brandon Gallas. "Night-sky reconstructions for linear digital imaging systems". En Signal Recovery and Synthesis. Washington, D.C.: Optica Publishing Group, 1998. http://dx.doi.org/10.1364/srs.1998.sthc.5.
Texto completoStankovic, Isidora, Milos Dakovic y Cornel Ioana. "Time-frequency signal reconstruction of nonsparse audio signals". En 2017 22nd International Conference on Digital Signal Processing (DSP). IEEE, 2017. http://dx.doi.org/10.1109/icdsp.2017.8096044.
Texto completoChetty, V., D. Hayden, J. Goncalves y S. Warnick. "Robust signal-structure reconstruction". En 2013 IEEE 52nd Annual Conference on Decision and Control (CDC). IEEE, 2013. http://dx.doi.org/10.1109/cdc.2013.6760369.
Texto completoTian, Jie, Xiaopu Zhang, Yong Chen, Peter Russhard y Hua Ouyang. "Sparse Reconstruction Method of Non-Uniform Sampling and its Application in Blade Tip Timing System". En ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/gt2020-14753.
Texto completoHonglin Huang y Anamitra Makur. "A new iterative reconstruction scheme for signal reconstruction". En APCCAS 2008 - 2008 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS). IEEE, 2008. http://dx.doi.org/10.1109/apccas.2008.4746028.
Texto completoO'Hagan, Daniel W., Motlatsi Setsubi y Stephen Paine. "Signal reconstruction of DVB-T2 signals in passive radar". En 2018 IEEE Radar Conference (RadarConf18). IEEE, 2018. http://dx.doi.org/10.1109/radar.2018.8378717.
Texto completoByrne, Charles L. y Michael A. Fiddy. "Signal Reconstruction as a Wiener Filter Approximation". En Photon Correlation Techniques and Applications. Washington, D.C.: Optica Publishing Group, 1988. http://dx.doi.org/10.1364/pcta.1988.pcmdr18.
Texto completoSheppard, CJR. "Microscope image reconstruction". En Signal Recovery and Synthesis. Washington, D.C.: Optica Publishing Group, 1998. http://dx.doi.org/10.1364/srs.1998.stue.2.
Texto completoShepard, Steven M. y Maria Frendberg Beemer. "Advances in thermographic signal reconstruction". En SPIE Sensing Technology + Applications, editado por Sheng-Jen (Tony) Hsieh y Joseph N. Zalameda. SPIE, 2015. http://dx.doi.org/10.1117/12.2176748.
Texto completoInformes sobre el tema "Reconstruction du signal"
Nguyen, C. T., C. Ganesh y S. E. Hammel. Advanced Techniques for Signal and Image Compression/Reconstruction with Wavelets. Fort Belvoir, VA: Defense Technical Information Center, mayo de 1995. http://dx.doi.org/10.21236/ada297037.
Texto completoGanesh, C., C. T. Nguyen, M. Marafino y S. E. Hammel. An Energy-Based Method for Signal Compression and Reconstruction with Wavelets. Fort Belvoir, VA: Defense Technical Information Center, septiembre de 1995. http://dx.doi.org/10.21236/ada305928.
Texto completoCasey, Stephen D. Signal Reconstruction and Analysis Via New Techniques in Harmonic and Complex Analysis. Fort Belvoir, VA: Defense Technical Information Center, agosto de 2005. http://dx.doi.org/10.21236/ada440756.
Texto completoDehghani, Hamid. Three Dimensional Reconstruction Algorithm for Imaging Pathophysiological Signal within Breast Tissue Using Near Infrared Light. Fort Belvoir, VA: Defense Technical Information Center, julio de 2004. http://dx.doi.org/10.21236/ada428927.
Texto completoCastiglioni, Whitmaur, Alex Himmel y 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), agosto de 2019. http://dx.doi.org/10.2172/1614720.
Texto completoNguyen, 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, marzo de 2008. http://dx.doi.org/10.21236/ada478887.
Texto completoTan, 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), febrero de 2011. http://dx.doi.org/10.2172/1009591.
Texto completoNguyen, 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, abril de 2009. http://dx.doi.org/10.21236/ada496571.
Texto completoGoodman, Joel, Keith Forsythe y Benjamin Miller. Efficient Reconstruction of Block-Sparse Signals. Fort Belvoir, VA: Defense Technical Information Center, enero de 2011. http://dx.doi.org/10.21236/ada541046.
Texto completoAltes, R. A., P. W. Moore y D. A. Helweg. Tomographic Image Reconstruction of MCM Targets Using Synthetic Dolphin Signals. Fort Belvoir, VA: Defense Technical Information Center, enero de 1998. http://dx.doi.org/10.21236/ada337008.
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