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Статті в журналах з теми "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.
Повний текст джерелаMingjiang 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.
Повний текст джерелаLiou, 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.
Повний текст джерелаAL-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.
Повний текст джерелаLu, 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.
Повний текст джерелаvan 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.
Повний текст джерелаXuan 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.
Повний текст джерелаZhang, 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.
Повний текст джерелаKöse, 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.
Повний текст джерелаLuo, 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.
Повний текст джерелаДисертації з теми "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.
Повний текст джерелаs 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/.
Повний текст джерелаMoose, Phillip J. "Approximate signal reconstruction from partial information." Thesis, This resource online, 1994. http://scholar.lib.vt.edu/theses/available/etd-06102009-063326/.
Повний текст джерелаScoular, 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.
Повний текст джерелаPillai, 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.
Повний текст джерелаFuller, Megan M. (Megan Marie). "Inverse filtering by signal reconstruction from phase." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/89858.
Повний текст джерелаThis 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.
Повний текст джерелаWigner 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.
Повний текст джерелаEm 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.
Повний текст джерелаScrofani, 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.
Повний текст джерелаThesis Advisor(s): Charles W. Therrien. Includes bibliographical references (p. 125-132). Also available online.
Книги з теми "Reconstruction du signal"
Beaumont, A. J. Signal reconstruction techniques for improved measurement of transient emissions. Warrendale, Pa: SAE International, 1990.
Знайти повний текст джерелаPhase retrieval and zero crossings: Mathematical methods in image reconstruction. Dordrecht: Kluwer Academic Publishers, 1989.
Знайти повний текст джерелаPetrović, Predrag. Digital Processing and Reconstruction of Complex AC Signals. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2009.
Знайти повний текст джерелаSignal, 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.
Знайти повний текст джерелаSchultz, Gerrit. Magnetic Resonance Imaging with Nonlinear Gradient Fields: Signal Encoding and Image Reconstruction. Wiesbaden: Springer Fachmedien Wiesbaden, 2013.
Знайти повний текст джерелаFeng, Zhiqiang. A signal processing method for the acoustic image reconstruction of planar objects. Portsmouth: Portsmouth Polytechnic, Dept. of Electrical and Electronic Engineering, 1988.
Знайти повний текст джерелаKong, Tse Chi, ed. Reconstruction of chaotic signals with applications to chaos-based communications. [Beijing, China]: Tsinghua University Press, 2008.
Знайти повний текст джерелаPrabahan, Basu, ed. Information theoretic approaches to signal and image restoration. Bellingham, Wash: SPIE, 2011.
Знайти повний текст джерелаL, 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.
Знайти повний текст джерелаL, 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.
Знайти повний текст джерелаЧастини книг з теми "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.
Повний текст джерелаMeister, 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.
Повний текст джерелаFeuer, 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.
Повний текст джерелаGopi, 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.
Повний текст джерелаVaswani, 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.
Повний текст джерелаKrémé, 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.
Повний текст джерелаSun, 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.
Повний текст джерелаKhan, 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.
Повний текст джерелаPizzolato, 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.
Повний текст джерелаBoyko, 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.
Повний текст джерелаТези доповідей конференцій з теми "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.
Повний текст джерелаClarkson, 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.
Повний текст джерелаStankovic, 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.
Повний текст джерелаChetty, 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.
Повний текст джерелаTian, 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.
Повний текст джерелаHonglin 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.
Повний текст джерелаO'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.
Повний текст джерелаByrne, 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.
Повний текст джерелаSheppard, 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.
Повний текст джерелаShepard, 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.
Повний текст джерелаЗвіти організацій з теми "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.
Повний текст джерелаGanesh, 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.
Повний текст джерелаCasey, 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.
Повний текст джерелаDehghani, 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.
Повний текст джерелаCastiglioni, 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.
Повний текст джерелаNguyen, 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.
Повний текст джерелаTan, 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.
Повний текст джерелаNguyen, 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.
Повний текст джерелаGoodman, 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.
Повний текст джерелаAltes, 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.
Повний текст джерела