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Автор: Grafiati
Опубліковано: 14 червня 2023

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Статті в журналах з теми "Reconstruction du signal":

1

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.

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Due to the capacity of compressing and recovering signal with low energy consumption, compressive sensing (CS) has drawn considerable attention in wireless telemonitoring of electrocardiogram (ECG) signals. However, most existing CS methods are designed for reconstructing single channel signal, and hence difficult to reconstruct multichannel ECG signals. In this paper, a spatio-temporal sparse model-based algorithm is proposed for the reconstruction of multichannel ECG signals by not only exploiting the temporal correlation in each individual channel signal, but also the spatial correlation among signals from different channels. In addition, a dictionary learning (DL) approach is developed to enhance the performance of the proposed reconstruction algorithm by using the sparsity of ECG signals in some transformed domain. The approach determines a dictionary by learning local dictionaries for each channel and merging them to form a global dictionary. Extensive simulations were performed to validate the proposed algorithms. Simulation results show that the proposed reconstruction algorithm has a better performance in recovering multichannel ECG signals as compared to the benchmarking methods. Moreover, the reconstruction performance of the algorithm can be further improved by using a dictionary matrix, which is obtained from the proposed DL algorithm.
2

Mingjiang 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.

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3

Liou, 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.

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Ultrasonic signal reconstruction for Structural Health Monitoring is a topic that has been discussed extensively. In this paper, we will apply the techniques of compressed sensing to reconstruct ultrasonic signals that are seriously damaged. To reconstruct the data, the application of conventional interpolation techniques is restricted under the criteria of Nyquist sampling theorem. The newly developed technique - compressed sensing breaks the limitations of Nyquist rate and provides effective results based upon sparse signal reconstruction. Sparse representation is constructed using Fourier transform basis. An l1-norm optimization is then applied for reconstruction. Signals with temperature characteristics were synthetically created. We seriously corrupted these signals and tested the efficacy of our approach under two different scenarios. Firstly, the signal is randomly sampled at very low rates. Secondly, selected intervals were completely blank out. Simulation results show that the signals are effectively reconstructed. It outperforms conventional Spline interpolation in signal-to-noise ratio (SNR) with low variation, especially under very low data rates. This research demonstrates very promising results of using compressed sensing for ultrasonic signal reconstruction.
4

AL-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.

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This paper addresses the problem of reconstructing a slowly-varying information-bearing signal from a parametrically modulated, nonstationary dynamical signal. A chaotic electronic oscillator model characterized by one control parameter and a double-scroll-like attractor is used throughout the study. Wavelet transforms are used to extract features of the chaotic signal resulting from parametric modulation of the control parameter by the useful signal. The vector of feature coefficients is fed into a feed-forward neural network that recovers the embedded information-bearing signal. The performance of the developed method is cross-validated through reconstruction of randomly-generated control parameter patterns. This method is applied to the reconstruction of speech signals, thus demonstrating its potential utility for secure communication applications. Our results are validated via numerical simulations.
5

Lu, 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.

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Aiming at the problems of low reconstruction rate and poor reconstruction precision when reconstructing sparse signals in wireless sensor networks, a sparse signal reconstruction algorithm based on the Limit-Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) quasi-Newton method is proposed. The L-BFGS quasi-Newton method uses a two-loop recursion algorithm to find the descent direction dk directly by calculating the step difference between m adjacent iteration points, and a matrix Hk approximating the inverse of the Hessian matrix is constructed. It solves the disadvantages of BFGS requiring the calculation and storage of Hk, reduces the algorithm complexity, and improves the reconstruction rate. Finally, the experimental results show that the L-BFGS quasi-Newton method has good experimental results for solving the problem of sparse signal reconstruction in wireless sensor networks.
6

van 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.

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Abstract:A technique is presented for the reconstruction of signals that suffered sampling-frequency decimation. Two assumptions are made: the original signal has to be repetitive, and no anti-aliasing filter has been used before frequency decimation. The performance of the technique is assessed by using test signals of which the original signal is known.
7

Xuan 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.

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8

Zhang, 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.

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During railway transportation, due to various factors such as road conditions and operating conditions and produced vibrations and shocks, this kind of vibration environment may cause fatigue damage to on-board equipment and transported goods. The authors propose a research on the numerical simulation method of the nonstationary random vibration signal sensor of railway transportation; first, they establish the mathematical model of the railway nonstationary random vibration signal sensor and then introduce the method of reconstructing the railway nonstationary random vibration signal sensor. For railway nonstationary non-Gaussian random vibration reconstruction signal, compare the time-domain characteristics of the sampled signal, and for railway nonstationary non-Gaussian random vibration reconstruction signal, compare the frequency domain characteristics of the sampled signal. The results show that the relative error of the RMSM function is within 6%, the relative error of the sliding bias function is within 10%, and the relative error of the sliding kurtosis function is within 8%. The energy distribution of the edge Hilbert amplitude spectrum is very similar, with absolute error less than 6%. The energy fluctuations are similar in each band, with absolute error rates less than 4% in most bands. The method proposed in this article, suitable for reconstruction of railway nonstationary Gaussian random vibration and nonstationary non-Gaussian vibration signal sensor, verifies the effectiveness and feasibility of the signal reconstruction method. The model and signal reconstruction method proposed in this paper are applied to the railway nonstationary Gaussian and nonstationary non-Gaussian random vibration sampling signals.
9

Köse, 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.

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Abstract. The meteorological observational period in Turkey, which starts ca. 1930 CE, is too short for understanding long-term climatic variability. Tree rings have been used intensively as proxy records to understand summer precipitation history of the region, primarily because they have a dominant precipitation signal. Yet, the historical context of temperature variability is unclear. Here, we used higher-order principle components of a network of 23 tree-ring chronologies to provide a high-resolution spring (March–April) temperature reconstruction over Turkey during the period 1800–2002. The reconstruction model accounted for 67 % (Adj. R2 = 0.64, p < 0.0001) of the instrumental temperature variance over the full calibration period (1930–2002). The reconstruction is punctuated by a temperature increase during the 20th century; yet extreme cold and warm events during the 19th century seem to eclipse conditions during the 20th century. We found significant correlations between our March–April spring temperature reconstruction and existing gridded spring temperature reconstructions for Europe over Turkey and southeastern Europe. Moreover, the precipitation signal obtained from the tree-ring network (first principle component) showed highly significant correlations with gridded summer drought index reconstruction over Turkey and Mediterranean countries. Our results showed that, beside the dominant precipitation signal, a temperature signal can be extracted from tree-ring series and they can be useful proxies in reconstructing past temperature variability.
10

Luo, 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.

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One of the main challenges in signal denoising is to accurately restore useful signals in low signal-to-noise ratio (SNR) scenarios. In this paper, we investigate the signal denoising problem for multi-component linear frequency modulated (LFM) signals. An effective time-frequency (TF) analysis-based approach is proposed. Compared to the existing approaches, our proposed one can further increase the noise suppressing performance and improve the quality of the reconstructed signal. Experimental results are presented to show that the proposed denoising approach is able to effectively separate the multi-component LFM signal from the strong noise environments.
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Статті в журналах Дисертації Книги

Дисертації з теми "Reconstruction du signal":

1

Serdaroglu, Bulent. "Signal Reconstruction From Nonuniform Samples." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12605850/index.pdf.

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Sampling and reconstruction is used as a fundamental signal processing operation since the history of signal theory. Classically uniform sampling is treated so that the resulting mathematics is simple. However there are various instances that nonuniform sampling and reconstruction of signals from their nonuniform samples are required. There exist two broad classes of reconstruction methods. They are the reconstruction according to a deterministic, and according to a stochastic model. In this thesis, the most fundamental aspects of nonuniform sampling and reconstruction, according to a deterministic model, is analyzed, implemented and tested by considering specific nonuniform reconstruction algorithms. Accuracy of reconstruction, computational efficiency and noise stability are the three criteria that nonuniform reconstruction algorithms are tested for. Specifically, four classical closed form interpolation algorithms proposed by Yen are discussed and implemented. These algorithms are tested, according to the proposed criteria, in a variety of conditions in order to identify their performances for reconstruction quality and robustness to noise and signal conditions. Furthermore, a filter bank approach is discussed for the interpolation from nonuniform samples in a computationally efficient manner. This approach is implemented and the efficiency as well as resulting filter characteristics is observed. In addition to Yen'
s 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.
2

Neuman, Bartosz P. "Signal processing in diffusion MRI : high quality signal reconstruction." Thesis, University of Nottingham, 2014. http://eprints.nottingham.ac.uk/27691/.

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Magnetic Resonance Imaging (MRI) is a medical imaging technique which is especially sensitive to different soft tissues, producing a good contrast between them. It allows for in vivo visualisation of internal structures in detail and became an indispensable tool in diagnosing and monitoring the brain related diseases and pathologies. Amongst others, MRI can be used to measure random incoherent motion of water molecules, which in turn allows to infer structural information. One of the main challenges in processing and analysing four dimensional diffusion MRI images is low signal quality. To improve the signal quality, either denoising algorithm or angular and spatial regularisations are utilised. Regularisation method based on Laplace--Beltrami smoothing operator was successfully applied to diffusion signal. In this thesis, a new regularisation strength selection scheme for diffusion signal regularisation is introduced. A mathematical model of diffusion signal is used in Monte--Carlo simulations, and a regularisation strength that optimally reconstructs the diffusion signal is sought. The regularisation values found in this research show a different trend than the currently used L-curve analysis, and further improve reconstruction accuracy. Additionally, as an alternative to regularisation methods a backward elimination regression for spherical harmonics is proposed. Instead of using the regularisation term as a low-pass filter, the statistical t-test is classifying regression terms into reliable and corrupted. Four algorithms that use this information are further introduced. As the result, a selective filtering is constructed that retains the angular sharpness of the signal, while at the same time reducing corruptive effect of measurement noise. Finally, a statistical approach for estimating diffusion signal is investigated. Based on the physical properties of water diffusion a prior knowledge for the diffusion signal is constructed. The spherical harmonic transform is then formulated as a Bayesian regression problem. Diffusion signal reconstructed with the addition of such prior knowledge is accurate, noise resilient, and of high quality.
3

Moose, Phillip J. "Approximate signal reconstruction from partial information." Thesis, This resource online, 1994. http://scholar.lib.vt.edu/theses/available/etd-06102009-063326/.

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4

Scoular, 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.

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5

Pillai, 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.

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An analog-to-digital converter (ADC) is a key component in many electronic systems. It is used to convert analog signals to the equivalent digital form. The conversion involves sampling which is the process of converting a continuous-time signal to a sequence of discrete-time samples, and quantization in which each sampled value is represented using a finite number of bits. The sampling rate and the effective resolution (number of bits) are two key ADC performance metrics. Today, ADCs form a major bottleneck in many applications like communication systems since it is difficult to simultaneously achieve high sampling rate and high resolution. Among the various ADC architectures, the time-interleaved analog-to-digital converter (TI-ADC) has emerged as a popular choice for achieving very high sampling rates and resolutions. At the principle level, by interleaving the outputs of M identical channel ADCs, a TI-ADC could achieve the same resolution as that of a channel ADC but with M times higher bandwidth. However, in practice, mismatches between the channel ADCs result in a nonuniformly sampled signal at the output of a TI-ADC which reduces the achievable resolution. Often, in TIADC implementations, digital reconstructors are used to recover the uniform-grid samples from the nonuniformly sampled signal at the output of the TI-ADC. Since such reconstructors operate at the TI-ADC output rate, reducing the number of computations required per corrected output sample helps to reduce the power consumed by the TI-ADC. Also, as the mismatch parameters change occasionally, the reconstructor should support online reconfiguration with minimal or no redesign. Further, it is advantageous to have reconstruction schemes that require fewer coefficient updates during reconfiguration. In this thesis, we focus on reducing the design and implementation complexities of nonrecursive finite-length impulse response (FIR) reconstructors. We propose efficient reconstruction schemes for three classes of nonuniformly sampled signals that can occur at the output of TI-ADCs. Firstly, we consider a class of nonuniformly sampled signals that occur as a result of static timing mismatch errors or due to channel mismatches in TI-ADCs. For this type of nonuniformly sampled signals, we propose three reconstructors which utilize a two-rate approach to derive the corresponding single-rate structure. The two-rate based reconstructors move part of the complexity to a symmetric filter and also simplifies the reconstruction problem. The complexity reduction stems from the fact that half of the impulse response coefficients of the symmetric filter are equal to zero and that, compared to the original reconstruction problem, the simplified problem requires only a simpler reconstructor. Next, we consider the class of nonuniformly sampled signals that occur when a TI-ADC is used for sub-Nyquist cyclic nonuniform sampling (CNUS) of sparse multi-band signals. Sub-Nyquist sampling utilizes the sparsities in the analog signal to sample the signal at a lower rate. However, the reduced sampling rate comes at the cost of additional digital signal processing that is needed to reconstruct the uniform-grid sequence from the sub-Nyquist sampled sequence obtained via CNUS. The existing reconstruction scheme is computationally intensive and time consuming and offsets the gains obtained from the reduced sampling rate. Also, in applications where the band locations of the sparse multi-band signal can change from time to time, the reconstructor should support online reconfigurability. Here, we propose a reconstruction scheme that reduces the computational complexity of the reconstructor and at the same time, simplifies the online reconfigurability of the reconstructor. Finally, we consider a class of nonuniformly sampled signals which occur at the output of TI-ADCs that use some of the input sampling instants for sampling a known calibration signal. The samples corresponding to the calibration signal are used for estimating the channel mismatch parameters. In such TI-ADCs, nonuniform sampling is due to the mismatches between the channel ADCs and due to the missing input samples corresponding to the sampling instants reserved for the calibration signal. We propose three reconstruction schemes for such nonuniformly sampled signals and show using design examples that, compared to a previous solution, the proposed schemes require substantially lower computational complexity.
6

Fuller, Megan M. (Megan Marie). "Inverse filtering by signal reconstruction from phase." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/89858.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.
This 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.
7

Cheng, Siuling. "Signal reconstruction from discrete-time Wigner distribution." Thesis, Virginia Tech, 1985. http://hdl.handle.net/10919/41550.

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Wigner 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
8

Santos, Dorabella Martins da Silva. "Signal reconstruction in structures with two channels." Doctoral thesis, Universidade de Aveiro, 2007. http://hdl.handle.net/10773/2211.

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Doutoramento em Engenharia Electrotécnica
Em 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).
9

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.

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Constrained by practical and economical considerations, one often uses seismic data with missing traces. The use of such data results in image artifacts and poor spatial resolution. Sometimes due to practical limitations, measurements may be available on a perturbed grid, instead of on the designated grid. Due to algorithmic requirements, when such measurements are viewed as those on the designated grid, the recovery procedures may result in additional artifacts. This paper interpolates incomplete data onto regular grid via the Fourier domain, using a recently developed greedy algorithm. The basic objective is to study experimentally as to what could be the size of the perturbation in measurement coordinates that allows for the measurements on the perturbed grid to be considered as on the designated grid for faithful recovery. Our experimental work shows that for compressible signals, a uniformly distributed perturbation can be offset with slightly more number of measurements.
10

Scrofani, 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.

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Анотація:
Thesis (Ph.D. in Electrical Engineering)--Naval Postgraduate School, September 2005.
Thesis Advisor(s): Charles W. Therrien. Includes bibliographical references (p. 125-132). Also available online.
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Книги з теми "Reconstruction du signal":

1

Beaumont, A. J. Signal reconstruction techniques for improved measurement of transient emissions. Warrendale, Pa: SAE International, 1990.

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2

Hurt, Norman. Phase retrieval and zero crossings: Mathematical methods in image reconstruction. Dordrecht: Kluwer Academic Publishers, 1989.

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3

Petrović, Predrag. Digital Processing and Reconstruction of Complex AC Signals. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2009.

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4

Signal, 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.

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5

Schultz, Gerrit. Magnetic Resonance Imaging with Nonlinear Gradient Fields: Signal Encoding and Image Reconstruction. Wiesbaden: Springer Fachmedien Wiesbaden, 2013.

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6

Feng, Zhiqiang. A signal processing method for the acoustic image reconstruction of planar objects. Portsmouth: Portsmouth Polytechnic, Dept. of Electrical and Electronic Engineering, 1988.

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7

Feng, Jiu Chao. Reconstruction of chaotic signals with applications to chaos-based communications. [Beijing, China]: Tsinghua University Press, 2008.

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8

Noonan, Joseph. Information theoretic approaches to signal and image restoration. Bellingham, Wash: SPIE, 2011.

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9

L, 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.

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10

L, 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.

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Частини книг з теми "Reconstruction du signal":

1

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.

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2

Meister, 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.

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3

Feuer, 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.

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4

Gopi, 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.

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5

Vaswani, 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.

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6

Krémé, 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.

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7

Sun, 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.

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8

Khan, 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.

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9

Pizzolato, 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.

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10

Boyko, 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.

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Тези доповідей конференцій з теми "Reconstruction du signal":

1

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.

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Анотація:
Medical image reconstruction is fraught with problems that are a result of noisy and incomplete data. The incomplete data give rise to null functions that are associated with the imaging operator, thus yielding an infinite number of solutions that fit the data equally well. Noise in the data often will lead to very rough reconstructions, which can be inconsistent with previous experience. The use of prior information can sometimes be introduced to help alleviate the aforementioned problems. If one knows that an object (or class of objects) possesses certain characteristics, then the reconstructions should possess the same characteristics.
2

Clarkson, 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.

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In tomographic and other digital imaging systems the goal is often to reconstruct an object function from a finite amount of noisy data generated by that function through a system operator. One way to determine the reconstructed function is to minimize the distance between the noiseless data vector it would generate via the system operator, and the data vector created through the system by the real object and noise. The former we will call the reconstructed data vector, and the latter the actual data vector. A reasonable constraint to place on this minimization problem is to require that the reconstructed function be non-negative everywhere. Different measures of distance in data space then result in different reconstruction methods. For example, the ordinary Euclidean distance results in a positively constrained least squares reconstruction, while the Kulback-Leibler distance results in a Poisson maximum likelihood reconstruction. In many cases though, if the reconstruction algorithm is continued until it converges, the end result is a reconstructed function that consists of many point-like structures and little else. These are called night-sky reconstructions, and they are usually avoided by stopping the reconstruction algorithm early or using regularization. The expectation-maximization algorithm for Poisson maximum likelihood reconstructions is an example of this situation.
3

Stankovic, 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.

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4

Chetty, 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.

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5

Tian, 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.

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Abstract Based on the blade vibration theory of turbomachinery and the basic principle of blade timing systems, a sparse reconstruction model is derived for the tip timing signal under an arbitrary sensor circumferential placement distribution. The proposed approach uses the sparsity of the tip timing signal in the frequency domain. The application of compressive sensing in reconstructing the blade tip timing signal and monitoring multi-mode blade vibrations is explored. To improve the reconstruction effect, a number of numerical experiments are conducted to examine the effects of various factors on synchronous and non-synchronous signals. This enables the specific steps involved in the compressive sensing reconstruction of tip timing signals to be determined. The proposed method is then applied to the tip timing data of a 27-blade rotor. The results show that the method accurately identifies the multi-mode blade vibrations at different rotation speeds. The proposed method has the advantages of low dependence on prior information, insensitivity to environmental noise, and simultaneous identification of synchronous and non-synchronous signals. The experimental results validate the effectiveness of the proposed approach in engineering applications.
6

Honglin 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.

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7

O'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.

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8

Byrne, 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.

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The problem of reconstructing a non-negative signal from a finite number of spectral data is a problem of finding an optimal approximation to one function by another. For example, for velocity measurement by crossed beam laser Doppler anemometry, a limited number of channels can provide high quality data on the autocorrelation function of the intensity of the scattered light. However, extrapolation of these data is required in order to estimate velocity distributions narrower than the point spread function determined by the number of channels, e.g. in the case of laminar flow. We describe here methods based on the theory of best approximation in weighted Hilbert spaces, (1). These methods have been under development for some time for use in a variety of 1-D and 2-D estimation problems. A new interpretation of these methods is now possible based on the close analogy between the reconstruction of a non-negative function from finitely many values of its Fourier transform, and the design of approximate Wiener filters,(2).
9

Sheppard, 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.

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In brightfield, phase-contrast or polarization microscopy, the image can be modeled by using scattering theory. The object, consisting of spatial variations in complex refractive index, scatters components of an angular spectrum of plane waves, and the image calculated by integration over incident and scattered waves. This approach takes into account the high aperture effects, important in microscope imaging. Rigorous methods can be used to calculate the scattering by the object.1 However, these methods, in addition to being in general very computationally intensive, result in the disadvantges that it is difficult to see trends in the behaviour and usually impracticable to reconstruct the object from the image data.
10

Shepard, 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.

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Звіти організацій з теми "Reconstruction du signal":

1

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.

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2

Ganesh, 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.

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3

Casey, 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.

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4

Dehghani, 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.

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5

Castiglioni, 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.

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6

Nguyen, 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.

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7

Tan, 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.

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8

Nguyen, 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.

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9

Goodman, 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.

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10

Altes, 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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