Journal articles: 'Reconstruction du signal'

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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.

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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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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.

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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.

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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.

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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.

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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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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.

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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.

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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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Kollár, Zsolt, Juraj Gazda, Péter Horváth, Lajos Varga, and Dušan Kocur. "Iterative signal reconstruction of deliberately clipped SMT signals." Science China Information Sciences 57, no. 2 (October 28, 2013): 1–13. http://dx.doi.org/10.1007/s11432-013-4921-7.

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LIANG, YANYAN, ZHANCHUAN CAI, DONGXU QI, and ZESHENG TANG. "SCALE-INVARIANT V-TRANSFORM AND ITS APPLICATION TO SIGNAL DE-NOISING." International Journal of Wavelets, Multiresolution and Information Processing 11, no. 05 (September 2013): 1350038. http://dx.doi.org/10.1142/s0219691313500380.

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An aporia of signal de-noising is that the local signal reconstruction at the singular points. Based on the analysis for the signal singular points, combining signal scaling and orthogonal transform, This paper present a novel method called Scale-Invariant V-Transform (SIVT) for signal de-noising based on V-System, which is polynomial multi-wavelets in invariant set. SIVT employs multiple redundant basis of various scale to suppress the artifacts appearing in the singular points of denoised signal. The test results reveal the SIVT reconstructions exhibit higher visual quality and numerical measurement of SNR than wavelet-based reconstructions. Existing theory of SIVT suggests that these new approaches can perform significantly better than wavelet methods in certain signal reconstruction problems.

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WANG, YANFEI, ZAIWEN WEN, ZUHAIR NASHED, and QIYU SUN. "ON DIRECT METHODS FOR TIME-LIMITED SIGNAL AND IMAGE RECONSTRUCTION AND ENHANCEMENT." International Journal of Wavelets, Multiresolution and Information Processing 05, no. 01 (January 2007): 51–68. http://dx.doi.org/10.1142/s0219691307001574.

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The discrete Fourier transform (DFT) can be considered as an observing system, which has an input f, an output F, and a response with additive noise E. In many applications, part of the frequency spectrum/frequency information is missing or unavailable due to the passage of the time-limited signal through a band-limited system, for example, the discrete Fourier system. We suggest improving the resolution of the reconstruction of signals and images using a novel approach for the solution of the discrete Fourier system and by image enhancement. We note that the reconstruction of a time-limited signal can be simply realized by only using either the real part or the imaginary part of the DFT matrix. Therefore, based on the study of the special structure of the real and imaginary parts of the discrete Fourier matrix, a fast direct computational method is developed that utilizes explicit formulas for the truncated singular value decomposition (TSVD) obtained recently by the authors. For improving the resolution of the reconstructions, enhancement by logarithm transform is applied. This fast direct computational method is superior to other direct methods such as LU decomposition, QR decomposition, classical SVD and classical TSVD. The explicit TSVD along with the enhancement can be considered as a useful tool for signal and image reconstructions. Numerical tests for signal and image reconstructions and enhancements are given as well.

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Stiebel, Tarek, and Dorit Merhof. "Brightness Invariant Deep Spectral Super-Resolution." Sensors 20, no. 20 (October 13, 2020): 5789. http://dx.doi.org/10.3390/s20205789.

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Spectral reconstruction from RGB or spectral super-resolution (SSR) offers a cheap alternative to otherwise costly and more complex spectral imaging devices. In recent years, deep learning based methods consistently achieved the best reconstruction quality in terms of spectral error metrics. However, there are important properties that are not maintained by deep neural networks. This work is primarily dedicated to scale invariance, also known as brightness invariance or exposure invariance. When RGB signals only differ in their absolute scale, they should lead to identical spectral reconstructions apart from the scaling factor. Scale invariance is an essential property that signal processing must guarantee for a wide range of practical applications. At the moment, scale invariance can only be achieved by relying on a diverse database during network training that covers all possibly occurring signal intensities. In contrast, we propose and evaluate a fundamental approach for deep learning based SSR that holds the property of scale invariance by design and is independent of the training data. The approach is independent of concrete network architectures and instead focuses on reevaluating what neural networks should actually predict. The key insight is that signal magnitudes are irrelevant for acquiring spectral reconstructions from camera signals and are only useful for a potential signal denoising.

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Sharma, Kamalesh Kumar, and Shiv Dutt Joshi. "Signal reconstruction from the undersampled signal samples." Optics Communications 268, no. 2 (December 2006): 245–52. http://dx.doi.org/10.1016/j.optcom.2006.07.045.

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Baczyk, Marcin, and Mateusz Malanowski. "Reconstruction of the Reference Signal in DVB-T-based Passive Radar." International Journal of Electronics and Telecommunications 57, no. 1 (March 1, 2011): 43–48. http://dx.doi.org/10.2478/v10177-011-0006-y.

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Reconstruction of the Reference Signal in DVB-T-based Passive RadarIn the paper the problem of decoding of digital television signal and its reconstruction for the purpose of using it in passive radar is presented. The main focus is the reconstruction of the signal using a general purpose receiver, not dedicated to digital television signal reception. The performance of the proposed method is verified on simulated and real-life signals.

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Fletcher, J. R., G. P. Swift, DeChang Dai, J. M. Chamberlain, and P. C. Upadhya. "Pulsed Terahertz Signal Reconstruction." Journal of Applied Physics 102, no. 11 (December 2007): 113105. http://dx.doi.org/10.1063/1.2818361.

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Tarczynski, A. "Sensitivity of signal reconstruction." IEEE Signal Processing Letters 4, no. 7 (July 1997): 192–94. http://dx.doi.org/10.1109/97.596883.

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Li, Yong Jie, Dong Jiao Xu, Xin Wang, and Ying Chang. "Signal Compression Reconstruction with Narrow-Band Interference." Applied Mechanics and Materials 668-669 (October 2014): 1110–13. http://dx.doi.org/10.4028/www.scientific.net/amm.668-669.1110.

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Compressive sensing (CS) implements sampling and compression to sparse or compressible signals simultaneously. Compressive signal processing is a new signal processing scheme base on compressive sensing theory. In this paper, the problem of signal compressive reconstruction base on narrow-band interference is researched. The reconstruction performance of BP, MP, and OMP algorithms with narrow-band interference is analyzed by computer simulations.

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Shinde, Ashok Naganath, Sanjay L. Lalbalwar, and Anil B. Nandgaonkar. "Modified meta-heuristic-oriented compressed sensing reconstruction algorithm for bio-signals." International Journal of Wavelets, Multiresolution and Information Processing 17, no. 05 (September 2019): 1950031. http://dx.doi.org/10.1142/s0219691319500310.

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In signal processing, several applications necessitate the efficient reprocessing and representation of data. Compression is the standard approach that is used for effectively representing the signal. In modern era, many new techniques are developed for compression at the sensing level. Compressed sensing (CS) is a rising domain that is on the basis of disclosure, which is a little gathering of a sparse signal’s linear projections including adequate information for reconstruction. The sampling of the signal is permitted by the CS at a rate underneath the Nyquist sampling rate while relying on the sparsity of the signals. Additionally, the reconstruction of the original signal from some compressive measurements can be authentically exploited using the varied reconstruction algorithms of CS. This paper intends to exploit a new compressive sensing algorithm for reconstructing the signal in bio-medical data. For this purpose, the signal can be compressed by undergoing three stages: designing of stable measurement matrix, signal compression and signal reconstruction. In this, the compression stage includes a new working model that precedes three operations. They are signal transformation, evaluation of [Formula: see text] and normalization. In order to evaluate the theta ([Formula: see text]) value, this paper uses the Haar wavelet matrix function. Further, this paper ensures the betterment of the proposed work by influencing the optimization concept with the evaluation procedure. The vector coefficient of Haar wavelet function is optimally selected using a new optimization algorithm called Average Fitness-based Glowworm Swarm Optimization (AF-GSO) algorithm. Finally, the performance of the proposed model is compared over the traditional methods like Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Firefly (FF), Crow Search (CS) and Glowworm Swarm Optimization (GSO) algorithms.

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Luo, Ying, Qun Zhang, Guozheng Wang, and Youqing Bai. "Exact CS Reconstruction Condition of Undersampled Spectrum-Sparse Signals." Journal of Applied Mathematics 2013 (2013): 1–6. http://dx.doi.org/10.1155/2013/715848.

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Compressive sensing (CS) reconstruction of a spectrum-sparse signal from undersampled data is, in fact, an ill-posed problem. In this paper, we mathematically prove that, in certain cases, the exact CS reconstruction of a spectrum-sparse signal from undersampled data is impossible. Then we present the exact CS reconstruction condition of undersampled spectrum-sparse signals, which is valuable for digital signal compression.

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Wang, Xiaoqing, Zhengguo Tan, Nick Scholand, Volkert Roeloffs, and Martin Uecker. "Physics-based reconstruction methods for magnetic resonance imaging." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 379, no. 2200 (May 10, 2021): 20200196. http://dx.doi.org/10.1098/rsta.2020.0196.

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Conventional magnetic resonance imaging (MRI) is hampered by long scan times and only qualitative image contrasts that prohibit a direct comparison between different systems. To address these limitations, model-based reconstructions explicitly model the physical laws that govern the MRI signal generation. By formulating image reconstruction as an inverse problem, quantitative maps of the underlying physical parameters can then be extracted directly from efficiently acquired k-space signals without intermediate image reconstruction—addressing both shortcomings of conventional MRI at the same time. This review will discuss basic concepts of model-based reconstructions and report on our experience in developing several model-based methods over the last decade using selected examples that are provided complete with data and code. This article is part of the theme issue ‘Synergistic tomographic image reconstruction: part 1’.

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Feng, Qi, Junyi Zhang, Li Chen, and Fang Liu. "Waveform Reconstruction of DSSS Signal Based on VAE-GAN." Wireless Communications and Mobile Computing 2022 (July 4, 2022): 1–10. http://dx.doi.org/10.1155/2022/3667592.

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The complex electromagnetic environment will limit the efficacy of communication equipment. It is critical to construct a complex electromagnetic environment to test communication equipment in order to maximize its capability. One of the most important methods for constructing a complex electromagnetic environment is signal reconstruction. This paper proposes a VAE-GAN-based method for reconstructing direct sequence spread spectrum (DSSS) signals. In this method, the deep residual shrinkage network (DRSN) and self-attention mechanism are added to the encoder and discriminator of VAE-GAN. In feature learning, the DRSNs can reduce the redundant information caused by noise in the collected signal. The self-attention mechanism can establish the long-distance dependence between the input sequences, making it easier for the network to learn the samples’ pseudonoise (PN) sequence features. In addition, feature loss is applied to the encoder and generator to improve network stability during training. The results of the experiments indicate that this method can reconstruct DSSS signals with the characteristics of the target signal.

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Chen, Long, Guitong Chen, Lei Huang, Yat-Sze Choy, and Weize Sun. "Multiple Sound Source Localization, Separation, and Reconstruction by Microphone Array: A DNN-Based Approach." Applied Sciences 12, no. 7 (March 28, 2022): 3428. http://dx.doi.org/10.3390/app12073428.

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Synchronistical localization, separation, and reconstruction for multiple sound sources are usually necessary in various situations, such as in conference rooms, living rooms, and supermarkets. To improve the intelligibility of speech signals, the application of deep neural networks (DNNs) has achieved considerable success in the area of time-domain signal separation and reconstruction. In this paper, we propose a hybrid microphone array signal processing approach for the nearfield scenario that combines the beamforming technique and DNN. Using this method, the challenge of identifying both the sound source location and content can be overcome. Moreover, the use of a sequenced virtual sound field reconstruction process enables the proposed approach to be quite suitable for a sound field which contains a dominant, stronger sound source and masked, weaker sound sources. Using this strategy, all traceable, mainly sound, sources can be discovered by loops in a given sound field. The operational duration and accuracy of localization are further improved by substituting the broadband weighted multiple signal classification (BW-MUSIC) method for the conventional delay-and-sum (DAS) beamforming algorithm. The effectiveness of the proposed method for localizing and reconstructing speech signals was validated by simulations and experiments with promising results. The localization results were accurate, while the similarity and correlation between the reconstructed and original signals was high.

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Zhang, Zheng Pu, Xing Feng Guo, and Bo Tian. "Analysis of the Effect of Noise in CosaMP Algorithm." Advanced Materials Research 926-930 (May 2014): 2992–95. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.2992.

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Compressive sensing is a new type of digital signal processing method. The novel objective of compressive Sensing is to reconstruct a signal accurately and efficiently from far fewer sampling points got by Nyquist sampling theorem. Compressive sensing theory combines the process of sampling and compression to reduce the complexity of signal processing, which is widely used in many fields. so there are wide application prospects in the areas of radar image, wireless sensor network (WSN), radio frequency communication, medical image processing, image device collecting and so on. One of the important tasks in CS is how to recover the signals more accurately and effectively, which is concerned by many researchers. Compressive sensing started late; there are many problems and research directions worthy of our in-depth research. At present, many researchers shove focused on reconstruction algorithms. Reconstruction algorithms are the core of compressive sensing, which are of great significance to reconstructing compressed signals and verifying the accuracy in sampling. These papers introduce CosaMP algorithm; and then study and analyze the Gaussian noise as the main content. Finally, the given signal and random signal, for example, we give a series of comparison results.

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Liu, Shuli, Yi Liu, Longjiang Shen, Lin Li, and Yiping Shen. "Compressed Sensing of Traction Motor Bearing Vibration Signals based on Discrete Fourier Transform and Generalized Orthogonal Matching Pursuit Algorithm." Journal of Physics: Conference Series 2477, no. 1 (April 1, 2023): 012071. http://dx.doi.org/10.1088/1742-6596/2477/1/012071.

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Abstract For the bogie traction motor bearings of rail vehicles under road transportation conditions, its vibration signal full-time domain data acquisition data volume, storage pressure, subsequent data analysis difficulties, and other problems. This study proposes a data compression acquisition and data reconstruction method for bearing vibration signals based on compressed sensing (CS), which can achieve compressed sampling, transmission, and storage of the original signal with fewer observations, and restore the original signal by combining reconstruction algorithms to achieve low error reconstruction. We study the effects of different sparse transform methods on signal sparsity, as well as the signal sparse performance based on different sparse transforms and the signal reconstruction effect of different reconstruction algorithms, and propose a sparse representation method based on the discrete Fourier transform (DFT) and the generalized orthogonal matching pursuit (gOMP) algorithm for CS. Through simulation and experimental comparison, it is concluded that the method can restore and reconstruct the original high-dimensional signal with small reconstruction error, and solve the problems of massive data, inconvenient transmission, and difficult data analysis in engineering.

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Si, Wei-Jian, Qiang Liu, and Zhi-An Deng. "Adaptive Reconstruction Algorithm Based on Compressed Sensing Broadband Receiver." Wireless Communications and Mobile Computing 2021 (January 15, 2021): 1–12. http://dx.doi.org/10.1155/2021/6673235.

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Existing greedy reconstruction algorithms require signal sparsity, and the remaining sparsity adaptive algorithms can be reconstructed but cannot achieve accurate sparsity estimation. To address this problem, a blind sparsity reconstruction algorithm is proposed in this paper, which is applied to compressed sensing radar receiver system. The proposed algorithm can realize the estimation of signal sparsity and channel position estimation, which mainly consists of two parts. The first part is to use fast search based on dichotomy search, which is based on the high probability reconstruction of greedy algorithm, and uses dichotomy search to cover the number of sparsity. The second part is the signal matching and tracking algorithm, which is mainly used to judge the signal position and reconstruct the signal. Combine the two parts together to realize the blind estimation of the sparsity and the accurate estimation of the number of signals when the number of signals is unknown. The experimental analyses are carried out to evaluate the performance of the reconstruction probability, the accuracy of sparsity estimation, the running time of the algorithm, and the signal-to-noise ratio.

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Mishali, M., and Y. C. Eldar. "Blind Multiband Signal Reconstruction: Compressed Sensing for Analog Signals." IEEE Transactions on Signal Processing 57, no. 3 (March 2009): 993–1009. http://dx.doi.org/10.1109/tsp.2009.2012791.

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Ma, Junhu, Jinwen Xie, and Lu Gan. "Compressive detection of unknown-parameters signals without signal reconstruction." Signal Processing 142 (January 2018): 114–18. http://dx.doi.org/10.1016/j.sigpro.2017.07.010.

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Ju, Mingchi, Yingjie Dai, Tailin Han, Yingzhi Wang, Bo Xu, and Xuan Liu. "Improved Compressed Sensing Reconfiguration Algorithm with Shockwave Dynamic Compensation Features." Shock and Vibration 2022 (May 5, 2022): 1–11. http://dx.doi.org/10.1155/2022/4035279.

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This paper proposes a regularized generalized orthogonal matching pursuit algorithm with dynamic compensation characteristics based on the application context of compressive sensing in shock wave signal testing. We add dynamic compensation denoising as a regularization condition to the reconstruction algorithm. The resonant noise is identified and suppressed according to the signal a priori characteristics, and the denoised signal is reconstructed directly from the original signal downsampling measurements. The signal-to-noise ratio of the output signal is improved while reducing the amount of data transmitted by the signal. The proposed algorithm’s applicability and internal parameter robustness are experimentally analyzed in the paper. We compare the proposed algorithm with similar compression-aware reconstruction and dynamic compensation algorithms under the shock tube test and measured shock wave signals. The results from the reconstruction signal-to-noise ratio and the number of measurements required for reconstruction verify the algorithm’s effectiveness in this paper.

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Liu, Xueyan, Limei Zhang, Yining Zhang, and Lishan Qiao. "A Photoacoustic Imaging Algorithm Based on Regularized Smoothed L0 Norm Minimization." Molecular Imaging 2021 (June 1, 2021): 1–13. http://dx.doi.org/10.1155/2021/6689194.

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The recently emerging technique of sparse reconstruction has received much attention in the field of photoacoustic imaging (PAI). Compressed sensing (CS) has large potential in efficiently reconstructing high-quality PAI images with sparse sampling signal. In this article, we propose a CS-based error-tolerant regularized smooth L0 (ReSL0) algorithm for PAI image reconstruction, which has the same computational advantages as the SL0 algorithm while having a higher degree of immunity to inaccuracy caused by noise. In order to evaluate the performance of the ReSL0 algorithm, we reconstruct the simulated dataset obtained from three phantoms. In addition, a real experimental dataset from agar phantom is also used to verify the effectiveness of the ReSL0 algorithm. Compared to three L0 norm, L1 norm, and TV norm-based CS algorithms for signal recovery and image reconstruction, experiments demonstrated that the ReSL0 algorithm provides a good balance between the quality and efficiency of reconstructions. Furthermore, the PSNR of the reconstructed image calculated by the introduced method was better than the other three methods. In particular, it can notably improve reconstruction quality in the case of noisy measurement.

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Wang, Yifan, Mingquan Zeng, Jinlong Gong, Ming Sun, Yifan Wang, Li Luo, Jiansheng Hu, and Junjie Ma. "IGBT Gate Waveform Acquisition Based on Compressed Sensing Technology." Journal of Physics: Conference Series 2370, no. 1 (November 1, 2022): 012009. http://dx.doi.org/10.1088/1742-6596/2370/1/012009.

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As the core device of power electronic equipment, IGBT (Insulated Gate Bipolar Transistor) is related to the reliability of the system, so its online health monitoring is particularly important. Due to the large amount of data and high sampling rate for IGBT online health monitoring, data transmission has become a major problem, the method of effectively reconstructing the original signal has become one of the current research hotspots. Based on the theory of compressed sensing, the acquisition of IGBT gate waveforms is studied in this paper. First, the theoretical principle of compressed sensing is expounded and analyzed, and the evaluation criteria for signal reconstruction performance are given. Then, from the theory and simulation, the sparseness of IGBT gate drive signals under different sparse bases and different measurement matrices are discussed and analyzed. Next, aiming at the shortcomings of the stagewise weak orthogonal matching pursuit algorithm, a SWOMP algorithm based on the backtracking strategy of different Sigmoid functions is proposed. Simulation and experimental results show that the improved SWOMP4 algorithm has the best reconstruction effect. Finally, the SWOMP4 algorithm is applied to the process of IGBT gate drive signal reconstruction. This paper mainly collects and reconstructs the IGBT gate drive signal through the improved SWOMP4 algorithm. The designed algorithm has high reconstruction accuracy and can be used in the IGBT module online health status system.

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Tolkova, Irina, and Holger Klinck. "Source separation with an acoustic vector sensor for terrestrial bioacoustics." Journal of the Acoustical Society of America 152, no. 2 (August 2022): 1123–34. http://dx.doi.org/10.1121/10.0013505.

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Passive acoustic monitoring is emerging as a low-cost, non-invasive methodology for automated species-level population surveys. However, systems for automating the detection and classification of vocalizations in complex soundscapes are significantly hindered by the overlap of calls and environmental noise. We propose addressing this challenge by utilizing an acoustic vector sensor to separate contributions from different sound sources. More specifically, we describe and implement an analytical pipeline consisting of (1) calculating direction-of-arrival, (2) decomposing the azimuth estimates into angular distributions for individual sources, and (3) numerically reconstructing source signals. Using both simulation and experimental recordings, we evaluate the accuracy of direction-of-arrival estimation through the active intensity method (AIM) against the baselines of white noise gain constraint beamforming (WNC) and multiple signal classification (MUSIC). Additionally, we demonstrate and compare source signal reconstruction with simple angular thresholding and a wrapped Gaussian mixture model. Overall, we show that AIM achieves higher performance than WNC and MUSIC, with a mean angular error of about 5°, robustness to environmental noise, flexible representation of multiple sources, and high fidelity in source signal reconstructions.

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Niang, Oumar, Abdoulaye Thioune, Éric Deléchelle, and Jacques Lemoine. "Spectral Intrinsic Decomposition Method for Adaptive Signal Representation." ISRN Signal Processing 2012 (December 13, 2012): 1–10. http://dx.doi.org/10.5402/2012/457152.

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We propose a new method called spectral intrinsic decomposition (SID) for the representation of nonlinear signals. This approach is based on the spectral decomposition of partial differential equation- (PDE-) based operators which interpolate the characteristic points of a signal. The SID’s components which are the eigenvectors of these PDE interpolation operators underlie the new signal decomposition-reconstruction method. The usefulness and the efficiency of this method is illustrated, in signal reconstruction or denoising aim, in some examples using artificial and pathological signals.

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Falcetelli, Francesco, Nicolas Venturini, Maria Barroso Romero, Marcias J. Martinez, Shashank Pant, and Enrico Troiani. "Broadband signal reconstruction for SHM: An experimental and numerical time reversal methodology." Journal of Intelligent Material Systems and Structures 32, no. 10 (February 23, 2021): 1043–58. http://dx.doi.org/10.1177/1045389x20972474.

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Structural Health Monitoring (SHM) aims to shift aircraft maintenance from a time-based to a condition-based approach. Within all the SHM techniques, Acoustic Emission (AE) allows for the monitoring of large areas by analyzing Lamb waves propagating in plate like structures. In this study, the authors proposed a Time Reversal (TR) methodology with the aim of reconstructing an original and unaltered signal from an AE event. Although the TR method has been applied in Narrow-Band (NwB) signal reconstruction, it fails when a Broad-Band (BdB) signal, such as a real AE event, is present. Therefore, a novel methodology based on the use of a Frequencies Compensation Transfer Function (FCTF), which is capable of reconstructing both NwB and real BdB signals, is presented. The study was carried out experimentally using several sensor layouts and materials with two different AE sources: (i) a Numerically Built Broadband (NBB) signal, (ii) a Pencil Lead Break (PLB). The results were validated numerically using Abaqus/CAETM with the implementation of absorbing boundaries to minimize edge reflections.

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Xie, Zhengguang, Hongwei Huang, and Xu Cai. "Matching Pursuit for Sparse Signal Reconstruction Based on Dual Thresholds." International Journal of Computer and Communication Engineering 5, no. 5 (2016): 341–49. http://dx.doi.org/10.17706/ijcce.2016.5.5.341-349.

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Song, Lei, Xin Rui Zhang, Lu Wei Su, and Yu Jiong Gu. "Fault Diagnosis Approach for Incipient Bearing Fault in Wind Turbine under Variable Conditions." Applied Mechanics and Materials 599-601 (August 2014): 312–20. http://dx.doi.org/10.4028/www.scientific.net/amm.599-601.312.

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For the extreme operating environment and variable working conditions of wind turbine and difficulty in finding fault feature accurately and promptly, a new incipient bearing fault method based on selecting optimal IMF (Intrinsic Mode Function) and Hilbert spectrum was proposed. Firstly, non-stationary time-domain signals are converted to stationary or quasi-stationary angle-domain signals; secondly, the EMD (Empirical Mode decomposition) method is used to decompose modal for angular waveform signal and obtain the IMF, and optimal IMF components are selected by cross-correlation criteria and kurtosis criteria to reconstructing signal. Finally, the reconstruction signal is processed by using Hilbert transformation to obtain the marginal spectrum. The paper finally verifies the effectiveness of the proposed method through experiment.

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Xiao, Hu, Shao, and Li. "A Low-Complexity Compressed Sensing Reconstruction Method for Heart Signal Biometric Recognition." Sensors 19, no. 23 (December 3, 2019): 5330. http://dx.doi.org/10.3390/s19235330.

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Biometric systems allow recognition and veriﬁcation of an individual through his or her physiological or behavioral characteristics. It is a growing ﬁeld of research due to the increasing demand for secure and trustworthy authentication systems. Compressed sensing is a data compression acquisition method that has been proposed in recent years. The sampling and compression of data is completed synchronously, avoiding waste of resources and meeting the requirements of small size and limited power consumption of wearable portable devices. In this work, a compression reconstruction method based on compression sensing was studied using bioelectric signals, which aimed to increase the limited resources of portable remote bioelectric signal recognition equipment. Using electrocardiograms (ECGs) and photoplethysmograms (PPGs) of heart signals as research data, an improved segmented weak orthogonal matching pursuit (OMP) algorithm was developed to compress and reconstruct the signals. Finally, feature values were extracted from the reconstructed signals for identification and analysis. The accuracy of the proposed method and the practicability of compression sensing in cardiac signal identification were verified. Experiments showed that the reconstructed ECG and PPG signal recognition rates were 95.65% and 91.31%, respectively, and that the residual value was less than 0.05 mV, which indicates that the proposed method can be effectively used for two bioelectric signal compression reconstructions.

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Stanković, Ljubiša, and Miloš Daković. "On a Gradient-Based Algorithm for Sparse Signal Reconstruction in the Signal/Measurements Domain." Mathematical Problems in Engineering 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/6212674.

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Sparse signals can be recovered from a reduced set of samples by using compressive sensing algorithms. In common compressive sensing methods the signal is recovered in the sparsity domain. A method for the reconstruction of sparse signals which reconstructs the missing/unavailable samples/measurements is recently proposed. This method can be efficiently used in signal processing applications where a complete set of signal samples exists. The missing samples are considered as the minimization variables, while the available samples are fixed. Reconstruction of the unavailable signal samples/measurements is preformed using a gradient-based algorithm in the time domain, with an adaptive step. Performance of this algorithm with respect to the step-size and convergence are analyzed and a criterion for the step-size adaptation is proposed in this paper. The step adaptation is based on the gradient direction angles. Illustrative examples and statistical study are presented. Computational efficiency of this algorithm is compared with other two commonly used gradient algorithms that reconstruct signal in the sparsity domain. Uniqueness of the recovered signal is checked using a recently introduced theorem. The algorithm application to the reconstruction of highly corrupted images is presented as well.

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Liu, Haoqiang, Hongbo Zhao, and Wenquan Feng. "Filtering-Based Regularized Sparsity Variable Step-Size Matching Pursuit and Its Applications in Vehicle Health Monitoring." Applied Sciences 11, no. 11 (May 24, 2021): 4816. http://dx.doi.org/10.3390/app11114816.

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Recent years have witnessed that real-time health monitoring for vehicles is gaining importance. Conventional monitoring scheme faces formidable challenges imposed by the massive signals generated with extremely heavy burden on storage and transmission. To address issues of signal sampling and transmission, compressed sensing (CS) has served as a promising solution in vehicle health monitoring, which performs signal sampling and compression simultaneously. Signal reconstruction is regarded as the most critical part of CS, while greedy reconstruction has been a research hotspot. However, the existing approaches either require prior knowledge of the sparse signal or perform with expensive computational complexity. To exploit the structure of the sparse signal, in this paper, we introduce an initial estimation approach for signal sparsity level firstly. Then, a novel greedy reconstruction algorithm that relies on no prior information of sparsity level while maintaining a good reconstruction performance is presented. The proposed algorithm integrates strategies of regularization and variable adaptive step size and further performs filtration. To verify the efficiency of the algorithm, typical voltage disturbance signals generated by the vehicle power system are taken as trial data. Preliminary simulation results demonstrate that the proposed algorithm achieves superior performance compared to the existing methods.

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Buccino, Alessio Paolo, Xinyue Yuan, Vishalini Emmenegger, Xiaohan Xue, Tobias Gänswein, and Andreas Hierlemann. "An automated method for precise axon reconstruction from recordings of high-density micro-electrode arrays." Journal of Neural Engineering 19, no. 2 (March 31, 2022): 026026. http://dx.doi.org/10.1088/1741-2552/ac59a2.

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Abstract Objective: Neurons communicate with each other by sending action potentials (APs) through their axons. The velocity of axonal signal propagation describes how fast electrical APs can travel. This velocity can be affected in a human brain by several pathologies, including multiple sclerosis, traumatic brain injury and channelopathies. High-density microelectrode arrays (HD-MEAs) provide unprecedented spatio-temporal resolution to extracellularly record neural electrical activity. The high density of the recording electrodes enables to image the activity of individual neurons down to subcellular resolution, which includes the propagation of axonal signals. However, axon reconstruction, to date, mainly relies on manual approaches to select the electrodes and channels that seemingly record the signals along a specific axon, while an automated approach to track multiple axonal branches in extracellular action-potential recordings is still missing. Approach: In this article, we propose a fully automated approach to reconstruct axons from extracellular electrical-potential landscapes, so-called ‘electrical footprints’ of neurons. After an initial electrode and channel selection, the proposed method first constructs a graph based on the voltage signal amplitudes and latencies. Then, the graph is interrogated to extract possible axonal branches. Finally, the axonal branches are pruned, and axonal action-potential propagation velocities are computed. Main results: We first validate our method using simulated data from detailed reconstructions of neurons, showing that our approach is capable of accurately reconstructing axonal branches. We then apply the reconstruction algorithm to experimental recordings of HD-MEAs and show that it can be used to determine axonal morphologies and signal-propagation velocities at high throughput. Significance: We introduce a fully automated method to reconstruct axonal branches and estimate axonal action-potential propagation velocities using HD-MEA recordings. Our method yields highly reliable and reproducible velocity estimations, which constitute an important electrophysiological feature of neuronal preparations.

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DEBBAL, S. M., and F. BEREKSI-REGUIG. "DISCRIMINATION OF PATHOLOGICAL CASES OF THE CARDIACS SOUNDS SIGNALS BY THE WAVELET TRANSFORM." Journal of Mechanics in Medicine and Biology 05, no. 04 (December 2005): 517–30. http://dx.doi.org/10.1142/s0219519405001679.

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In order to highlight the cardiac sounds or phonocardiogram (PCG) signals analysis according to their added murmur importance, we try to apply the wavelet transform in its multi resolution analysis version. We then look for reconstruction error between the original signal and the synthesized signal. In this case, the original PCG signal is decomposed over seven levels and the seventh detail of decomposition is considered as the synthesized signal. According to the results we obtain, the reconstruction error can be considered as an important parameter in the classification and discrimination of the pathological severity of the PCG signals.

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Bellazzi, Riccardo, Paolo Magni, and Giuseppe De Nicolao. "Gibbs Sampling for Signal Reconstruction." IFAC Proceedings Volumes 30, no. 2 (March 1997): 271–76. http://dx.doi.org/10.1016/s1474-6670(17)44583-6.

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Qiu, Kai, Xianghui Mao, Xinyue Shen, Xiaohan Wang, Tiejian Li, and Yuantao Gu. "Time-Varying Graph Signal Reconstruction." IEEE Journal of Selected Topics in Signal Processing 11, no. 6 (September 2017): 870–83. http://dx.doi.org/10.1109/jstsp.2017.2726969.

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Holland, Stephen D. "Thermographic signal reconstruction for vibrothermography." Infrared Physics & Technology 54, no. 6 (November 2011): 503–11. http://dx.doi.org/10.1016/j.infrared.2011.07.004.

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Balan, Radu, Pete Casazza, and Dan Edidin. "On signal reconstruction without phase." Applied and Computational Harmonic Analysis 20, no. 3 (May 2006): 345–56. http://dx.doi.org/10.1016/j.acha.2005.07.001.

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Chaoang, Xiao, Tang Hesheng, and Ren Yan. "Compressed sensing reconstruction for axial piston pump bearing vibration signals based on adaptive sparse dictionary model." Measurement and Control 53, no. 3-4 (January 25, 2020): 649–61. http://dx.doi.org/10.1177/0020294019898725.

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Aiming at the mechanical equipment in the fault diagnosis process, the traditional Shannon–Nyquist sampling theorem is used for data collection, which faces main problems of storage, transmission, and processing of mechanical vibration signals. This paper presents a novel method of compressed sensing reconstruction for axial piston pump bearing vibration signals based on the adaptive sparse dictionary model. First, vibration signals were divided into blocks, and an energy sequence was produced in accordance with the energy of each signal block. Second, the energy sequence of each signal block was classified by the quantum particle swarm optimization algorithm. Finally, the reconstruction of machinery vibration signals was carried out using the K-SVD dictionary algorithm. The average relative error of the reconstructed signal obtained by the proposed algorithm is 4.25%, and the reconstruction time decreases by 43.6% when the compression ratio is 1.6.

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Wei, Ziran, Jianlin Zhang, Zhiyong Xu, Yongmei Huang, Yong Liu, and Xiangsuo Fan. "Gradient Projection with Approximate L0 Norm Minimization for Sparse Reconstruction in Compressed Sensing." Sensors 18, no. 10 (October 9, 2018): 3373. http://dx.doi.org/10.3390/s18103373.

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In the reconstruction of sparse signals in compressed sensing, the reconstruction algorithm is required to reconstruct the sparsest form of signal. In order to minimize the objective function, minimal norm algorithm and greedy pursuit algorithm are most commonly used. The minimum L1 norm algorithm has very high reconstruction accuracy, but this convex optimization algorithm cannot get the sparsest signal like the minimum L0 norm algorithm. However, because the L0 norm method is a non-convex problem, it is difficult to get the global optimal solution and the amount of calculation required is huge. In this paper, a new algorithm is proposed to approximate the smooth L0 norm from the approximate L2 norm. First we set up an approximation function model of the sparse term, then the minimum value of the objective function is solved by the gradient projection, and the weight of the function model of the sparse term in the objective function is adjusted adaptively by the reconstruction error value to reconstruct the sparse signal more accurately. Compared with the pseudo inverse of L2 norm and the L1 norm algorithm, this new algorithm has a lower reconstruction error in one-dimensional sparse signal reconstruction. In simulation experiments of two-dimensional image signal reconstruction, the new algorithm has shorter image reconstruction time and higher image reconstruction accuracy compared with the usually used greedy algorithm and the minimum norm algorithm.

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Li, Zhong Qun, Kai Xie, Ying Hao Ye, Rong Bin Guo, and Xu Fei Wang. "Reconstruct the PWM Signal of Switching Power Supplies from the Near Field Radiation." Advanced Materials Research 718-720 (July 2013): 1792–96. http://dx.doi.org/10.4028/www.scientific.net/amr.718-720.1792.

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A non-contact testing method is proposed for encapsulation treated or insulation coated switching power supplies, which is implemented by reconstructing the pulse width modulation (PWM) signal of switching converters from the near field radiation of magnetic components. The radiation pattern of a buck converter is investigated, and the magnetic field sensing probe and PWM signal reconstruction circuit are also illustrated. The reconstruction testing is carried out on a buck converter; the duty cycle error of the reconstructed PWM signal is less than 0.2%, which validates the proposed method.

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Zhang, Hanfei, Shungen Xiao, and Ping Zhou. "A Matching Pursuit Algorithm for Backtracking Regularization Based on Energy Sorting." Symmetry 12, no. 2 (February 3, 2020): 231. http://dx.doi.org/10.3390/sym12020231.

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The signal reconstruction quality has become a critical factor in compressed sensing at present. This paper proposes a matching pursuit algorithm for backtracking regularization based on energy sorting. This algorithm uses energy sorting for secondary atom screening to delete individual wrong atoms through the regularized orthogonal matching pursuit (ROMP) algorithm backtracking. The support set is continuously updated and expanded during each iteration. While the signal energy distribution is not uniform, or the energy distribution is in an extreme state, the reconstructive performance of the ROMP algorithm becomes unstable if the maximum energy is still taken as the selection criterion. The proposed method for the regularized orthogonal matching pursuit algorithm can be adopted to improve those drawbacks in signal reconstruction due to its high reconstruction efficiency. The experimental results show that the algorithm has a proper reconstruction.

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Journal articles on the topic 'Reconstruction du signal'

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