Fast Sparse Reconstruction of Sound Field Via Bayesian Compressive Sensing

2019 ◽  
Vol 141 (4) ◽  
Author(s):  
Ding-Yu Hu ◽  
Xin-Yue Liu ◽  
Yue Xiao ◽  
Yu Fang
Keyword(s):  
Compressive Sensing ◽  
Sound Field ◽  
Sparse Reconstruction ◽  
Limited Attention ◽  
Reconstruction Method ◽  
Sound Sources ◽  
Computation Efficiency ◽  
Reconstruction Methods ◽  
Spatially Extended ◽  
Value Decomposition

To overcome the contradiction between the resolution and the measurement cost, various algorithms for reconstructing the sound field with sparse measurement have been developed. However, limited attention is paid to the computation efficiency. In this study, a fast sparse reconstruction method is proposed based on the Bayesian compressive sensing. First, the reconstruction problem is modeled by a sparse decomposition of the sound field via singular value decomposition. Then, the Bayesian compressive sensing is adapted to reconstruct the sound field with sparse measurement of sound pressure. Numerical results demonstrate that the proposed method is applicable to either the spatially sparse distributed sound sources or the spatially extended sound sources. And comparisons with other two sparse reconstruction methods show that the proposed one has the advantages in terms of reconstruction accuracy and computational efficiency. In addition, as it is developed in the framework of multitask compressive sensing, the method can use multiple snapshots to perform reconstruction, which greatly enhances the robustness to noise. The validity and the advantage of the proposed method are further proved by experimental results.

Sparse Reconstruction Method of Non-Uniform Sampling and its Application in Blade Tip Timing System

2020 ◽  
Author(s):  
Jie Tian ◽  
Xiaopu Zhang ◽  
Yong Chen ◽  
Peter Russhard ◽  
Hua Ouyang
Keyword(s):  
Compressive Sensing ◽  
Sparse Reconstruction ◽  
Reconstruction Method ◽  
Blade Vibration ◽  
Uniform Sampling ◽  
Blade Vibrations ◽  
Timing Data ◽  
Blade Tip ◽  
Simultaneous Identification ◽  
Multi Mode

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.

scholarly journals A sparse reconstruction method for the estimation of multi-resolution emission fields via atmospheric inversion

2015 ◽  
Vol 8 (4) ◽  
pp. 1259-1273 ◽  
Author(s):  
J. Ray ◽  
J. Lee ◽  
V. Yadav ◽  
S. Lefantzi ◽  
A. M. Michalak ◽  
...  
Keyword(s):  
Field Model ◽  
Computational Cost ◽  
Estimation Method ◽  
Sparse Reconstruction ◽  
Model Parameters ◽  
Reconstruction Method ◽  
Reconstruction Procedure ◽  
Reconstruction Methods ◽  
Atmospheric Inversion ◽  
Atmospheric Inversions

Abstract. Atmospheric inversions are frequently used to estimate fluxes of atmospheric greenhouse gases (e.g., biospheric CO2 flux fields) at Earth's surface. These inversions typically assume that flux departures from a prior model are spatially smoothly varying, which are then modeled using a multi-variate Gaussian. When the field being estimated is spatially rough, multi-variate Gaussian models are difficult to construct and a wavelet-based field model may be more suitable. Unfortunately, such models are very high dimensional and are most conveniently used when the estimation method can simultaneously perform data-driven model simplification (removal of model parameters that cannot be reliably estimated) and fitting. Such sparse reconstruction methods are typically not used in atmospheric inversions. In this work, we devise a sparse reconstruction method, and illustrate it in an idealized atmospheric inversion problem for the estimation of fossil fuel CO2 (ffCO2) emissions in the lower 48 states of the USA. Our new method is based on stagewise orthogonal matching pursuit (StOMP), a method used to reconstruct compressively sensed images. Our adaptations bestow three properties to the sparse reconstruction procedure which are useful in atmospheric inversions. We have modified StOMP to incorporate prior information on the emission field being estimated and to enforce non-negativity on the estimated field. Finally, though based on wavelets, our method allows for the estimation of fields in non-rectangular geometries, e.g., emission fields inside geographical and political boundaries. Our idealized inversions use a recently developed multi-resolution (i.e., wavelet-based) random field model developed for ffCO2 emissions and synthetic observations of ffCO2 concentrations from a limited set of measurement sites. We find that our method for limiting the estimated field within an irregularly shaped region is about a factor of 10 faster than conventional approaches. It also reduces the overall computational cost by a factor of 2. Further, the sparse reconstruction scheme imposes non-negativity without introducing strong nonlinearities, such as those introduced by employing log-transformed fields, and thus reaps the benefits of simplicity and computational speed that are characteristic of linear inverse problems.

Global Inhomogeneity Index Evaluation of a DCT-based EIT Lung Imaging

2020 ◽  
Vol 6 (3) ◽  
pp. 36-39
Author(s):  
Rongqing Chen ◽  
Knut Möller
Keyword(s):  
Classical Method ◽  
Lung Imaging ◽  
Imaging Method ◽  
Reconstruction Method ◽  
Essential Information ◽  
Preliminary Results ◽  
Reconstruction Methods ◽  
Comparison Results ◽  
Index Evaluation ◽  
Inhomogeneity Index

AbstractPurpose: To evaluate a novel structural-functional DCT-based EIT lung imaging method against the classical EIT reconstruction. Method: Taken retrospectively from a former study, EIT data was evaluated using both reconstruction methods. For different phases of ventilation, EIT images are analyzed with respect to the global inhomogeneity (GI) index for comparison. Results: A significant less variant GI index was observed in the DCTbased method, compared to the index from classical method. Conclusion: The DCT-based method generates more accurate lung contour yet decreasing the essential information in the image which affects the GI index. These preliminary results must be consolidated with more patient data in different breathing states.

scholarly journals Compressive Sensing Based Sampling and Reconstruction for Wireless Sensor Array Network

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Ming Yin ◽  
Kai Yu ◽  
Zhi Wang
Keyword(s):  
Compressive Sensing ◽  
Sensor Array ◽  
Superior Performance ◽  
Wireless Sensor ◽  
Prototype System ◽  
Reconstruction Method ◽  
Compressed Sampling ◽  
Data Volume ◽  
Simulation Results ◽  
Sparsity Structure

For low-power wireless systems, transmission data volume is a key property, which influences the energy cost and time delay of transmission. In this paper, we introduce compressive sensing to propose a compressed sampling and collaborative reconstruction framework, which enables real-time direction of arrival estimation for wireless sensor array network. In sampling part, random compressed sampling and 1-bit sampling are utilized to reduce sample data volume while making little extra requirement for hardware. In reconstruction part, collaborative reconstruction method is proposed by exploiting similar sparsity structure of acoustic signal from nodes in the same array. Simulation results show that proposed framework can reach similar performances as conventional DoA methods while requiring less than 15% of transmission bandwidth. Also the proposed framework is compared with some data compression algorithms. While simulation results show framework’s superior performance, field experiment data from a prototype system is presented to validate the results.

scholarly journals Compressive Sensing in Signal Processing: Algorithms and Transform Domain Formulations

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Irena Orović ◽  
Vladan Papić ◽  
Cornel Ioana ◽  
Xiumei Li ◽  
Srdjan Stanković
Keyword(s):  
Signal Processing ◽  
Fourier Transform ◽  
Compressive Sensing ◽  
Signal Reconstruction ◽  
Signal Acquisition ◽  
Transform Domain ◽  
Hermite Transform ◽  
Time Frequency ◽  
Reconstruction Methods ◽  
Fourier Transform Domain

Compressive sensing has emerged as an area that opens new perspectives in signal acquisition and processing. It appears as an alternative to the traditional sampling theory, endeavoring to reduce the required number of samples for successful signal reconstruction. In practice, compressive sensing aims to provide saving in sensing resources, transmission, and storage capacities and to facilitate signal processing in the circumstances when certain data are unavailable. To that end, compressive sensing relies on the mathematical algorithms solving the problem of data reconstruction from a greatly reduced number of measurements by exploring the properties of sparsity and incoherence. Therefore, this concept includes the optimization procedures aiming to provide the sparsest solution in a suitable representation domain. This work, therefore, offers a survey of the compressive sensing idea and prerequisites, together with the commonly used reconstruction methods. Moreover, the compressive sensing problem formulation is considered in signal processing applications assuming some of the commonly used transformation domains, namely, the Fourier transform domain, the polynomial Fourier transform domain, Hermite transform domain, and combined time-frequency domain.

scholarly journals POLARIMETRIC SAR TOMOGRAPHY USING l2,1 MIXED NORM SPARSE RECONSTRUCTION METHOD

2012 ◽  
Vol 130 ◽  
pp. 105-130 ◽  
Author(s):  
Shiqi Xing ◽  
Dahai Dai ◽  
Yongzhen Li ◽  
Xuesong Wang
Keyword(s):  
Sparse Reconstruction ◽  
Mixed Norm ◽  
Reconstruction Method ◽  
Polarimetric Sar

Numerical Study on Air-Reed Instruments With LES

2011 ◽  
Author(s):  
Kin’ya Takahashi ◽  
Masataka Miyamoto ◽  
Yasunori Ito ◽  
Toshiya Takami ◽  
Taizo Kobayashi ◽  
...  
Keyword(s):  
Numerical Study ◽  
Sound Field ◽  
Acoustic Analogy ◽  
Empirical Theory ◽  
Sound Sources ◽  
Aerodynamic Sound ◽  
Eddy Simulation ◽  
Large Eddy ◽  
Semi Empirical ◽  
2D And 3D

The acoustic mechanisms of 2D and 3D edge tones and a 2D small air-reed instrument have been studied numerically with compressible Large Eddy Simulation (LES). Sound frequencies of the 2D and 3D edge tones obtained numerically change with the jet velocity well following Brown’s semi-empirical equation, while that of the 2D air-reed instrument behaves in a different manner and obeys the semi-empirical theory, so called Cremer-Ising-Coltman theory. We have also calculated aerodynamic sound sources for the 2D edge tone and the 2D air-reed instrument relying on Ligthhill’s acoustic analogy and have discussed similarities and differences between them. The sound source of the air-reed instrument is more localized around the open mouth compared with that of the edge tone due to the effect of the strong sound field excited in the resonator.

scholarly journals Novel Reconstruction Using Roux-en-Y Plus Antral Obstruction Following Proximal Gastrectomy: Feasibility and Quality of Life Benefits

2019 ◽  
Author(s):  
Dejun Yang ◽  
Changming Wang ◽  
Hongbing Fu ◽  
Ziran Wei ◽  
Xin Zhang ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Reflux Esophagitis ◽  
Proximal Gastrectomy ◽  
Partial Gastrectomy ◽  
Incidence Rates ◽  
Reconstruction Method ◽  
Reflux Symptoms ◽  
Reconstruction Methods ◽  
Postoperative Reflux

Abstract Background and Aims Routine gastroesophagostomy has been shown to have adverse effects on the recovery of digestive functions and quality of life because patients typically experience reflux symptoms after proximal gastrectomy. This study was performed to assess the feasibility and quality of life benefits of a novel reconstruction method termed Roux-en-Y anastomosis plus antral obstruction (RYAO) following proximal partial gastrectomy. Methods A total of 73 patients who underwent proximal gastrectomy from June 2015 to June 2017 were divided into two groups according to digestive reconstruction methods [RYAO (37 patients) and conventional esophagogastric anastomosis with pyloroplasty (EGPP, 36 patients)]. Clinical data were compared between the two groups retrospectively. Results The mean operative time for digestive reconstruction was slightly longer in the RYAO group than in the EGPP group. However, the incidence of postoperative short-term complications did not differ between the RYAO and the EGPP groups. At the 6-month follow-up, the incidence rates of both reflux esophagitis and gastritis were lower in the RYAO group than in the EGPP group (P = 0.002). Additionally, body weight recovery was better in the RYAO group (P = 0.028). The scale tests indicated that compared with the patients in the EGPP group, the patients in the RYAO group had significantly reduced reflux, nausea and vomiting and reported improvements in their overall health status and quality of life (all P < 0.05). Conclusion RYAO reconstruction may be a feasible procedure to reduce postoperative reflux symptoms and the incidence of reflux esophagitis and gastritis, thus improving patient quality of life after proximal gastrectomy.

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