scholarly journals A sinusoidal signal reconstruction method for the inversion of the mel-spectrogram

2021 ◽  
Author(s):  
Anastasia Natsiou ◽  
Sean O'Leary
Keyword(s):  
Signal Reconstruction ◽  
Sinusoidal Signal ◽  
Reconstruction Method

Image Reconstruction with the Fourier Coefficients for Magnetic Induction Tomography

2020 ◽  
Vol 16 (2) ◽  
pp. 156-163
Author(s):  
Jingwen Wang ◽  
Xu Wang ◽  
Dan Yang ◽  
Kaiyang Wang
Keyword(s):  
Inverse Problem ◽  
Image Reconstruction ◽  
Magnetic Induction ◽  
Fourier Coefficients ◽  
Signal Reconstruction ◽  
Reconstruction Algorithm ◽  
Reconstruction Method ◽  
Measurement Equation ◽  
Image Reconstruction Method ◽  
Magnetic Induction Tomography

Background: Image reconstruction of magnetic induction tomography (MIT) is a typical ill-posed inverse problem, which means that the measurements are always far from enough. Thus, MIT image reconstruction results using conventional algorithms such as linear back projection and Landweber often suffer from limitations such as low resolution and blurred edges. Methods: In this paper, based on the recent finite rate of innovation (FRI) framework, a novel image reconstruction method with MIT system is presented. Results: This is achieved through modeling and sampling the MIT signals in FRI framework, resulting in a few new measurements, namely, fourier coefficients. Because each new measurement contains all the pixel position and conductivity information of the dense phase medium, the illposed inverse problem can be improved, by rebuilding the MIT measurement equation with the measurement voltage and the new measurements. Finally, a sparsity-based signal reconstruction algorithm is presented to reconstruct the original MIT image signal, by solving this new measurement equation. Conclusion: Experiments show that the proposed method has better indicators such as image error and correlation coefficient. Therefore, it is a kind of MIT image reconstruction method with high accuracy.

scholarly journals Fractional Differential Equation-Based Instantaneous Frequency Estimation for Signal Reconstruction

2021 ◽  
Vol 5 (3) ◽  
pp. 83
Author(s):  
Bilgi Görkem Yazgaç ◽  
Mürvet Kırcı
Keyword(s):  
Differential Equation ◽  
Fractional Differential Equation ◽  
Frequency Estimation ◽  
Signal Reconstruction ◽  
Reconstruction Method ◽  
Reconstruction Algorithms ◽  
Instantaneous Frequency Estimation ◽  
Proposed Model ◽  
Fractional Differential ◽  
Instantaneous Frequencies

In this paper, we propose a fractional differential equation (FDE)-based approach for the estimation of instantaneous frequencies for windowed signals as a part of signal reconstruction. This approach is based on modeling bandpass filter results around the peaks of a windowed signal as fractional differential equations and linking differ-integrator parameters, thereby determining the long-range dependence on estimated instantaneous frequencies. We investigated the performance of the proposed approach with two evaluation measures and compared it to a benchmark noniterative signal reconstruction method (SPSI). The comparison was provided with different overlap parameters to investigate the performance of the proposed model concerning resolution. An additional comparison was provided by applying the proposed method and benchmark method outputs to iterative signal reconstruction algorithms. The proposed FDE method received better evaluation results in high resolution for the noniterative case and comparable results with SPSI with an increasing iteration number of iterative methods, regardless of the overlap parameter.

Signal reconstruction and frequency estimation of a biased and noisy sinusoidal signal

2010 ◽  
Author(s):  
Da Zheng ◽  
Lei Jiang ◽  
Zhengyun Ren ◽  
Jian'an Fang ◽  
Xiumei Wu
Keyword(s):  
Frequency Estimation ◽  
Signal Reconstruction ◽  
Sinusoidal Signal

Gravity anomaly reconstruction based on nonequispaced Fourier transform

Geophysics ◽  
2019 ◽  
Vol 84 (6) ◽  
pp. G83-G92
Author(s):  
Ya Xu ◽  
Fangzhou Nan ◽  
Weiping Cao ◽  
Song Huang ◽  
Tianyao Hao
Keyword(s):  
Fourier Transform ◽  
Compressed Sensing ◽  
Gravity Data ◽  
Signal Reconstruction ◽  
Gaussian Function ◽  
Reconstruction Algorithm ◽  
Data Reconstruction ◽  
Reconstruction Method ◽  
Grid Data ◽  
2D Data

Irregular sampled gravity data are often interpolated into regular grid data for convenience of data processing and interpretation. The compressed sensing theory provides a signal reconstruction method that can recover a sparse signal from far fewer samples. We have introduced a gravity data reconstruction method based on the nonequispaced Fourier transform (NFT) in the framework of compressed sensing theory. We have developed a sparsity analysis and a reconstruction algorithm with an iterative cooling thresholding method and applied to the gravity data of the Bishop model. For 2D data reconstruction, we use two methods to build the weighting factors: the Gaussian function and the Voronoi method. Both have good reconstruction results from the 2D data tests. The 2D reconstruction tests from different sampling rates and comparison with the minimum curvature and the kriging methods indicate that the reconstruction method based on the NFT has a good reconstruction result even with few sampling data.

Accuracy-Complexity Trade-Off for the Signal Reconstruction of Multifunctional Sensors

2013 ◽  
Vol 706-708 ◽  
pp. 618-622
Author(s):  
Xian Guang Fan ◽  
Xin Wang ◽  
Jing Lin Wu ◽  
Yong Zuo
Keyword(s):  
Optimization Design ◽  
Signal Reconstruction ◽  
Spline Approximation ◽  
Significant Loss ◽  
Reconstruction Method ◽  
Reconstruction Accuracy ◽  
Trade Off ◽  
B Spline ◽  
On Chip ◽  
Chip Signal

The on-chip signal reconstruction method based on B-spline approximation and Extended Kalman Filter (EKF) for multifunctional sensors has been studied previously. In this paper, we focus on the design for reducing the complexity of the reconstruction method without significant loss of reconstruction accuracy. The two-objective optimization design framework is proposed, where the reconstruction accuracy and complexity are considering as two conflicting costs to be decreased jointly. Genetic Algorithm (GA) is presented to achieve the accuracy-complexity trade-off by optimizing the B-spline structure parameters, i.e. the dimensions of knot vectors. The experimental results show that the proposed method provides a good improvement to the B-spline and EKF based on-chip signal reconstruction method.

scholarly journals A Non-Uniformly Under-Sampled Blade Tip-Timing Signal Reconstruction Method for Blade Vibration Monitoring

Sensors ◽  
2015 ◽  
Vol 15 (2) ◽  
pp. 2419-2437 ◽  
Author(s):  
Zheng Hu ◽  
Jun Lin ◽  
Zhong-Sheng Chen ◽  
Yong-Min Yang ◽  
Xue-Jun Li
Keyword(s):  
Signal Reconstruction ◽  
Vibration Monitoring ◽  
Reconstruction Method ◽  
Blade Vibration ◽  
Blade Tip
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