scholarly journals Research on Sparse Representation Method of Acoustic Microimaging Signals

2022 ◽  
Vol 12 (2) ◽  
pp. 642
Author(s):  
Kun Wang ◽  
Tao Leng ◽  
Jie Mao ◽  
Guoxuan Lian ◽  
Changzhi Zhou
Keyword(s):  
Sparse Representation ◽  
Frequency Domain ◽  
Matching Pursuit ◽  
Super Resolution ◽  
Electronic Packages ◽  
Destructive Testing ◽  
Time Frequency ◽  
Resolution Imaging ◽  
Representation Method ◽  
Longitudinal Resolution

Acoustic microimaging (AMI), a technology for high-resolution imaging of materials using a scanning acoustic microscope, has been widely used for non-destructive testing and evaluation of electronic packages. Recently, the internal features and defects of electronic packages have reached the resolution limits of conventional time domain or frequency domain AMI methods with the miniaturization of electronic packages. Various time-frequency domain AMI methods have been developed to achieve super-resolution. In this paper, the sparse representation of AMI signals is studied, and a constraint dictionary-based sparse representation (CD-SR) method is proposed. First, the time-frequency parameters of the atom dictionary are constrained according to the AMI signal to constitute a constraint dictionary. Then, the AMI signal is sparsely decomposed using the matching pursuit algorithm, and echoes selection and echoes reconstruction are performed. The performance of CD-SR was quantitatively evaluated by simulated and experimental ultrasonic A-scan signals. The results demonstrated that CD-SR has superior longitudinal resolution and robustness.

scholarly journals Super-resolution for doubly-dispersive channel estimation

2021 ◽  
Vol 19 (2) ◽  
Author(s):  
Robert Beinert ◽  
Peter Jung ◽  
Gabriele Steidl ◽  
Tom Szollmann
Keyword(s):  
Matching Pursuit ◽  
Super Resolution ◽  
Gradient Algorithm ◽  
Linear Combinations ◽  
Time Frequency ◽  
Dispersive Channel ◽  
Adaptive Grid Refinement ◽  
High Resolution Radar ◽  
Matching Pursuit Algorithm ◽  
Refinement Strategy

AbstractIn this work we consider the problem of identification and reconstruction of doubly-dispersive channel operators which are given by finite linear combinations of time-frequency shifts. Such operators arise as time-varying linear systems for example in radar and wireless communications. In particular, for information transmission in highly non-stationary environments the channel needs to be estimated quickly with identification signals of short duration and for vehicular application simultaneous high-resolution radar is desired as well. We consider the time-continuous setting and prove an exact resampling reformulation of the involved channel operator when applied to a trigonometric polynomial as identifier in terms of sparse linear combinations of real-valued atoms. Motivated by recent works of Heckel et al. we present an exact approach for off-the-grid super-resolution which allows to perform the identification with realizable signals having compact support. Then we show how an alternating descent conditional gradient algorithm can be adapted to solve the reformulated problem. Numerical examples demonstrate the performance of this algorithm, in particular in comparison with a simple adaptive grid refinement strategy and an orthogonal matching pursuit algorithm.

scholarly journals A Modified Sparse Representation Method for Facial Expression Recognition

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Wei Wang ◽  
LiHong Xu
Keyword(s):  
Facial Expression ◽  
Sparse Representation ◽  
Facial Expression Recognition ◽  
Matching Pursuit ◽  
Iteration Process ◽  
Orthogonal Matching Pursuit ◽  
Expression Recognition ◽  
Training Samples ◽  
Reduction Methods ◽  
Representation Method

In this paper, we carry on research on a facial expression recognition method, which is based on modified sparse representation recognition (MSRR) method. On the first stage, we use Haar-like+LPP to extract feature and reduce dimension. On the second stage, we adopt LC-K-SVD (Label Consistent K-SVD) method to train the dictionary, instead of adopting directly the dictionary from samples, and add block dictionary training into the training process. On the third stage, stOMP (stagewise orthogonal matching pursuit) method is used to speed up the convergence of OMP (orthogonal matching pursuit). Besides, a dynamic regularization factor is added to iteration process to suppress noises and enhance accuracy. We verify the proposed method from the aspect of training samples, dimension, feature extraction and dimension reduction methods and noises in self-built database and Japan’s JAFFE and CMU’s CK database. Further, we compare this sparse method with classic SVM and RVM and analyze the recognition effect and time efficiency. The result of simulation experiment has shown that the coefficient of MSRR method contains classifying information, which is capable of improving the computing speed and achieving a satisfying recognition result.

scholarly journals A fractional Fourier transform analysis of the scattering of ultrasonic waves

2015 ◽  
Vol 471 (2175) ◽  
pp. 20140958 ◽  
Author(s):  
Katherine M.M. Tant ◽  
Anthony J. Mulholland ◽  
Matthias Langer ◽  
Anthony Gachagan
Keyword(s):  
Fourier Transform ◽  
Frequency Domain ◽  
Fractional Fourier Transform ◽  
Signal To Noise Ratio ◽  
Homogeneous Solution ◽  
Ultrasonic Waves ◽  
Destructive Testing ◽  
Time Frequency ◽  
Forcing Function ◽  
Linear Chirp

Many safety critical structures, such as those found in nuclear plants, oil pipelines and in the aerospace industry, rely on key components that are constructed from heterogeneous materials. Ultrasonic non-destructive testing (NDT) uses high-frequency mechanical waves to inspect these parts, ensuring they operate reliably without compromising their integrity. It is possible to employ mathematical models to develop a deeper understanding of the acquired ultrasonic data and enhance defect imaging algorithms. In this paper, a model for the scattering of ultrasonic waves by a crack is derived in the time–frequency domain. The fractional Fourier transform (FrFT) is applied to an inhomogeneous wave equation where the forcing function is prescribed as a linear chirp, modulated by a Gaussian envelope. The homogeneous solution is found via the Born approximation which encapsulates information regarding the flaw geometry. The inhomogeneous solution is obtained via the inverse Fourier transform of a Gaussian-windowed linear chirp excitation. It is observed that, although the scattering profile of the flaw does not change, it is amplified. Thus, the theory demonstrates the enhanced signal-to-noise ratio permitted by the use of coded excitation, as well as establishing a time–frequency domain framework to assist in flaw identification and classification.

Photoswitchable fluorescent nanoparticles and their emerging applications

Nanoscale ◽  
2015 ◽  
Vol 7 (46) ◽  
pp. 19342-19357 ◽  
Author(s):  
Yuanlin Zhang ◽  
Kaiquan Zhang ◽  
Jie Wang ◽  
Zhiyuan Tian ◽  
Alexander D. Q. Li
Keyword(s):  
Frequency Domain ◽  
Super Resolution ◽  
Fluorescent Nanoparticles ◽  
Recent Advances ◽  
Dual Color ◽  
Resolution Imaging ◽  
Novel Applications

This review summarizes the recent advances in photoswitchable nanoparticles and their novel applications in super resolution imaging, frequency domain imaging, and anti-phase dual color correlation imaging.

Sign in / Sign up

Export Citation Format

Share Document