An innovative multi-level singular value decomposition and compressed sensing based framework for noise removal from heart sounds

2017 ◽  
Vol 38 ◽  
pp. 34-43 ◽  
Author(s):  
Yineng Zheng ◽  
Xingming Guo ◽  
Hong Jiang ◽  
Benmei Zhou
Keyword(s):  
Singular Value Decomposition ◽  
Compressed Sensing ◽  
Noise Removal ◽  
Singular Value ◽  
Heart Sounds ◽  
Multi Level ◽  
Value Decomposition

Enhancement of 3D GPR datasets using singular value decomposition applied in 2D the spectral domain for clutter noise removal

2021 ◽  
Author(s):  
Rui Jorge Oliveira ◽  
Bento Caldeira ◽  
Teresa Teixidó ◽  
José Fernando Borges
Keyword(s):  
Singular Value Decomposition ◽  
Filter Design ◽  
Archaeological Site ◽  
Noise Removal ◽  
Computational Effort ◽  
Singular Value ◽  
Spectral Domain ◽  
Buried Structures ◽  
3D Gpr ◽  
Value Decomposition

<p>The ground-penetrating radar (GPR) datasets obtained in archaeological environments have substantial problems related the presence of clutter noise. These noisy reflections are generated by the heterogeneities of the ground and by the collapses of structures buried in the ground, that can prevent a good assessment of the subsurface with this method. The classic filtering operations available can fail to remove it effectively. This work presents an approach to filtering the GPR data in the 2D spectral domain through the singular value decomposition (SVD) factorization technique. The spectral domain present advantages such as the circular symmetry of the transformed data that turns easy the filter parametrisation and the constant computational effort whatever the amount of data considered. SVD allows the decreasing of the user dependency to parametrize the filter. The main propose of this method is to classify automatically the datasets into useful information, corresponding to buried structures, and noise, to remove the last. This approach was conceived based on the study of the GPR signal in the 2D spectral domain and the manual filter design. The tests were performed with different datasets, one from a laboratory experiment (controlled environment) and the other from a field acquisition in an archaeological site (uncontrolled environment) with subsequent excavation to proof the results. The proposed approach is effective to remove the clutter noise in the GPR datasets and constitutes a complementary operation to those already existing in the commercial software.</p><p> </p><p>Acknowledgment: The work was supported by the Portuguese Foundation for Science and Technology (FCT) project UIDB/04683/2020 - ICT (Institute of Earth Sciences) and by the INTERREG 2014-2020 Program, through the "Innovación abierta e inteligente en la EUROACE" Project, with the reference 0049_INNOACE_4_E.</p>

Compressed sensing MRI with singular value decomposition-based sparsity basis

2011 ◽  
Vol 56 (19) ◽  
pp. 6311-6325 ◽  
Author(s):  
Mingjian Hong ◽  
Yeyang Yu ◽  
Hua Wang ◽  
Feng Liu ◽  
Stuart Crozier
Keyword(s):  
Singular Value Decomposition ◽  
Compressed Sensing ◽  
Singular Value ◽  
Value Decomposition

scholarly journals Fusion Based Watermarking Scheme with Multi Level DWT & SVD

2018 ◽  
Vol 7 (3.6) ◽  
pp. 270
Author(s):  
Ch Hima Bindu ◽  
Maruturi Haribabu ◽  
K Veera Swamy
Keyword(s):  
Singular Value Decomposition ◽  
Mean Square Error ◽  
Singular Value ◽  
Cover Image ◽  
Discrete Wavelet ◽  
Mean Square ◽  
Watermark Image ◽  
Multi Level ◽  
Value Decomposition ◽  
Watermarking Scheme

This paper proposes “Fusion based watermarking with multi level DWT & singular value decomposition” has been implemented. In watermarking scheme, maintaining security and robustness is major hurdle. To address this issue we proposed a novel non blind embedding scheme with Discrete Wavelet transform (DWT) and Singular Value Decomposition (SVD) techniques. This paper details the design of the proposed watermarking scheme and analyses its robustness in the presence of various possible security attacks that involves in degrading the quality of watermark. In the beginning, the cover image color component (especially Red Component) is decomposed into LL, LH, HL, HH with 2 level DWT: these LH & HL coefficients are further divided into 8*8 blocks and each block is compared to build a fused coefficient. Apply SVD on fused coefficient of cover image and watermark image to embed the singular values with sigmod scaling factor. Finally the watermarked image is generated, after applying inverse SVD & 2 level DWT. At receiver side the inverse process has been implemented to extract watermark image. The efficiency and performance of the proposed method is verified with   Peak Signal to Noise Ratio (PSNR), Root mean square error (RMSE) and Mean Square error (MSE) and compared with recent works of santhi [12] and harsha [13].  

Partial Discharge Random Noise Removal Using Hankel Matrix-Based Fast Singular Value Decomposition

2020 ◽  
Vol 69 (7) ◽  
pp. 4093-4102 ◽  
Author(s):  
Suganya Govindarajan ◽  
Jayalalitha Subbaiah ◽  
Andrea Cavallini ◽  
Kannan Krithivasan ◽  
Jaikanth Jayakumar
Keyword(s):  
Singular Value Decomposition ◽  
Random Noise ◽  
Partial Discharge ◽  
Hankel Matrix ◽  
Noise Removal ◽  
Singular Value ◽  
Value Decomposition
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