scholarly journals Observation Strategies Based on Singular Value Decomposition for Ocean Analysis and Forecast

Water ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3445
Author(s):  
Maria Fattorini ◽  
Carlo Brandini
Keyword(s):  
Singular Value Decomposition ◽  
Maximum Error ◽  
Ocean Model ◽  
Singular Value ◽  
Practical Applications ◽  
Positioning Strategy ◽  
Value Decomposition ◽  
Observation Strategies

In this article, we discuss possible observing strategies for a simplified ocean model (Double Gyre (DG)), used as a preliminary tool to understand the observation needs for real analysis and forecasting systems. Observations are indeed fundamental to improve the quality of forecasts when data assimilation techniques are employed to obtain reliable analysis results. In addition, observation networks, particularly in situ observations, are expensive and require careful positioning of instruments. A possible strategy to locate observations is based on Singular Value Decomposition (SVD). SVD has many advantages when a variational assimilation method such as the 4D-Var is available, with its computation being dependent on the tangent linear and adjoint models. SVD is adopted as a method to identify areas where maximum error growth occurs and assimilating observations can give particular advantages. However, an SVD-based observation positioning strategy may not be optimal; thus, we introduce other criteria based on the correlation between points, as the information observed on neighboring locations can be redundant. These criteria are easily replicable in practical applications, as they require rather standard studies to obtain prior information.

scholarly journals Sampling strategies based on the Singular Value Decomposition for ocean analysis and forecast

2018 ◽  
Author(s):  
Maria Fattorini ◽  
Carlo Brandini
Keyword(s):  
Singular Value Decomposition ◽  
Sampling Strategy ◽  
Maximum Error ◽  
Ocean Model ◽  
Singular Value ◽  
Sampling Strategies ◽  
Analysis Model ◽  
Practical Applications ◽  
Value Decomposition ◽  
Observation Networks

Abstract. In this article we discuss some possible optimal sampling strategies for a simplified ocean model, used as a preliminary tool to understand the observation needs for real analysis and forecasting systems. Indeed, observations are mostly useful for improving the reliability of forecasting models, which require upstream an analysis model in which Data Assimilation techniques are used. In addition, observation networks and in particular in-situ networks are expensive and require careful positioning of a limited number of observation instruments. As in other literature studies, the Singular Value Decomposition has been adopted, which has many advantages, especially when we dispose of a variational assimilation method like the 4D-Var, also because the calculation of Singular Vectors and Singular Values is linked to the availability of tangent linear and adjoint models. SVD has been adopted here as a method for identifying areas where maximum error growth occurs, within which sampling gives particular advantages. However, a SVD-based sampling strategy may not be unique, and we need to introduce other criteria, based on the correlation between points, since the information observed on neighbouring points can be redundant. The criteria adopted are easily replicable in practical applications, and require rather standard studies to obtain prior information (for example, climatological and correlation studies), to be carried out in order to properly design observation networks.

scholarly journals Singular value automata and approximate minimization

2019 ◽  
Vol 29 (9) ◽  
pp. 1444-1478 ◽  
Author(s):  
Borja Balle ◽  
Prakash Panangaden ◽  
Doina Precup
Keyword(s):  
Singular Value Decomposition ◽  
Finite Rank ◽  
Singular Value ◽  
Linear Operators ◽  
Hankel Matrices ◽  
Weighted Automata ◽  
Approximate Minimization ◽  
Value Decomposition ◽  
Svd Decomposition

AbstractThe present paper uses spectral theory of linear operators to construct approximatelyminimal realizations of weighted languages. Our new contributions are: (i) a new algorithm for the singular value decomposition (SVD) decomposition of finite-rank infinite Hankel matrices based on their representation in terms of weighted automata, (ii) a new canonical form for weighted automata arising from the SVD of its corresponding Hankelmatrix, and (iii) an algorithmto construct approximateminimizations of given weighted automata by truncating the canonical form.We give bounds on the quality of our approximation.

scholarly journals A Novel Blind and Robust Video Watermarking Technique in Fast Motion Frames Based on SVD and MR-SVD

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Imen Nouioua ◽  
Nouredine Amardjia ◽  
Sarra Belilita
Keyword(s):  
Singular Value Decomposition ◽  
Video Quality ◽  
Singular Value ◽  
Video Watermarking ◽  
Digital Video Watermarking ◽  
Video Frames ◽  
Fast Motion ◽  
Good Transparency ◽  
Value Decomposition

In this work, a novel and efficient digital video watermarking technique based on the Singular Value Decomposition performed in the Multiresolution Singular Value Decomposition domain is proposed. While most of the existing watermarking schemes embed the watermark in all the video frames, which is time-consuming and also affects the perceptibly of the video quality, the proposed method chooses only the fast motion frames in each shot to host the watermark. In doing so, the number of frames to be processed is consequently reduced and a better quality of the watermarked video is also ensured since the human visual system cannot notice the variations in fast moving regions. The watermark information is embedded by Quantization Index Modulation which is a blind watermarking algorithm. The experimental results demonstrate that the proposed method can achieve a very good transparency, while being robust against various kinds of attacks such as filtering, noising, compression, and frame collusion. Compared with several methods found in the literature, the proposed method gives a better robustness.

Singular Value Decomposition-Based Generalized Side Lobe Canceller Beamforming Method for Ultrasound Imaging

2018 ◽  
Vol 32 (07) ◽  
pp. 1854017 ◽  
Author(s):  
Han-Wu Luo ◽  
Fang Li ◽  
Guang Sun ◽  
Shi-Gang Cui ◽  
Nan Lin
Keyword(s):  
Singular Value Decomposition ◽  
Contrast Ratio ◽  
Side Lobe ◽  
Minimum Variance ◽  
Singular Value ◽  
Signal Space ◽  
Velocity Error ◽  
Sample Covariance ◽  
Value Decomposition

In the previous studies, eigenspace-based minimum variance (ESBMV) algorithms were proposed, however, the quality of the algorithm will degrade in low signal to noise occasions. In this study, a singular value decomposition generalized side lobe canceller (SVD-GSC) beamforming method based on the GSC is proposed. The sample covariance matrix is eigendecomposed, and a kind of further SVD is introduced to establish the noise space and the signal space, respectively. After that, the weighting vectors acquired by GSC are projected into the left singular space of the desired signal space. The performance of the proposed method is investigated by both of the simulation and experimental data. And the sound velocity error is also investigated in this paper. The imaging quality of point targets are measured by the [Formula: see text][Formula: see text]dB main lobe width and the peak side lobe (PSL). The contrast ratio (CR) is introduced to describe the quality of cyst phantom. Both the point targets and cyst phantom simulation show that the proposed SVD-GSC performs better in terms of spatial resolution, PSL and CR. Furthermore, the proposed method has a stronger robustness than the traditional GSC.

scholarly journals CRITIQUE OF SOLUTIONS IN LINEARIZED INVERSE PROBLEMS: NUMERICAL EXPERIMENTS IN TRAVELTIME TOMOGRAPHY

2017 ◽  
Vol 34 (4) ◽  
Author(s):  
Silvia L. Bejarano ◽  
Amin Bassrei
Keyword(s):  
Singular Value Decomposition ◽  
Inverse Problems ◽  
Numerical Experiments ◽  
Singular Value ◽  
Traveltime Tomography ◽  
Value Decomposition

ABSTRACT. In this work, we evaluated the quality of the solution of numerical experiments in traveltime tomography, in linear and linearized cases, using singular value decomposition. The simulations were performed using...Keywords: inverse problems, traveltime tomography, resolution matrices, Barbieri method, regularization. RESUMO. Neste trabalho avaliamos a qualidade da solução em experimentos numéricos em tomografia de tempo de trânsito, nos casos linear e linearizado, utilizando o método de decomposição por valores singulares...Palavras-chave: problemas inversos, tomografia de tempos de trânsito, matrizes de resolução, método de Barbieri, regularização.

Data Hiding Schemes Based on Singular Value Decomposition

2013 ◽  
pp. 400-434
Author(s):  
Victor Pomponiu ◽  
Davide Cavagnino ◽  
Alessandro Basso ◽  
Annamaria Vernone
Keyword(s):  
Singular Value Decomposition ◽  
Digital Watermarking ◽  
Data Hiding ◽  
Information Hiding ◽  
Singular Value ◽  
Practical Applications ◽  
Advantages And Disadvantages ◽  
Multimedia Contents ◽  
Video And Audio ◽  
Value Decomposition

Information hiding techniques are acquiring an always increasing importance, due to the widespread diffusion of multimedia contents. Several schemes have been devised in the fields of steganography and digital watermarking, exploiting the properties of different domains. In this chapter, the authors focus on the SVD transform, with the aim of providing an exhaustive overview (more than 100 papers are analyzed) on those steganography and watermarking techniques leveraging on the important properties of such a transform. The large number of algorithms operating in the image, video and audio contexts is first classified by means of a general approach, then analyzed, to highlight the advantages and disadvantages of each method. The authors also give a detailed discussion about the applicability of each reviewed and compared data hiding scheme, in order to identify the most appropriate candidates for practical applications.

Digital Watermarking in the Transform Domain with Emphasis on SVD

2011 ◽  
pp. 46-66
Author(s):  
Maria Calagna
Keyword(s):  
Singular Value Decomposition ◽  
Digital Watermarking ◽  
Spatial Data ◽  
Singular Value ◽  
Transform Domain ◽  
Value Decomposition ◽  
Watermarking Scheme ◽  
Multimedia File

The chapter illustrates watermarking based on the transform domain. It argues that transform-based watermarking is robust to possible attacks and imperceptible with respect to the quality of the multimedia file we would like to protect. Among those transforms commonly used in communications, we emphasize the use of singular value decomposition (SVD) for digital watermarking. The main advantage of this choice is flexibility of application. In fact, SVD may be applied in several fields where data are organized as matrices, including multimedia and communications. We present a robust SVD-based watermarking scheme for images. According to the detection steps, the watermark can be determined univocally, while other related works present flaws in watermark detection. A case study of our approach refers to the protection of geographical and spatial data in case of the raster representation model of maps.

scholarly journals GPR Clutter Reflection Noise-Filtering through Singular Value Decomposition in the Bidimensional Spectral Domain

2021 ◽  
Vol 13 (10) ◽  
pp. 2005
Author(s):  
Rui Jorge Oliveira ◽  
Bento Caldeira ◽  
Teresa Teixidó ◽  
José Fernando Borges
Keyword(s):  
Singular Value Decomposition ◽  
Singular Value ◽  
Spectral Domain ◽  
Roman Villa ◽  
Useful Signal ◽  
3D Gpr ◽  
Effective Signal ◽  
Value Decomposition ◽  
Ground Penetrating

Usually, in ground-penetrating radar (GPR) datasets, the user defines the limits between the useful signal and the noise through standard filtering to isolate the effective signal as much as possible. However, there are true reflections that mask the coherent reflectors that can be considered noise. In archaeological sites these clutter reflections are caused by scattering with origin in subsurface elements (e.g., isolated masonry, ceramic objects, and archaeological collapses). Its elimination is difficult because the wavelet parameters similar to coherent reflections and there is a risk of creating artefacts. In this study, a procedure to filter the clutter reflection noise (CRN) from GPR datasets is presented. The CRN filter is a singular value decomposition-based method (SVD), applied in the 2D spectral domain. This CRN filtering was tested in a dataset obtained from a controlled laboratory environment, to establish a mathematical control of this algorithm. Additionally, it has been applied in a 3D-GPR dataset acquired in the Roman villa of Horta da Torre (Fronteira, Portugal), which is an uncontrolled environment. The results show an increase in the quality of archaeological GPR planimetry that was verified via archaeological excavation.

scholarly journals A Robust Medical Images Watermarking Using FDCuT-DCT-SVD

2020 ◽  
Vol 13 (6) ◽  
pp. 266-278
Author(s):  
Ledya Novamizanti ◽  
◽  
Ida Wahidah ◽  
Ni Wardana ◽  
◽  
...  
Keyword(s):  
Singular Value Decomposition ◽  
Discrete Cosine Transform ◽  
High Frequency ◽  
Medical Image ◽  
Medical Images ◽  
Singular Values ◽  
Singular Value ◽  
Watermark Image ◽  
Value Decomposition

One way to prevent image duplication is by applying watermarking techniques. In this work, the watermarking process is applied to medical images using the Fast Discrete Curvelet Transforms (FDCuT), Discrete Cosine Transform (DCT), and Singular Value Decomposition (SVD) methods. The medical image of the host is transformed using FDCuT so that three subbands are obtained. High Frequency (HF) subband selected for DCT and SVD applications. Meanwhile, SVD was also applied to the watermark image. The singular value on the host image is exchanged with the singular value on the watermark. Insertion of tears by exchanging singular values does not cause the quality of medical images to decrease significantly. The experimental results prove that the proposed FDCuT-DCT-SVD algorithm produces good imperceptibility. The proposed algorithm is also resistant to various types of attacks, including JPEG compression, noise enhancement attacks, filtering attacks, and other common attacks.

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