Relevant bibliographies by topics / Singular-Value Decomposition (SVD)

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Singular-Value Decomposition (SVD)'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 September 2023

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Singular-Value Decomposition (SVD).'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Singular-Value Decomposition (SVD)"

1

KOH, MIN-SUNG. "A QUINTET SINGULAR VALUE DECOMPOSITION THROUGH EMPIRICAL MODE DECOMPOSITIONS." Advances in Adaptive Data Analysis 06, no. 02n03 (April 2014): 1450010. http://dx.doi.org/10.1142/s1793536914500101.

Full text
Abstract:
A particular quintet singular valued decomposition (Quintet-SVD) is introduced in this paper via empirical mode decompositions (EMDs). The Quintet-SVD results in four specific orthogonal matrices with a diagonal matrix of singular values. Furthermore, this paper shows relationships between the Quintet-SVD and traditional SVD, generalized low rank approximations of matrices (GLRAM) of one single matrix, and EMDs. One application of the Quintet-SVD for speech enhancement is shown and compared with an application of traditional SVD.
APA, Harvard, Vancouver, ISO, and other styles
2

Lem, Kong Hoong. "Truncated singular value decomposition in ripped photo recovery." ITM Web of Conferences 36 (2021): 04008. http://dx.doi.org/10.1051/itmconf/20213604008.

Full text
Abstract:
Singular value decomposition (SVD) is one of the most useful matrix decompositions in linear algebra. Here, a novel application of SVD in recovering ripped photos was exploited. Recovery was done by applying truncated SVD iteratively. Performance was evaluated using the Frobenius norm. Results from a few experimental photos were decent.
APA, Harvard, Vancouver, ISO, and other styles
3

Caltenco, J. H., José Luis Lopez-Bonilla, B. E. Carvajal-Gámez, and P. Lam-Estrada. "Singular Value Decomposition." Bulletin of Society for Mathematical Services and Standards 11 (September 2014): 13–20. http://dx.doi.org/10.18052/www.scipress.com/bsmass.11.13.

Full text
Abstract:
We study the SVD of an arbitrary matrix Anxm, especially its subspaces of activation, which leads in natural manner to pseudoinverse of Moore-Bjenhammar-Penrose. Besides, we analyze the compatibility of linear systems and the uniqueness of the corresponding solution, and our approach gives the Lanczos classification for these systems.
APA, Harvard, Vancouver, ISO, and other styles
4

Liu, Bowen, Balázs Pejó, and Qiang Tang. "Privacy-Preserving Federated Singular Value Decomposition." Applied Sciences 13, no. 13 (June 21, 2023): 7373. http://dx.doi.org/10.3390/app13137373.

Full text
Abstract:
Singular value decomposition (SVD) is a fundamental technique widely used in various applications, such as recommendation systems and principal component analyses. In recent years, the need for privacy-preserving computations has been increasing constantly, which concerns SVD as well. Federated SVD has emerged as a promising approach that enables collaborative SVD computation without sharing raw data. However, existing federated approaches still need improvements regarding privacy guarantees and utility preservation. This paper moves a step further towards these directions: we propose two enhanced federated SVD schemes focusing on utility and privacy, respectively. Using a recommendation system use-case with real-world data, we demonstrate that our schemes outperform the state-of-the-art federated SVD solution. Our utility-enhanced scheme (utilizing secure aggregation) improves the final utility and the convergence speed by more than 2.5 times compared with the existing state-of-the-art approach. In contrast, our privacy-enhancing scheme (utilizing differential privacy) provides more robust privacy protection while improving the same aspect by more than 25%.
APA, Harvard, Vancouver, ISO, and other styles
5

GYONGYOSI, LASZLO, and SANDOR IMRE. "QUANTUM SINGULAR VALUE DECOMPOSITION BASED APPROXIMATION ALGORITHM." Journal of Circuits, Systems and Computers 19, no. 06 (October 2010): 1141–62. http://dx.doi.org/10.1142/s0218126610006797.

Full text
Abstract:
Singular Value Decomposition (SVD) is one of the most useful techniques for analyzing data in linear algebra. SVD decomposes a rectangular real or complex matrix into two orthogonal matrices and one diagonal matrix. The proposed Quantum-SVD algorithm interpolates the non-uniform angles in the Fourier domain. The error of the Quantum-SVD approach is some orders lower than the error given by ordinary Quantum Fourier Transformation. Our Quantum-SVD algorithm is a fundamentally novel approach for the computation of the Quantum Fourier Transformation (QFT) of non-uniform states. The presented Quantum-SVD algorithm is based on the singular value decomposition mechanism, and the computation of Quantum Fourier Transformation of non-uniform angles of a quantum system. The Quantum-SVD approach provides advantages in terms of computational structure, being based on QFT and multiplications.
APA, Harvard, Vancouver, ISO, and other styles
6

de Franco, Roberto, and Gemma Musacchio. "Polarization filter with singular value decomposition." GEOPHYSICS 66, no. 3 (May 2001): 932–38. http://dx.doi.org/10.1190/1.1444983.

Full text
Abstract:
We present a singular value decomposition (SVD) based algorithm for polarization filtering of triaxial seismic recordings based on the assumption that the particle motion trajectory is essentially 2-D (elliptical polarization). The filter is the sum of the first two eigenimages of the SVD on the signal matrix. Weighing functions, which are strictly dependent on the intensity (linearity and planarity) of the polarization, are applied. The efficiency of the filter is tested on synthetic traces and on real data, and found to be superior to solely covariance‐based filter algorithms. Although SVD and covariance‐based methods have similar theoretical approach to the solution of the eigenvalue problem, SVD does not require any further rotation along the polarization ellipsoid principal axes. The algorithm presented here is a robust and fast filter that properly reproduces polarization attributes, amplitude, and phase of the original signal. A major novelty is the enhancement of both elliptical and linear polarized signals. Moreover as SVD preserves the amplitude ratios across the triaxial recordings, the particle motion ellipse before and after filtering retains a correct orientation, overcoming a typical artifact of the covariance‐based methods.
APA, Harvard, Vancouver, ISO, and other styles
7

Akritas, Alkiviadis G., and Gennadi I. Malaschonok. "Applications of singular-value decomposition (SVD)." Mathematics and Computers in Simulation 67, no. 1-2 (September 2004): 15–31. http://dx.doi.org/10.1016/j.matcom.2004.05.005.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Xu, Peng Fei, Hong Bin Zhang, Xin Feng Wang, and Zheng Yong Yu. "Color Image Compression Using Block Singular Value Decomposition." Applied Mechanics and Materials 303-306 (February 2013): 2122–25. http://dx.doi.org/10.4028/www.scientific.net/amm.303-306.2122.

Full text
Abstract:
This paper looks at the application of Singular Value Decomposition (SVD) to color image compression. Based on the basic principle and characteristics of SVD, combined with the image of the matrix structure. A block SVD-based image compression scheme is demonstrated and the usage feasibility of Block SVD-based image compression is proved.
APA, Harvard, Vancouver, ISO, and other styles
9

Galo, André Luiz, and Márcio Francisco Colombo. "Singular Value Decomposition and Ligand Binding Analysis." Journal of Spectroscopy 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/372596.

Full text
Abstract:
Singular values decomposition (SVD) is one of the most important computations in linear algebra because of its vast application for data analysis. It is particularly useful for resolving problems involving least-squares minimization, the determination of matrix rank, and the solution of certain problems involving Euclidean norms. Such problems arise in the spectral analysis of ligand binding to macromolecule. Here, we present a spectral data analysis method using SVD (SVD analysis) and nonlinear fitting to determine the binding characteristics of intercalating drugs to DNA. This methodology reduces noise and identifies distinct spectral species similar to traditional principal component analysis as well as fitting nonlinear binding parameters. We applied SVD analysis to investigate the interaction of actinomycin D and daunomycin with native DNA. This methodology does not require prior knowledge of ligand molar extinction coefficients (free and bound), which potentially limits binding analysis. Data are acquired simply by reconstructing the experimental data and by adjusting the product of deconvoluted matrices and the matrix of model coefficients determined by the Scatchard and McGee and von Hippel equation.
APA, Harvard, Vancouver, ISO, and other styles
10

Balle, Borja, Prakash Panangaden, and Doina Precup. "Singular value automata and approximate minimization." Mathematical Structures in Computer Science 29, no. 9 (May 27, 2019): 1444–78. http://dx.doi.org/10.1017/s0960129519000094.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Singular-Value Decomposition (SVD)"

1

Ek, Christoffer. "Singular Value Decomposition." Thesis, Linnéuniversitetet, Institutionen för datavetenskap, fysik och matematik, DFM, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-21481.

Full text
Abstract:
Digital information och kommunikation genom digitala medier är ett växande område. E-post och andra kommunikationsmedel används dagligen över hela världen. Parallellt med att området växer så växer även intresset av att hålla informationen säker. Transmission via antenner är inom signalbehandling ett välkänt område. Transmission från en sändare till en mottagare genom fri rymd är ett vanligt exempel. I en tuff miljö som till exempel ett rum med reflektioner och oberoende elektriska apparater kommer det att finnas en hel del distorsion i systemet och signalen som överförs kan, på grund av systemets egenskaper och buller förvrängas.Systemidentifiering är ett annat välkänt begrepp inom signalbehandling. Denna avhandling fokuserar på systemidentifiering i en tuff miljö med okända system. En presentation ges av matematiska verktyg från den linjära algebran samt en tillämpning inom signalbehandling. Denna avhandling grundar sig främst på en matrisfaktorisering känd som Singular Value Decomposition (SVD). SVD’n används här för att lösa komplicerade matrisinverser och identifiera system.Denna avhandling utförs i samarbete med Combitech AB. Deras expertis inom signalbehandling var till stor hjälp när teorin praktiserades. Med hjälp av ett välkänt programmeringsspråk känt som LabView praktiserades de matematiska verktygen och kunde synkroniseras med diverse instrument som användes för att generera signaler och system.
Digital information transmission is a growing field. Emails, videos and so on are transmitting around the world on a daily basis. Along the growth of using digital devises there is in some cases a great interest of keeping this information secure. In the field of signal processing a general concept is antenna transmission. Free space between an antenna transmitter and a receiver is an example of a system. In a rough environment such as a room with reflections and independent electrical devices there will be a lot of distortion in the system and the signal that is transmitted might, due to the system characteristics and noise be distorted. System identification is another well-known concept in signal processing. This thesis will focus on system identification in a rough environment and unknown systems. It will introduce mathematical tools from the field of linear algebra and applying them in signal processing. Mainly this thesis focus on a specific matrix factorization called Singular Value Decomposition (SVD). This is used to solve complicated inverses and identifying systems. This thesis is formed and accomplished in collaboration with Combitech AB. Their expertise in the field of signal processing was of great help when putting the algorithm in practice. Using a well-known programming script called LabView the mathematical tools were synchronized with the instruments that were used to generate the systems and signals.
APA, Harvard, Vancouver, ISO, and other styles
2

Jolly, Vineet Kumar. "Activity Recognition using Singular Value Decomposition." Thesis, Virginia Tech, 2006. http://hdl.handle.net/10919/35219.

Full text
Abstract:
A wearable device that accurately records a user's daily activities is of substantial value. It can be used to enhance medical monitoring by maintaining a diary that lists what a person was doing and for how long. The design of a wearable system to record context such as activity recognition is influenced by a combination of variables. A flexible yet systematic approach for building a software classification environment according to a set of variables is described. The integral part of the software design is the use of a unique robust classifier that uses principal component analysis (PCA) through singular value decomposition (SVD) to perform real-time activity recognition. The thesis describes the different facets of the SVD-based approach and how the classifier inputs can be modified to better differentiate between activities. This thesis presents the design and implementation of a classification environment used to perform activity detection for a wearable e-textile system.
Master of Science
APA, Harvard, Vancouver, ISO, and other styles
3

Renkjumnong, Wasuta. "SVD and PCA in Image Processing." Digital Archive @ GSU, 2007. http://digitalarchive.gsu.edu/math_theses/31.

Full text
Abstract:
The Singular Value Decomposition is one of the most useful matrix factorizations in applied linear algebra, the Principal Component Analysis has been called one of the most valuable results of applied linear algebra. How and why principal component analysis is intimately related to the technique of singular value decomposition is shown. Their properties and applications are described. Assumptions behind this techniques as well as possible extensions to overcome these limitations are considered. This understanding leads to the real world applications, in particular, image processing of neurons. Noise reduction, and edge detection of neuron images are investigated.
APA, Harvard, Vancouver, ISO, and other styles
4

Haque, S. M. Rafizul. "Singular Value Decomposition and Discrete Cosine Transform based Image Watermarking." Thesis, Blekinge Tekniska Högskola, Avdelningen för för interaktion och systemdesign, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-5269.

Full text
Abstract:
Rapid evolution of digital technology has improved the ease of access to digital information enabling reliable, faster and efficient storage, transfer and processing of digital data. It also leads to the consequence of making the illegal production and redistribution of digital media easy and undetectable. Hence, the risk of copyright violation of multimedia data has increased due to the enormous growth of computer networks that provides fast and error free transmission of any unauthorized duplicate and possibly manipulated copy of multimedia information. One possible solution may be to embed a secondary signal or pattern into the image that is not perceivable and is mixed so well with the original digital data that it is inseparable and remains unaffected against any kind of multimedia signal processing. This embedded secondary information is digital watermark which is, in general, a visible or invisible identification code that may contain some information about the intended recipient, the lawful owner or author of the original data, its copyright etc. in the form of textual data or image. In order to be effective for copyright protection, digital watermark must be robust which are difficult to remove from the object in which they are embedded despite a variety of possible attacks. Several types of watermarking algorithms have been developed so far each of which has its own advantages and limitations. Among these, recently Singular Value Decomposition (SVD) based watermarking algorithms have attracted researchers due to its simplicity and some attractive mathematical properties of SVD. Here a number of pure and hybrid SVD based watermarking schemes have been investigated and finally a RST invariant modified SVD and Discrete Cosine Transform (DCT) based algorithm has been developed. A preprocessing step before the watermark extraction has been proposed which makes the algorithm resilient to geometric attack i.e. RST attack. Performance of this watermarking scheme has been analyzed by evaluating the robustness of the algorithm against geometric attack including rotation, scaling, translation (RST) and some other attacks. Experimental results have been compared with existing algorithm which seems to be promising.
Phone number: +88041730212
APA, Harvard, Vancouver, ISO, and other styles
5

Kaufman, Jason R. "Digital video watermarking using singular value decomposition and two-dimensional principal component analysis." Ohio : Ohio University, 2006. http://www.ohiolink.edu/etd/view.cgi?ohiou1141855950.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Brown, Michael J. "SINGULAR VALUE DECOMPOSITION AND 2D PRINCIPAL COMPONENT ANALYSIS OF IRIS-BIOMETRICS FOR AUTOMATIC HUMAN IDENTIFICATION." Ohio University / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1149187904.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Chu, Yue. "SVD-BAYES: A SINGULAR VALUE DECOMPOSITION-BASED APPROACH UNDER BAYESIAN FRAMEWORK FOR INDIRECT ESTIMATION OF AGE-SPECIFIC FERTILITY AND MORTALITY." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1609638415015896.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Campbell, Kathlleen. "Extension of Kendall's tau Using Rank-Adapted SVD to Identify Correlation and Factions Among Rankers and Equivalence Classes Among Ranked Elements." Diss., Temple University Libraries, 2014. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/284578.

Full text
Abstract:
Statistics
Ph.D.
The practice of ranking objects, events, and people to determine relevance, importance, or competitive edge is ancient. Recently, the use of rankings has permeated into daily usage, especially in the fields of business and education. When determining the association among those creating the ranks (herein called sources), the traditional assumption is that all sources compare a list of the same items (herein called elements). In the twenty-first century, it is rare that any two sources choose identical elements to rank. Adding to this difficulty, the number of credible sources creating and releasing rankings is increasing. In statistical literature, there is no current methodology that adequately assesses the association among multiple sources. We introduce rank-adapted singular value decomposition (R-A SVD), a new method that uses Kendall's tau as the underlying correlation method. We begin with (P), a matrix of data ranks. The first step is to factor the covariance matrix (K) as follows: K = cov(P) = V D^2 V Here, (V) is an orthonormal basis for the rows that is useful in identifying when sources agree as to the rank order and specifically which sources. D is a diagonal of eigenvalues. By analogy with singular value decomposition (SVD), we define U^* as U^* = PVD^(-1) The diagonal matrix, D, provides the factored eigenvalues in decreasing order. The largest eigenvalue is used to assess the overall association among the sources and is a conservative unbiased method comparable to Kendall's W. Anderson's test determines whether this association is significant and also identifies other significant eigenvalues produced by the covariance matrix.. Using Anderson's test (1963) we identify the a significantly large eigenvalues from D. When one or more eigenvalues is significant, there is evidence that the association among the sources is significant. Focusing on the a corresponding vectors of V specifically identifies which sources agree. In cases where more than one eigenvalue is significant, the $a$ significant vectors of V provide insight into factions. When more than one set of sources is in agreement, each group of agreeing sources is considered a faction. In many cases, more than one set of sources will be in agreement with one another but not necessarily with another set of sources; each group that is in agreement would be considered a faction. Using the a significant vectors of U^* provides different but equally important results. In many cases, the elements that are being ranked can be subdivided into equivalence classes. An equivalence class is defined as subpopulations of ranked elements that are similar to one another but dissimilar from other classes. When these classes exist, U^* provides insight as to how many classes and which elements belong in each class. In summary, the R-A SVD method gives the user the ability to assess whether there is any underlying association among multiple rank sources. It then identifies when sources agree and allows for more useful and careful interpretation when analyzing rank data.
Temple University--Theses
APA, Harvard, Vancouver, ISO, and other styles
9

Idrees, Zunera, and Eliza Hashemiaghjekandi. "Image Compression by Using Haar Wavelet Transform and Singualr Value Decomposition." Thesis, Linnéuniversitetet, Institutionen för datavetenskap, fysik och matematik, DFM, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-11467.

Full text
Abstract:
The rise in digital technology has also rose the use of digital images. The digital imagesrequire much storage space. The compression techniques are used to compress the dataso that it takes up less storage space. In this regard wavelets play important role. Inthis thesis, we studied the Haar wavelet system, which is a complete orthonormal systemin L2(R): This system consists of the functions j the father wavelet, and y the motherwavelet. The Haar wavelet transformation is an example of multiresolution analysis. Ourpurpose is to use the Haar wavelet basis to compress an image data. The method ofaveraging and differencing is used to construct the Haar wavelet basis. We have shownthat averaging and differencing method is an application of Haar wavelet transform. Afterdiscussing the compression by using Haar wavelet transform we used another method tocompress that is based on singular value decomposition. We used mathematical softwareMATLAB to compress the image data by using Haar wavelet transformation, and singularvalue decomposition.
APA, Harvard, Vancouver, ISO, and other styles
10

Gunyan, Scott Nathan. "An Examination into the Statistics of the Singular Vectors for the Multi-User MIMO Wireless Channel." Diss., CLICK HERE for online access, 2004. http://contentdm.lib.byu.edu/ETD/image/etd539.pdf.

Full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Book chapters on the topic "Singular-Value Decomposition (SVD)"

1

Yanai, Haruo, Kei Takeuchi, and Yoshio Takane. "Singular Value Decomposition (SVD)." In Projection Matrices, Generalized Inverse Matrices, and Singular Value Decomposition, 125–49. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9887-3_5.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Dongarra, Jack, Piotr Luszczek, Felix Wolf, Jesper Larsson Träff, Patrice Quinton, Hermann Hellwagner, Martin Fränzle, et al. "Singular-Value Decomposition (SVD)." In Encyclopedia of Parallel Computing, 1827. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-09766-4_2076.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Bertero, Mario, Patrizia Boccacci, and Christine De MoI. "Singular value decomposition (SVD)." In Introduction to Inverse Problems in Imaging, 163–82. 2nd ed. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003032755-7.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Gallier, Jean. "Singular Value Decomposition (SVD) and Polar Form." In Texts in Applied Mathematics, 333–51. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4613-0137-0_12.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Gallier, Jean. "Singular Value Decomposition (SVD) and Polar Form." In Texts in Applied Mathematics, 367–85. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9961-0_13.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Kothari, Ashish M., Vedvyas Dwivedi, and Rohit M. Thanki. "Singular Value Decomposition (SVD)-Based Video Watermarking." In Watermarking Techniques for Copyright Protection of Videos, 63–80. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-92837-1_4.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Abdulla, Hussam Dahwa, and Vaclav Snasel. "Search Result Clustering using a Singular Value Decomposition (SVD)." In Proceedings of the First International Conference on Intelligent Human Computer Interaction, 336–43. New Delhi: Springer India, 2009. http://dx.doi.org/10.1007/978-81-8489-203-1_33.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Thanki, Rohit M., Vedvyas J. Dwivedi, and Komal R. Borisagar. "Multibiometric Watermarking Technique Using Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD)." In Multibiometric Watermarking with Compressive Sensing Theory, 115–36. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73183-4_6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

El-Shahed, Reham A., M. N. Al-Berry, Hala M. Ebeid, and Howida A. Shedeed. "Multi-resolution Video Steganography Technique Based on Stationary Wavelet Transform (SWT) and Singular Value Decomposition (SVD)." In Advances in Intelligent Systems and Computing, 157–69. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3071-2_15.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Xiao, Tingting, and Wanshe Li. "A Novel Robust Adaptive Color Image Watermarking Scheme Based on Artificial Bee Colony." In Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications, 1006–17. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_101.

Full text
Abstract:
AbstractThis paper proposes a new robust adaptive watermarking scheme based on dual-tree quaternion wavelet and artificial bee colony, wherein the host images and watermark images are both color images. Color host images and watermark images in RGB space are transformed into YCbCr space. Then, apply Arnold chaotic map on their luminance components and use the artificial bee colony optimization algorithm to generate embedding watermark strength factor. Dual-tree quaternion wavelet transform is performed on the luminance component of the scrambled host image. Apply singular value decomposition on its low-frequency amplitude sub-band to obtain the principal component (PC). Embed the watermark into the principal component. Analysis and experimental results show that the proposed scheme is better as compared to the RDWT-SVD scheme and the QWT-DCT scheme.
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Singular-Value Decomposition (SVD)"

1

Patil, Nilesh M., and Milind U. Nemade. "Audio signal deblurring using singular value decomposition (SVD)." In 2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI). IEEE, 2017. http://dx.doi.org/10.1109/icpcsi.2017.8391912.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Meng, Fanshuo, Peng Li, Weibei Fan, Hongjun Zhang, Zhuangzhuang Xue, and Haitao Cheng. "BPTTD: Block-Parallel Singular Value Decomposition(SVD) Based Tensor Train Decomposition." In 2023 26th International Conference on Computer Supported Cooperative Work in Design (CSCWD). IEEE, 2023. http://dx.doi.org/10.1109/cscwd57460.2023.10152799.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Kaur, Sandeep, and Alka Jindal. "Singular Value Decomposition (SVD) based Image Tamper Detection Scheme." In 2020 International Conference on Inventive Computation Technologies (ICICT). IEEE, 2020. http://dx.doi.org/10.1109/icict48043.2020.9112432.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Zhou, Xu, and Mayank Tyagi. "Evaluation of Singular Value Decomposition (SVD) Enhanced Upscaling in Reservoir Simulation." In ASME 2020 39th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/omae2020-19259.

Full text
Abstract:
Abstract Reservoir upscaling is an important step in reservoir modeling for converting highly detailed geological models to simulation grids. It substitutes a heterogeneous model that consists of high-resolution fine grid cells with a lower resolution reduced-dimension homogeneous model using averaging schemes. Its objective is to use a coarse grid model to represent a fine grid model, thus to reduce simulation time. The benefit of upscaling in reservoir simulation is that it efficiently saves simulation time, and effectively preserves key features of data for flow simulation. Singular Vector Decomposition (SVD) is a matrix decomposition method. It has been used for image processing and compressing. It has been proved to be capable of providing a good compression ratio, and effectively saves digital image storage space. SVD also has been used in noise suppression and signal enhancement. It has been shown to be effective in reducing noise components arising from both the sound sampling and delivery system. This study evaluates the effect of SVD in parameterization and upscaling for reservoir simulation. A two-phase flow reservoir model was created using data from the SPE tenth comparative solution project [1]. Simulation results show that SVD is valid in the parameterization of permeability values. The reconstructed permeability matrices using certain amount of singular values are good approximations of the original permeability values. Simulation results from SVD processed permeability values are similar to that using the original values. SVD is then applied on the upscaled permeability value to evaluate the effectiveness on upscaling. Simulation results were compared between the base case, upscaled case, and SVD upscaled case. The simulation results did not show a significant improvement in the accuracy of predicting oil production by applying SVD on the upscaled permeability values. It could be because the reconstructed permeability matrix has the same dimension before and after the SVD processing, thus the model accuracy and efficiency are not significantly improved. Future work includes adding more cases to further explore the effect of SVD on upscaling. The number of grid blocks may be increased, and more layers can be added to investigate whether SVD enhance upscaling for larger scale reservoir simulation models.
APA, Harvard, Vancouver, ISO, and other styles
5

Gu, Zhouye, Weisi Lin, Bu-sung Lee, Chiew Tong Lau, and Manoranjan Paul. "Two dimensional Singular Value Decomposition (2D-SVD) based video coding." In 2010 17th IEEE International Conference on Image Processing (ICIP 2010). IEEE, 2010. http://dx.doi.org/10.1109/icip.2010.5650998.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Elgamel, Dalia, Roy Greeff, and David Ovard. "Passivity Verification and Macromodel Interpolation Using Singular Value Decomposition (SVD)." In 2015 IEEE Workshop on Microelectronics and Electron Devices (WMED). IEEE, 2015. http://dx.doi.org/10.1109/wmed.2015.7093692.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Larsson, Felix, Ludvig Knöös Franzén, Christopher Reichenwallner, and Alessandro Dell’Amico. "An Estimator for Aircraft Actuator Characteristics Using Singular Value Decomposition." In Workshop on Innovative Engineering for Fluid Power. Linköping University Electronic Press, 2023. http://dx.doi.org/10.3384/ecp196004.

Full text
Abstract:
This paper illustrates how a Singular Value Decomposition (SVD) and regression analyses can be used to create estimation models for aircraft actuator components by use of industrial data. The estimation models are at the end used to show how an electromechanical actuator´s weight and size will evolve with respect to output force. An essential step in the early design of aircraft is to be able to predict the weight and size of a resulting concept. This weight and size typically include contributions of main components such as wing and fuselage. Weight and size estimations at this stage can also range down to components at a sub-system level, for example, the aircraft actuators. The weight and size of an actuator depends on many parameters, and it is desirable to understand any underlying relationship to make qualified estimations of an actuator’s characteristics. However, the knowledge about a design is often limited at an early design stage and the required information is not always available. Consequently, estimations must be made from limited information and desired properties of the actuator. One way to approach this problem is to use SVD. An SVD analysis determines the most influential parameters in a data set and uses these to create an estimation model that only requires a few inputs for estimating the remaining parameters in the data set. An SVD can thereby be used for both identifying the driving parameters in a statistical dataset of existing solutions and to estimate the characteristics of new designs to be developed.
APA, Harvard, Vancouver, ISO, and other styles
8

Winck, Ryder C., and Wayne J. Book. "A Control Loop Structure Based on Singular Value Decomposition for Input-Coupled Systems." In ASME 2011 Dynamic Systems and Control Conference and Bath/ASME Symposium on Fluid Power and Motion Control. ASMEDC, 2011. http://dx.doi.org/10.1115/dscc2011-6116.

Full text
Abstract:
This paper introduces a control structure based on the singular value decomposition (SVD) to control multiple subsystems with reduced inputs. The SVD System permits simultaneous, dependent control of sets of subsystems coupled by a row-column input design. The use of the SVD differs from previous applications because it is used to obtain a low-rank approximation of desired inputs. The row-column system allows many actuators to be controlled by a few inputs. Current control methods using the row-column system rely on scheduling techniques that permit independent actuator control but are too slow for many applications. The inspiration for this new control construct is a pin array human machine interface, called Digital Clay. Some useful properties of the SVD will be discussed and the SVD System will be described and demonstrated in a simulation of Digital Clay.
APA, Harvard, Vancouver, ISO, and other styles
9

Lim Song Li and Norashikin Yahya. "Face recognition technique using Gabor wavelets and Singular Value Decomposition (SVD)." In 2014 IEEE International Conference on Control System, Computing and Engineering (ICCSCE). IEEE, 2014. http://dx.doi.org/10.1109/iccsce.2014.7072762.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Saad, Zuraidi, Muhammad Khusairi Osman, Zuli Imran Zulkafli, and Sopiah Ishak. "Vehicle Recognition System Using Singular Value Decomposition (SVD) and Levenberg-Marquardt." In 2009 International Conference on Computational Intelligence, Modelling and Simulation. IEEE, 2009. http://dx.doi.org/10.1109/cssim.2009.39.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Singular-Value Decomposition (SVD)"

1

Su, Dong. Internal Alignment of the SLD Vertex Detector using a Matrix Singular Value Decomposition Technique. Office of Scientific and Technical Information (OSTI), January 2002. http://dx.doi.org/10.2172/798957.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Upadhyaya, Shrini K., Abraham Shaviv, Abraham Katzir, Itzhak Shmulevich, and David S. Slaughter. Development of A Real-Time, In-Situ Nitrate Sensor. United States Department of Agriculture, March 2002. http://dx.doi.org/10.32747/2002.7586537.bard.

Full text
Abstract:
Although nitrate fertilizers are critical for enhancing crop production, excess application of nitrate fertilizer can result in ground water contamination leading to the so called "nitrate problem". Health and environmental problems related to this "nitrate problem" have led to serious concerns in many parts of the world including the United States and Israel. These concerns have resulted in legislation limiting the amount of nitrate N in drinking water to 10mg/g. Development of a fast, reliable, nitrate sensor for in-situ application can be extremely useful in dynamic monitoring of environmentally sensitive locations and applying site-specific amounts of nitrate fertilizer in a precision farming system. The long range objective of this study is to develop a fast, reliable, real-time nitrate sensor. The specific objective of this one year feasibility study was to explore the possible use of nitrate sensor based on mid-IR spectroscopy developed at UCD along with the silver halide fiber ATR (i.e. attenuated total internal reflection) sensor developed at TAU to detect nitrate content in solution and soil paste in the presence of interfering compounds. Experiments conducted at Technion and UCD clearly demonstrate the feasibility of detecting nitrate content in solutions as well as soil pastes using mid-IR spectroscopy and an ATR technique. When interfering compounds such as carbonates, bicarbonates, organic matter etc. are present special data analysis technique such as singular value decomposition (SYD) or cross correlation was necessary to detect nitrate concentrations successfully. Experiments conducted in Israel show that silver halide ATR fiber based FEWS, particularly flat FEWS, resulted in low standard error and high coefficient of determination (i.e. R² values) indicating the potential of the flat Fiberoptic Evanescent Wave Spectroscopy (FEWS) for direct determinations of nitrate. Moreover, they found that it was possible to detect nitrate and other anion concentrations using anion exchange membranes and M1R spectroscopy. The combination of the ion-exchange membranes with fiberoptices offers one more option to direct determination of nitrate in environmental systems.
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Singular-Value Decomposition (SVD)' for particular source types:
Journal articles Dissertations / Theses
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography