Showing 1-25 of 23,995 resultsfor "Index Terms":Singular Vectors
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The reaserch on parameters of spectral clustering based on SVD
Proceedings of 2013 3rd International Conference on Computer Science and Network Technology
Year: 2013 | Conference Paper |
The spectral clustering algorithm, which cluster by using the eigenvalues and eigenvectors of Laplacian matrix, may not be obtained the desired clustering results in some cases. It is possible to remedy this deficiency by using the singular value decomposition (SVD) in the spectral clustering algorithm. It is presented in this article the algorithm of spectral clustering based on SVD which use sin...Show More
A preliminary study of IoT-device control using gestures recognition
2017 56th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE)
Year: 2017 | Conference Paper |
Cited by: Papers (3)
So called “IoT devices”, which has embedded computer and internet connectivity, are widely spreading in these days. And user interface for such devices is very important issue to be discussed. One attractive solution is gesture control. It might be easy and intuitive to use. Therefore, many researchers have been proposing gesture recognition by using camera or data grove. However, these devices ar...Show More
Generalizing discriminant analysis using the generalized singular value decomposition
Discriminant analysis has been used for decades to extract features that preserve class separability. It is commonly defined as an optimization problem involving covariance matrices that represent the scatter within and between clusters. The requirement that one of these matrices be nonsingular limits its application to data sets with certain relative dimensions. We examine a number of optimizatio...Show More
Efficient Adaptive SVD Algorithm for MIMO Applications
Many multiple-input multiple-output (MIMO) applications require the computation of some or all of the nonzero singular values and the corresponding left and right singular vectors of a time-varying channel response matrix. An adaptive algorithm is derived to achieve this goal, based on a first-order perturbation, which updates a full or partial singular value decomposition (SVD) using input and no...Show More
A new ECG data compression method based on adaptive vector quantization and residual error compensation
2017 5th International Conference on Electrical Engineering - Boumerdes (ICEE-B)
Year: 2017 | Conference Paper |
In the last few years, data compression has become essential for both storage and transmission especially in the biomedical domain. In this paper, we propose a new technique for the electrocardiogram data compression. It is based on the singular value decomposition and a new adaptive vector quantization. In the proposed method, the codebook is generated adaptively at each quantization stage. The r...Show More
Floating Small Target Detection in Sea Clutter Based on the Singular Value Decomposition of Low Rank Perturbed Random Matrices
2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)
Year: 2021 | Conference Paper |
Cited by: Papers (5)
Detection of sea-surface small floating target is always a challenging problem under the influence of sea clutter. In this paper, a feature-based detector is proposed for effective and robust detection of floating small target in sea clutter. Three features have been extracted from the received signal based on the singular value decomposition (SVD) of large dimensional rectangular random matrix to...Show More
The use of an efficient QRS detection algorithm for the compression of ECG signals with large QRS waves
2017 First International Conference on Embedded & Distributed Systems (EDiS)
Year: 2017 | Conference Paper |
Cited by: Papers (1)
Recently, the development of storage and transmission technologies and the evolution of information systems, require the handling of large amount of data. Hence, data compression has become essential for both storage and transmission. In this paper, we investigate the influence of an efficient QRS detection technique on the compression quality of a new proposed compression method, in the case of t...Show More
Reconstruction of GPR Signals by Spectral Analysis of the SVD Components of the Data Matrix
This letter considers the problem of reconstructing total-time responses from noisy data collected by ground-penetrating radar (GPR). The well-known singularity expansion method (SEM) - a theory - for late-time response representation is generalized to establish a matrix model (data matrix) representing total-time responses of radar scattering waveforms. Using singular value decomposition of the d...Show More
Image watermarking algorithm based on grey relational analysis and singular value decomposition in wavelet domain
2017 International Conference on Grey Systems and Intelligent Services (GSIS)
Year: 2017 | Conference Paper |
Cited by: Papers (1)
An image watermarking algorithm based on grey relational analysis and singular value decomposition in wavelet domain is proposed. Firstly, the host image is processed with one-level of discrete wavelet transform. The low frequency coefficients LL1 can be obtained from mentioned operation, and LL1 is divided into non-overlapping blocks whose size is same as watermarking. Secondly, through the gaine...Show More
A parallel implementation of Singular Value Decomposition based on Map-Reduce and PARPACK
Proceedings of 2011 International Conference on Computer Science and Network Technology
Year: 2011 | Conference Paper |
In the e-commerce on the Web, recommender systems become a powerful technology for extracting valuable information from its customer databases. These systems also help customers find products they want to buy from a business sites. Singular Value Decomposition(SVD) is a useful technology to speedup the recommendations with very fast online performance, requiring just a few simple arithmetic operat...Show More
Fault diagnosis of gas turbine based on support vector machine
Weihong Hu;Jiyuan Liu;Jianguo Cui;Yang Gao;Bo Cui;Liying Jiang
The 26th Chinese Control and Decision Conference (2014 CCDC)
Year: 2014 | Conference Paper |
Cited by: Papers (4)
In this paper, a fault diagnosis method based on support vector machine (SVM) is proposed for gas turbine bearing. Firstly, through analysis and processing of vibration signals, the singular value decomposition related EEMD technique is applied to extract feature vectors of the signals. The results are used as the input of SVM classifier model. Then, by using the SVM network intelligence, the turb...Show More
Analysis and control of the 3- and 5-phase voltage source inverters using the averaged output voltage method
2009 International Conference on Power Electronics and Drive Systems (PEDS)
Year: 2009 | Conference Paper |
A systematic analysis for the multi-phase voltage source inverter (VSI) is illustrated in this paper. By applying the singular value decomposition, the instantaneous output voltage of VSI can be categorized into two complementary groups with the same magnitude and different sign to each other. The switching strategy for minimum conduction time which yields the maximum linear modulation index is pr...Show More
Robust change detection in highly dynamic guided wave signals with singular value decomposition
Chang Liu;Joel Harley;Nicholas O'Donoughue;Yujie Ying;Martin H. Altschul;Mario Bergés;James H. Garrett;David W. Greve;José M. F. Moura;Irving J. Oppenheim;Lucio Soibelman
Ultrasonic guided waves are sensitive to small scatterers and can, in principle, be used to detect damage in pipe structures. However, pipes are often subjected to varying environmental and operational conditions (EOC), which can produce false positives or mask the change of interest. We apply singular value decomposition as a robust change detection method in ultrasonic signals. We test the metho...Show More
An adaptive hybrid image compression method and its application to medical images
Ali Al-Fayadh;Abir Jaafar Hussain;Paulo Lisboa;Dhiya Al-Jumeily
2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro
Year: 2008 | Conference Paper |
Cited by: Papers (2)
An efficient adaptive lossy image compression technique using classified vector quantiser and singular value decomposition for compression of medical magnetic resonance-brain images is presented. The proposed method is called adaptive hybrid classified vector quantisation. A simple but efficient classifier based gradient method without employing any threshold to determine the class of the input im...Show More
Invariance analysis and application of the zoomed-and-shrunk image singular value vector
2010 2nd International Conference on Software Technology and Engineering
Year: 2010 | Conference Paper |
The image singular value vector has been applied extensively to image processing and recognition for its stability and invariance in the transformation of translation,rotation, transposition and mirroration. This paper is to try to analyse how the image singular value vector to change with image size changing by performing matrix operation and experiment; then apply the conclusions to template-mat...Show More
Study on Boundaries of Eigenvalues in SVD Method for Autonomous Star Identification
2014 Sixth International Conference on Intelligent Human-Machine Systems and Cybernetics
Year: 2014 | Conference Paper |
Cited by: Papers (2)
The singular values are important invariant features in singular value decomposition (SVD) method for autonomous star identification. This paper theoretically analyzes the inherent relationship between the star vectors in field of view (FOV) and the eigenvalues of the Hermitian matrix formed by star vectors, which is performed as an equivalent study on singular values of the star vector matrix. Fi...Show More
Singular value decomposition and space/spatial-frequency analysis applied to digital watermarking
2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO)
Year: 2012 | Conference Paper |
A watermarking procedure in the space/spatial-frequency domain, based on the singular value decomposition is proposed. Singular values are used for selection of pixels suitable for watermarking. Generally, regions for watermarking are characterized as: busy and moderate busy. For pixels from busy regions the entire middle frequency content in the space/spatial frequency domain is used for watermar...Show More
Hybrid Classified Vector Quantisation and Its Application to Image Compression
Ali Al-Fayadh;Abir Jaafar Hussain;Paulo Lisboa;Dhiya Al-Jumeily
EUROCON 2007 - The International Conference on "Computer as a Tool"
Year: 2007 | Conference Paper |
Cited by: Papers (1)
A novel image compression technique using classified vector quantiser and singular value decomposition is presented for the efficient representation of still images. The proposed method is called hybrid classified vector quantisation. A simple but efficient classifier based gradient method which employs only one threshold to determine the class of the input image block that results in a good image...Show More
A Hybrid Classified Vector Quantisation and Its Application to Image Compression
Ali Al-Fayadh;Abir Jaafar Hussain;Paulo Lisboa;Dhiya Al-Jumeily
2007 IEEE International Conference on Signal Processing and Communications
Year: 2007 | Conference Paper |
A novel image compression technique using classified vector quantiser and singular value decomposition is presented for the efficient representation of still images. The proposed method is called hybrid classified vector quantisation. It involves a simple, but efficient, classifier based gradient method in the spatial domain which employs only one threshold to determine the class of the input imag...Show More
Star Chart Recognition Algorithm Based on Singular Value Decomposition
2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)
Year: 2021 | Conference Paper |
Cited by: Papers (2)
The problem of all-sky autonomous star chart recognition is a key technology in star-sensitive device technology, the traditional star chart recognition algorithm is mainly characterized by the angular distance or its derived form, such methods generally require a relatively large storage space, the real-time performance is not good and the recognition rate is generally not high. In this paper, a ...Show More
Using a Matrix Decomposition for Clustering Data
2009 International Conference on Computational Aspects of Social Networks
Year: 2009 | Conference Paper |
Cited by: Papers (1)
There are many search engines in the web and when asked, they return a long list of search results, ranked by their relevancies to the given query. Web users have to go through the list and examine the titles and (short) snippets sequentially to identify their required results. In this paper we present how usage of Matrix Decomposition (Singular Value Decomposition (SVD) and Nonnegative Matrix Fac...Show More
Study on Identification Method of Tool Wear Based on Singular Spectrum Analysis and Support Vector Machine
2011 Second International Conference on Digital Manufacturing & Automation
Year: 2011 | Conference Paper |
Acoustic Emission signal reflecting the tool wear state is made by phase space reconstruction that uses mutual information method and Cao method to determine time delay and embedding dimension for constructing phase space matrix. After reconstruction, by calculating singular spectral of phase space matrix, based on which characteristic vector is constructed. These characteristic vectors are combin...Show More
An Adaptive On-Line Inspection Method Based on Singular Value Decomposition
2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics
Year: 2013 | Conference Paper |
Singular value decomposition(SVD) is an effective method of algebraic feature extraction. It has the stability, rotation invariability, brightness invariability and other important features. In this thesis, through autonomous learning in small sample space and extracting the SVD feature, the similarity calculation method of singular value feature is given, the similarity is used to recognition. Th...Show More
An explanation of linear discrete time system behavior by singular value decomposition of the system matrix
Proceedings of 28th Southeastern Symposium on System Theory
Year: 1996 | Conference Paper |
This study formulates the single-input-single-output (SISO) output controllability problem based on singular value decomposition (SVD) of the system matrix. With the approach, the authors show if any input trajectory is along a right singular vector, the output trajectory will be along the corresponding left singular vector and will mirror the input. In addition, the authors derive a relationship ...Show More
Fault Diagnosis of Commutation Failures in the HVDC System Based on Wavelet Singular Value and Support Vector Machine
2009 Asia-Pacific Power and Energy Engineering Conference
Year: 2009 | Conference Paper |
Cited by: Papers (9)
The paper based on the singular value decomposition and support vector machines, a new fault diagnosis of commutation failures method in HVDC system was proposed. The coefficient matrix acquired from wavelet package transform is first decomposed on singular value, by which fault current are mapped to different time-frequency sub-space. Then the singular value is put into support vector machines to...Show More
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