In this paper, we will demonstrate three-dimensional tomographic reconstruction of space-borne highresolution SAR data using Shanghai as our test site. The high density of high-rise buildings in Shanghai leads to a rather complicated backscattering regime, which is difficult to handle with conventional interferometric processing. For the tomographic signal reconstruction, we use three different...

In this paper, we present a unified approach to the (related) problems of recovering signal parameters from noisy observations and the identification of linear system model parameters from observed input/output signals, both using singular value decomposition (SVD) techniques. Both known and new SVD-based identification methods are classified in a subspaceoriented scheme. The singular value dec...

A new method for implementing complex wavelet transform (CWT) based on analog sampled-data circuit and singular value decomposition (SVD) algorithm is presented. To begin with, the real and imaginary parts functions of the complex wavelet base are approximated by using SVD algorithm. As the main advantage of this approximation approach is its computational simplicity and general applicability. ...

Large repositories of data contain sensitive information that must be protected against unauthorized access. The protection of the confidentiality of this information has been a long-term goal for the database security research community and for the government statistical agencies. Recent advances in data mining and machine learning algorithms have increased the disclosure risks that one may en...

A new method is described for computing the singular value decomposition (SVD). It begins by computing the polar decomposition and then computes the spectral decomposition of the Hermitian polar factor. The method is particularly attractive for shared memory parallel computers with a relatively small number of processors, because the polar decomposition can be computed efficiently on such machi...

The main aim of this paper is to perform the analysis of Singular Value Decomposition (SVD) as a Dimensionality Reduction technique and Sparse Representation Classifier (SRC) as a Post Classifier for the Classification of Epilepsy Risk levels from Electroencephalography signals. The data acquisition of the EEG signals is performed initially. Then SVD is applied here as a dimensionality reductio...

Bridge decks deteriorate over time as a result of freezing-and-thawing, heavy use, and water penetration resulting in internal defects. Ground penetrating radar (GPR) can be used as a non-destructive method for detecting such defects. Unfortunately, reflections from closely spaced objects overlap which prevents the accurate estimation of the round-trip travel time of GPR waves to the closely sp...

The paper proposes a digital watermarking algorithm for audio copyright protection combined characteristics of singular value decomposition (SVD) and discrete cosine transform (DCT). The audio are split into blocks, and each block are decomposed on discrete cosine transform (DCT), then the DCT coefficients are decomposed on singular value decomposition (SVD) transform and embedded the watermark...

The increasing popularity of Singular Value Decomposition Algorithms, used in real time signal processing, demands a rapid development of their fast and reliable implementations. This paper shows several modiications to the Jacobi-like parallel algorithm for Singular Value Decomposition (SVD) and their impact on the algorithm's performance. In particular, the optimisations for the parallel memo...

Numerical techniques for data analysis and feature extraction are discussed using the framework of matrix rank reduction. The singular value decomposition (SVD) and its properties are reviewed, and the relation to Latent Semantic Indexing (LSI) and Principal Component Analysis (PCA) is described. Methods that approximate the SVD are reviewed. A few basic methods for linear regression, in partic...