Version Of Singular Value Decomposition Research Articles

Enhancing the SVD compression losslessly

Orthonormality is the foundation of a number of matrix decomposition methods. For example, Singular Value Decomposition (SVD) implements the compression by factoring a matrix with orthonormal parts and is pervasively utilized in various fields. Orthonormality, however, inherently includes constraints that would induce redundant information, preventing SVD from deeper compression and even making it frustrated as the data fidelity is strictly required. In this paper, we theoretically prove that these redundances resulted by orthonormality can be completely eliminated in a lossless manner. An enhanced version of SVD, namely E-SVD, is accordingly established to losslessly and quickly release constraints and recover the orthonormal parts in SVD. According to our theory, advantages of E-SVD over SVD become increasingly evident with the rising requirement of data fidelity. In particular, E-SVD will reduce 25% storage units as SVD reaches its limitation and fails to compress data. Empirical evidences from remote sensing and internet of things justify our theory by demonstrating the consistent compression superiority of E-SVD over SVD, and the additional calculation cost of E-SVD is of the same magnitude with SVD. Digital image compression experiment with typical size 375 × 500 also shows that E-SVD needs the least storage space as compared to alternative compression solutions when the rank is sufficient to maintain a good visual quality. The presented theory sheds insightful lights on the constraint solution in orthonormal matrices and E-SVD, guaranteed by which will profoundly enhance the SVD-based compression in the context of explosive growth in both data acquisition and fidelity levels.

SVM-PSO based rotation-invariant image texture classification in SVD and DWT domains

The paper presents a new image classification technique which first extracts rotation-invariant image texture features in singular value decomposition (SVD) and discrete wavelet transform (DWT) domains. Subsequently, it exploits a support vector machine (SVM) to perform image texture classification. For convenience, it is called the SRITCSD method hereafter. First, the method applies the SVD to enhance image textures of an image. Then, it extracts the texture features in the DWT domain of the SVD version of the image. Also, the SRITCSD method employs the SVM to serve as a multiclassifier for image texture features. Meanwhile, the particle swarm optimization (PSO) algorithm is utilized to optimize the SRITCSD method, which is exploited to select a nearly optimal combination of features and a set of parameters utilized in the SVM. The experimental results demonstrate that the SRITCSD method can achieve satisfying results and outperform other existing methods under considerations here.

Potential of Acoustic Emission in Unsupervised Monitoring of Gas-Fuelled Engines

Abstract: The aim of this paper is to scrutinise closely information that contains in the acoustic emission of a gas-fuelled engine and to define the potential of knock condition detection. The recently introduced technique of nonstationary system identification is investigated. At first, it utilises the wavelet transform to reveal time-frequency energy density of data. Then the modified version of singular value decomposition is applied to extract dominant frequency components from the data buried in background noise. The efficacy of knock feature extraction is investigated using three sources of data: in-cylinder pressure, engine structure vibration and engine noise sensed by a microphone.

A new DWT-SVD based robust watermarking scheme for real property rights

In this paper, a new robust digital image watermarking algorithm which was based on singular value decomposition (SVD) and discrete wavelet transform (DWT) was proposed and simulated for protecting real property rights. A gray scale logo image, rather than a randomly generated Gaussian noise type watermark, was used as a watermark. Its embedding algorithm hid a watermark LL sub-band blocks in the low–low (LL) and high-high (HH) sub-bands of a target non-overlapping block of the host image by modifying singular values on SVD version of these blocks. A semi-blind watermark extraction was designed to estimate the original coefficients.
 Experimental results showed that the proposed scheme made significant improvements in terms of both transparency and robustness and was superior to the existing methods which were considered in this paper.

An SVD-based image watermarking in wavelet domain using SVR and PSO

The paper presents a novel blind watermarking scheme for image copyright protection, which is developed in the discrete wavelet transform (DWT) and is based on the singular value decomposition (SVD) and the support vector regression (SVR). Its embedding algorithm hides a watermark bit in the low–low (LL) subband of a target non-overlap block of the host image by modifying a coefficient of U component on SVD version of the block. A blind watermark-extraction is designed using a trained SVR to estimate original coefficients. Subsequently, the watermark bit can be computed using the watermarked coefficient and its corresponding estimate coefficient. Additionally, the particle swarm optimization (PSO) is further utilized to optimize the proposed scheme. Experimental results show the proposed scheme possesses significant improvements in both transparency and robustness, and is superior to existing methods under consideration here.