Papers by Keyword: Singular Value Decomposition

Abstract: Landsat satellite images are images that represent the ocean and land areas of the earth. Image data can be used for various purposes such as environmental analysis, remote sensing, mapping, and others. However, the quality of Landsat imagery is often unsatisfactory due to interference or noise from sources such as sensors, transmission, atmosphere, and storage. Therefore, they can reduce the contrast, sharpness, and information of landsat satellite images. Some of these disturbances prevent people from obtaining clear geographical locations. In order to overcome this problem, an effective and efficient method of Landsat satellite image quality improvement is needed. This research uses an image improvement method, namely discrete cosine transformation. The discrete cosine transformation method is used to reduce image noise by dividing it into each basic element. The method can perform the calculation process metematically and applicatively in the process of Landsat satellite image improvement. The processed results obtained are used to design and implement Landsat satellite image enhancement using the discrete cosine transformation method.

Abstract: Recommender Systems (RS) systems help users to select items and recommend useful items to target customers who are interested in them, such as movies, music, books, and jokes. Traditional recommendation algorithms are primarily concerned with improving performance accuracy; as a result, these algorithms prefer to promote only popular products. Variability is also an important inaccurate number of personalized recommendations that suggest unfamiliar or different things. Multi objective development strategies, which magnify these contradictory measures simultaneously, are used to measure accuracy and variability. Existing algorithms have an important feature because they are not flexible enough to control competing targets. We suggest creating a recommendation system based on shared filtering. Instead of finding out the preferences and preferences of users openly, we can find out by publicly analyzing historical and real-time data. This is done through a process called matrix factorization. Matrix factorization algorithms work by decomposing the interactive matrix of the user object into a product of two rectangular matrices with a minimum size. This type of recommendation has the added advantage of finding invisible and unmeasured relationships that are not possible with standard content-based filters.

Abstract: The 2D scattering problem is studied for the case of a two-dimensional cylindrical invisibility cloak, incorporating a tiny layer of perfect electromagnetic conductor (PEMC). The solution of the corresponding Helmholtz equation with variable coefficients is seeked as cylindrical functions Fourier series. The coefficients of these series are determined by solving the system of four linear algebraic equations with respect to four unknown coefficients with badly-conditioned matrix. The Singular Value Decomposition (SVD) method is applied for solving the system. Based on this idea the efficient numerical method is developed for solving the cloaking problem under study. The properties of this algorithm are studied and some results of numerical experiments are discussed.

Abstract: We consider the problem of decreasing the scattering from arbitrary 2D object by surrounding it the shell composed of M layers of homogeneous anisotropic materials. The solution of the scattering problem under study is obtained by solving corresponding 2D Helmholtz equation using cylindrical functions expansion. The coefficients of these expansions are determined by solving system of 4M+3 linear algebraic equations. Efficient numerical algorithm of cloaking problem is developed which is based on singular value decomposition of the coefficient matrix. Properties of the algorithm are studied and the results of numerical experiments are discussed.

Abstract: Singular value decomposition (SVD) is an important part of the numerical calculateion.It is widely used in biology, meteorology, quantum mechanics and other fields. It is discovered that the speed of calculation and accuracy has become the two basic questions of singular value decomposition during the construction process. With the era of big data,there are more and more cases of largescale data analysis using SVD. Singular value decomposition was originally an algorithm for computing resources are consumed, if still using the traditional stand-alone mode, will consume a lot of time cost. In order to improve the computing speed and accuracy, the system implement the parallel SVD algorithm which is based on unilateral jacobi method.It is used to analyze large-scale matrix about medicine for finding similarity of medicine efficacy.

Abstract: The principle of registration of the 3D point cloud data and the current algorithms are compared, and ICP algorithm is chosen since its fast convergence speed, high precision, and simple objective function. On the basis of ICP algorithm, singular value decomposition and four-array method are analysed by programming program, and all the mathematical algorithms is transformed into programming language by Matlab software.

Abstract: Singular Value Decomposition used in spectrum feature extraction, often discards small component that may be important for identifying mineral oil products. This work presents a new method using the Singular Value Division (SVD) on Wavelet Transform (WT) with three-dimensional fluorescence spectra as the source of oil features. WT-SVD feature based fuzzy classification (FCM) is implemented and comparable or better results are yielded in more accurate, and more robust than SVD performance under random noise conditions. The result means that WT-SVD method can strike a balance between data compression and preservation of small valid information in feature extraction of three-dimensional fluorescence spectra of mineral oils. This method is conducive to oil discrimination and pollution analysis in water environment monitoring.

Abstract: Digital watermarking technology has become an important means of integrity and respect for people's authenticity, as well as users of copyright and intellectual property security and other interests such as the protection of digital works. In this paper, we proposed a watermarking algorithm based on discrete wavelet transform (DWT) of image and singular valued composition (SVD). The original image is decomposed with DWT,the watermarking image is decomposed with SVD after chaotic scrambling,and then the singular values of watermarking are embedded into some coefficients of decomposed original image. In this algorithm, after decomposing the original host image into four bands, we apply the SVD to watermark image,and modify DWT coefficients of the host image with the singular values of the watermark image. The outstanding features of the proposed algorithm are that it provides larger watermarking capacity and is more robust than others.

Abstract: Watermark technique is one of the technologies of digital product property rights protection. This article from the basic principle, implementation, application, the experimental results were introduced from the aspects such as watermark technology, and analyzes the digital water marking technology based on singular value decomposition.

Abstract: In this letter, a digital watermarking method which combines the singular value decomposition (SVD) and the discrete cosine transform (DCT) is presented. First of all, The Arnold scrambling and the DCT are performed on the watermark and the original image, respectively. The SVD transform is performed on the image when scrambled watermark is embedded, then the watermarked image is formed. Finally, we extract watermark from the image which is handled by halftone screens. The experimental results show that this method can achieve the highest possible robustness without losing the transparency. Also it can anti printing and scanning. All the processes do not require any additional equipment. It will bring new hope to the field of anti-counterfeiting printing with singular value decomposition is applied to the half-tone image area.