SVD

The human's Micro-Doppler signatures resulting from breathing, arm, foot and other periodic motion can provide valuable information about the structure of the moving parts and may be used for identification and classification purposes. In this paper, we carry out simulate with FDTD method and through wall experiment with UWB radar for human being's periodic motion detection. In addition, Advancements signal processing methods are presented to classify and to extract the human's periodic motion characteristic information, such as Micro-Doppler shift and motion frequency. Firstly, we apply the Principal Component Analysis (PCA) with singular value decomposition (SVD) to denoise and extract the human motion signal. Then, we present the results base on the Hilbert-Huang transform (HHT) and the S transform to classify and to identify the human's micro-Doppler shift characteristics. The results demonstrate that the combination of UWB radar and various processing methods has potential to detect human's Doppler signatures effectively. Downloaded From: http://spiedigitallibrary.org/ on 03/18/2014 Terms of Use: http://spiedl.org/terms Proc. of SPIE Vol. 8734 873402-2 Downloaded From: http://spiedigitallibrary.org/ on 03/18/2014 Terms of Use: http://spiedl.org/terms

The Nyquist constraint for regular sampling of multiband signals is that the sampling rate must fall in an "allowed band." For random sampling, the sampling rate only has to exceed the Nyquist-Landau rate (information rate), which is a simpler constraint. We demonstrate this flexibility in a simulation of a communication channel.

Sebagian besar dokumen web memiliki bentuk berupa blog. Pelabelan dokumen web dengan tag membutuhkan waktu lebih lama karena banyaknya tag di dalamnya. Oleh karena itu, pada penelitian ini dimaksudkan untuk membuat sistem rekomendasi pemberian tag yang berfungsi sebagai navigasi dan titik temu hasil pencarian dokumen. Adapun metode yang digunakan adalah Latent Semantic Indexing (LSI) dengan menggunakan pendekatan matematis sehingga mendapatkan hubungan yang tidak nampak antara kata kunci yang terdapat pada teks dokumen, yaitu Tag-In (TI) dan kata kunci yang tidak terdapat pada teks dokumen, yaitu Tag-Out (TO). Ekstraksi dari TI pada dokumen didapatkan dengan menghitung bobot kata. Dari sejumlah TI yang memiliki bobot tertinggi, didapatkan TO yang muncul bersama sejumlah TI tersebut dari graf data latih. Kemudian dibentuk sebuah matriks yang berisi nilai co-occurrence antara TI dan TO yang telah diperoleh. Matriks tersebut didekomposisi menggunakan Singular Value Decomposition (SVD). Hasil dekomposisi yang merupakan vektor masing-masing TO dihitung kedekatannya dengan vektor query dan diambil jarak yang terdekat. Hasil pengujian menunjukkan nilai recall terbaik dicapai pada saat TI=3 dan TO=6, yaitu 0.69, dimana jumlah rata-rata tag tiap dokumen uji adalah 5 tag. Nilai k yang tepat dan menghasilkan akurasi terbaik bergantung pada rank matriks dengan F-Measure terbaik untuk rank = 2.

The purpose of this article is to determine the usefulness of the Graphics Processing Unit (GPU) calculations used to implement the Latent Semantic Indexing (LSI) reduction of the TERM-BY-DOCUMENT matrix. Considered reduction of the matrix is based on the use of the SVD (Singular Value Decomposition) decomposition. A high computational complexity of the SVD decomposition-O(n 3), causes that a reduction of a large indexing structure is a difficult task. In this article there is a comparison of the time complexity and accuracy of the algorithms implemented for two different environments. The first environment is associated with the CPU and MATLAB R2011a. The second environment is related to graphics processors and the CULA library. The calculations were carried out on generally available benchmark matrices, which were combined to achieve the resulting matrix of high size. For both considered environments computations were performed for double and single precision data.

To ensure the quality of the nanoimprint fabricated optical gratings, optical scatterometry (OS) is an efficient and effective mean to diagnose the actual fabricated geometry. To facilitate the diagnosis process, efficient pattern matching algorithms over a huge database are of great importance.

This paper presents a new hybrid optimization strategy for training feedforward neural networks. The algorithm combines gradient-based optimization of nonlinear weights with singular value decomposition (SVD) computation of linear weights in one integrated routine. It is described for the multilayer perceptron (MLP) and radial basis function (RBF) networks and then extended to the local model network (LMN), a new feedforward structure in which a global nonlinear model is constructed from a set of locally valid submodels. Simulation results are presented demonstrating the superiority of the new hybrid training scheme compared to second-order gradient methods. It is particularly effective for the LMN architecture where the linear to nonlinear parameter ratio is large.

This work deals with the use of multiple correspondence analysis (MCA) and a weighted Euclidean distance (the tolerance distance) as an exploratory tool in developing predictive logistic models. The method was applied to a living-donor kidney transplant data set with 109 cases and 13 predictors. This approach, followed by backward and forward selection procedures, yielded two models, one with four and another with two predictors. These models were compared to two other models, ordinarily built by backward and forward stepwise selection, which yielded, respectively, five and two predictors. After internal validation, the models performance statistics showed similar results. Likelihood ratio tests suggested that backward approach achieved a better fit than the forward modelling in both methods and the Vuong's non-nested test between backward-built models suggested that these were undistinguishable. We conclude that the tolerance distance, in combination with MCA, could be a feasible method for variable selection in logistic modelling, when there are several categorical predictors. c o m p u t e r m e t h o d s a n d p r o g r a m s i n b i o m e d i c i n e 9 5 ( 2 0 0 9 ) 116-128 117 1.

Recently laser scanning technologies become widely used in many areas of the modern economy. In the following paper authors show a potential spectrum of use Terrestrial Laser Scanning (TLS) in diagnostics of reinforced concrete elements. Based on modes of failure analysis of reinforcement concrete beam authors describe downsides and advantages of adaptation of terrestrial laser scanning to this purpose, moreover reveal under which condition this technology might be used. Research studies were conducted by Faculty of Civil and Environmental Engineering at Gdansk University of Technology. An experiment involved bending of reinforced concrete beam, the process was registered by the terrestrial laser scanner. Reinforced concrete beam was deliberately overloaded and eventually failed by shear. Whole failure process was tracing and recording by scanner Leica ScanStation C10 and verified by synchronous photographic registration supported by digital photogrammetry methods. Obtained data were post-processed in Leica Cyclone (dedicated software) and MeshLab (program on GPL license). The main goal of this paper is to prove the effectiveness of TLS in diagnostics of reinforced concrete elements. Authors propose few methods and procedures to virtually reconstruct failure process, measure geometry and assess a condition of structure.

A code transaction modifies a set of code files and runs a set of tests to make sure that it does not break any of the existing features. But how do we choose the target set of tests? There could be 1000s of tests. Do we run all those tests? In that case, are we not wasting resources, and delaying the whole process of merging a code change? Can we choose a static set of tests? In that case, we have a chance of missing a set of tests which may be related to the code changes. How do we find out the optimal set of tests for a given code change-is the goal which we are trying to solve using the Recommendation System.

Nowadays, recommendation systems are used successfully to provide items (example: movies, music, books, news, images) tailored to user preferences. Amongst the approaches existing to recommend adequate content, we use the collaborative filtering approach of finding the information that satisfies the user by using the reviews of other users. These reviews are stored in matrices that their sizes increase exponentially to predict whether an item is relevant or not. The evaluation shows that these systems provide unsatisfactory recommendations because of what we call the cold start factor. Our objective is to apply a hybrid approach to improve the quality of our recommendation system. The benefit of this approach is the fact that it does not require a new algorithm for calculating the predictions. We are going to apply two algorithms: k-nearest neighbours (KNN) and the matrix factorization algorithm of collaborative filtering which are based on the method of (singular-value-decomposition). Our combined model has a very high precision and the experiments show that our method can achieve better results.

This issue (DOI: 10.13140/RG.2.2.10242.07361) includes the following articles; P1151637518, Author="Géraud Azehoun-Pazou and Kokou Assogba", Title="Automatic Recognition of Lesion-like Regions in Black Skin Medical Images" P1151636515, Author="Khalid A. Al-Afandy and EL-Sayed M. EL-Rabaie and Fathi E. Abd El-Samie and Osama S. Faragallah and Ahmed ELmhalaway and A. M. Shehata", Title="Robust Color Image Watermarking Technique Using DWT and Homomorphic based SVD" P1151638519, Author="TALLURI. SUNIL KUMAR and T.V.RAJINIKANTH and B. ESWARA REDDY", Title="Region Based Integrated Approach for Image Retrieval" P1151616490, Author="Rafika Ben Salem and Karim Saheb Ettabaa and Mohamed Ali Hamdi", Title="Spectral-Spatial Classification of Hyperspectral Image based on Oversampling and Multi-Feature Kernels"

Medical image security provides challenges and opportunities, watermarking and encryption of medical images provides the necessary control over the flow of medical information. In this paper a dual security approach is employed .A medical image is considered as watermark and is watermarked inside a natural image. This approach is to wean way the potential attacker by disguising the medical image as a natural image. To further enhance the security the watermarked image is encrypted using encryption algorithms. In this paper a multi–objective optimization approach optimized using different metaheuristic approaches like Genetic Algorithm (GA), Differential Evolution ( DE) and Bacterial Foraging Optimization (BFOA) is proposed. Such optimization helps in preserving the structural integrity of the medical images, which is of utmost importance. The water marking is proposed to be implemented using both Lifted Wavelet Transforms (LWT) and Singular Value Decomposition (SVD) technique. The encryption is done using RSA and AES encryption algorithms. A Graphical User Interface (GUI) which enables the user to have ease of operation in loading the image, watermark it, encrypt it and also retrieve the original image whenever necessary is also designed and presented in this paper.

Parkinson's disease is a common and debilitating condition, caused by aberrant activity in a complex basal ganglia-thalamocortical circuit. Therapeutic advances rely on characterising interactions in this circuit. However, recording electrophysiological responses over the entire circuit is impractical. Dynamic causal modelling offers large-scale models of predictive value based on a limited or partial sampling of complex networks. Using dynamic causal modelling, we determined the network changes underlying the pathological excess of beta oscillations that characterise the Parkinsonian state. We modelled data from five patients undergoing surgery for deep brain stimulation of more than one target. We found that connections to and from the subthalamic nucleus were strengthened and promoted beta synchrony, in the untreated compared to the treated Parkinsonian state. Dynamic causal modelling was able to replicate the effects of lesioning this nucleus and may provide a new means of directing the search for therapeutic targets.

The paper proposes a novel robust watermarking technique based on newly introduced Nonsubsampled contourlet transform(NSCT) and singular value decomposition(SVD) for multimedia copyright protection. The NSCT can give the asymptotic optimal representation of the edges and contours in image by virtue of the characteristics of good multi resolution shift invariance and multi directionality. After decomposing the host image into sub bands, we choose the low frequency directional sub band and apply singular value decomposition. The singular values of the original image are then modified by the singular values of nonsubsampled contourlet transformed
visual grayscale logo watermark image. This hybrid approach improves the performance of the watermarking technique compared to earlier techniques. Experimental results shows that the hybrid technique is resilient to various linear and non linear filtering ,JPEG compression, JPEG2000 compression, Histogram equalization, Grayscale inversion, Contrast adjustment, gamma correction, alpha mean ,cropping ,Gaussian noise, scaling etc.

Phương pháp Delphi là một kỹ thuật hỗ trợ quá trình thảo luận nhóm để đưa ra giải pháp cho một vấn đề cụ thể. Cụ thể hơn, Phương pháp Delphi là một quá trình thảo luận có bài bản để nhóm các chuyên gia tích lũy thông tin và thể hiện tri thức. Thực tế cho thấy, tri thức được thu thập qua các bảng câu hỏi và tri thức của các chuyên gia trong nhóm không bao giờ có điểm chung. Do đó, quá trình thảo luận nhóm và kết quả cuối cùng của nó không bao giờ "đi theo người dẫn đầu" và thường gây trở ngại cho chất lượng của ý kiến chung trong quá trình thảo luận trực tiếp. Phương pháp Delphi dựa trên triết lý "Điều tra biện chứng", nghĩa là quá trình thảo luận nhóm đi từ chính đề (đưa ra một ý kiến). Hay nói cách khác, Phương pháp Delphi dùng các mâu thuẫn nảy sinh giữa các ý kiến trái ngược trong quá trình thảo luận nhóm, tập trung quanh vấn đề cụ thể để tìm ra giải pháp mới.

Most of the wireless communication technologies are not able to provide substantial gain in terms of reliability and capacity due to the effect of multipath fading and limited spectrum. A best solution to overcome these problems is multiple antennas techniques. This paper investigates the effect of multiple antennas at the transmitter and receiver end of SU-MIMO and MU-MIMO, using a SVD based single and multiple beamforming technique, for various Modulation schemes namely ASK, BPSK, QPSK, 8-PSK and 8-QAM. The effectiveness of these approaches has been validated with help of MATLAB simulations.

In this paper, we will focus on the collaborative MIMO in combination with the LTE-advanced system. In this system, two or more users each using multiple antennas are transmitting their data within the same frequency/time grid. In this paper, we investigate the MIMO enhancements introduced to the LTE uplink of the LTE-advanced system. User equipment here is equipped by 2-4 antennas which can be used along the collaborative MIMO system to increase data rate, enhance system performance and match channel conditions according to the channel knowledge at transmitter. We propose a space frequency block codes (SFBC) precoding for the uplink to achieve space diversity in accompany with spatial multiplexing gain achieved before using the collaborative system. Then, we propose a combination between the collaborative system and the precoded MIMO whether ideally (SVD precoding) or suboptimally using codebook precoding. The results show dramatic enhancement in performance and achieves the peak spectral efficiency of the uplink mode of the LTE-advanced.

In order to contribute to the security of sharing and transferring medical images, we had presented a multiple watermarking technique for multiple protections; it was based on the combination of three transformations: the discrete wavelet transform (DWT), the fast Walsh-Hadamard transform (FWHT) and, the singular value decomposition (SVD). In this paper, three watermark images of sizes 512x 512 were inserted into a single medical image of various modalities such as magnetic resonance imaging (MRI), computed tomography (CT), and X-Radiation (X-ray). After applying DWT up to the third level on the original image, the high-resolution sub-bands were being selected subsequently to apply FWHT and then SVD. The singular values of the three watermark images were inserted into the singular values of the cover medical image. The experimental results showed the effectiveness of the proposed method in terms of quality and robustness compared to other reported techniques cited in the literature.

Cloud computing is a well-known subject in IT
system and is based on network and computer utilities.
Cloud computing is the main source of storage
facilities for small, medium and large companies in
recent years. Since, many customers look for cloud
computing facilities and services, thus the security of
information must be guaranteed to give a full
confidence for that customer who has trusted to use
these facilities and ready to store their valuable
information on the cloud computing and distributed
networks. The fundamental problem in cloud
computing is the security of the stored data.[1]
This paper describes the process of storing the images
and scanned documents over the cloud by using our
proposed security model which is based on the
biometric features and multilevel encryption. On the
other hand, it will discuss about cloud computing
environment, issues and concerns regarding to
security, authentication by using biometric features,
and new security algorithms and models. The
Security algorithms, which are used in this paper, are
consisting of the scrambling algorithm and two level
encryption methods. In addition, we have proposed
two different scenarios that improve the DEPSKY
model.
Biometrics is the knowledge of setting the identity of
an individual related to the inherent physical or
attitude characteristics connected with the person. The
relationship between the perceived authentication
content and perceived content of biometric features is
studied. The process of developing the algorithms and
the model is documented in the proposed section.

A pilfered duplicate of a digital video would be easily disseminated to the global audience because of the rapid high-speed internet. Due to impeccably replicable nature of digital video, numerous unlawful duplicates of the original video can be made. A video can undergo several intentional attacks like frame dropping, averaging, cropping and median filtering and unintentional attacks like the addition of noise and compression which can compromise copyright information, thereby denying the authentication. Hence techniques are needed to secure copyrights of the proprietor and counteract illegal copying. One of the techniques is Video Watermarking strategy for concealing some sort of information into digital video sequences that are orders of successive still frames. In this paper, we study properties of video watermarking, the arrangement of computerized video watermarking systems, watermark attacks, its applications, issues and challenges for video watermarking. At last, we propose some future research directions.

This paper focuses on no-reference image quality assessment(NR-IQA)metrics. In the literature, a wide range of algorithms are proposed to automatically estimate the perceived quality of visual data. However, most of them are not able to effectively quantify the various degradations and artifacts that the image may undergo. Thus, merging of diverse metrics operating in different information domains is hoped to yield better performances, which is the main theme of the proposed work. In particular, the metric proposed in this paper is based on three well-known NR-IQA objective metrics that depend on natural scene statistical attributes from three different domains to extract a vector of image features. Then, Singular Value Decomposition (SVD) based dominant eigenvectors method is used to select the most relevant image quality attributes. These latter are used as input to Relevance Vector Machine (RVM) to derive the overall quality index. Validation experiments are divided into two groups; in the first group, learning process (training and test phases) is applied on one single image quality database whereas in the second group of simulations, training and test phases are separated on two distinct datasets. Obtained results demonstrate that the proposed metric performs very well in terms of correlation, monotonicity and accuracy in both the two scenarios.

In this paper we propose a semi-blind watermarking scheme using Discrete Wavelet Transform and Singular Value Decomposition for copyright protection. We used a gray scale image as a watermark to hide in another gray scale image as a cover image. The cover image is modified (Zig-Zag) and divided to number of blocks of size n x n. We find the spatial frequency of each block and kept a threshold on this spatial frequency to form a reference image. Then the reference image is transformed into wavelet domain. We hide the watermark into reference image by modifying the singular values of reference image with the singular values of watermark. The proposed algorithm provides a good imperceptibility and robust for various attacks.

A photochemically-induced fluorescence system combined with second-order chemometric analysis for the determination of the anticonvulsant carbamazepine (CBZ) is presented. CBZ is a widely used drug for the treatment of epilepsy and is included in the group of emerging contaminant present in the aquatic environment. CBZ is not fluorescent in solution but can be converted into a fluorescent compound through a photochemical reaction in a strong acid medium. The determination is carried out by measuring excitation-emission photoinduced fluorescence matrices of the products formed upon ultraviolet light irradiation in a laboratoryconstructed reactor constituted by two simple 4 W germicidal tubes. Working conditions related to both the reaction medium and the photoreactor geometry are optimized by an experimental design. The developed approach enabled the determination of CBZ at trace levels without the necessity of applying separation steps, and in the presence of uncalibrated interferences which also display photoinduced fluorescence and may be potentially present in the investigated samples. Different second-order algorithms were tested and successful resolution was achieved using multivariate curve resolution-alternating least-squares (MCR-ALS). The study is employed for the discussion of the scopes and yields of each of the applied second-order chemometric tools. The quality of the proposed method is probed through the determination of the studied emerging pollutant in both environmental and drinking water samples. After a pre-concentration step on a C18 membrane using 50.0 mL of real water samples, a prediction relative error of 2 % and limits of detection and quantification of 0.2 and 0.6 ng mL-1 were respectively obtained.

Secret key extraction is a crucial issue in physical layer security and a promising technique for the next generation of 5G and beyond. Unlike previous works on this topic in which Orthogonal Frequency Division Multiplexing (OFDM) sub-channels are considered to be independent, the effect of correlation among sub-channels on the secret key rate is addressed in this paper. We assume a realistic model for dependency among the sub-channels. Moreover, a new approach with low computational complexity for key extraction and optimal Mutual Information (MI) is presented in our study. To do this, we utilize a Singular Value Decomposition-based (SVD-based) precoding to obtain an optimal SVD-based approach for key extraction. Simulation results show that the rate of the key exchange link may drop up to 72% when the signal to noise ratio (SNR) is considered to be 8dB. The low computational complexity of our proposed approach makes it a promising candidate for developing secure and high-speed networks.

The combination texture feature extraction approach for texture image retrieval is proposed in this paper. Two kinds of low level texture features were combined in the approach. One of them was extracted from singular value decomposition (SVD) based dual-tree complex wavelet transform (DTCWT) coefficients, and the other one was extracted from multi-scale local binary patterns (LBPs). The fusion features of SVD based multi-directional wavelet features and multi-scale LBP features have short dimensions of feature vector. The comparing experiments are conducted on Brodatz and Vistex datasets. According to the experimental results, the proposed method has a relatively better performance in aspect of retrieval accuracy and time complexity upon the existing methods.

Despite the popularity of Matrix factorization methods (MF) in Recommender Systems (RSs), it is not clear the practical distinction among them in real scenarios. In this context, this work organizes existing methods, proposing a taxonomic organization for the area, and characterizes their performance, correlating the application success with domain characteristics. Aiming at the practical use of this research, we present a characterization framework for MF methods applied to RSs, useful for contrasting the performance of different methods. Analyses of actual collections have shown that recommendations generated via SVD tend to focus on non-popular items, while recommendations issued by PCA focuses on popular items.

A method for determining the bidomain conductivity values is developed. The study was generated because the different sets of measured conductivity values reported in the literature each produce significantly different bidomain simulation results. The method involves mapping the propagation of the electrical activation of cardiac tissue, initiated by point stimulation, via extracellular electrodes. A timedependent bidomain model is used to simulate the electrical phenomena. The optimum set of conductivity values is achieved by minimizing the difference between the bidomain model output and the measured extracellular potential, by means of inverse techniques in parameter estimation leastsquares and singular value decomposition. The method is validated with synthetic data with added random noise. Other parameters in the model such as membrane capacitance and fiber angle can also be estimated. The method takes a different approach to the conventional four-electrode technique, as it does not require the small electrode separation needed to separate the extracellular current from the intracellular.

This paper presents secure transmission for digital watermarking using four level stationary or redundant wavelet transform (SWT/RWT) and singular value decomposition (SVD). In the embedding process. For copyright protection and image quality, we used 4-SWT-SVD for digital watermarking. In our method, SWT is applied to decompose the cover image and produce the low and high frequency coefficients to take benefits of “power compaction” goods. SVD is additionally performed to acquire the singular values and improve the strength of the algorithm. The experimental outcomes give good results up-to 49% as compared to previous results. Several results show that the presented method is able to withstand under various attacks. In this study, we determined the performance of this algorithm on the basis of Peak Signal Noise Ratio (PSNR) and Normalized Correlation (NC) value of various watermarked images.

This paper examines the implementation of the Singular Value Decomposition (SVD) method to detect the presence of wireless signal. The method is used to find the maximum and minimum eigenvalues. We simulated the algorithm using common digital signal in wireless communication namely rectangular pulse shape, raised cosine and root-raised cosine to test the performance of the signal detector. The SVD-based signal detector was found to be more efficient in sensing signal without knowing the properties of the transmitted signal. The execution time is acceptable compared to the favorable energy detection. The computational complexity of SVD-based detector is medium compared to the energy detector. The algorithm is suitable for blind spectrum sensing where the properties of the signal to be detected are unknown. This is also the advantage of the algorithm since any signal would interfere and subsequently affect the quality of service (QoS) of the IEEE 802.22 connection. Furthermore, the algorithm performed better in the low signal-to-noise ratio (SNR) environment.

This dissertation deals with a class of nonlinear adjustment problems that has a direct least squares solutionfor certain weighting cases.  In the literature of mathematical statistics these problems are expressed in anonlinear model called Errors-In-Variables (EIV) and their solution became popular as total least squares(TLS). The TLS solution is direct and involves the use of singular value decomposition (SVD), presented inmost cases for adjustment problems with equally weighted and uncorrelated measurements.  Additionally,several weighted total least squares (WTLS) algorithms have been published in the last years for derivingiterative solutions, when more general weighting cases have to be taken into account and without linearizingthe problem in any step of the solution process.This research provides  rstly a well de ned mathematical relationship between TLS and direct least squaressolutions.  As a by-product, a systematic approach for the direct solution of these adjustments is established,using a consistent and complete mathematical formalization.  By transforming the problem to the solutionof a quadratic or cubic algebraic equation, which is identical with those resulting from TLS, it will be shownthat TLS is an algorithmic approach already known to the geodetic community and not a new method.A second contribution of this work is the clear overview of weighted least squares solutions for the discussedclass of problems, i.e.  the WTLS solution in the terminology of the statistical community.  It will be shownthat for certain weighting cases a direct solution still exists, for which two new solution strategies will beproposed. Further, stochastic models with more general weight matrices are examined, including correlationsbetween the measurements or even singular cofactor matrices.  New algorithms are developed and presented,that provide iterative weighted least squares solutions without linearizing the original nonlinear problem.The aim of this work is the popularization of the TLS approach, by presenting a complete framework forobtaining a (weighted) least squares solution for the investigated class of nonlinear adjustment problems.The proposed approaches and the implemented algorithms can be employed for obtaining direct solutionsin engineering tasks for which efficiency is important, while iterative solutions can be derived for stochasticmodels with more general weights.

In this study we will provide a new way of images compression based on two mathematic concepts, these two concepts are Singular Value Decomposition (SVD) and Block Truncation Coding. The input image either is in JPEG format or in BMP format and the current way suitable for both color and gray scale images. The input image will be compressed first by reducing the image matrix rank, which achieved by using the SVD process and then the result matrix compressed by using BTC. The proposed algorithm gives further lossless compression to JPEG compression process and the rate of compression reach more than 99%.

In system identification and parametric spectral estimation by two-dimensional (2-D) autoregressive (AR) and 2-D autoregressive moving average (ARMA) models, the order selection problem is often required. In this correspondence, we show that the information of 2-D AR model order is implicitly contained in a correlation matrix. An algorithm for 2-D quarter-plane AR model order determination is proposed. Numerical simulations are presented to show the efficiency of the proposed singular value decomposition (SVD) based algorithm.

This paper highlights an algorithm for detecting the presence of wireless signal using the Singular Value Decomposition (SVD) technique. We simulated the algorithm to detect common digital signals in wireless communication to test the performance of the signal detector. The SVD-based signal detector was found to be more efficient in detecting a signal without knowing the properties of the transmitted signal. The performance of the algorithm is better compared to the favorable energy detection. The algorithm is suitable for blind spectrum sensing where the properties of the signal to be detected are unknown. This is also the advantage of the algorithm since any signal would interfere and subsequently affect the quality of service (QoS) of the IEEE 802.22 connection.

The ability of biogeochemical ecosystem models to represent agro-ecosystems depends on their correct integration with field observations. We report simultaneous calibration of 67 DayCent model parameters using multiple observation types through inverse modeling using the PEST parameter estimation software. Parameter estimation reduced the total sum of weighted squared residuals by 56% and improved model fit to crop productivity, soil carbon, volumetric soil water content, soil temperature, N2O, and soil NO3- compared to the default simulation. Inverse modeling substantially reduced predictive model error relative to the default model for all model predictions, except for soil NO3- and NH4+. Post-processing analyses provided insights into parameter–observation relationships based on parameter correlations, sensitivity and identifiability. Inverse modeling tools are shown to be a powerful way to systematize and accelerate the process of biogeochemical model interrogation, improving ou...

An algorithm is described that identifies the shape of the impulse within the impulse noise component of the HFC upstream channel. The cross-correlation function (CCF) is used t o align samples of the impulse waveforms in time. The singular value decomposition (SVD) finds the dominant basis waveforms that represent the collection of samples. We find that two frequency-modulated basis waveforms sufficiently span our set of 75 measured impulses. The joint statistics of the basis coefficients is given.

The purpose of this article is to determine the usefulness of the Graphics Processing Unit (GPU) calculations used to implement the Latent Semantic Indexing (LSI) reduction of the TERM-BY-DOCUMENT matrix. Considered reduction of the matrix is based on the use of the SVD (Singular Value Decomposition) decomposition. A high computational complexity of the SVD decomposition-O(n 3), causes that a reduction of a large indexing structure is a difficult task. In this article there is a comparison of the time complexity and accuracy of the algorithms implemented for two different environments. The first environment is associated with the CPU and MATLAB R2011a. The second environment is related to graphics processors and the CULA library. The calculations were carried out on generally available benchmark matrices, which were combined to achieve the resulting matrix of high size. For both considered environments computations were performed for double and single precision data.

Recommender systems use statistical and knowledge discovery techniques in order to recommend products to users and to mitigate the problem of information overload. The evaluation of the quality of recommender systems has become an important issue for choosing the best learning algorithms. In this paper we propose an evaluation methodology for collaborative filtering (CF) algorithms. This methodology carries out a clear, guided and repeatable evaluation of a CF algorithm. We apply the methodology on two datasets, with different characteristics, using two CF algorithms: singular value decomposition and naivem bayesian networks 1.

This work deals with the use of multiple correspondence analysis (MCA) and a weighted Euclidean distance (the tolerance distance) as an exploratory tool in developing predictive logistic models. The method was applied to a living-donor kidney transplant data set with 109 cases and 13 predictors. This approach, followed by backward and forward selection procedures, yielded two models, one with four and another with two predictors. These models were compared to two other models, ordinarily built by backward and forward stepwise selection, which yielded, respectively, five and two predictors. After internal validation, the models performance statistics showed similar results. Likelihood ratio tests suggested that backward approach achieved a better fit than the forward modelling in both methods and the Vuong's non-nested test between backward-built models suggested that these were undistinguishable. We conclude that the tolerance distance, in combination with MCA, could be a feasible method for variable selection in logistic modelling, when there are several categorical predictors. c o m p u t e r m e t h o d s a n d p r o g r a m s i n b i o m e d i c i n e 9 5 ( 2 0 0 9 ) 116-128 117 1.

It is known that ramp-based models are not sufficient for accurate timing modeling. In this paper, we develop a technique that accurately models the waveforms, and also allows a flexible trade-off of accuracy vs. computational and representational cost. The technique is based on Singular Value Decomposition (SVD) and it naturally leads to a more general gate delay model which can be applied in any timing analysis engine with minor modifications. We demonstrate its application in timing analysis by propagating a waveform along a path. When compared with Spice, the proposed model shows good accuracy.