Abstract: This paper presents a method for identification
of a linear time invariant (LTI) autonomous all pole system
using singular value decomposition. The novelty of this paper
is two fold: First, MUSIC algorithm for estimating complex
frequencies from real measurements is proposed. Secondly,
using the proposed algorithm, we can identify the coefficients
of differential equation that determines the LTI system by
switching off our input signal. For this purpose, we need only
to switch off the input, apply our complex MUSIC algorithm
and determine the coefficients as symmetric polynomials in the
complex frequencies. This method can be applied to unstable
system and has higher resolution as compared to time series
solution when, noisy data are used. The classical performance
bound, Cramer Rao bound (CRB), has been used as a basis for
performance comparison of the proposed method for multiple
poles estimation in noisy exponential signal.
Abstract: This paper deals with the coordinated tuning of the Power System Stabilizer (PSS) controller and Power Oscillation Damping (POD) Controller of Flexible AC Transmission System (FACTS) in a multi-machine power systems. The coordinated tuning is based on the critical eigenvalues of the power system and a model reduction technique where the Hankel Singular Value method is applied. Through the linearized system model and the parameter-constrained nonlinear optimization algorithm, it can compute the parameters of both controllers. Moreover, the parameters are optimized simultaneously obtaining the gains of both controllers. Then, the nonlinear simulation to observe the time response of the controller is performed.
Abstract: A sparse representation speech denoising method based on adapted stopping residue error was presented in this paper. Firstly, the cross-correlation between the clean speech spectrum and the noise spectrum was analyzed, and an estimation method was proposed. In the denoising method, an over-complete dictionary of the clean speech power spectrum was learned with the K-singular value decomposition (K-SVD) algorithm. In the sparse representation stage, the stopping residue error was adaptively achieved according to the estimated cross-correlation and the adjusted noise spectrum, and the orthogonal matching pursuit (OMP) approach was applied to reconstruct the clean speech spectrum from the noisy speech. Finally, the clean speech was re-synthesised via the inverse Fourier transform with the reconstructed speech spectrum and the noisy speech phase. The experiment results show that the proposed method outperforms the conventional methods in terms of subjective and objective measure.
Abstract: In this paper, we present a technique of secure watermarking of grayscale and color images. This technique consists in applying the Singular Value Decomposition (SVD) in LWT (Lifting Wavelet Transform) domain in order to insert the watermark image (grayscale) in the host image (grayscale or color image). It also uses signature in the embedding and extraction steps. The technique is applied on a number of grayscale and color images. The performance of this technique is proved by the PSNR (Pick Signal to Noise Ratio), the MSE (Mean Square Error) and the SSIM (structural similarity) computations.
Abstract: Digital images are widely used in computer
applications. To store or transmit the uncompressed images
requires considerable storage capacity and transmission bandwidth.
Image compression is a means to perform transmission or storage of
visual data in the most economical way. This paper explains about
how images can be encoded to be transmitted in a multiplexing
time-frequency domain channel. Multiplexing involves packing
signals together whose representations are compact in the working
domain. In order to optimize transmission resources each 4 × 4
pixel block of the image is transformed by a suitable polynomial
approximation, into a minimal number of coefficients. Less than
4 × 4 coefficients in one block spares a significant amount of
transmitted information, but some information is lost. Different
approximations for image transformation have been evaluated as
polynomial representation (Vandermonde matrix), least squares +
gradient descent, 1-D Chebyshev polynomials, 2-D Chebyshev
polynomials or singular value decomposition (SVD). Results have
been compared in terms of nominal compression rate (NCR),
compression ratio (CR) and peak signal-to-noise ratio (PSNR)
in order to minimize the error function defined as the difference
between the original pixel gray levels and the approximated
polynomial output. Polynomial coefficients have been later encoded
and handled for generating chirps in a target rate of about two
chirps per 4 × 4 pixel block and then submitted to a transmission
multiplexing operation in the time-frequency domain.
Abstract: This paper describes a subarray based low
computational design method of multiuser massive multiple
input multiple output (MIMO) system. In our previous works, use of
large array is assumed only in transmitter, but this study considers
the case both of transmitter and receiver sides are equipped with
large array antennas. For this aim, receive arrays are also divided
into several subarrays, and the former proposed method is modified
for the synthesis of a large array from subarrays in both ends.
Through computer simulations, it is verified that the performance
of the proposed method is degraded compared with the original
approach, but it can achieve the improvement in the aspect of
complexity, namely, significant reduction of the computational load
to the practical level.
Abstract: This paper treats different aspects of entropy measure
in classical information theory and statistical quantum mechanics, it
presents the possibility of extending the definition of Von Neumann
entropy to image and array processing. In the first part, we generalize
the quantum entropy using singular values of arbitrary rectangular
matrices to measure the randomness and the quality of denoising
operation, this new definition of entropy can be implemented to
compare the performance analysis of filtering methods. In the second
part, we apply the concept of pure state in quantum formalism
to generalize the maximum entropy method for narrowband and
farfield source localization problem. Several computer simulation
results are illustrated to demonstrate the effectiveness of the proposed
techniques.
Abstract: Singular value decomposition based optimisation of
geometric design parameters of a 5-speed gearbox is studied. During
the optimisation, a four-degree-of freedom torsional vibration model
of the pinion gear-wheel gear system is obtained and the minimum
singular value of the transfer matrix is considered as the objective
functions. The computational cost of the associated singular value
problems is quite low for the objective function, because it is only
necessary to compute the largest and smallest singular values (μmax
and μmin) that can be achieved by using selective eigenvalue solvers;
the other singular values are not needed. The design parameters are
optimised under several constraints that include bending stress,
contact stress and constant distance between gear centres. Thus, by
optimising the geometric parameters of the gearbox such as, the
module, number of teeth and face width it is possible to obtain a
light-weight-gearbox structure. It is concluded that the all optimised
geometric design parameters also satisfy all constraints.
Abstract: Subspace channel estimation methods have been
studied widely, where the subspace of the covariance matrix is
decomposed to separate the signal subspace from noise subspace. The
decomposition is normally done by using either the eigenvalue
decomposition (EVD) or the singular value decomposition (SVD) of
the auto-correlation matrix (ACM). However, the subspace
decomposition process is computationally expensive. This paper
considers the estimation of the multipath slow frequency hopping
(FH) channel using noise space based method. In particular, an
efficient method is proposed to estimate the multipath time delays by
applying multiple signal classification (MUSIC) algorithm which is
based on the null space extracted by the rank revealing LU (RRLU)
factorization. As a result, precise information is provided by the
RRLU about the numerical null space and the rank, (i.e., important
tool in linear algebra). The simulation results demonstrate the
effectiveness of the proposed novel method by approximately
decreasing the computational complexity to the half as compared
with RRQR methods keeping the same performance.
Abstract: Image or document encryption is needed through egovernment
data base. Really in this paper we introduce two matrices
images, one is the public, and the second is the secret (original). The
analyses of each matrix is achieved using the transformation of
singular values decomposition. So each matrix is transformed or
analyzed to three matrices say row orthogonal basis, column
orthogonal basis, and spectral diagonal basis. Product of the two row
basis is calculated. Similarly the product of the two column basis is
achieved. Finally we transform or save the files of public, row
product and column product. In decryption stage, the original image
is deduced by mutual method of the three public files.
Abstract: Key frame extraction methods select the most
representative frames of a video, which can be used in different areas
of video processing such as video retrieval, video summary, and video
indexing. In this paper we present a novel approach for extracting key
frames from video sequences. The frame is characterized uniquely by
his contours which are represented by the dominant blocks. These
dominant blocks are located on the contours and its near textures.
When the video frames have a noticeable changement, its dominant
blocks changed, then we can extracte a key frame. The dominant
blocks of every frame is computed, and then feature vectors are
extracted from the dominant blocks image of each frame and arranged
in a feature matrix. Singular Value Decomposition is used to calculate
sliding windows ranks of those matrices. Finally the computed ranks
are traced and then we are able to extract key frames of a video.
Experimental results show that the proposed approach is robust
against a large range of digital effects used during shot transition.
Abstract: The present work deals with the optimal placement of piezoelectric actuators on a thin plate using Modified Control Matrix and Singular Value Decomposition (MCSVD) approach. The problem has been formulated using the finite element method using ten piezoelectric actuators on simply supported plate to suppress first six modes. The sizes of ten actuators are combined to outline one actuator by adding the ten columns of control matrix to form a column matrix. The singular value of column control matrix is considered as the fitness function and optimal positions of the actuators are obtained by maximizing it with GA. Vibration suppression has been studied for simply supported plate with piezoelectric patches in optimal positions using Linear Quadratic regulator) scheme. It is observed that MCSVD approach has given the position of patches adjacent to each-other, symmetric to the centre axis and given greater vibration suppression than other previously published results on SVD.
Abstract: Robust stability and performance are the two most
basic features of feedback control systems. The harmonic balance
analysis technique enables to analyze the stability of limit cycles
arising from a neural network control based system operating over
nonlinear plants. In this work a robust stability analysis based on the
harmonic balance is presented and applied to a neural based control
of a non-linear binary distillation column with unstructured
uncertainty. We develop ways to describe uncertainty in the form of
neglected nonlinear dynamics and high harmonics for the plant and
controller respectively. Finally, conclusions about the performance of
the neural control system are discussed using the Nyquist stability
margin together with the structured singular values of the uncertainty
as a robustness measure.
Abstract: This paper deals with modeling and parameter
identification of nonlinear systems described by Hammerstein model
having Piecewise nonlinear characteristics such as Dead-zone
nonlinearity characteristic. The simultaneous use of both an easy
decomposition technique and the triangular basis functions leads to a
particular form of Hammerstein model. The approximation by using
Triangular basis functions for the description of the static nonlinear
block conducts to a linear regressor model, so that least squares
techniques can be used for the parameter estimation. Singular Values
Decomposition (SVD) technique has been applied to separate the
coupled parameters. The proposed approach has been efficiently
tested on academic examples of simulation.
Abstract: As a method of expanding a higher-order tensor data to tensor products of vectors we have proposed the Third-order Orthogonal Tensor Product Expansion (3OTPE) that did similar expansion as Higher-Order Singular Value Decomposition (HOSVD). In this paper we provide a computation algorithm to improve our previous method, in which SVD is applied to the matrix that constituted by the contraction of original tensor data and one of the expansion vector obtained. The residual of the improved method is smaller than the previous method, truncating the expanding tensor products to the same number of terms. Moreover, the residual is smaller than HOSVD when applying to color image data. It is able to be confirmed that the computing time of improved method is the same as the previous method and considerably better than HOSVD.
Abstract: In this paper, we investigate a blind channel estimation method for Multi-carrier CDMA systems that use a subspace decomposition technique. This technique exploits the orthogonality property between the noise subspace and the received user codes to obtain channel of each user. In the past we used Singular Value Decomposition (SVD) technique but SVD have most computational complexity so in this paper use a new algorithm called URV Decomposition, which serve as an intermediary between the QR decomposition and SVD, replaced in SVD technique to track the noise space of the received data. Because of the URV decomposition has almost the same estimation performance as the SVD, but has less computational complexity.
Abstract: Monitoring the tool flank wear without affecting the
throughput is considered as the prudent method in production
technology. The examination has to be done without affecting the
machining process. In this paper we proposed a novel work that is
used to determine tool flank wear by observing the sound signals
emitted during the turning process. The work-piece material we used
here is steel and aluminum and the cutting insert was carbide
material. Two different cutting speeds were used in this work. The
feed rate and the cutting depth were constant whereas the flank wear
was a variable. The emitted sound signal of a fresh tool (0 mm flank
wear) a slightly worn tool (0.2 -0.25 mm flank wear) and a severely
worn tool (0.4mm and above flank wear) during turning process were
recorded separately using a high sensitive microphone. Analysis
using Singular Value Decomposition was done on these sound
signals to extract the feature sound components. Observation of the
results showed that an increase in tool flank wear correlates with an
increase in the values of SVD features produced out of the sound
signals for both the materials. Hence it can be concluded that wear
monitoring of tool flank during turning process using SVD features
with the Fuzzy C means classification on the emitted sound signal is
a potential and relatively simple method.
Abstract: Digital watermarking is one of the techniques for
copyright protection. In this paper, a normalization-based robust
image watermarking scheme which encompasses singular value
decomposition (SVD) and discrete cosine transform (DCT)
techniques is proposed. For the proposed scheme, the host image is
first normalized to a standard form and divided into non-overlapping
image blocks. SVD is applied to each block. By concatenating the
first singular values (SV) of adjacent blocks of the normalized image,
a SV block is obtained. DCT is then carried out on the SV blocks to
produce SVD-DCT blocks. A watermark bit is embedded in the highfrequency
band of a SVD-DCT block by imposing a particular
relationship between two pseudo-randomly selected DCT
coefficients. An adaptive frequency mask is used to adjust local
watermark embedding strength. Watermark extraction involves
mainly the inverse process. The watermark extracting method is blind
and efficient. Experimental results show that the quality degradation
of watermarked image caused by the embedded watermark is visually
transparent. Results also show that the proposed scheme is robust
against various image processing operations and geometric attacks.
Abstract: The main objective of this work is to provide a fault detection and isolation based on Markov parameters for residual generation and a neural network for fault classification. The diagnostic approach is accomplished in two steps: In step 1, the system is identified using a series of input / output variables through an identification algorithm. In step 2, the fault is diagnosed comparing the Markov parameters of faulty and non faulty systems. The Artificial Neural Network is trained using predetermined faulty conditions serves to classify the unknown fault. In step 1, the identification is done by first formulating a Hankel matrix out of Input/ output variables and then decomposing the matrix via singular value decomposition technique. For identifying the system online sliding window approach is adopted wherein an open slit slides over a subset of 'n' input/output variables. The faults are introduced at arbitrary instances and the identification is carried out in online. Fault residues are extracted making a comparison of the first five Markov parameters of faulty and non faulty systems. The proposed diagnostic approach is illustrated on benchmark problems with encouraging results.
Abstract: A proof of convergence of a new continuation algorithm for computing the Analytic SVD for a large sparse parameter– dependent matrix is given. The algorithm itself was developed and numerically tested in [5].