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: In recent decades, flapping wing aerodynamics has attracted great interest. Understanding the physics of biological flyers such as birds and insects can help improve the performance of micro air vehicles. The present research focuses on the aerodynamics of insect-like flapping wing flight with the approach of numerical computation. Insect model of hawkmoth is adopted in the numerical study with rigid wing assumption currently. The numerical model integrates the computational fluid dynamics of the flow and active control of wing kinematics to achieve stable flight. The computation grid is a hybrid consisting of background Cartesian nodes and clouds of mesh-free grids around immersed boundaries. The generalized finite difference method is used in conjunction with single value decomposition (SVD-GFD) in computational fluid dynamics solver to study the dynamics of a free hovering hummingbird hawkmoth. The longitudinal dynamics of the hovering flight is governed by three control parameters, i.e., wing plane angle, mean positional angle and wing beating frequency. In present work, a PID controller works out the appropriate control parameters with the insect motion as input. The controller is adjusted to acquire desired maneuvering of the insect flight. The numerical scheme in present study is proven to be accurate and stable to simulate the flight of the hummingbird hawkmoth, which has relatively high Reynolds number. The PID controller is responsive to provide feedback to the wing kinematics during the hovering flight. The simulated hovering flight agrees well with the real insect flight. The present numerical study offers a promising route to investigate the free flight aerodynamics of insects, which could overcome some of the limitations of experiments.
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: 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: To evaluate the factors which predetermine the
coronary artery disease in patients having positive Exercise Tolerance
Test (ETT) that is treadmill results and coronary artery findings. This
descriptive study was conducted at Department of Cardiology,
Ibrahim Cardiac Hospital & Research Institute, Dhaka, Bangladesh
from 1st January, 2014 to 31st August, 2014. All patients who had
done ETT (treadmill) for chest pain diagnosis were studied. One
hundred and four patients underwent coronary angiogram after
positive treadmill result. Patients were divided into two groups
depending upon the angiographic findings, i.e. true positive and false
positive. Positive treadmill test patients who have coronary artery
involvement these are called true positive and who have no
involvement they are called false positive group. Both groups were
compared with each other. Out of 104 patients, 81 (77.9%) patients
had true positive ETT and 23 (22.1%) patients had false positive
ETT. The mean age of patients in positive ETT was 53.46± 8.06
years and male mean age was 53.63±8.36 years and female was
52.87±7.0 years. Sixty nine (85.19%) male patients and twelve
(14.81%) female patients had true positive ETT, whereas 15
(65.21%) males and 8 (34.79%) females had false positive ETT, this
was statistically significant (p
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 paper focuses on the problem of the point
correspondence matching in stereo images. The proposed matching
algorithm is based on the combination of simpler methods such as
normalized sum of squared differences (NSSD) and a more complex
phase correlation based approach, by considering the noise and other
factors, as well. The speed of NSSD and the preciseness of the
phase correlation together yield an efficient approach to find the best
candidate point with sub-pixel accuracy in stereo image pairs. The
task of the NSSD in this case is to approach the candidate pixel
roughly. Afterwards the location of the candidate is refined by an
enhanced phase correlation based method which in contrast to the
NSSD has to run only once for each selected pixel.
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: 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: In this paper, Optimum adaptive loading algorithms
are applied to multicarrier system with Space-Time Block Coding
(STBC) scheme associated with space-time processing based on
singular-value decomposition (SVD) of the channel matrix over
Rayleigh fading channels. SVD method has been employed in
MIMO-OFDM system in order to overcome subchannel interference.
Chaw-s and Compello-s algorithms have been implemented to obtain
a bit and power allocation for each subcarrier assuming instantaneous
channel knowledge. The adaptive loaded SVD-STBC scheme is
capable of providing both full-rate and full-diversity for any number
of transmit antennas. The effectiveness of these techniques has
demonstrated through the simulation of an Adaptive loaded SVDSTBC
system, and the comparison shown that the proposed
algorithms ensure better performance in the case of MIMO.
Abstract: The COSvd Ciphers has been proposed by Filiol and others (2004). It is a strengthened version of COS stream cipher family denoted COSvd that has been adopted for at least one commercial standard. We propose a distinguish attack on this version, and prove that, it is distinguishable from a random stream. In the COSvd Cipher used one S-Box (10×8) on the final part of cipher. We focus on S-Box and use weakness this S-Box for distinguish attack. In addition, found a leak on HNLL that the sub s-boxes don-t select uniformly. We use this property for an Improve distinguish attack.
Abstract: Automatic reusability appraisal could be helpful in
evaluating the quality of developed or developing reusable software
components and in identification of reusable components from
existing legacy systems; that can save cost of developing the software
from scratch. But the issue of how to identify reusable components
from existing systems has remained relatively unexplored. In this
paper, we have mentioned two-tier approach by studying the
structural attributes as well as usability or relevancy of the
component to a particular domain. Latent semantic analysis is used
for the feature vector representation of various software domains. It
exploits the fact that FeatureVector codes can be seen as documents
containing terms -the idenifiers present in the components- and so
text modeling methods that capture co-occurrence information in
low-dimensional spaces can be used. Further, we devised Neuro-
Fuzzy hybrid Inference System, which takes structural metric values
as input and calculates the reusability of the software component.
Decision tree algorithm is used to decide initial set of fuzzy rules for
the Neuro-fuzzy system. The results obtained are convincing enough
to propose the system for economical identification and retrieval of
reusable software components.
Abstract: In manufacturing industries, development of measurement leads to increase the number of monitoring variables and eventually the importance of multivariate control comes to the fore. Statistical process control (SPC) is one of the most widely used as multivariate control chart. Nevertheless, SPC is restricted to apply in processes because its assumption of data as following specific distribution. Unfortunately, process data are composed by the mixture of several processes and it is hard to estimate as one certain distribution. To alternative conventional SPC, therefore, nonparametric control chart come into the picture because of the strength of nonparametric control chart, the absence of parameter estimation. SVDD based control chart is one of the nonparametric control charts having the advantage of flexible control boundary. However,basic concept of SVDD has been an oversight to the important of data characteristic, density distribution. Therefore, we proposed DW-SVDD (Density Weighted SVDD) to cover up the weakness of conventional SVDD. DW-SVDD makes a new attempt to consider dense of data as introducing the notion of density Weight. We extend as control chart using new proposed SVDD and a simulation study of various distributional data is conducted to demonstrate the improvement of performance.
Abstract: We study the performance of compressed beamforming
weights feedback technique in generalized triangular decomposition
(GTD) based MIMO system. GTD is a beamforming technique that
enjoys QoS flexibility. The technique, however, will perform at its
optimum only when the full knowledge of channel state information
(CSI) is available at the transmitter. This would be impossible in
the real system, where there are channel estimation error and limited
feedback. We suggest a way to implement the quantized beamforming
weights feedback, which can significantly reduce the feedback data,
on GTD-based MIMO system and investigate the performance of
the system. Interestingly, we found that compressed beamforming
weights feedback does not degrade the BER performance of the
system at low input power, while the channel estimation error
and quantization do. For comparison, GTD is more sensitive to
compression and quantization, while SVD is more sensitive to the
channel estimation error. We also explore the performance of GTDbased
MU-MIMO system, and find that the BER performance starts
to degrade largely at around -20 dB channel estimation error.
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].