Abstract: Controlling the flow of fluids is a challenging problem
that arises in many fields. Burgers’ equation is a fundamental
equation for several flow phenomena such as traffic, shock waves,
and turbulence. The optimal feedback control method, so-called
model predictive control, has been proposed for Burgers’ equation.
However, the model predictive control method is inapplicable to
systems whose all state variables are not exactly known. In practical
point of view, it is unusual that all the state variables of systems are
exactly known, because the state variables of systems are measured
through output sensors and limited parts of them can be only
available. In fact, it is usual that flow velocities of fluid systems
cannot be measured for all spatial domains. Hence, any practical
feedback controller for fluid systems must incorporate some type of
state estimator. To apply the model predictive control to the fluid
systems described by Burgers’ equation, it is needed to establish
a state estimation method for Burgers’ equation with limited
measurable state variables. To this purpose, we apply unscented
Kalman filter for estimating the state variables of fluid systems
described by Burgers’ equation. The objective of this study is to
establish a state estimation method based on unscented Kalman filter
for Burgers’ equation. The effectiveness of the proposed method is
verified by numerical simulations.
Abstract: In this paper, an approach is presented to investigate the performance of Pixel Factor Mapping (PFM) and Extended PMM (Pixel Mapping Method) through the qualitative and quantitative approach. These methods are tested against a number of well-known image similarity metrics and statistical distribution techniques. The PFM has been performed in spatial domain as well as frequency domain and the Extended PMM has also been performed in spatial domain through large set of images available in the internet.
Abstract: In this paper, we propose an improved face recognition algorithm using histogram-based features in spatial and frequency domains. For adding spatial information of the face to improve recognition performance, a region-division (RD) method is utilized. The facial area is firstly divided into several regions, then feature vectors of each facial part are generated by Binary Vector Quantization (BVQ) histogram using DCT coefficients in low frequency domains, as well as Local Binary Pattern (LBP) histogram in spatial domain. Recognition results with different regions are first obtained separately and then fused by weighted averaging. Publicly available ORL database is used for the evaluation of our proposed algorithm, which is consisted of 40 subjects with 10 images per subject containing variations in lighting, posing, and expressions. It is demonstrated that face recognition using RD method can achieve much higher recognition rate.
Abstract: This paper presents two techniques, local feature
extraction using image spectrum and low frequency spectrum
modelling using GMM to capture the underlying statistical
information to improve the performance of face recognition
system. Local spectrum features are extracted using overlap sub
block window that are mapped on the face image. For each of this
block, spatial domain is transformed to frequency domain using
DFT. A low frequency coefficient is preserved by discarding high
frequency coefficients by applying rectangular mask on the
spectrum of the facial image. Low frequency information is non-
Gaussian in the feature space and by using combination of several
Gaussian functions that has different statistical properties, the best
feature representation can be modelled using probability density
function. The recognition process is performed using maximum
likelihood value computed using pre-calculated GMM components.
The method is tested using FERET datasets and is able to achieved
92% recognition rates.
Abstract: Anti-scatter grids used in radiographic imaging for the contrast enhancement leave specific artifacts. Those artifacts may be visible or may cause Moiré effect when digital image is resized on a diagnostic monitor. In this paper we propose an automated grid artifactsdetection and suppression algorithm which is still an actual problem. Grid artifacts detection is based on statistical approach in spatial domain. Grid artifacts suppression is based on Kaiser bandstop filter transfer function design and application avoiding ringing artifacts. Experimental results are discussed and concluded with description of advantages over existing approaches.
Abstract: In this work we propose a novel Steganographic
method for hiding information within the spatial domain of the gray
scale image. The proposed approach works by dividing the cover into
blocks of equal sizes and then embeds the message in the edge of the
block depending on the number of ones in left four bits of the pixel.
The proposed approach is tested on a database consists of 100
different images. Experimental results, compared with other
methods, showed that the proposed approach hide more large
information and gave a good visual quality stego-image that can be
seen by human eyes.
Abstract: This paper presents a study of laminar to turbulent transition on a profile specifically designed for wind turbine blades, the DU91-W2-250, which belongs to a class of wind turbine dedicated airfoils, developed by Delft University of Technology. A comparison between the experimental behavior of the airfoil studied at Delft wind tunnel and the numerical predictions of the commercial CFD solver ANSYS FLUENT® has been performed. The prediction capabilities of the Spalart-Allmaras turbulence model and of the γ-θ Transitional model have been tested. A sensitivity analysis of the numerical results to the spatial domain discretization has also been performed using four different computational grids, which have been created using the mesher GAMBIT®. The comparison between experimental measurements and CFD results have allowed to determine the importance of the numerical prediction of the laminar to turbulent transition, in order not to overestimate airfoil friction drag due to a fully turbulent-regime flow computation.
Abstract: Assessment for image quality traditionally needs its
original image as a reference. The conventional method for assessment
like Mean Square Error (MSE) or Peak Signal to Noise Ratio (PSNR)
is invalid when there is no reference. In this paper, we present a new
No-Reference (NR) assessment of image quality using blur and noise.
The recent camera applications provide high quality images by help of
digital Image Signal Processor (ISP). Since the images taken by the
high performance of digital camera have few blocking and ringing
artifacts, we only focus on the blur and noise for predicting the
objective image quality. The experimental results show that the
proposed assessment method gives high correlation with subjective
Difference Mean Opinion Score (DMOS). Furthermore, the proposed
method provides very low computational load in spatial domain and
similar extraction of characteristics to human perceptional assessment.
Abstract: Neural processors have shown good results for
detecting a certain character in a given input matrix. In this paper, a
new idead to speed up the operation of neural processors for character
detection is presented. Such processors are designed based on cross
correlation in the frequency domain between the input matrix and the
weights of neural networks. This approach is developed to reduce the
computation steps required by these faster neural networks for the
searching process. The principle of divide and conquer strategy is
applied through image decomposition. Each image is divided into
small in size sub-images and then each one is tested separately by
using a single faster neural processor. Furthermore, faster character
detection is obtained by using parallel processing techniques to test the
resulting sub-images at the same time using the same number of faster
neural networks. In contrast to using only faster neural processors, the
speed up ratio is increased with the size of the input image when using
faster neural processors and image decomposition. Moreover, the
problem of local subimage normalization in the frequency domain is
solved. The effect of image normalization on the speed up ratio of
character detection is discussed. Simulation results show that local
subimage normalization through weight normalization is faster than
subimage normalization in the spatial domain. The overall speed up
ratio of the detection process is increased as the normalization of
weights is done off line.
Abstract: Over last two decades, due to hostilities of environment
over the internet the concerns about confidentiality of information
have increased at phenomenal rate. Therefore to safeguard the information
from attacks, number of data/information hiding methods have
evolved mostly in spatial and transformation domain.In spatial domain
data hiding techniques,the information is embedded directly on
the image plane itself. In transform domain data hiding techniques the
image is first changed from spatial domain to some other domain and
then the secret information is embedded so that the secret information
remains more secure from any attack. Information hiding algorithms
in time domain or spatial domain have high capacity and relatively
lower robustness. In contrast, the algorithms in transform domain,
such as DCT, DWT have certain robustness against some multimedia
processing.In this work the authors propose a novel steganographic
method for hiding information in the transform domain of the gray
scale image.The proposed approach works by converting the gray
level image in transform domain using discrete integer wavelet
technique through lifting scheme.This approach performs a 2-D
lifting wavelet decomposition through Haar lifted wavelet of the cover
image and computes the approximation coefficients matrix CA and
detail coefficients matrices CH, CV, and CD.Next step is to apply the
PMM technique in those coefficients to form the stego image. The
aim of this paper is to propose a high-capacity image steganography
technique that uses pixel mapping method in integer wavelet domain
with acceptable levels of imperceptibility and distortion in the cover
image and high level of overall security. This solution is independent
of the nature of the data to be hidden and produces a stego image
with minimum degradation.
Abstract: This paper addresses the development of an intelligent vision system for human-robot interaction. The two novel contributions of this paper are 1) Detection of human faces and 2) Localizing the eye. The method is based on visual attributes of human skin colors and geometrical analysis of face skeleton. This paper introduces a spatial domain filtering method named ?Fuzzily skewed filter' which incorporates Fuzzy rules for deciding the gray level of pixels in the image in their neighborhoods and takes advantages of both the median and averaging filters. The effectiveness of the method has been justified over implementing the eye tracking commands to an entertainment robot, named ''AIBO''.
Abstract: In this paper a hybrid technique of Genetic Algorithm
and Simulated Annealing (HGASA) is applied for Fractal Image
Compression (FIC). With the help of this hybrid evolutionary
algorithm effort is made to reduce the search complexity of matching
between range block and domain block. The concept of Simulated
Annealing (SA) is incorporated into Genetic Algorithm (GA) in order
to avoid pre-mature convergence of the strings. One of the image
compression techniques in the spatial domain is Fractal Image
Compression but the main drawback of FIC is that it involves more
computational time due to global search. In order to improve the
computational time along with acceptable quality of the decoded
image, HGASA technique has been proposed. Experimental results
show that the proposed HGASA is a better method than GA in terms
of PSNR for Fractal image Compression.
Abstract: We present a general comparison of punctual kriging based image restoration for different neighbourhood sizes. The formulation of the technique under consideration is based on punctual kriging and fuzzy concepts for image restoration in spatial domain. Three different neighbourhood windows are considered to estimate the semivariance at different lags for studying its effect in reduction of negative weights resulted in punctual kriging, consequently restoration of degraded images. Our results show that effect of neighbourhood size higher than 5x5 on reduction in negative weights is insignificant. In addition, image quality measures, such as structure similarity indices, peak signal to noise ratios and the new variogram based quality measures; show that 3x3 window size gives better performance as compared with larger window sizes.
Abstract: We present in this paper a new approach for specific JPEG steganalysis and propose studying statistics of the compressed DCT coefficients. Traditionally, steganographic algorithms try to preserve statistics of the DCT and of the spatial domain, but they cannot preserve both and also control the alteration of the compressed data. We have noticed a deviation of the entropy of the compressed data after a first embedding. This deviation is greater when the image is a cover medium than when the image is a stego image. To observe this deviation, we pointed out new statistic features and combined them with the Multiple Embedding Method. This approach is motivated by the Avalanche Criterion of the JPEG lossless compression step. This criterion makes possible the design of detectors whose detection rates are independent of the payload. Finally, we designed a Fisher discriminant based classifier for well known steganographic algorithms, Outguess, F5 and Hide and Seek. The experiemental results we obtained show the efficiency of our classifier for these algorithms. Moreover, it is also designed to work with low embedding rates (< 10-5) and according to the avalanche criterion of RLE and Huffman compression step, its efficiency is independent of the quantity of hidden information.
Abstract: Several methods have been proposed for color image
compression but the reconstructed image had very low signal to noise
ratio which made it inefficient. This paper describes a lossy
compression technique for color images which overcomes the
drawbacks. The technique works on spatial domain where the pixel
values of RGB planes of the input color image is mapped onto two
dimensional planes. The proposed technique produced better results
than JPEG2000, 2DPCA and a comparative study is reported based
on the image quality measures such as PSNR and MSE.Experiments
on real time images are shown that compare this methodology with
previous ones and demonstrate its advantages.
Abstract: The optimal grid spacing and turbulence model for the
2D numerical analysis of a vertical-axis water turbine (VAWaterT)
operating in a 2 m/s freestream current has been investigated. The
results of five different spatial domain discretizations and two
turbulence models (k-ω SST and k-ε RNG) have been compared, in
order to gain the optimal y+ parameter distribution along the blade
walls during a full rotor revolution. The resulting optimal mesh has
appeared to be quite similar to that obtained for the numerical
analysis of a vertical-axis wind turbine.
Abstract: Terminal localization for indoor Wireless Local Area
Networks (WLANs) is critical for the deployment of location-aware
computing inside of buildings. A major challenge is obtaining high
localization accuracy in presence of fluctuations of the received signal
strength (RSS) measurements caused by multipath fading. This paper
focuses on reducing the effect of the distance-varying noise by spatial
filtering of the measured RSS. Two different survey point geometries
are tested with the noise reduction technique: survey points arranged
in sets of clusters and survey points uniformly distributed over the
network area. The results show that the location accuracy improves
by 16% when the filter is used and by 18% when the filter is applied
to a clustered survey set as opposed to a straight-line survey set.
The estimated locations are within 2 m of the true location, which
indicates that clustering the survey points provides better localization
accuracy due to superior noise removal.
Abstract: In recent years, everything is trending toward digitalization
and with the rapid development of the Internet technologies,
digital media needs to be transmitted conveniently over the network.
Attacks, misuse or unauthorized access of information is of great
concern today which makes the protection of documents through
digital media a priority problem. This urges us to devise new data
hiding techniques to protect and secure the data of vital significance.
In this respect, steganography often comes to the fore as a tool for
hiding information. Steganography is a process that involves hiding
a message in an appropriate carrier like image or audio. It is of
Greek origin and means "covered or hidden writing". The goal of
steganography is covert communication. Here the carrier can be sent
to a receiver without any one except the authenticated receiver only
knows existence of the information. Considerable amount of work
has been carried out by different researchers on steganography. In this
work the authors propose a novel Steganographic method for hiding
information within the spatial domain of the gray scale image. The
proposed approach works by selecting the embedding pixels using
some mathematical function and then finds the 8 neighborhood of
the each selected pixel and map each bit of the secret message in
each of the neighbor pixel coordinate position in a specified manner.
Before embedding a checking has been done to find out whether the
selected pixel or its neighbor lies at the boundary of the image or not.
This solution is independent of the nature of the data to be hidden
and produces a stego image with minimum degradation.
Abstract: In this paper, a new technique for fast painting with
different colors is presented. The idea of painting relies on applying
masks with different colors to the background. Fast painting is
achieved by applying these masks in the frequency domain instead of
spatial (time) domain. New colors can be generated automatically as a
result from the cross correlation operation. This idea was applied
successfully for faster specific data (face, object, pattern, and code)
detection using neural algorithms. Here, instead of performing cross
correlation between the input input data (e.g., image, or a stream of
sequential data) and the weights of neural networks, the cross
correlation is performed between the colored masks and the
background. Furthermore, this approach is developed to reduce the
computation steps required by the painting operation. The principle of
divide and conquer strategy is applied through background
decomposition. Each background is divided into small in size subbackgrounds
and then each sub-background is processed separately by
using a single faster painting algorithm. Moreover, the fastest painting
is achieved by using parallel processing techniques to paint the
resulting sub-backgrounds using the same number of faster painting
algorithms. In contrast to using only faster painting algorithm, the
speed up ratio is increased with the size of the background when using
faster painting algorithm and background decomposition. Simulation
results show that painting in the frequency domain is faster than that in
the spatial domain.