Abstract: In this paper, an image adaptive, invisible digital
watermarking algorithm with Orthogonal Polynomials based
Transformation (OPT) is proposed, for copyright protection of digital
images. The proposed algorithm utilizes a visual model to determine
the watermarking strength necessary to invisibly embed the
watermark in the mid frequency AC coefficients of the cover image,
chosen with a secret key. The visual model is designed to generate a
Just Noticeable Distortion mask (JND) by analyzing the low level
image characteristics such as textures, edges and luminance of the
cover image in the orthogonal polynomials based transformation
domain. Since the secret key is required for both embedding and
extraction of watermark, it is not possible for an unauthorized user to
extract the embedded watermark. The proposed scheme is robust to
common image processing distortions like filtering, JPEG
compression and additive noise. Experimental results show that the
quality of OPT domain watermarked images is better than its DCT
counterpart.
Abstract: The selective recovery of heavy metals of Cu, Zn, Ni and Cr from a mixed plating sludge by sulfidation and oxidation treatment was targeted in this study. At first, the mixed plating sludge was simultaneously subjected to an extraction and Cu sulfidation process at pH=1.5 to dissolve heavy metals and to precipitate Cu2+ as CuS. In the next step, the sulfidation treatment of Zn was carried out at pH=4.5 and the residual solution was subjected to an oxidation treatment of chromium with H2O2 at pH=10.0. After the experiments, the selectivity of metal precipitation and the chromium oxidation ratio were evaluated. As results, it was found that the filter cake obtained after selective sulfidation of Cu was composed of 96.6% of Cu (100% equals to the sum of Cu, Zn, Ni and Cr contents). Such findings confirmed that almost complete extraction of heavy metals was achieved at pH=1.5 and also that Cu could be selectively recovered as CuS. Further, the filter cake obtained at pH=4.5 was composed of 91.5% Zn and 6.83% of Cr. Regarding the chromium oxidation step, the chromium oxidation ratio was found to increase with temperature and the addition of oxidation agent of H2O2, but only oxidation ratio of 59% was achieved at a temperature of 60°C and H2O2 to Cr3+ equivalent ratio of 180.
Abstract: Wrist pulse analysis for identification of health status
is found in Ancient Indian as well as Chinese literature. The preprocessing
of wrist pulse is necessary to remove outlier pulses and
fluctuations prior to the analysis of pulse pressure signal. This paper
discusses the identification of irregular pulses present in the pulse
series and intricacies associated with the extraction of time domain
pulse features. An approach of Dynamic Time Warping (DTW) has
been utilized for the identification of outlier pulses in the wrist pulse
series. The ambiguity present in the identification of pulse features is
resolved with the help of first derivative of Ensemble Average of
wrist pulse series. An algorithm for detecting tidal and dicrotic notch
in individual wrist pulse segment is proposed.
Abstract: Using bottom-up image processing algorithms to predict human eye fixations and extract the relevant embedded information in images has been widely applied in the design of active machine vision systems. Scene text is an important feature to be extracted, especially in vision-based mobile robot navigation as many potential landmarks such as nameplates and information signs contain text. This paper proposes an edge-based text region extraction algorithm, which is robust with respect to font sizes, styles, color/intensity, orientations, and effects of illumination, reflections, shadows, perspective distortion, and the complexity of image backgrounds. Performance of the proposed algorithm is compared against a number of widely used text localization algorithms and the results show that this method can quickly and effectively localize and extract text regions from real scenes and can be used in mobile robot navigation under an indoor environment to detect text based landmarks.
Abstract: The evaluation and measurement of human body
dimensions are achieved by physical anthropometry. This research
was conducted in view of the importance of anthropometric indices
of the face in forensic medicine, surgery, and medical imaging. The
main goal of this research is to optimization of facial feature point by
establishing a mathematical relationship among facial features and
used optimize feature points for age classification. Since selected
facial feature points are located to the area of mouth, nose, eyes and
eyebrow on facial images, all desire facial feature points are extracted
accurately. According this proposes method; sixteen Euclidean
distances are calculated from the eighteen selected facial feature
points vertically as well as horizontally. The mathematical
relationships among horizontal and vertical distances are established.
Moreover, it is also discovered that distances of the facial feature
follows a constant ratio due to age progression. The distances
between the specified features points increase with respect the age
progression of a human from his or her childhood but the ratio of the
distances does not change (d = 1 .618 ) . Finally, according to the
proposed mathematical relationship four independent feature
distances related to eight feature points are selected from sixteen
distances and eighteen feature point-s respectively. These four feature
distances are used for classification of age using Support Vector
Machine (SVM)-Sequential Minimal Optimization (SMO) algorithm
and shown around 96 % accuracy. Experiment result shows the
proposed system is effective and accurate for age classification.
Abstract: With the explosive growth of information sources available on the World Wide Web, it has become increasingly difficult to identify the relevant pieces of information, since web pages are often cluttered with irrelevant content like advertisements, navigation-panels, copyright notices etc., surrounding the main content of the web page. Hence, tools for the mining of data regions, data records and data items need to be developed in order to provide value-added services. Currently available automatic techniques to mine data regions from web pages are still unsatisfactory because of their poor performance and tag-dependence. In this paper a novel method to extract data items from the web pages automatically is proposed. It comprises of two steps: (1) Identification and Extraction of the data regions based on visual clues information. (2) Identification of data records and extraction of data items from a data region. For step1, a novel and more effective method is proposed based on visual clues, which finds the data regions formed by all types of tags using visual clues. For step2 a more effective method namely, Extraction of Data Items from web Pages (EDIP), is adopted to mine data items. The EDIP technique is a list-based approach in which the list is a linear data structure. The proposed technique is able to mine the non-contiguous data records and can correctly identify data regions, irrespective of the type of tag in which it is bound. Our experimental results show that the proposed technique performs better than the existing techniques.
Abstract: With the globalized production and logistics
environment, the need for reducing the product development interval
and lead time, having a faster response to orders, conforming to quality
standards, fair tracking, and boosting information exchanging
activities with customers and partners, and coping with changes in the
management environment, manufacturers are in dire need of an
information management system in their manufacturing environments.
There are lots of information systems that have been designed to
manage the condition or operation of equipment in the field but
existing systems have a decentralized architecture, which is not
unified. Also, these systems cannot effectively handle the status data
extraction process upon encountering a problem related to protocols or
changes in the equipment or the setting. In this regard, this paper will
introduce a system for processing and saving the status info of
production equipment, which uses standard representation formats, to
enable flexible responses to and support for variables in the field
equipment. This system can be used for a variety of manufacturing and
equipment settings and is capable of interacting with higher-tier
systems such as MES.
Abstract: In this paper, we present the region based hidden Markov random field model (RBHMRF), which encodes the characteristics of different brain regions into a probabilistic framework for brain MR image segmentation. The recently proposed TV+L1 model is used for region extraction. By utilizing different spatial characteristics in different brain regions, the RMHMRF model performs beyond the current state-of-the-art method, the hidden Markov random field model (HMRF), which uses identical spatial information throughout the whole brain. Experiments on both real and synthetic 3D MR images show that the segmentation result of the proposed method has higher accuracy compared to existing algorithms.
Abstract: In this paper, an efficient local appearance feature
extraction method based the multi-resolution Curvelet transform is
proposed in order to further enhance the performance of the well
known Linear Discriminant Analysis(LDA) method when applied
to face recognition. Each face is described by a subset of band
filtered images containing block-based Curvelet coefficients. These
coefficients characterize the face texture and a set of simple statistical
measures allows us to form compact and meaningful feature vectors.
The proposed method is compared with some related feature extraction
methods such as Principal component analysis (PCA), as well
as Linear Discriminant Analysis LDA, and independent component
Analysis (ICA). Two different muti-resolution transforms, Wavelet
(DWT) and Contourlet, were also compared against the Block Based
Curvelet-LDA algorithm. Experimental results on ORL, YALE and
FERET face databases convince us that the proposed method provides
a better representation of the class information and obtains much
higher recognition accuracies.
Abstract: The design of a pattern classifier includes an attempt
to select, among a set of possible features, a minimum subset of
weakly correlated features that better discriminate the pattern classes.
This is usually a difficult task in practice, normally requiring the
application of heuristic knowledge about the specific problem
domain. The selection and quality of the features representing each
pattern have a considerable bearing on the success of subsequent
pattern classification. Feature extraction is the process of deriving
new features from the original features in order to reduce the cost of
feature measurement, increase classifier efficiency, and allow higher
classification accuracy. Many current feature extraction techniques
involve linear transformations of the original pattern vectors to new
vectors of lower dimensionality. While this is useful for data
visualization and increasing classification efficiency, it does not
necessarily reduce the number of features that must be measured
since each new feature may be a linear combination of all of the
features in the original pattern vector. In this paper a new approach is
presented to feature extraction in which feature selection, feature
extraction, and classifier training are performed simultaneously using
a genetic algorithm. In this approach each feature value is first
normalized by a linear equation, then scaled by the associated weight
prior to training, testing, and classification. A knn classifier is used to
evaluate each set of feature weights. The genetic algorithm optimizes
a vector of feature weights, which are used to scale the individual
features in the original pattern vectors in either a linear or a nonlinear
fashion. By this approach, the number of features used in classifying
can be finely reduced.
Abstract: A new approach based on the consideration that electroencephalogram (EEG) signals are chaotic signals was presented for automated diagnosis of electroencephalographic changes. This consideration was tested successfully using the nonlinear dynamics tools, like the computation of Lyapunov exponents. This paper presented the usage of statistics over the set of the Lyapunov exponents in order to reduce the dimensionality of the extracted feature vectors. Since classification is more accurate when the pattern is simplified through representation by important features, feature extraction and selection play an important role in classifying systems such as neural networks. Multilayer perceptron neural network (MLPNN) architectures were formulated and used as basis for detection of electroencephalographic changes. Three types of EEG signals (EEG signals recorded from healthy volunteers with eyes open, epilepsy patients in the epileptogenic zone during a seizure-free interval, and epilepsy patients during epileptic seizures) were classified. The selected Lyapunov exponents of the EEG signals were used as inputs of the MLPNN trained with Levenberg- Marquardt algorithm. The classification results confirmed that the proposed MLPNN has potential in detecting the electroencephalographic changes.
Abstract: The recovery of metal values and safe disposal of
spent catalyst is gaining interest due to both its hazardous nature and
increased regulation associated with disposal methods. Prior to the
recovery of the valuable metals, removal of entrained deposits limit
the diffusion of lixiviate resulting in low recovery of metals must be
taken into consideration. Therefore, petroleum refinery spent catalyst
was subjected to acetone washing and roasting at 500oC. The treated
samples were investigated for metals bioleaching using
Acidithiobacillus ferrooxidans in batch reactors and the leaching
efficiencies were compared. It was found out that acetone washed
spent catalysts results in better metal recovery compare to roasted
spent. About 83% Ni, 20% Al, 50% Mo and 73% V were leached
using the acetone washed spent catalyst. In both the cases, Ni, V and
Mo was high compared to Al.
Abstract: This paper makes an attempt to solve the problem of
searching and retrieving of similar MRI photos via Internet services
using morphological features which are sourced via the original
image. This study is aiming to be considered as an additional tool of
searching and retrieve methods. Until now the main way of the
searching mechanism is based on the syntactic way using keywords.
The technique it proposes aims to serve the new requirements of
libraries. One of these is the development of computational tools for
the control and preservation of the intellectual property of digital
objects, and especially of digital images. For this purpose, this paper
proposes the use of a serial number extracted by using a previously
tested semantic properties method. This method, with its center being
the multi-layers of a set of arithmetic points, assures the following
two properties: the uniqueness of the final extracted number and the
semantic dependence of this number on the image used as the
method-s input. The major advantage of this method is that it can
control the authentication of a published image or its partial
modification to a reliable degree. Also, it acquires the better of the
known Hash functions that the digital signature schemes use and
produces alphanumeric strings for cases of authentication checking,
and the degree of similarity between an unknown image and an
original image.
Abstract: Subcritical water extraction was investigated as a
novel and alternative technology in the food and pharmaceutical
industry for the separation of Mannitol from olive leaves and its
results was compared with those of Soxhlet extraction. The effects of
temperature, pressure, and flow rate of water and also momentum
and mass transfer dimensionless variables such as Reynolds and
Peclet Numbers on extraction yield and equilibrium partition
coefficient were investigated. The 30-110 bars, 60-150°C, and flow
rates of 0.2-2 mL/min were the water operating conditions. The
results revealed that the highest Mannitol yield was obtained at
100°C and 50 bars. However, extraction of Mannitol was not
influenced by the variations of flow rate. The mathematical modeling
of experimental measurements was also investigated and the model is
capable of predicting the experimental measurements very well. In
addition, the results indicated higher extraction yield for the
subcritical water extraction in contrast to Soxhlet method.
Abstract: An image texture analysis and target recognition approach of using an improved image texture feature coding method (TFCM) and Support Vector Machine (SVM) for target detection is presented. With our proposed target detection framework, targets of interest can be detected accurately. Cascade-Sliding-Window technique was also developed for automated target localization. Application to mammogram showed that over 88% of normal mammograms and 80% of abnormal mammograms can be correctly identified. The approach was also successfully applied to Synthetic Aperture Radar (SAR) and Ground Penetrating Radar (GPR) images for target detection.
Abstract: Text Mining is an important step of Knowledge
Discovery process. It is used to extract hidden information from notstructured
o semi-structured data. This aspect is fundamental because
much of the Web information is semi-structured due to the nested
structure of HTML code, much of the Web information is linked,
much of the Web information is redundant. Web Text Mining helps
whole knowledge mining process to mining, extraction and
integration of useful data, information and knowledge from Web
page contents.
In this paper, we present a Web Text Mining process able to
discover knowledge in a distributed and heterogeneous multiorganization
environment. The Web Text Mining process is based on
flexible architecture and is implemented by four steps able to
examine web content and to extract useful hidden information
through mining techniques. Our Web Text Mining prototype starts
from the recovery of Web job offers in which, through a Text Mining
process, useful information for fast classification of the same are
drawn out, these information are, essentially, job offer place and
skills.
Abstract: This paper describes a novel and effective approach to content-based image retrieval (CBIR) that represents each image in the database by a vector of feature values called “Standard deviation of mean vectors of color distribution of rows and columns of images for CBIR". In many areas of commerce, government, academia, and hospitals, large collections of digital images are being created. This paper describes the approach that uses contents as feature vector for retrieval of similar images. There are several classes of features that are used to specify queries: colour, texture, shape, spatial layout. Colour features are often easily obtained directly from the pixel intensities. In this paper feature extraction is done for the texture descriptor that is 'variance' and 'Variance of Variances'. First standard deviation of each row and column mean is calculated for R, G, and B planes. These six values are obtained for one image which acts as a feature vector. Secondly we calculate variance of the row and column of R, G and B planes of an image. Then six standard deviations of these variance sequences are calculated to form a feature vector of dimension six. We applied our approach to a database of 300 BMP images. We have determined the capability of automatic indexing by analyzing image content: color and texture as features and by applying a similarity measure Euclidean distance.
Abstract: Data mining is an extraordinarily demanding field referring to extraction of implicit knowledge and relationships, which are not explicitly stored in databases. A wide variety of methods of data mining have been introduced (classification, characterization, generalization...). Each one of these methods includes more than algorithm. A system of data mining implies different user categories,, which mean that the user-s behavior must be a component of the system. The problem at this level is to know which algorithm of which method to employ for an exploratory end, which one for a decisional end, and how can they collaborate and communicate. Agent paradigm presents a new way of conception and realizing of data mining system. The purpose is to combine different algorithms of data mining to prepare elements for decision-makers, benefiting from the possibilities offered by the multi-agent systems. In this paper the agent framework for data mining is introduced, and its overall architecture and functionality are presented. The validation is made on spatial data. Principal results will be presented.
Abstract: This paper presents a novel method for data hiding based on neighborhood pixels information to calculate the number of bits that can be used for substitution and modified Least Significant Bits technique for data embedding. The modified solution is independent of the nature of the data to be hidden and gives correct results along with un-noticeable image degradation. The technique, to find the number of bits that can be used for data hiding, uses the green component of the image as it is less sensitive to human eye and thus it is totally impossible for human eye to predict whether the image is encrypted or not. The application further encrypts the data using a custom designed algorithm before embedding bits into image for further security. The overall process consists of three main modules namely embedding, encryption and extraction cm.
Abstract: The Automatic Speech Recognition (ASR) applied to
Arabic language is a challenging task. This is mainly related to the
language specificities which make the researchers facing multiple
difficulties such as the insufficient linguistic resources and the very
limited number of available transcribed Arabic speech corpora. In
this paper, we are interested in the development of a HMM-based
ASR system for Standard Arabic (SA) language. Our fundamental
research goal is to select the most appropriate acoustic parameters
describing each audio frame, acoustic models and speech recognition
unit. To achieve this purpose, we analyze the effect of varying frame
windowing (size and period), acoustic parameter number resulting
from features extraction methods traditionally used in ASR, speech
recognition unit, Gaussian number per HMM state and number of
embedded re-estimations of the Baum-Welch Algorithm. To evaluate
the proposed ASR system, a multi-speaker SA connected-digits
corpus is collected, transcribed and used throughout all experiments.
A further evaluation is conducted on a speaker-independent continue
SA speech corpus. The phonemes recognition rate is 94.02% which is
relatively high when comparing it with another ASR system
evaluated on the same corpus.