Abstract: In recent years image watermarking has become an
important research area in data security, confidentiality and image
integrity. Many watermarking techniques were proposed for medical
images. However, medical images, unlike most of images, require
extreme care when embedding additional data within them because
the additional information must not affect the image quality and
readability. Also the medical records, electronic or not, are linked to
the medical secrecy, for that reason, the records must be confidential.
To fulfill those requirements, this paper presents a lossless
watermarking scheme for DICOM images. The proposed a fragile
scheme combines two reversible techniques based on difference
expansion for patient's data hiding and protecting the region of
interest (ROI) with tamper detection and recovery capability.
Patient's data are embedded into ROI, while recovery data are
embedded into region of non-interest (RONI). The experimental
results show that the original image can be exactly extracted from the
watermarked one in case of no tampering. In case of tampered ROI,
tampered area can be localized and recovered with a high quality
version of the original area.
Abstract: Data clustering is an important data exploration technique
with many applications in data mining. We present an enhanced
version of the well known single link clustering algorithm. We will
refer to this algorithm as DCBOR. The proposed algorithm alleviates
the chain effect by removing the outliers from the given dataset.
So this algorithm provides outlier detection and data clustering
simultaneously. This algorithm does not need to update the distance
matrix, since the algorithm depends on merging the most k-nearest
objects in one step and the cluster continues grow as long as possible
under specified condition. So the algorithm consists of two phases;
at the first phase, it removes the outliers from the input dataset. At
the second phase, it performs the clustering process. This algorithm
discovers clusters of different shapes, sizes, densities and requires
only one input parameter; this parameter represents a threshold for
outlier points. The value of the input parameter is ranging from 0 to
1. The algorithm supports the user in determining an appropriate
value for it. We have tested this algorithm on different datasets
contain outlier and connecting clusters by chain of density points,
and the algorithm discovers the correct clusters. The results of
our experiments demonstrate the effectiveness and the efficiency of
DCBOR.
Abstract: In this paper a new fast simplification method is presented. Such method realizes Karnough map with large number of variables. In order to accelerate the operation of the proposed method, a new approach for fast detection of group of ones is presented. Such approach implemented in the frequency domain. The search operation relies on performing cross correlation in the frequency domain rather than time one. It is proved mathematically and practically that the number of computation steps required for the presented method is less than that needed by conventional cross correlation. Simulation results using MATLAB confirm the theoretical computations. Furthermore, a powerful solution for realization of complex functions is given. The simplified functions are implemented by using a new desigen for neural networks. Neural networks are used because they are fault tolerance and as a result they can recognize signals even with noise or distortion. This is very useful for logic functions used in data and computer communications. Moreover, the implemented functions are realized with minimum amount of components. This is done by using modular neural nets (MNNs) that divide the input space into several homogenous regions. Such approach is applied to implement XOR function, 16 logic functions on one bit level, and 2-bit digital multiplier. Compared to previous non- modular designs, a clear reduction in the order of computations and hardware requirements is achieved.
Abstract: In this paper, an algorithm for detecting and attenuating
puff noises frequently generated under the mobile environment is
proposed. As a baseline system, puff detection system is designed
based on Gaussian Mixture Model (GMM), and 39th Mel Frequency
Cepstral Coefficient (MFCC) is extracted as feature parameters. To
improve the detection performance, effective acoustic features for puff
detection are proposed. In addition, detected puff intervals are
attenuated by high-pass filtering. The speech recognition rate was
measured for evaluation and confusion matrix and ROC curve are used
to confirm the validity of the proposed system.
Abstract: To analyze the behavior of Petri nets, the accessibility
graph and Model Checking are widely used. However, if the
analyzed Petri net is unbounded then the accessibility graph becomes
infinite and Model Checking can not be used even for small Petri
nets. ECATNets [2] are a category of algebraic Petri nets. The main
feature of ECATNets is their sound and complete semantics based on
rewriting logic [8] and its language Maude [9]. ECATNets analysis
may be done by using techniques of accessibility analysis and Model
Checking defined in Maude. But, these two techniques supported by
Maude do not work also with infinite-states systems. As a category
of Petri nets, ECATNets can be unbounded and so infinite systems.
In order to know if we can apply accessibility analysis and Model
Checking of Maude to an ECATNet, we propose in this paper an
algorithm allowing the detection if the ECATNet is bounded or not.
Moreover, we propose a rewriting logic based tool implementing this
algorithm. We show that the development of this tool using the
Maude system is facilitated thanks to the reflectivity of the rewriting
logic. Indeed, the self-interpretation of this logic allows us both the
modelling of an ECATNet and acting on it.
Abstract: The study of proteomics reached unexpected levels of
interest, as a direct consequence of its discovered influence over
some complex biological phenomena, such as problematic diseases
like cancer. This paper presents a new technique that allows for an
accurate analysis of the human interactome network. It is basically
a two-step analysis process that involves, at first, the detection of
each protein-s absolute importance through the betweenness centrality
computation. Then, the second step determines the functionallyrelated
communities of proteins. For this purpose, we use a community
detection technique that is based on the edge betweenness
calculation. The new technique was thoroughly tested on real biological
data and the results prove some interesting properties of those proteins that are involved in the carcinogenesis process. Apart from its
experimental usefulness, the novel technique is also computationally
effective in terms of execution times. Based on the analysis- results, some topological features of cancer mutated proteins are presented
and a possible optimization solution for cancer drugs design is suggested.
Abstract: Despite the fact that Arabic language is currently one
of the most common languages worldwide, there has been only a
little research on Arabic speech recognition relative to other
languages such as English and Japanese. Generally, digital speech
processing and voice recognition algorithms are of special
importance for designing efficient, accurate, as well as fast automatic
speech recognition systems. However, the speech recognition process
carried out in this paper is divided into three stages as follows: firstly,
the signal is preprocessed to reduce noise effects. After that, the
signal is digitized and hearingized. Consequently, the voice activity
regions are segmented using voice activity detection (VAD)
algorithm. Secondly, features are extracted from the speech signal
using Mel-frequency cepstral coefficients (MFCC) algorithm.
Moreover, delta and acceleration (delta-delta) coefficients have been
added for the reason of improving the recognition accuracy. Finally,
each test word-s features are compared to the training database using
dynamic time warping (DTW) algorithm. Utilizing the best set up
made for all affected parameters to the aforementioned techniques,
the proposed system achieved a recognition rate of about 98.5%
which outperformed other HMM and ANN-based approaches
available in the literature.
Abstract: In this paper we present a modification to existed model of threshold for shot cut detection, which is able to adapt itself to the sequence statistics and operate in real time, because it use for calculation only previously evaluated frames. The efficiency of proposed modified adaptive threshold scheme was verified through extensive test experiment with several similarity metrics and achieved results were compared to the results reached by the original model. According to results proposed threshold scheme reached higher accuracy than existed original model.
Abstract: Many applications of speech communication and speaker
identification suffer from the problem of co-channel speech. This
paper deals with a multi-resolution dyadic wavelet transform method
for usable segments of co-channel speech detection that could be
processed by a speaker identification system. Evaluation of this
method is performed on TIMIT database referring to the Target to
Interferer Ratio measure. Co-channel speech is constructed by
mixing all possible gender speakers. Results do not show much
difference for different mixtures. For the overall mixtures 95.76% of
usable speech is correctly detected with false alarms of 29.65%.
Abstract: This paper proposes a new performance characterization for the test strategy intended for second order filters denominated Transient Analysis Method (TRAM). We evaluate the ability of the addressed test strategy for detecting deviation faults under simultaneous statistical fluctuation of the non-faulty parameters. For this purpose, we use Monte Carlo simulations and a fault model that considers as faulty only one component of the filter under test while the others components adopt random values (within their tolerance band) obtained from their statistical distributions. The new data reported here show (for the filters under study) the presence of hard-to-test components and relatively low fault coverage values for small deviation faults. These results suggest that the fault coverage value obtained using only nominal values for the non-faulty components (the traditional evaluation of TRAM) seem to be a poor predictor of the test performance.
Abstract: Automatic detection of syllable repetition is one of the
important parameter in assessing the stuttered speech objectively.
The existing method which uses artificial neural network (ANN)
requires high levels of agreement as prerequisite before attempting to
train and test ANNs to separate fluent and nonfluent. We propose
automatic detection method for syllable repetition in read speech for
objective assessment of stuttered disfluencies which uses a novel
approach and has four stages comprising of segmentation, feature
extraction, score matching and decision logic. Feature extraction is
implemented using well know Mel frequency Cepstra coefficient
(MFCC). Score matching is done using Dynamic Time Warping
(DTW) between the syllables. The Decision logic is implemented by
Perceptron based on the score given by score matching. Although
many methods are available for segmentation, in this paper it is done
manually. Here the assessment by human judges on the read speech
of 10 adults who stutter are described using corresponding method
and the result was 83%.
Abstract: In this research study, an intelligent detection system
to support medical diagnosis and detection of abnormal lesions by
processing endoscopic images is presented. The images used in this
study have been obtained using the M2A Swallowable Imaging
Capsule - a patented, video color-imaging disposable capsule.
Schemes have been developed to extract texture features from the
fuzzy texture spectra in the chromatic and achromatic domains for a
selected region of interest from each color component histogram of
endoscopic images. The implementation of an advanced fuzzy
inference neural network which combines fuzzy systems and
artificial neural networks and the concept of fusion of multiple
classifiers dedicated to specific feature parameters have been also
adopted in this paper. The achieved high detection accuracy of the
proposed system has provided thus an indication that such intelligent
schemes could be used as a supplementary diagnostic tool in
endoscopy.
Abstract: Exploring an autistic child in Elementary school is a
difficult task that must be fully thought out and the teachers should
be aware of the many challenges they face raising their child
especially the behavioral problems of autistic children. Hence there
arises a need for developing Artificial intelligence (AI)
Contemporary Techniques to help diagnosis to discover autistic
people.
In this research, we suggest designing architecture of expert
system that combine Cognitive Maps (CM) with Case Based
Reasoning technique (CBR) in order to reduce time and costs of
traditional diagnosis process for the early detection to discover
autistic children. The teacher is supposed to enter child's information
for analyzing by CM module. Then, the reasoning processor would
translate the output into a case to be solved a current problem by
CBR module. We will implement a prototype for the model as a
proof of concept using java and MYSQL.
This will be provided a new hybrid approach that will achieve new
synergies and improve problem solving capabilities in AI. And we
will predict that will reduce time, costs, the number of human errors
and make expertise available to more people who want who want to
serve autistic children and their families.
Abstract: Existing ground movement surveillance technologies
at airports are subjected to limitations due to shadowing effects or
multiple reflections. Therefore, there is a strong demand for a new
sensing technology, which will be cost effective and will provide
detection of non-cooperative targets under any weather conditions.
This paper aims to present a new intelligent system, developed
within the framework of the EC-funded ISMAEL project, which is
based on a new magnetic sensing technology and provides detection,
tracking and automatic classification of targets moving on the airport
surface. The system is currently being installed at two European
airports. Initial experimental results under real airport traffic
demonstrate the great potential of the proposed system.
Abstract: Gaussian mixture background model is widely used in
moving target detection of the image sequences. However, traditional
Gaussian mixture background model usually considers the time
continuity of the pixels, and establishes background through statistical
distribution of pixels without taking into account the pixels- spatial
similarity, which will cause noise, imperfection and other problems.
This paper proposes a new Gaussian mixture modeling approach,
which combines the color and gradient of the spatial information, and
integrates the spatial information of the pixel sequences to establish
Gaussian mixture background. The experimental results show that the
movement background can be extracted accurately and efficiently, and
the algorithm is more robust, and can work in real time in tracking
applications.
Abstract: To evaluate genetic variation of wheat (Triticum aestivum) affected by heat and drought stress on eight Australian wheat genotypes that are parents of Doubled Haploid (HD) mapping populations at the vegetative stage, the water stress experiment was conducted at 65% field capacity in growth room. Heat stress experiment was conducted in the research field under irrigation over summer. Result show that water stress decreased dry shoot weight and RWC but increased osmolarity and means of Fv/Fm values in all varieties except for Krichauff. Krichauff and Kukri had the maximum RWC under drought stress. Trident variety was shown maximum WUE, osmolarity (610 mM/Kg), dry mater, quantum yield and Fv/Fm 0.815 under water stress condition. However, the recovery of quantum yield was apparent between 4 to 7 days after stress in all varieties. Nevertheless, increase in water stress after that lead to strong decrease in quantum yield. There was a genetic variation for leaf pigments content among varieties under heat stress. Heat stress decreased significantly the total chlorophyll content that measured by SPAD. Krichauff had maximum value of Anthocyanin content (2.978 A/g FW), chlorophyll a+b (2.001 mg/g FW) and chlorophyll a (1.502 mg/g FW). Maximum value of chlorophyll b (0.515 mg/g FW) and Carotenoids (0.234 mg/g FW) content belonged to Kukri. The quantum yield of all varieties decreased significantly, when the weather temperature increased from 28 ÔùªC to 36 ÔùªC during the 6 days. However, the recovery of quantum yield was apparent after 8th day in all varieties. The maximum decrease and recovery in quantum yield was observed in Krichauff. Drought and heat tolerant and moderately tolerant wheat genotypes were included Trident, Krichauff, Kukri and RAC875. Molineux, Berkut and Excalibur were clustered into most sensitive and moderately sensitive genotypes. Finally, the results show that there was a significantly genetic variation among the eight varieties that were studied under heat and water stress.
Abstract: The fault detection and diagnosis of complicated
production processes is one of essential tasks needed to run the process
safely with good final product quality. Unexpected events occurred in
the process may have a serious impact on the process. In this work,
triangular representation of process measurement data obtained in an
on-line basis is evaluated using simulation process. The effect of using
linear and nonlinear reduced spaces is also tested. Their diagnosis
performance was demonstrated using multivariate fault data. It has
shown that the nonlinear technique based diagnosis method produced
more reliable results and outperforms linear method. The use of
appropriate reduced space yielded better diagnosis performance. The
presented diagnosis framework is different from existing ones in that it
attempts to extract the fault pattern in the reduced space, not in the
original process variable space. The use of reduced model space helps
to mitigate the sensitivity of the fault pattern to noise.
Abstract: Scale Time Offset Robust Modulation (STORM) [1]–
[3] is a high bandwidth waveform design that adds time-scale
to embedded reference modulations using only time-delay [4]. In
an environment where each user has a specific delay and scale,
identification of the user with the highest signal power and that
user-s phase is facilitated by the STORM processor. Both of these
parameters are required in an efficient multiuser detection algorithm.
In this paper, the STORM modulation approach is evaluated with
a direct sequence spread quadrature phase shift keying (DS-QPSK)
system. A misconception of the STORM time scale modulation is that
a fine temporal resolution is required at the receiver. STORM will
be applied to a QPSK code division multiaccess (CDMA) system
by modifying the spreading codes. Specifically, the in-phase code
will use a typical spreading code, and the quadrature code will
use a time-delayed and time-scaled version of the in-phase code.
Subsequently, the same temporal resolution in the receiver is required
before and after the application of STORM. In this paper, the bit error
performance of STORM in a synchronous CDMA system is evaluated
and compared to theory, and the bit error performance of STORM
incorporated in a single user WCDMA downlink is presented to
demonstrate the applicability of STORM in a modern communication
system.
Abstract: We here propose improved version of elastic graph matching (EGM) as a face detector, called the multi-scale EGM (MS-EGM). In this improvement, Gabor wavelet-based pyramid reduces computational complexity for the feature representation often used in the conventional EGM, but preserving a critical amount of information about an image. The MS-EGM gives us higher detection performance than Viola-Jones object detection algorithm of the AdaBoost Haar-like feature cascade. We also show rapid detection speeds of the MS-EGM, comparable to the Viola-Jones method. We find fruitful benefits in the MS-EGM, in terms of topological feature representation for a face.
Abstract: This paper proposes new hybrid approaches for face
recognition. Gabor wavelets representation of face images is an
effective approach for both facial action recognition and face
identification. Perform dimensionality reduction and linear
discriminate analysis on the down sampled Gabor wavelet faces can
increase the discriminate ability. Nearest feature space is extended to
various similarity measures. In our experiments, proposed Gabor
wavelet faces combined with extended neural net feature space
classifier shows very good performance, which can achieve 93 %
maximum correct recognition rate on ORL data set without any preprocessing
step.