Abstract: A Wireless sensor network (WSN) consists of a set of battery-powered nodes, which collaborate to perform sensing tasks in a given environment. Each node in WSN should be capable to act for long periods of time with scrimpy or no external management. One requirement for this independent is: in the presence of adverse positions, the sensor nodes must be capable to configure themselves. Hence, the nodes for determine the existence of unusual events in their surroundings should make use of position awareness mechanisms. This work approaches the problem by considering the possible unusual events as diseases, thus making it possible to diagnose them through their symptoms, namely, their side effects. Considering these awareness mechanisms as a foundation for highlevel monitoring services, this paper also shows how these mechanisms are included in the primal plan of an intrusion detection system.
Abstract: Traditionally, terror groups have been formed by ideologically aligned actors who perceive a lack of options for achieving political or social change. However, terrorist attacks have been increasingly carried out by small groups of actors or lone individuals who may be only ideologically affiliated with larger, formal terrorist organizations. The formation of these groups represents the inverse of traditional organizational growth, whereby structural de-evolution within issue-based organizations leads to the formation of small, independent terror cells. Ideological franchising – the bypassing of formal affiliation to the “parent" organization – represents the de-evolution of traditional concepts of organizational structure in favor of an organic, independent, and focused unit. Traditional definitions of dark networks that are issue-based include focus on an identified goal, commitment to achieving this goal through unrestrained actions, and selection of symbolic targets. The next step in the de-evolution of small dark networks is the miniorganization, consisting of only a handful of actors working toward a common, violent goal. Information-sharing through social media platforms, coupled with civil liberties of democratic nations, provide the communication systems, access to information, and freedom of movement necessary for small dark networks to flourish without the aid of a parent organization. As attacks such as the 7/7 bombings demonstrate the effectiveness of small dark networks, terrorist actors will feel increasingly comfortable aligning with an ideology only, without formally organizing. The natural result of this de-evolving organization is the single actor event, where an individual seems to subscribe to a larger organization-s violent ideology with little or no formal ties.
Abstract: Since the conception of JML, many tools, applications and implementations have been done. In this context, the users or developers who want to use JML seem surounded by many of these tools, applications and so on. Looking for a common infrastructure and an independent language to provide a bridge between these tools and JML, we developed an approach to embedded contracts in XML for Java: XJML. This approach offer us the ability to separate preconditions, posconditions and class invariants using JML and XML, so we made a front-end which can process Runtime Assertion Checking, Extended Static Checking and Full Static Program Verification. Besides, the capabilities for this front-end can be extended and easily implemented thanks to XML. We believe that XJML is an easy way to start the building of a Graphic User Interface delivering in this way a friendly and IDE independency to developers community wich want to work with JML.
Abstract: Space Vector Modulation (SVM) is an optimum Pulse Width Modulation (PWM) technique for an inverter used in a variable frequency drive applications. It is computationally rigorous and hence limits the inverter switching frequency. Increase in switching frequency can be achieved using Neural Network (NN) based SVM, implemented on application specific chips. This paper proposes a neural network based SVM technique for a Voltage Source Inverter (VSI). The network proposed is independent of switching frequency. Different architectures are investigated keeping the total number of neurons constant. The performance of the inverter is compared for various switching frequencies for different architectures of NN based SVM. From the results obtained, the network with minimum resource and appropriate word length is identified. The bit precision required for this application is identified. The network with 8-bit precision is implemented in the IC XCV 400 and the results are presented. The performance of NN based general purpose SVM with higher bit precision is discussed.
Abstract: In this paper, many techniques for blind identification of moving average (MA) process are presented. These methods utilize third- and fourth-order cumulants of the noisy observations of the system output. The system is driven by an independent and identically distributed (i.i.d) non-Gaussian sequence that is not observed. Two nonlinear optimization algorithms, namely the Gradient Descent and the Gauss-Newton algorithms are exposed. An algorithm based on the joint-diagonalization of the fourth-order cumulant matrices (FOSI) is also considered, as well as an improved version of the classical C(q, 0, k) algorithm based on the choice of the Best 1-D Slice of fourth-order cumulants. To illustrate the effectiveness of our methods, various simulation examples are presented.
Abstract: This paper presents data annotation models at five levels of granularity (database, relation, column, tuple, and cell) of relational data to address the problem of unsuitability of most relational databases to express annotations. These models do not require any structural and schematic changes to the underlying database. These models are also flexible, extensible, customizable, database-neutral, and platform-independent. This paper also presents an SQL-like query language, named Annotation Query Language (AnQL), to query annotation documents. AnQL is simple to understand and exploits the already-existent wide knowledge and skill set of SQL.
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: Human immunodeficiency virus infection and
acquired immunodeficiency syndrome is a global pandemic with
cases reporting from virtually every country and continues to be a
common infection in developing country like India.
Microalbuminuria is a manifestation of human immunodeficiency
virus associated nephropathy. Therefore, microalbuminuria may be
an early marker of human immunodeficiency virus associated
nephropathy, and screening for its presence may be beneficial. A
strikingly high prevalence of microalbuminuria among human
immunodeficiency virus infected patients has been described in
various studies. Risk factors for clinically significant proteinuria
include African - American race, higher human immunodeficiency
virus ribonucleic acid level and lower CD4 lymphocyte count. The
cardiovascular risk factors of increased systolic blood pressure and
increase fasting blood sugar level are strongly associated with
microalbuminuria in human immunodeficiency virus patient. These
results suggest that microalbuminuria may be a sign of current
endothelial dysfunction and micro-vascular disease and there is
substantial risk of future cardiovascular disease events. Positive
contributing factors include early kidney disease such as human
immunodeficiency virus associated nephropathy, a marker of end
organ damage related to co morbidities of diabetes or hypertension,
or more diffuse endothelial cells dysfunction. Nevertheless after
adjustment for non human immunodeficiency virus factors, human
immunodeficiency virus itself is a major risk factor. The presence of
human immunodeficiency virus infection is independent risk to
develop microalbuminuria in human immunodeficiency virus patient.
Cardiovascular risk factors appeared to be stronger predictors of
microalbuminuria than markers of human immunodeficiency virus
severity person with human immunodeficiency virus infection and
microalbuminuria therefore appear to potentially bear the burden of
two separate damage related to known vascular end organ damage
related to know vascular risk factors, and human immunodeficiency
virus specific processes such as the direct viral infection of kidney
cells.The higher prevalence of microalbuminuria among the human
immunodeficiency virus infected could be harbinger of future
increased risks of both kidney and cardiovascular disease. Further
study defining the prognostic significance of microalbuminuria
among human immunodeficiency virus infected persons will be
essential. Microalbuminuria seems to be a predictor of cardiovascular
disease in diabetic and non diabetic subjects, hence it can also be
used for early detection of micro vascular disease in human
immunodeficiency virus positive patients, thus can help to diagnose
the disease at the earliest.
Abstract: This article proposes a voltage-mode
multifunction filter using differential voltage current
controllable current conveyor transconductance amplifier
(DV-CCCCTA). The features of the circuit are that: the
quality factor and pole frequency can be tuned independently
via the values of capacitors: the circuit description is very
simple, consisting of merely 1 DV-CCCCTA, and 2
capacitors. Without any component matching conditions, the
proposed circuit is very appropriate to further develop into
an integrated circuit. Additionally, each function response
can be selected by suitably selecting input signals with
digital method. The PSpice simulation results are depicted.
The given results agree well with the theoretical anticipation.
Abstract: There is no doubt that Internet technology is widely used by hotels and its demand is constantly booming. Hotels have largely adopted website information services through using different interactive tools, dimensions and attributes to achieve excellence in functionality and usability but these do not necessary equate with website effectiveness. One way to investigate the effectiveness of hotel website is from the perspective ofe-consumers. This exploratory research is to investigate the perceived importance of websites effectiveness of some selected independent small and medium-sized hotels (SMHs) located in Dubai, United Arab Emirates, from the perspective of Omanie-consumers by using non-random sampling method. From 400 questionnaire addressed to respondents in 27 organizations in Muscat the capital city of Oman, 173 are valid. Findings of this study assist SMHs management in Dubai with the reallocation of their resources and efforts in order to supportebusiness development and to sustain a competitive advantage.
Abstract: Most simple nonlinear thresholding rules for
wavelet- based denoising assume that the wavelet coefficients are independent. However, wavelet coefficients of natural images have significant dependencies. This paper attempts to give a recipe for selecting one of the popular image-denoising algorithms based
on VisuShrink, SureShrink, OracleShrink, BayesShrink and BiShrink and also this paper compares different Bivariate models used for image denoising applications. The first part of the paper
compares different Shrinkage functions used for image-denoising.
The second part of the paper compares different bivariate models
and the third part of this paper uses the Bivariate model with modified marginal variance which is based on Laplacian assumption. This paper gives an experimental comparison on six 512x512 commonly used images, Lenna, Barbara, Goldhill,
Clown, Boat and Stonehenge. The following noise powers 25dB,26dB, 27dB, 28dB and 29dB are added to the six standard images and the corresponding Peak Signal to Noise Ratio (PSNR) values
are calculated for each noise level.
Abstract: We consider linear regression models where both input data (the values of independent variables) and output data (the observations of the dependent variable) are interval-censored. We introduce a possibilistic generalization of the least squares estimator, so called OLS-set for the interval model. This set captures the impact of the loss of information on the OLS estimator caused by interval censoring and provides a tool for quantification of this effect. We study complexity-theoretic properties of the OLS-set. We also deal with restricted versions of the general interval linear regression model, in particular the crisp input – interval output model. We give an argument that natural descriptions of the OLS-set in the crisp input – interval output cannot be computed in polynomial time. Then we derive easily computable approximations for the OLS-set which can be used instead of the exact description. We illustrate the approach by an example.
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: IEEE 802.11e is the enhanced version of the IEEE
802.11 MAC dedicated to provide Quality of Service of wireless
network. It supports QoS by the service differentiation and
prioritization mechanism. Data traffic receives different priority
based on QoS requirements. Fundamentally, applications are divided
into four Access Categories (AC). Each AC has its own buffer queue
and behaves as an independent backoff entity. Every frame with a
specific priority of data traffic is assigned to one of these access
categories. IEEE 802.11e EDCA (Enhanced Distributed Channel
Access) is designed to enhance the IEEE 802.11 DCF (Distributed
Coordination Function) mechanisms by providing a distributed
access method that can support service differentiation among
different classes of traffic. Performance of IEEE 802.11e MAC layer
with different ACs is evaluated to understand the actual benefits
deriving from the MAC enhancements.
Abstract: In this paper, we consider a multi user multiple input
multiple output (MU-MIMO) based cooperative reporting system for
cognitive radio network. In the reporting network, the secondary
users forward the primary user data to the common fusion center
(FC). The FC is equipped with linear equalizers and an energy
detector to make the decision about the spectrum. The primary user
data are considered to be a digital video broadcasting - terrestrial
(DVB-T) signal. The sensing channel and the reporting channel are
assumed to be an additive white Gaussian noise and an independent
identically distributed Raleigh fading respectively. We analyzed the
detection probability of MU-MIMO system with linear equalizers and
arrived at the closed form expression for average detection
probability. Also the system performance is investigated under
various MIMO scenarios through Monte Carlo simulations.
Abstract: In theoretical computer science, the Turing machine has played a number of important roles in understanding and exploiting basic concepts and mechanisms in computing and information processing [20]. It is a simple mathematical model of computers [9]. After that, M.Blum and C.Hewitt first proposed two-dimensional automata as a computational model of two-dimensional pattern processing, and investigated their pattern recognition abilities in 1967 [7]. Since then, a lot of researchers in this field have been investigating many properties about automata on a two- or three-dimensional tape. On the other hand, the question of whether processing fourdimensional digital patterns is much more difficult than two- or threedimensional ones is of great interest from the theoretical and practical standpoints. Thus, the study of four-dimensional automata as a computasional model of four-dimensional pattern processing has been meaningful [8]-[19],[21]. This paper introduces a cooperating system of four-dimensional finite automata as one model of four-dimensional automata. A cooperating system of four-dimensional finite automata consists of a finite number of four-dimensional finite automata and a four-dimensional input tape where these finite automata work independently (in parallel). Those finite automata whose input heads scan the same cell of the input tape can communicate with each other, that is, every finite automaton is allowed to know the internal states of other finite automata on the same cell it is scanning at the moment. In this paper, we mainly investigate some accepting powers of a cooperating system of eight- or seven-way four-dimensional finite automata. The seven-way four-dimensional finite automaton is an eight-way four-dimensional finite automaton whose input head can move east, west, south, north, up, down, or in the fu-ture, but not in the past on a four-dimensional input tape.
Abstract: An effective approach for realizing the binary tree structure, representing a combinational logic functionality with enhanced throughput, is discussed in this paper. The optimization in maximum operating frequency was achieved through delay minimization, which in turn was possible by means of reducing the depth of the binary network. The proposed synthesis methodology has been validated by experimentation with FPGA as the target technology. Though our proposal is technology independent, yet the heuristic enables better optimization in throughput even after technology mapping for such Boolean functionality; whose reduced CNF form is associated with a lesser literal cost than its reduced DNF form at the Boolean equation level. For cases otherwise, our method converges to similar results as that of [12]. The practical results obtained for a variety of case studies demonstrate an improvement in the maximum throughput rate for Spartan IIE (XC2S50E-7FT256) and Spartan 3 (XC3S50-4PQ144) FPGA logic families by 10.49% and 13.68% respectively. With respect to the LUTs and IOBUFs required for physical implementation of the requisite non-regenerative logic functionality, the proposed method enabled savings to the tune of 44.35% and 44.67% respectively, over the existing efficient method available in literature [12].
Abstract: We propose a new perspective on speech
communication using blind source separation. The original speech is
mixed with key signals which consist of the mixing matrix, chaotic
signals and a random noise. However, parts of the keys (the mixing
matrix and the random noise) are not necessary in decryption. In
practice implement, one can encrypt the speech by changing the noise
signal every time. Hence, the present scheme obtains the advantages
of a One Time Pad encryption while avoiding its drawbacks in key
exchange. It is demonstrated that the proposed scheme is immune
against traditional attacks.
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.