Abstract: This paper describes the study of cryptographic hash functions, one of the most important classes of primitives used in recent techniques in cryptography. The main aim is the development of recent crypt analysis hash function. We present different approaches to defining security properties more formally and present basic attack on hash function. We recall Merkle-Damgard security properties of iterated hash function. The Main aim of this paper is the development of recent techniques applicable to crypt Analysis hash function, mainly from SHA family. Recent proposed attacks an MD5 & SHA motivate a new hash function design. It is designed not only to have higher security but also to be faster than SHA-256. The performance of the new hash function is at least 30% better than that of SHA-256 in software. And it is secure against any known cryptographic attacks on hash functions.
Abstract: The main goal of the present work is to decrease the
computational burden for optimum design of steel frames with
frequency constraints using a new type of neural networks called
Wavelet Neural Network. It is contested to train a suitable neural
network for frequency approximation work as the analysis program.
The combination of wavelet theory and Neural Networks (NN)
has lead to the development of wavelet neural networks.
Wavelet neural networks are feed-forward networks using
wavelet as activation function. Wavelets are mathematical
functions within suitable inner parameters, which help them to
approximate arbitrary functions. WNN was used to predict the
frequency of the structures. In WNN a RAtional function with
Second order Poles (RASP) wavelet was used as a transfer
function. It is shown that the convergence speed was faster
than other neural networks. Also comparisons of WNN with
the embedded Artificial Neural Network (ANN) and with
approximate techniques and also with analytical solutions are
available in the literature.
Abstract: Recently there has been a growing interest in the field
of bio-mimetic robots that resemble the behaviors of an insect or an
aquatic animal, among many others. One of various bio-mimetic robot
applications is to explore pipelines, spotting any troubled areas or
malfunctions and reporting its data. Moreover, the robot is able to
prepare for and react to any abnormal routes in the pipeline. Special
types of mobile robots are necessary for the pipeline monitoring tasks.
In order to move effectively along a pipeline, the robot-s movement
will resemble that of insects or crawling animals. When situated in
massive pipelines with complex routes, the robot places fixed sensors
in several important spots in order to complete its monitoring. This
monitoring task is to prevent a major system failure by preemptively
recognizing any minor or partial malfunctions. Areas uncovered by
fixed sensors are usually impossible to provide real-time observation
and examination, and thus are dependent on periodical offline
monitoring. This paper proposes a monitoring system that is able to
monitor the entire area of pipelines–with and without fixed
sensors–by using the bio-mimetic robot.
Abstract: The aim of this paper to characterize a larger set of
wavelet functions for implementation in a still image compression
system using SPIHT algorithm. This paper discusses important
features of wavelet functions and filters used in sub band coding to
convert image into wavelet coefficients in MATLAB. Image quality
is measured objectively using peak signal to noise ratio (PSNR) and
its variation with bit rate (bpp). The effect of different parameters is
studied on different wavelet functions. Our results provide a good
reference for application designers of wavelet based coder.
Abstract: A heuristic conceptual model for to develop the
Reliability Centered Maintenance (RCM), especially in preventive
strategy, has been explored during this paper. In most real cases
which complicity of system obligates high degree of reliability, this
model proposes a more appropriate reliability function between life
time distribution based and another which is based on relevant
Extreme Value (EV) distribution. A statistical and mathematical
approach is used to estimate and verify these two distribution
functions. Then best one is chosen just among them, whichever is
more reliable. A numeric Industrial case study will be reviewed to
represent the concepts of this paper, more clearly.
Abstract: A neurofuzzy approach for a given set of input-output training data is proposed in two phases. Firstly, the data set is partitioned automatically into a set of clusters. Then a fuzzy if-then rule is extracted from each cluster to form a fuzzy rule base. Secondly, a fuzzy neural network is constructed accordingly and parameters are tuned to increase the precision of the fuzzy rule base. This network is able to learn and optimize the rule base of a Sugeno like Fuzzy inference system using Hybrid learning algorithm, which combines gradient descent, and least mean square algorithm. This proposed neurofuzzy system has the advantage of determining the number of rules automatically and also reduce the number of rules, decrease computational time, learns faster and consumes less memory. The authors also investigate that how neurofuzzy techniques can be applied in the area of control theory to design a fuzzy controller for linear and nonlinear dynamic systems modelling from a set of input/output data. The simulation analysis on a wide range of processes, to identify nonlinear components on-linely in a control system and a benchmark problem involving the prediction of a chaotic time series is carried out. Furthermore, the well-known examples of linear and nonlinear systems are also simulated under the Matlab/Simulink environment. The above combination is also illustrated in modeling the relationship between automobile trips and demographic factors.
Abstract: Type 2 diabetes mellitus (T2DM) is a complex
metabolic disorder that characterized by the presence of high glucose
in blood that cause from insulin resistance and insufficiency due to
deterioration β-cell Langerhans functions. T2DM is commonly
caused by the combination of inherited genetic variations as well as
our own lifestyle. Metallothionein (MT) is a known cysteine-rich
protein responsible in helping zinc homeostasis which is important in
insulin signaling and secretion as well as protection our body from
reactive oxygen species (ROS). MT scavenged ROS and free
radicals in our body happen to be one of the reasons of T2DM and its
complications. The objective of this study was to investigate the
association of MT1A and MT2A polymorphisms between T2DM and
control subjects among Malay populations. This study involved 150
T2DM and 120 Healthy individuals of Malay ethnic with mixed
genders. The genomic DNA was extracted from buccal cells and
amplified for MT1A and MT2A loci; the 347bp and 238bp banding
patterns were respectively produced by mean of the Polymerase
Chain Reaction (PCR). The PCR products were digested with Mlucl
and Tsp451 restriction enzymes respectively and producing
fragments lengths of (158/189/347bp) and (103/135/238bp)
respectively. The ANOVA test was conducted and it shown that there
was a significant difference between diabetic and control subjects for
age, BMI, WHR, SBP, FPG, HBA1C, LDL, TG, TC and family
history with (P0.05). The genotype
frequency for AA, AG and GG of MT1A polymorphisms was 72.7%,
22.7% and 4.7% in cases and 15%, 55% and 30% in control
respectively. As for MT2A, genotype frequency of GG, GC and CC
was 42.7%, 27.3% and 30% in case and 5%, 40% and 55% for
control respectively. Both polymorphisms show significant difference
between two investigated groups with (P=0.000). The Post hoc test
was conducted and shows a significant difference between the
genotypes within each polymorphism (P=0. 000). The MT1A and
MT2A polymorphisms were believed to be the reliable molecular
markers to distinguish the T2DM subjects from healthy individuals in
Malay populations.
Abstract: A complex valued neural network is a neural network
which consists of complex valued input and/or weights and/or thresholds
and/or activation functions. Complex-valued neural networks
have been widening the scope of applications not only in electronics
and informatics, but also in social systems. One of the most important
applications of the complex valued neural network is in signal
processing. In Neural networks, generalized mean neuron model
(GMN) is often discussed and studied. The GMN includes a new
aggregation function based on the concept of generalized mean of all
the inputs to the neuron. This paper aims to present exhaustive results
of using Generalized Mean Neuron model in a complex-valued neural
network model that uses the back-propagation algorithm (called
-Complex-BP-) for learning. Our experiments results demonstrate the
effectiveness of a Generalized Mean Neuron Model in a complex
plane for signal processing over a real valued neural network. We
have studied and stated various observations like effect of learning
rates, ranges of the initial weights randomly selected, error functions
used and number of iterations for the convergence of error required on
a Generalized Mean neural network model. Some inherent properties
of this complex back propagation algorithm are also studied and
discussed.
Abstract: Human Computer Interaction (HCI) has been an
emerging field that draws in the experts from various fields to
enhance the application of computer programs and the ease of
computer users. HCI has much to do with learning and cognition and
an emerging approach to learning and problem-solving is problembased
learning (PBL). The processes of PBL involve important
cognitive functions in the various stages. This paper will illustrate
how closely related fields to HCI, PBL and cognitive psychology can
benefit from informing each other through analysing various
cognitive functions. Several cognitive functions from cognitive
function disc (CFD) would be presented and discussed in relation to
human-computer interface. This paper concludes with the
implications of bridging the gaps amongst these disciplines.
Abstract: MBMS is a unidirectional point-to-multipoint bearer
service in which data are transmitted from a single source entity to
multiple recipients. For a mobile to support the MBMS, MBMS client
functions as well as MBMS radio protocols should be designed and
implemented. In this paper, we analyze the MBMS client functions
and describe the implementation of them in our mobile test-bed. User
operations and signaling flows between protocol entities to control the
MBMS functions are designed in detail. Service announcement
utilizing the file download MBMS service and four MBMS user
services are demonstrated in the test-bed to verify the MBMS client
functions.
Abstract: There has been a growing interest in the field of
bio-mimetic robots that resemble the shape of an insect or an aquatic
animal, among many others. One bio-mimetic robot serves the
purpose of exploring pipelines, spotting any troubled areas or
malfunctions and reporting its data. Moreover, the robot is able to
prepare for and react to any abnormal routes in the pipeline. In order
to move effectively inside a pipeline, the robot-s movement will
resemble that of a lizard. When situated in massive pipelines with
complex routes, the robot places fixed sensors in several important
spots in order to complete its monitoring. This monitoring task is to
prevent a major system failure by preemptively recognizing any minor
or partial malfunctions. Areas uncovered by fixed sensors are usually
impossible to provide real-time observation and examination, and thus
are dependant on periodical offline monitoring. This paper provides
the Monitoring System that is able to monitor the entire area of
pipelines–with and without fixed sensors–by using the bio-mimetic
robot.
Abstract: In the traditional theory of non-uniform torsion the
axial displacement field is expressed as the product of the unit twist
angle and the warping function. The first one, variable along the
beam axis, is obtained by a global congruence condition; the second
one, instead, defined over the cross-section, is determined by solving
a Neumann problem associated to the Laplace equation, as well as for
the uniform torsion problem.
So, as in the classical theory the warping function doesn-t punctually
satisfy the first indefinite equilibrium equation, the principal aim of
this work is to develop a new theory for non-uniform torsion of
beams with axial symmetric cross-section, fully restrained on both
ends and loaded by a constant torque, that permits to punctually
satisfy the previous equation, by means of a trigonometric expansion
of the axial displacement and unit twist angle functions.
Furthermore, as the classical theory is generally applied with good
results to the global and local analysis of ship structures, two beams
having the first one an open profile, the second one a closed section,
have been analyzed, in order to compare the two theories.
Abstract: This paper presents a methodology towards the emulation of the electrical power consumption of the RF device during the cellular phone/handset transmission mode using the LTE technology. The emulation methodology takes the physical environmental variables and the logical interface between the baseband and the RF system as inputs to compute the emulated power dissipation of the RF device. The emulated power, in between the measured points corresponding to the discrete values of the logical interface parameters is computed as a polynomial interpolation using polynomial basis functions. The evaluation of polynomial and spline curve fitting models showed a respective divergence (test error) of 8% and 0.02% from the physically measured power consumption. The precisions of the instruments used for the physical measurements have been modeled as intervals. We have been able to model the power consumption of the RF device operating at 5MHz using homotopy between 2 continuous power consumptions of the RF device operating at the bandwidths 3MHz and 10MHz.
Abstract: In this paper, a new approach for target recognition based on the Empirical mode decomposition (EMD) algorithm of Huang etal. [11] and the energy tracking operator of Teager [13]-[14] is introduced. The conjunction of these two methods is called Teager-Huang analysis. This approach is well suited for nonstationary signals analysis. The impulse response (IR) of target is first band pass filtered into subsignals (components) called Intrinsic mode functions (IMFs) with well defined Instantaneous frequency (IF) and Instantaneous amplitude (IA). Each IMF is a zero-mean AM-FM component. In second step, the energy of each IMF is tracked using the Teager energy operator (TEO). IF and IA, useful to describe the time-varying characteristics of the signal, are estimated using the Energy separation algorithm (ESA) algorithm of Maragos et al .[16]-[17]. In third step, a set of features such as skewness and kurtosis are extracted from the IF, IA and IMF energy functions. The Teager-Huang analysis is tested on set of synthetic IRs of Sonar targets with different physical characteristics (density, velocity, shape,? ). PCA is first applied to features to discriminate between manufactured and natural targets. The manufactured patterns are classified into spheres and cylinders. One hundred percent of correct recognition is achieved with twenty three echoes where sixteen IRs, used for training, are free noise and seven IRs, used for testing phase, are corrupted with white Gaussian noise.
Abstract: Herein, the organic semiconductor methyl orange
(MO), is investigated for the first time for its electronic applications.
For this purpose, Al/MO/n-Si heterojunction is fabricated through
economical cheap and simple “drop casting” technique. The currentvoltage
(I-V) measurements of the device are made at room
temperature under dark conditions. The I-V characteristics of
Al/MO/n-Si junction exhibits asymmetrical and rectifying behavior
that confirms the formation of diode. The diode parameters such as
rectification ratio (RR), turn on voltage (Vturn on), reverse saturation
current (I0), ideality factor (n), barrier height ( b
f ), series resistance
(Rs) and shunt resistance (Rsh) are determined from I-V curves using
Schottky equations. These values of these parameters are also
extracted and verified by applying Cheung’s functions. The
conduction mechanisms are explained from the forward bias I-V
characteristics using the power law.
Abstract: Search for a tertiary substructure that geometrically
matches the 3D pattern of the binding site of a well-studied protein provides a solution to predict protein functions. In our previous work,
a web server has been built to predict protein-ligand binding sites
based on automatically extracted templates. However, a drawback of such templates is that the web server was prone to resulting in many
false positive matches. In this study, we present a sequence-order constraint to reduce the false positive matches of using automatically
extracted templates to predict protein-ligand binding sites. The binding site predictor comprises i) an automatically constructed template library and ii) a local structure alignment algorithm for
querying the library. The sequence-order constraint is employed to
identify the inconsistency between the local regions of the query protein and the templates. Experimental results reveal that the sequence-order constraint can largely reduce the false positive matches and is effective for template-based binding site prediction.
Abstract: Imperfect knowledge cannot be avoided all the time. Imperfections may have several forms; uncertainties, imprecision and incompleteness. When we look to classification of methods for the management of imperfect knowledge we see fuzzy set-based techniques. The choice of a method to process data is linked to the choice of knowledge representation, which can be numerical, symbolic, logical or semantic and it depends on the nature of the problem to be solved for example decision support, which will be mentioned in our study. Fuzzy Logic is used for its ability to manage imprecise knowledge, but it can take advantage of the ability of neural networks to learn coefficients or functions. Such an association of methods is typical of so-called soft computing. In this study a new method was used for the management of imprecision for collected knowledge which related to economic analysis of construction industry in Turkey. Because of sudden changes occurring in economic factors decrease competition strength of construction companies. The better evaluation of these changes in economical factors in view of construction industry will made positive influence on company-s decisions which are dealing construction.
Abstract: The empirical mode decomposition (EMD) represents any time series into a finite set of basis functions. The bases are termed as intrinsic mode functions (IMFs) which are mutually orthogonal containing minimum amount of cross-information. The EMD successively extracts the IMFs with the highest local frequencies in a recursive way, which yields effectively a set low-pass filters based entirely on the properties exhibited by the data. In this paper, EMD is applied to explore the properties of the multi-year air temperature and to observe its effects on climate change under global warming. This method decomposes the original time-series into intrinsic time scale. It is capable of analyzing nonlinear, non-stationary climatic time series that cause problems to many linear statistical methods and their users. The analysis results show that the mode of EMD presents seasonal variability. The most of the IMFs have normal distribution and the energy density distribution of the IMFs satisfies Chi-square distribution. The IMFs are more effective in isolating physical processes of various time-scales and also statistically significant. The analysis results also show that the EMD method provides a good job to find many characteristics on inter annual climate. The results suggest that climate fluctuations of every single element such as temperature are the results of variations in the global atmospheric circulation.
Abstract: In this paper, we consider the design of pulse shaping
filter using orthogonal Hermite-Rodriguez basis functions. The pulse
shaping filter design problem has been formulated and solved as a
quadratic programming problem with linear inequality constraints.
Compared with the existing approaches reported in the literature, the
use of Hermite-Rodriguez functions offers an effective alternative to
solve the constrained filter synthesis problem. This is demonstrated
through a numerical example which is concerned with the design of
an equalization filter for a digital transmission channel.
Abstract: Modern information and communication technologies
offer a variety of support options for the efficient handling of
customer relationships. CRM systems have been developed, which
are designed to support the processes in the areas of marketing, sales
and service. Along with technological progress, CRM systems are
constantly changing, i.e. the systems are continually enhanced by
new functions. However, not all functions are suitable for every
company because of different frameworks and business processes. In
this context the question arises whether or not CRM systems are
widely used in Austrian companies and which business processes are
most frequently supported by CRM systems. This paper aims to shed
light on the popularity of CRM systems in Austrian companies in
general and the use of different functions to support their daily
business. First of all, the paper provides a theoretical overview of the
structure of modern CRM systems and proposes a categorization of
currently available software functionality for collaborative,
operational and analytical CRM processes, which provides the
theoretical background for the empirical study. Apart from these
theoretical considerations, the paper presents the empirical results of
a field survey on the use of CRM systems in Austrian companies and
analyzes its findings.