Abstract: The paper proposes a novel technique for iris
recognition using texture and phase features. Texture features are
extracted on the normalized iris strip using Haar Wavelet while phase
features are obtained using LOG Gabor Wavelet. The matching
scores generated from individual modules are combined using sum of
score technique. The system is tested on database obtained from Bath
University and Indian Institute of Technology Kanpur and is giving
an accuracy of 95.62% and 97.66% respectively. The FAR and FRR
of the combined system is also reduced comparatively.
Abstract: This paper presents a new sufficient condition for the
existence, uniqueness and global asymptotic stability of the equilibrium point for Cohen-Grossberg neural networks with multiple time delays. The results establish a relationship between the network parameters
of the neural system independently of the delay parameters. The results are also compared with the previously reported results in
the literature.
Abstract: In this paper the development of neural network based fuzzy inference system for electricity consumption prediction is considered. The electricity consumption depends on number of factors, such as number of customers, seasons, type-s of customers, number of plants, etc. It is nonlinear process and can be described by chaotic time-series. The structure and algorithms of neuro-fuzzy system for predicting future values of electricity consumption is described. To determine the unknown coefficients of the system, the supervised learning algorithm is used. As a result of learning, the rules of neuro-fuzzy system are formed. The developed system is applied for predicting future values of electricity consumption of Northern Cyprus. The simulation of neuro-fuzzy system has been performed.
Abstract: Multi-loop (De-centralized) Proportional-Integral-
Derivative (PID) controllers have been used extensively in process
industries due to their simple structure for control of multivariable
processes. The objective of this work is to design multiple-model
adaptive multi-loop PID strategy (Multiple Model Adaptive-PID)
and neural network based multi-loop PID strategy (Neural Net
Adaptive-PID) for the control of multivariable system. The first
method combines the output of multiple linear PID controllers,
each describing process dynamics at a specific level of operation.
The global output is an interpolation of the individual multi-loop
PID controller outputs weighted based on the current value of the
measured process variable. In the second method, neural network
is used to calculate the PID controller parameters based on the
scheduling variable that corresponds to major shift in the process
dynamics. The proposed control schemes are simple in structure with
less computational complexity. The effectiveness of the proposed
control schemes have been demonstrated on the CSTR process,
which exhibits dynamic non-linearity.
Abstract: As emails communications have no consistent
authentication procedure to ensure the authenticity, we present an
investigation analysis approach for detecting forged emails based on
Random Forests and Naïve Bays classifiers. Instead of investigating
the email headers, we use the body content to extract a unique writing
style for all the possible suspects. Our approach consists of four main
steps: (1) The cybercrime investigator extract different effective
features including structural, lexical, linguistic, and syntactic
evidence from previous emails for all the possible suspects, (2) The
extracted features vectors are normalized to increase the accuracy
rate. (3) The normalized features are then used to train the learning
engine, (4) upon receiving the anonymous email (M); we apply the
feature extraction process to produce a feature vector. Finally, using
the machine learning classifiers the email is assigned to one of the
suspects- whose writing style closely matches M. Experimental
results on real data sets show the improved performance of the
proposed method and the ability of identifying the authors with a
very limited number of features.
Abstract: The Bayesian Optimization Algorithm (BOA) is an algorithm based on the estimation of distributions. It uses techniques from modeling data by Bayesian networks to estimating the joint distribution of promising solutions. To obtain the structure of Bayesian network, different search algorithms can be used. The key point that BOA addresses is whether the constructed Bayesian network could generate new and useful solutions (strings), which could lead the algorithm in the right direction to solve the problem. Undoubtedly, this ability is a crucial factor of the efficiency of BOA. Varied search algorithms can be used in BOA, but their performances are different. For choosing better ones, certain suitable method to present their ability difference is needed. In this paper, a greedy search algorithm and a stochastic search algorithm are used in BOA to solve certain optimization problem. A method using Kullback-Leibler (KL) Divergence to reflect their difference is described.
Abstract: This paper presents the visual control flow support of Visual Modeling and Transformation System (VMTS), which facilitates composing complex model transformations out of simple transformation steps and executing them. The VMTS Visual Control Flow Language (VCFL) uses stereotyped activity diagrams to specify control flow structures and OCL constraints to choose between different control flow branches. This work discusses the termination properties of VCFL and provides an algorithm to support the termination analysis of VCFL transformations.
Abstract: This paper discusses the classification process for medical data. In this paper, we use the data from ACM KDDCup 2008 to demonstrate our classification process based on latent topic discovery. In this data set, the target set and outliers are quite different in their nature: target set is only 0.6% size in total, while the outliers consist of 99.4% of the data set. We use this data set as an example to show how we dealt with this extremely biased data set with latent topic discovery and noise reduction techniques. Our experiment faces two major challenge: (1) extremely distributed outliers, and (2) positive samples are far smaller than negative ones. We try to propose a suitable process flow to deal with these issues and get a best AUC result of 0.98.
Abstract: This study suggests a model of a new set of evaluation criteria that will be used to measure the efficiency of real-world E-commerce websites. Evaluation criteria include design, usability and performance for websites, the Data Envelopment Analysis (DEA) technique has been used to measure the websites efficiency. An efficient Web site is defined as a site that generates the most outputs, using the smallest amount of inputs. Inputs refer to measurements representing the amount of effort required to build, maintain and perform the site. Output is amount of traffic the site generates. These outputs are measured as the average number of daily hits and the average number of daily unique visitors.
Abstract: Most of the concurrent transactional protocols consider
serializability as a correctness criterion of the transactions execution.
Usually, the proof of the serializability relies on mathematical proofs
for a fixed finite number of transactions. In this paper, we introduce
a protocol to deal with an infinite number of transactions which are
iterated infinitely often. We specify serializability of the transactions
and the protocol using a specification language based on temporal
logics. It is worthwhile using temporal logics such as LTL (Lineartime
Temporal Logic) to specify transactions, to gain full automatic
verification by using model checkers.
Abstract: In this paper, gate leakage current has been mitigated
by the use of novel nanoscale MOSFET with Source/Drain-to-Gate
Non-overlapped and high-k spacer structure for the first time. A
compact analytical model has been developed to study the gate
leakage behaviour of proposed MOSFET structure. The result
obtained has found good agreement with the Sentaurus Simulation.
Fringing gate electric field through the dielectric spacer induces
inversion layer in the non-overlap region to act as extended S/D
region. It is found that optimal Source/Drain-to-Gate Non-overlapped
and high-k spacer structure has reduced the gate leakage current to
great extent as compared to those of an overlapped structure. Further,
the proposed structure had improved off current, subthreshold slope
and DIBL characteristic. It is concluded that this structure solves the
problem of high leakage current without introducing the extra series
resistance.
Abstract: The requirements analysis, modeling, and simulation have consistently been one of the main challenges during the development of complex systems. The scenarios and the state machines are two successful models to describe the behavior of an interactive system. The scenarios represent examples of system execution in the form of sequences of messages exchanged between objects and are a partial view of the system. In contrast, state machines can represent the overall system behavior. The automation of processing scenarios in the state machines provide some answers to various problems such as system behavior validation and scenarios consistency checking. In this paper, we propose a method for translating scenarios in state machines represented by Discreet EVent Specification and procedure to detect implied scenarios. Each induced DEVS model represents the behavior of an object of the system. The global system behavior is described by coupling the atomic DEVS models and validated through simulation. We improve the validation process with integrating formal methods to eliminate logical inconsistencies in the global model. For that end, we use the Z notation.
Abstract: Ringing effect is one of the most annoying visual
artifacts in digital video. It is a significant factor of subjective quality
deterioration. However, there is a widely-accepted misunderstanding
of its cause. In this paper, we propose a reasonable interpretation of the
cause of ringing effect. Based on the interpretation, we suggest further
two methods to reduce ringing effect in DCT-based video coding. The
methods adaptively adjust quantizers according to video features. Our
experiments proved that the methods could efficiently improve
subjective quality with acceptable additional computing costs.
Abstract: Authentication of multimedia contents has gained much attention in recent times. In this paper, we propose a secure semi-fragile watermarking, with a choice of two watermarks to be embedded. This technique operates in integer wavelet domain and makes use of semi fragile watermarks for achieving better robustness. A self-recovering algorithm is employed, that hides the image digest into some Wavelet subbands to detect possible malevolent object manipulation undergone by the image (object replacing and/or deletion). The Semi-fragility makes the scheme tolerant for JPEG lossy compression as low as quality of 70%, and locate the tempered area accurately. In addition, the system ensures more security because the embedded watermarks are protected with private keys. The computational complexity is reduced using parameterized integer wavelet transform. Experimental results show that the proposed scheme guarantees the safety of watermark, image recovery and location of the tempered area accurately.
Abstract: Reentry trajectory optimization is a multi-constraints
optimal control problem which is hard to solve. To tackle it, we
proposed a new algorithm named CDEN(Constrained Differential
Evolution Newton-Raphson Algorithm) based on Differential Evolution(
DE) and Newton-Raphson.We transform the infinite dimensional
optimal control problem to parameter optimization which is finite
dimensional by discretize control parameter. In order to simplify
the problem, we figure out the control parameter-s scope by process
constraints. To handle constraints, we proposed a parameterless constraints
handle process. Through comprehensive analyze the problem,
we use a new algorithm integrated by DE and Newton-Raphson to
solve it. It is validated by a reentry vehicle X-33, simulation results
indicated that the algorithm is effective and robust.
Abstract: Social learning network analysis has drawn attention
for most researcher on e-learning research domain. This is due to the
fact that it has the capability to identify the behavior of student
during their social interaction inside e-learning. Normally, the social
network analysis (SNA) is treating the students' interaction merely as
node and edge with less meaning. This paper focuses on providing an
ontology structure of e-learning Moodle that can enrich the
relationships among students, as well as between the students and the
teacher. This ontology structure brings great benefit to the future
development of e-learning system.
Abstract: Face Recognition is a field of multidimensional
applications. A lot of work has been done, extensively on the most of
details related to face recognition. This idea of face recognition using
PCA is one of them. In this paper the PCA features for Feature
extraction are used and matching is done for the face under
consideration with the test image using Eigen face coefficients. The
crux of the work lies in optimizing Euclidean distance and paving the
way to test the same algorithm using Matlab which is an efficient tool
having powerful user interface along with simplicity in representing
complex images.
Abstract: Although achieving zero-defect software release is
practically impossible, software industries should take maximum
care to detect defects/bugs well ahead in time allowing only bare
minimums to creep into released version. This is a clear indicator of
time playing an important role in the bug detection. In addition to
this, software quality is the major factor in software engineering
process. Moreover, early detection can be achieved only through
static code analysis as opposed to conventional testing.
BugCatcher.Net is a static analysis tool, which detects bugs in .NET®
languages through MSIL (Microsoft Intermediate Language)
inspection. The tool utilizes a Parser based on Finite State Automata
to carry out bug detection. After being detected, bugs need to be
corrected immediately. BugCatcher.Net facilitates correction, by
proposing a corrective solution for reported warnings/bugs to end
users with minimum side effects. Moreover, the tool is also capable
of analyzing the bug trend of a program under inspection.
Abstract: This paper uses the radial basis function neural
network (RBFNN) for system identification of nonlinear systems.
Five nonlinear systems are used to examine the activity of RBFNN in
system modeling of nonlinear systems; the five nonlinear systems are
dual tank system, single tank system, DC motor system, and two
academic models. The feed forward method is considered in this
work for modelling the non-linear dynamic models, where the KMeans
clustering algorithm used in this paper to select the centers of
radial basis function network, because it is reliable, offers fast
convergence and can handle large data sets. The least mean square
method is used to adjust the weights to the output layer, and
Euclidean distance method used to measure the width of the Gaussian
function.
Abstract: MicroRNAs (miRNAs) are a class of non-coding
RNAs that hybridize to mRNAs and induce either translation
repression or mRNA cleavage. Recently, it has been reported that
miRNAs could possibly play an important role in human diseases. By
integrating miRNA target genes, cancer genes, miRNA and mRNA
expression profiles information, a database is developed to link
miRNAs to cancer target genes. The database provides experimentally
verified human miRNA target genes information, including oncogenes
and tumor suppressor genes. In addition, fragile sites information for
miRNAs, and the strength of the correlation of miRNA and its target
mRNA expression level for nine tissue types are computed, which
serve as an indicator for suggesting miRNAs could play a role in
human cancer. The database is freely accessible at
http://ppi.bioinfo.asia.edu.tw/mirna_target/index.html.