Abstract: Visual secret sharing (VSS) was proposed by Naor and Shamir in 1995. Visual secret sharing schemes encode a secret image into two or more share images, and single share image can’t obtain any information about the secret image. When superimposes the shares, it can restore the secret by human vision. Due to the traditional VSS have some problems like pixel expansion and the cost of sophisticated. And this method only can encode one secret image. The schemes of encrypting more secret images by random grids into two shares were proposed by Chen et al. in 2008. But when those restored secret images have much distortion, those schemes are almost limited in decoding. In the other words, if there is too much distortion, we can’t encrypt too much information. So, if we can adjust distortion to very small, we can encrypt more secret images. In this paper, four new algorithms which based on Chang et al.’s scheme be held in 2010 are proposed. First algorithm can adjust distortion to very small. Second algorithm distributes the distortion into two restored secret images. Third algorithm achieves no distortion for special secret images. Fourth algorithm encrypts three secret images, which not only retain the advantage of VSS but also improve on the problems of decoding.
Abstract: Text categorization - the assignment of natural language documents to one or more predefined categories based on their semantic content - is an important component in many information organization and management tasks. Performance of neural networks learning is known to be sensitive to the initial weights and architecture. This paper discusses the use multilayer neural network initialization with decision tree classifier for improving text categorization accuracy. An adaptation of the algorithm is proposed in which a decision tree from root node until a final leave is used for initialization of multilayer neural network. The experimental evaluation demonstrates this approach provides better classification accuracy with Reuters-21578 corpus, one of the standard benchmarks for text categorization tasks. We present results comparing the accuracy of this approach with multilayer neural network initialized with traditional random method and decision tree classifiers.
Abstract: The optimal design of PI controller for Automatic Generation Control in two area is presented in this paper. The concept of Dual mode control is applied in the PI controller, such that the proportional mode is made active when the rate of change of the error is sufficiently larger than a specified limit otherwise switched to the integral mode. A digital simulation is used in conjunction with the Hooke-Jeeve’s optimization technique to determine the optimum parameters (individual gain of proportional and integral controller) of the PI controller. Integrated Square of the Error (ISE), Integrated Time multiplied by Absolute Error(ITAE) , and Integrated Absolute Error(IAE) performance indices are considered to measure the appropriateness of the designed controller. The proposed controller are tested for a two area single nonreheat thermal system considering the practical aspect of the problem such as Deadband and Generation Rate Constraint(GRC). Simulation results show that dual mode with optimized values of the gains improved the control performance than the commonly used Variable Structure .
Abstract: Recent scientific investigations indicate that
multimodal biometrics overcome the technical limitations of
unimodal biometrics, making them ideally suited for everyday life
applications that require a reliable authentication system. However,
for a successful adoption of multimodal biometrics, such systems
would require large heterogeneous datasets with complex multimodal
fusion and privacy schemes spanning various distributed
environments. From experimental investigations of current
multimodal systems, this paper reports the various issues related to
speed, error-recovery and privacy that impede the diffusion of such
systems in real-life. This calls for a robust mechanism that caters to
the desired real-time performance, robust fusion schemes,
interoperability and adaptable privacy policies.
The main objective of this paper is to present a framework that
addresses the abovementioned issues by leveraging on the
heterogeneous resource sharing capacities of Grid services and the
efficient machine learning capabilities of artificial neural networks
(ANN). Hence, this paper proposes a Grid-based neural network
framework for adopting multimodal biometrics with the view of
overcoming the barriers of performance, privacy and risk issues that
are associated with shared heterogeneous multimodal data centres.
The framework combines the concept of Grid services for reliable
brokering and privacy policy management of shared biometric
resources along with a momentum back propagation ANN (MBPANN)
model of machine learning for efficient multimodal fusion and
authentication schemes. Real-life applications would be able to adopt
the proposed framework to cater to the varying business requirements
and user privacies for a successful diffusion of multimodal
biometrics in various day-to-day transactions.
Abstract: Face authentication for access control is a face
membership authentication which passes the person of the incoming
face if he turns out to be one of an enrolled person based on face
recognition or rejects if not. Face membership authentication belongs
to the two class classification problem where SVM(Support Vector
Machine) has been successfully applied and shows better performance
compared to the conventional threshold-based classification. However,
most of previous SVMs have been trained using image feature vectors
extracted from face images of each class member(enrolled
class/unenrolled class) so that they are not robust to variations in
illuminations, poses, and facial expressions and much affected by
changes in member configuration of the enrolled class
In this paper, we propose an effective face membership
authentication method based on SVM using class discriminating
features which represent an incoming face image-s associability with
each class distinctively. These class discriminating features are weakly
related with image features so that they are less affected by variations
in illuminations, poses and facial expression.
Through experiments, it is shown that the proposed face
membership authentication method performs better than the threshold
rule-based or the conventional SVM-based authentication methods and
is relatively less affected by changes in member size and membership.
Abstract: A network of coupled stochastic oscillators is
proposed for modeling of a cluster of entangled qubits that is
exploited as a computation resource in one-way quantum
computation schemes. A qubit model has been designed as a
stochastic oscillator formed by a pair of coupled limit cycle
oscillators with chaotically modulated limit cycle radii and
frequencies. The qubit simulates the behavior of electric field of
polarized light beam and adequately imitates the states of two-level
quantum system. A cluster of entangled qubits can be associated
with a beam of polarized light, light polarization degree being
directly related to cluster entanglement degree. Oscillatory network,
imitating qubit cluster, is designed, and system of equations for
network dynamics has been written. The constructions of one-qubit
gates are suggested. Changing of cluster entanglement degree caused
by measurements can be exactly calculated.
Abstract: The purpose of this paper is to propose an integrated
consumer health informatics utilization framework that can be used
to gauge the online health information needs and usage patterns
among Malaysian women. The proposed framework was developed
based on four different theories/models: Use and Gratification
Theory, Technology Acceptance 3 Model, Health Belief Model, and
Multi-level Model of Information Seeking. The relevant constructs
and research hypotheses are also presented in this paper. The
framework will be tested in order for it to be used successfully to
identify Malaysian women-s preferences of online health information
resources and health information seeking activities.
Abstract: In this paper, a novel scheme is proposed for Ownership Identification and Color Image Authentication by deploying Cryptography & Digital Watermarking. The color image is first transformed from RGB to YST color space exclusively designed for watermarking. Followed by color space transformation, each channel is divided into 4×4 non-overlapping blocks with selection of central 2×2 sub-blocks. Depending upon the channel selected two to three LSBs of each central 2×2 sub-block are set to zero to hold the ownership, authentication and recovery information. The size & position of sub-block is important for correct localization, enhanced security & fast computation. As YS ÔèÑ T so it is suitable to embed the recovery information apart from the ownership and authentication information, therefore 4×4 block of T channel along with ownership information is then deployed by SHA160 to compute the content based hash that is unique and invulnerable to birthday attack or hash collision instead of using MD5 that may raise the condition i.e. H(m)=H(m'). For recovery, intensity mean of 4x4 block of each channel is computed and encoded upto eight bits. For watermark embedding, key based mapping of blocks is performed using 2DTorus Automorphism. Our scheme is oblivious, generates highly imperceptible images with correct localization of tampering within reasonable time and has the ability to recover the original work with probability of near one.
Abstract: This paper presents a methodical approach for designing and optimizing process parameters in oil blending industries. Twenty seven replicated experiments were conducted for production of A-Z crown super oil (SAE20W/50) employing L9 orthogonal array to establish process response parameters. Power law model was fitted to experimental data and the obtained model was optimized applying the central composite design (CCD) of response surface methodology (RSM). Quadratic model was found to be significant for production of A-Z crown supper oil. The study recognized and specified four new lubricant formulations that conform to ISO oil standard in the course of analyzing the batch productions of A-Z crown supper oil as: L1: KV = 21.8293Cst, BS200 = 9430.00Litres, Ad102=11024.00Litres, PVI = 2520 Litres, L2: KV = 22.513Cst, BS200 = 12430.00 Litres, Ad102 = 11024.00 Litres, PVI = 2520 Litres, L3: KV = 22.1671Cst, BS200 = 9430.00 Litres, Ad102 = 10481.00 Litres, PVI= 2520 Litres, L4: KV = 22.8605Cst, BS200 = 12430.00 Litres, Ad102 = 10481.00 Litres, PVI = 2520 Litres. The analysis of variance showed that quadratic model is significant for kinematic viscosity production while the R-sq value statistic of 0.99936 showed that the variation of kinematic viscosity is due to its relationship with the control factors. This study therefore resulted to appropriate blending proportions of lubricants base oil and additives and recommends the optimal kinematic viscosity of A-Z crown super oil (SAE20W/50) to be 22.86Cst.
Abstract: Producing IT products/services required carefully
designed. IT development process is intangible and labour intensive.
Making optimal use of available resources, both soft (knowledge,
skill-set etc.) and hard (computer system, ancillary equipment etc.),
is vital if IT development is to achieve sensible economical
advantages. Apart from the norm of Project Life Cycle and System
Development Life Cycle (SDLC), there is an urgent need to establish
a general yet widely acceptable guideline on the most effective and
efficient way to precede an IT project in the broader view of Product
Life Cycle. The current paper proposes such a framework with two
major areas of concern: (1) an integration of IT Products and IT
Services within an existing IT Process architecture and; (2) how IT
Product and IT Services are built into the framework of Product Life
Cycle, Project Life Cycle and SDLC.
Abstract: The main objective of this work is to provide a fault detection and isolation based on Markov parameters for residual generation and a neural network for fault classification. The diagnostic approach is accomplished in two steps: In step 1, the system is identified using a series of input / output variables through an identification algorithm. In step 2, the fault is diagnosed comparing the Markov parameters of faulty and non faulty systems. The Artificial Neural Network is trained using predetermined faulty conditions serves to classify the unknown fault. In step 1, the identification is done by first formulating a Hankel matrix out of Input/ output variables and then decomposing the matrix via singular value decomposition technique. For identifying the system online sliding window approach is adopted wherein an open slit slides over a subset of 'n' input/output variables. The faults are introduced at arbitrary instances and the identification is carried out in online. Fault residues are extracted making a comparison of the first five Markov parameters of faulty and non faulty systems. The proposed diagnostic approach is illustrated on benchmark problems with encouraging results.
Abstract: A sophisticated simulator provides a cost-effective measure to carry out preliminary mission testing and diagnostic while reducing potential failures for real life at sea trials. The presented simulation framework covers three key areas: AUV modeling, sensor modeling, and environment modeling. AUV modeling mainly covers the area of AUV dynamics. Sensor modeling deals with physics and mathematical models that govern each sensor installed onto the AUV. Environment model incorporates the hydrostatic, hydrodynamics, and ocean currents that will affect the AUV in a real-time mission. Based on this designed simulation framework, custom scenarios provided by the user can be modeled and its corresponding behaviors can be observed. This paper focuses on the accuracy of the simulated data from AUV model and environmental model derived from a developed AUV test-bed which was jointly upgraded by DSTO and the University of Adelaide. The main contribution of this paper is to experimentally verify the accuracy of the proposed simulation framework.
Abstract: This paper presents an new vision technique for
robotic manipulation of randomly oriented objects in industrial
applications. The proposed approach uses 2D and 3D vision for
efficiently extracting the 3D pose of an object in the presence of
multiple randomly positioned objects. 2D vision permits to quickly
select the objects of interest for 3D processing with a new modified
ICP algorithm (FaR-ICP), thus reducing significantly the processing
time. The extracted 3D pose is then sent to the robot manipulator for
picking. The tests show that the proposed system achieves high
performances
Abstract: A concept of switched beam antennas consisting of
2×2 rectangular array spaced by λ/4 accompanied with a null locating
has been proposed in the previous work. In this letter, the
performance evaluations of its prototype are presented. The benefits
of using proposed system have been clearly measured in term of
signal quality, throughput and delays. Also, the impact of position
shift which mesh router is not located on the expected beam direction
has also been investigated.
Abstract: Portuguese diet has been gradually diverging from the basic principles of healthy eating, leading to an unbalanced dietary pattern which, associated with increasing sedentary lifestyle, has a negative impact on public health. The main objective of this work was to characterize the dietary habits of university students in Viseu, Portugal. The study consisted of a sample of 80 university students, aged between 18 and 28 years. Anthropometric data (weight (kg) and height (m)) were collected and Body Mass Index (BMI) was calculated. The dietary habits were assessed through a three-day food record and the software Medpoint was used to convert food into energy and nutrients. The results showed that students present a normal body mass index. Female university students made a higher number of daily meals than male students, and these last skipped breakfast more frequently. The values of average daily intake of energy, macronutrients and calcium were higher in males. The food pattern was characterized by a predominant consumption of meat, cereal, fats and sugar. Dietary intake of dairy products, fruits, vegetables and legumes does not meet the recommendations, revealing inadequate food habits such as hypoglycemic, hyperprotein and hyperlipidemic diet. Our findings suggest that preventive interventions should be focus in promoting healthy eating habits and physical activity in adulthood.
Abstract: Processing the data by computers and performing
reasoning tasks is an important aim in Computer Science. Semantic
Web is one step towards it. The use of ontologies to enhance the
information by semantically is the current trend. Huge amount of
domain specific, unstructured on-line data needs to be expressed in
machine understandable and semantically searchable format.
Currently users are often forced to search manually in the results
returned by the keyword-based search services. They also want to use
their native languages to express what they search. In this paper, an
ontology-based automated question answering system on software
test documents domain is presented. The system allows users to enter
a question about the domain by means of natural language and
returns exact answer of the questions. Conversion of the natural
language question into the ontology based query is the challenging
part of the system. To be able to achieve this, a new algorithm
regarding free text to ontology based search engine query conversion
is proposed. The algorithm is based on investigation of suitable
question type and parsing the words of the question sentence.
Abstract: A multi fingered dexterous anthropomorphic hand is
being developed by the authors. The focus of the hand is the
replacement of human operators in hazardous environments and also
in environments where zero tolerance is observed for the human
errors. The robotic hand will comprise of five fingers (four fingers
and one thumb) each having four degrees of freedom (DOF) which
can perform flexion, extension, abduction, adduction and also
circumduction. For the actuation purpose pneumatic muscles and
springs will be used. The paper exemplifies the mechanical design for
the robotic hand. It also describes different mechanical designs that
have been developed before date.
Abstract: In this work, we present a novel active learning approach
for learning a visual object detection system. Our system
is composed of an active learning mechanism as wrapper around
a sub-algorithm which implement an online boosting-based learning
object detector. In the core is a combination of a bootstrap procedure
and a semi automatic learning process based on the online boosting
procedure. The idea is to exploit the availability of classifier during
learning to automatically label training samples and increasingly
improves the classifier. This addresses the issue of reducing labeling
effort meanwhile obtain better performance. In addition, we propose
a verification process for further improvement of the classifier.
The idea is to allow re-update on seen data during learning for
stabilizing the detector. The main contribution of this empirical study
is a demonstration that active learning based on an online boosting
approach trained in this manner can achieve results comparable or
even outperform a framework trained in conventional manner using
much more labeling effort. Empirical experiments on challenging data
set for specific object deteciton problems show the effectiveness of
our approach.
Abstract: In this study, a classification-based video
super-resolution method using artificial neural network (ANN) is
proposed to enhance low-resolution (LR) to high-resolution (HR)
frames. The proposed method consists of four main steps:
classification, motion-trace volume collection, temporal adjustment,
and ANN prediction. A classifier is designed based on the edge
properties of a pixel in the LR frame to identify the spatial information.
To exploit the spatio-temporal information, a motion-trace volume is
collected using motion estimation, which can eliminate unfathomable
object motion in the LR frames. In addition, temporal lateral process is
employed for volume adjustment to reduce unnecessary temporal
features. Finally, ANN is applied to each class to learn the complicated
spatio-temporal relationship between LR and HR frames. Simulation
results show that the proposed method successfully improves both
peak signal-to-noise ratio and perceptual quality.
Abstract: Direct Torque Control is a control technique in AC
drive systems to obtain high performance torque control. The
conventional DTC drive contains a pair of hysteresis comparators.
DTC drives utilizing hysteresis comparators suffer from high torque
ripple and variable switching frequency. The most common solution
to those problems is to use the space vector depends on the reference
torque and flux. In this Paper The space vector modulation technique
(SVPWM) is applied to 2 level inverter control in the proposed
DTC-based induction motor drive system, thereby dramatically
reducing the torque ripple. Then the controller based on space vector
modulation is designed to be applied in the control of Induction
Motor (IM) with a three-level Inverter. This type of Inverter has
several advantages over the standard two-level VSI, such as a greater
number of levels in the output voltage waveforms, Lower dV/dt, less
harmonic distortion in voltage and current waveforms and lower
switching frequencies. This paper proposes a general SVPWM
algorithm for three-level based on standard two-level SVPWM. The
proposed scheme is described clearly and simulation results are
reported to demonstrate its effectiveness. The entire control scheme is
implemented with Matlab/Simulink.