Abstract: This paper analyzes different techniques of the fine grained security of relational databases for the two variables-data accessibility and inference. Data accessibility measures the amount of data available to the users after applying a security technique on a table. Inference is the proportion of information leakage after suppressing a cell containing secret data. A row containing a secret cell which is suppressed can become a security threat if an intruder generates useful information from the related visible information of the same row. This paper measures data accessibility and inference associated with row, cell, and column level security techniques. Cell level security offers greatest data accessibility as it suppresses secret data only. But on the other hand, there is a high probability of inference in cell level security. Row and column level security techniques have least data accessibility and inference. This paper introduces cell plus innocent security technique that utilizes the cell level security method but suppresses some innocent data to dodge an intruder that a suppressed cell may not necessarily contain secret data. Four variations of the technique namely cell plus innocent 1/4, cell plus innocent 2/4, cell plus innocent 3/4, and cell plus innocent 4/4 respectively have been introduced to suppress innocent data equal to 1/4, 2/4, 3/4, and 4/4 percent of the true secret data inside the database. Results show that the new technique offers better control over data accessibility and inference as compared to the state-of-theart security techniques. This paper further discusses the combination of techniques together to be used. The paper shows that cell plus innocent 1/4, 2/4, and 3/4 techniques can be used as a replacement for the cell level security.
Abstract: In this paper we proposed a novel method to acquire
the ROI (Region of interest) of unsupervised and touch-less palmprint
captured from a web camera in real-time. We use Viola-Jones
approach and skin model to get the target area in real time. Then an
innovative course-to-fine approach to detect the key points on the hand
is described. A new algorithm is used to find the candidate key points
coarsely and quickly. In finely stage, we verify the hand key points
with the shape context descriptor. To make the user much comfortable,
it can process the hand image with different poses, even the hand is
closed. Experiments show promising result by using the proposed
method in various conditions.
Abstract: Quantitative methods of economic decision-making as
the methodological base of the so called operational research
represent an important set of tools for managing complex economic
systems,both at the microeconomic level and on the macroeconomic
scale. Mathematical models of controlled and controlling processes
allow, by means of artificial experiments, obtaining information
foroptimalor optimum approaching managerial decision-making.The
quantitative methods of economic decision-making usually include a
methodology known as structural analysis -an analysisof
interdisciplinary production-consumption relations.
Abstract: This paper provides a flexible way of controlling
Variable-Bit-Rate (VBR) of compressed digital video, applicable to
the new H264 video compression standard. The entire video
sequence is assessed in advance and the quantisation level is then set
such that bit rate (and thus the frame rate) remains within
predetermined limits compatible with the bandwidth of the
transmission system and the capabilities of the remote end, while at
the same time providing constant quality similar to VBR encoding.
A process for avoiding buffer starvation by selectively eliminating
frames from the encoded output at times when the frame rate is slow
(large number of bits per frame) will be also described. Finally, the
problem of buffer overflow will be solved by selectively eliminating
frames from the received input to the decoder. The decoder detects
the omission of the frames and resynchronizes the transmission by
monitoring time stamps and repeating frames if necessary.
Abstract: Web services are pieces of software that can be invoked via a standardized protocol. They can be combined via formalized taskflow languages. The Open Knowledge system is a fully distributed system using P2P technology, that allows users to publish the setaskflows, and programmers to register their web services or publish implementations of them, for the roles described in these workflows.Besides this, the system offers the functionality to select a peer that could coordinate such an interaction model and inform web services when it is their 'turn'. In this paper we describe the architecture and implementation of the Open Knowledge Kernel which provides the core functionality of the Open Knowledge system.
Abstract: The aim of this work was to detect genetic variability among the set of 40 castor genotypes using 8 RAPD markers. Amplification of genomic DNA of 40 genotypes, using RAPD analysis, yielded in 66 fragments, with an average of 8.25 polymorphic fragments per primer. Number of amplified fragments ranged from 3 to 13, with the size of amplicons ranging from 100 to 1200 bp. Values of the polymorphic information content (PIC) value ranged from 0.556 to 0.895 with an average of 0.784 and diversity index (DI) value ranged from 0.621 to 0.896 with an average of 0.798. The dendrogram based on hierarchical cluster analysis using UPGMA algorithm was prepared and analyzed genotypes were grouped into two main clusters and only two genotypes could not be distinguished. Knowledge on the genetic diversity of castor can be used for future breeding programs for increased oil production for industrial uses.
Abstract: Heart sound is an acoustic signal and many techniques
used nowadays for human recognition tasks borrow speech recognition
techniques. One popular choice for feature extraction of accoustic
signals is the Mel Frequency Cepstral Coefficients (MFCC) which
maps the signal onto a non-linear Mel-Scale that mimics the human
hearing. However the Mel-Scale is almost linear in the frequency
region of heart sounds and thus should produce similar results with
the standard cepstral coefficients (CC). In this paper, MFCC is
investigated to see if it produces superior results for PCG based
human identification system compared to CC. Results show that the
MFCC system is still superior to CC despite linear filter-banks in
the lower frequency range, giving up to 95% correct recognition rate
for MFCC and 90% for CC. Further experiments show that the high
recognition rate is due to the implementation of filter-banks and not
from Mel-Scaling.
Abstract: Among neural models the Support Vector Machine
(SVM) solutions are attracting increasing attention, mostly because
they eliminate certain crucial questions involved by neural network
construction. The main drawback of standard SVM is its high
computational complexity, therefore recently a new technique, the
Least Squares SVM (LS–SVM) has been introduced. In this paper we
present an extended view of the Least Squares Support Vector
Regression (LS–SVR), which enables us to develop new
formulations and algorithms to this regression technique. Based on
manipulating the linear equation set -which embodies all information
about the regression in the learning process- some new methods are
introduced to simplify the formulations, speed up the calculations
and/or provide better results.
Abstract: At present time, competition, unpredictable fluctuations have made communication engineering education in the global sphere really difficult. Confront with new situation in the engineering education sector. Communication engineering education has to be reformed and ready to use more advanced technologies. We realized that one of the general problems of student`s education is that after graduating from their universities, they are not prepared to face the real life challenges and full skilled to work in industry. They are prepared only to think like engineers and professionals but they also need to possess some others non-technical skills. In today-s environment, technical competence alone is not sufficient for career success. Employers want employees (graduate engineers) who have good oral and written communication (soft) skills. It does require for team work, business awareness, organization, management skills, responsibility, initiative, problem solving and IT competency. This proposed curriculum brings interactive, creative, interesting, effective learning methods, which includes online education, virtual labs, practical work, problem-based learning (PBL), and lectures given by industry experts. Giving short assignments, presentations, reports, research papers and projects students can significantly improve their non-technical skills. Also, we noticed the importance of using ICT technologies in engineering education which used by students and teachers, and included that into proposed teaching and learning methods. We added collaborative learning between students through team work which builds theirs skills besides course materials. The prospective on this research that we intent to update communication engineering curriculum in order to get fully constructed engineer students to ready for real industry work.
Abstract: This paper proposes an architecture of dynamically
reconfigurable arithmetic circuit. Dynamic reconfiguration is a
technique to realize required functions by changing hardware
construction during operations. The proposed circuit is based on a
complex number multiply-accumulation circuit which is used
frequently in the field of digital signal processing. In addition, the
proposed circuit performs real number double precision arithmetic
operations. The data formats are single and double precision floating
point number based on IEEE754. The proposed circuit is designed
using VHDL, and verified the correct operation by simulations and
experiments.
Abstract: Renewable energy resources are inexhaustible, clean as compared with conventional resources. Also, it is used to supply regions with no grid, no telephone lines, and often with difficult accessibility by common transport. Satellite earth stations which located in remote areas are the most important application of renewable energy. Neural control is a branch of the general field of intelligent control, which is based on the concept of artificial intelligence. This paper presents the mathematical modeling of satellite earth station power system which is required for simulating the system.Aswan is selected to be the site under consideration because it is a rich region with solar energy. The complete power system is simulated using MATLAB–SIMULINK.An artificial neural network (ANN) based model has been developed for the optimum operation of earth station power system. An ANN is trained using a back propagation with Levenberg–Marquardt algorithm. The best validation performance is obtained for minimum mean square error. The regression between the network output and the corresponding target is equal to 96% which means a high accuracy. Neural network controller architecture gives satisfactory results with small number of neurons, hence better in terms of memory and time are required for NNC implementation. The results indicate that the proposed control unit using ANN can be successfully used for controlling the satellite earth station power system.
Abstract: This work concerns the evolution and the maintenance
of an ontological resource in relation with the evolution of the corpus
of texts from which it had been built.
The knowledge forming a text corpus, especially in dynamic domains,
is in continuous evolution. When a change in the corpus occurs, the
domain ontology must evolve accordingly. Most methods manage
ontology evolution independently from the corpus from which it is
built; in addition, they treat evolution just as a process of knowledge
addition, not considering other knowledge changes. We propose a
methodology for managing an evolving ontology from a text corpus
that evolves over time, while preserving the consistency and the
persistence of this ontology.
Our methodology is based on the changes made on the corpus to
reflect the evolution of the considered domain - augmented surgery
in our case. In this context, the results of text mining techniques,
as well as the ARCHONTE method slightly modified, are used to
support the evolution process.
Abstract: Proprietary sensor network systems are typically expensive, rigid and difficult to incorporate technologies from other vendors. When using competing and incompatible technologies, a non-proprietary system is complex to create because it requires significant technical expertise and effort, which can be more expensive than a proprietary product. This paper presents the Sensor Abstraction Layer (SAL) that provides middleware architectures with a consistent and uniform view of heterogeneous sensor networks, regardless of the technologies involved. SAL abstracts and hides the hardware disparities and specificities related to accessing, controlling, probing and piloting heterogeneous sensors. SAL is a single software library containing a stable hardware-independent interface with consistent access and control functions to remotely manage the network. The end-user has near-real-time access to the collected data via the network, which results in a cost-effective, flexible and simplified system suitable for novice users. SAL has been used for successfully implementing several low-cost sensor network systems.
Abstract: Revolutions Applications such as telecommunications, hands-free communications, recording, etc. which need at least one microphone, the signal is usually infected by noise and echo. The important application is the speech enhancement, which is done to remove suppressed noises and echoes taken by a microphone, beside preferred speech. Accordingly, the microphone signal has to be cleaned using digital signal processing DSP tools before it is played out, transmitted, or stored. Engineers have so far tried different approaches to improving the speech by get back the desired speech signal from the noisy observations. Especially Mobile communication, so in this paper will do reconstruction of the speech signal, observed in additive background noise, using the Kalman filter technique to estimate the parameters of the Autoregressive Process (AR) in the state space model and the output speech signal obtained by the MATLAB. The accurate estimation by Kalman filter on speech would enhance and reduce the noise then compare and discuss the results between actual values and estimated values which produce the reconstructed signals.
Abstract: To increase precision and reliability of automatic control systems, we have to take into account of random factors affecting the control system. Thus, operational matrix technique is used for statistical analysis of first order plus time delay system with uniform random parameter. Examples with deterministic and stochastic disturbance are considered to demonstrate the validity of the method. Comparison with Monte Carlo method is made to show the computational effectiveness of the method.
Abstract: The article presents the whole model of IS/IT
architecture exception governance. As first, the assumptions of
presented model are set. As next, there is defined a generic
governance model that serves as a basis for the architecture exception
governance. The architecture exception definition and its attributes
follow. The model respects well known approaches to the area that
are described in the text, but it adopts higher granularity in
description and expands the process view with all the next necessary
governance components as roles, principles and policies, tools to
enable the implementation of the model into organizations. The
architecture exception process is decomposed into a set of processes
related to the architecture exception lifecycle consisting of set of
phases and architecture exception states. Finally, there is information
about my future research related to this area.
Abstract: In this paper, a study on the applications of the
optimization and regression techniques for optimal calculation of
partial ratios of helical gearboxes with second-step double gear-sets
for minimal cross section dimension is introduced. From the condition
of the moment equilibrium of a mechanic system including three gear
units and their regular resistance condition, models for calculation of
the partial ratios of helical gearboxes with second-step double
gear-sets were given. Especially, by regression analysis, explicit
models for calculation of the partial ratios are introduced. These
models allow determining the partial ratios accurately and simply.
Abstract: In this paper three different approaches for person
verification and identification, i.e. by means of fingerprints, face and
voice recognition, are studied. Face recognition uses parts-based
representation methods and a manifold learning approach. The
assessment criterion is recognition accuracy. The techniques under
investigation are: a) Local Non-negative Matrix Factorization
(LNMF); b) Independent Components Analysis (ICA); c) NMF with
sparse constraints (NMFsc); d) Locality Preserving Projections
(Laplacianfaces). Fingerprint detection was approached by classical
minutiae (small graphical patterns) matching through image
segmentation by using a structural approach and a neural network as
decision block. As to voice / speaker recognition, melodic cepstral
and delta delta mel cepstral analysis were used as main methods, in
order to construct a supervised speaker-dependent voice recognition
system. The final decision (e.g. “accept-reject" for a verification
task) is taken by using a majority voting technique applied to the
three biometrics. The preliminary results, obtained for medium
databases of fingerprints, faces and voice recordings, indicate the
feasibility of our study and an overall recognition precision (about
92%) permitting the utilization of our system for a future complex
biometric card.
Abstract: In this paper, a frequency-variation based method has
been proposed for transistor parameter estimation in a commonemitter
transistor amplifier circuit. We design an algorithm to estimate
the transistor parameters, based on noisy measurements of the output
voltage when the input voltage is a sine wave of variable frequency
and constant amplitude. The common emitter amplifier circuit has
been modelled using the transistor Ebers-Moll equations and the
perturbation technique has been used for separating the linear and
nonlinear parts of the Ebers-Moll equations. This model of the amplifier
has been used to determine the amplitude of the output sinusoid as
a function of the frequency and the parameter vector. Then, applying
the proposed method to the frequency components, the transistor
parameters have been estimated. As compared to the conventional
time-domain least squares method, the proposed method requires
much less data storage and it results in more accurate parameter
estimation, as it exploits the information in the time and frequency
domain, simultaneously. The proposed method can be utilized for
parameter estimation of an analog device in its operating range of
frequencies, as it uses data collected from different frequencies output
signals for parameter estimation.