Abstract: The changing economic climate has made global
manufacturing a growing reality over the last decade, forcing
companies from east and west and all over the world to
collaborate beyond geographic boundaries in the design,
manufacture and assemble of products. The ISO10303 and
ISO14649 Standards (STEP and STEP-NC) have been
developed to introduce interoperability into manufacturing
enterprises so as to meet the challenge of responding to
production on demand. This paper describes and illustrates a
STEP compliant CAD/CAPP/CAM System for the manufacture
of rotational parts on CNC turning centers. The information
models to support the proposed system together with the data
models defined in the ISO14649 standard used to create the NC
programs are also described. A structured view of a STEP
compliant CAD/CAPP/CAM system framework supporting the
next generation of intelligent CNC controllers for turn/mill
component manufacture is provided. Finally a proposed
computational environment for a STEP-NC compliant system
for turning operations (SCSTO) is described. SCSTO is the
experimental part of the research supported by the specification
of information models and constructed using a structured
methodology and object-oriented methods. SCSTO was
developed to generate a Part 21 file based on machining
features to support the interactive generation of process plans
utilizing feature extraction. A case study component has been
developed to prove the concept for using the milling and turning
parts of ISO14649 to provide a turn-mill CAD/CAPP/CAM
environment.
Abstract: Image segmentation is an important step in image
processing. Major developments in medical imaging allow
physicians to use potent and non-invasive methods in order to
evaluate structures, performance and to diagnose human diseases. In
this study, an active contour was used to extract vessel networks
from color retina images. Automatic analysis of retina vessels
facilitates calculation of arterial index which is required to diagnose
some certain retinopathies.
Abstract: The cost of developing the software from scratch can
be saved by identifying and extracting the reusable components from
already developed and existing software systems or legacy systems
[6]. But the issue of how to identify reusable components from
existing systems has remained relatively unexplored. We have used
metric based approach for characterizing a software module. In this
present work, the metrics McCabe-s Cyclometric Complexity
Measure for Complexity measurement, Regularity Metric, Halstead
Software Science Indicator for Volume indication, Reuse Frequency
metric and Coupling Metric values of the software component are
used as input attributes to the different types of Neural Network
system and reusability of the software component is calculated. The
results are recorded in terms of Accuracy, Mean Absolute Error
(MAE) and Root Mean Squared Error (RMSE).
Abstract: An experiment was conducted under controlled
conditions to study the effect of water extract of leaves, shoots and
roots of either Sisymbrium irio L. =SISIR and/or Descurainia
sophia (L.) Schur =DESSO on the germination and primary growth
of wheat. A split-split plot experiment in CRD with three
replications was used. The main plots were the type of weed: i.e.
SISIR and DESSO and the sub-plots were type of organ: i.e. leaf,
stem and root and, the sub-sub plots were concentration of the
water extract of each organ of the weeds: i.e. 0, 2, 4 and 8 % w/v.
The plant materials were cut in 2-3 cm pieces and then were
ground in a blender. The crushed materials were weighed
according to experimental protocol and the final volume was
reached to 100 ml in distilled water in dark bottles. All bottles were
put on a shaker for 24 hours. The solutions were filtered by muslin
cloth. Whatman paper, 9 cm in diameter, were put in petri dishes
and twenty seeds of wheat were put on it and 5 ml distilled water
or water extract of weeds were added to each petri dish. All petri
dishes were put in constant temperature of 15 0C incubator.
The results showed that the SISIR water extract had a greater
inhibitory effects on germination and primary growth of wheat
than those of DESSO water extract. The water extracts of the
leaves of both weeds had the greatest inhibitory effects on
germination and primary growth of wheat, compared to those of
stems and roots. Increasing the concentration of water extract of
leaves, stems and roots of both weeds up to 8 % caused the greatest
inhibitory effects to wheat and reduced the germination rate and
primary growth of it linearly.
Abstract: The challenge in the case of image authentication is that in many cases images need to be subjected to non malicious operations like compression, so the authentication techniques need to be compression tolerant. In this paper we propose an image authentication system that is tolerant to JPEG lossy compression operations. A scheme for JPEG grey scale images is proposed based on a data embedding method that is based on a secret key and a secret mapping vector in the frequency domain. An encrypted feature vector extracted from the image DCT coefficients, is embedded redundantly, and invisibly in the marked image. On the receiver side, the feature vector from the received image is derived again and compared against the extracted watermark to verify the image authenticity. The proposed scheme is robust against JPEG compression up to a maximum compression of approximately 80%,, but sensitive to malicious attacks such as cutting and pasting.
Abstract: Due to the dynamic nature of the Cloud, continuous monitoring of QoS requirements is necessary to manage the Cloud computing environment. The process of QoS monitoring and SLA violation detection consists of: collecting low and high level information pertinent to the service, analyzing the collected information, and taking corrective actions when SLA violations are detected. In this paper, we detail the architecture and the implementation of the first step of this process. More specifically, we propose an event-based approach to obtain run time information of services developed as BPEL processes. By catching particular events (i.e., the low level information), our approach recognizes the run-time execution path of a monitored service and uses the BPEL execution patterns to compute QoS of the composite service (i.e., the high level information).
Abstract: The main goal of data mining is to extract accurate, comprehensible and interesting knowledge from databases that may be considered as large search spaces. In this paper, a new, efficient type of Genetic Algorithm (GA) called uniform two-level GA is proposed as a search strategy to discover truly interesting, high-level prediction rules, a difficult problem and relatively little researched, rather than discovering classification knowledge as usual in the literatures. The proposed method uses the advantage of uniform population method and addresses the task of generalized rule induction that can be regarded as a generalization of the task of classification. Although the task of generalized rule induction requires a lot of computations, which is usually not satisfied with the normal algorithms, it was demonstrated that this method increased the performance of GAs and rapidly found interesting rules.
Abstract: Verification of real-time software systems can be
expensive in terms of time and resources. Testing is the main method
of proving correctness but has been shown to be a long and time
consuming process. Everyday engineers are usually unwilling to
adopt formal approaches to correctness because of the overhead
associated with developing their knowledge of such techniques.
Performance modelling techniques allow systems to be evaluated
with respect to timing constraints. This paper describes PARTES, a
framework which guides the extraction of performance models from
programs written in an annotated subset of C.
Abstract: Multimedia, as it stands now is perhaps the most
diverse and rich culture around the globe. One of the major needs of
Multimedia is to have a single system that enables people to
efficiently search through their multimedia catalogues. Many
Domain Specific Systems and architectures have been proposed but
up till now no generic and complete architecture is proposed. In this
paper, we have suggested a generic architecture for Multimedia
Database. The main strengths of our architecture besides being
generic are Semantic Libraries to reduce semantic gap, levels of
feature extraction for more specific and detailed feature extraction
according to classes defined by prior level, and merging of two types
of queries i.e. text and QBE (Query by Example) for more accurate
yet detailed results.
Abstract: This paper describes Independent Component Analysis (ICA) based fixed-point algorithm for the blind separation of the convolutive mixture of speech, picked-up by a linear microphone array. The proposed algorithm extracts independent sources by non- Gaussianizing the Time-Frequency Series of Speech (TFSS) in a deflationary way. The degree of non-Gaussianization is measured by negentropy. The relative performances of algorithm under random initialization and Null beamformer (NBF) based initialization are studied. It has been found that an NBF based initial value gives speedy convergence as well as better separation performance
Abstract: Arbitrarily shaped video objects are an important
concept in modern video coding methods. The techniques presently
used are not based on image elements but rather video objects having
an arbitrary shape. In this paper, spatial shape error concealment
techniques to be used for object-based image in error-prone
environments are proposed. We consider a geometric shape
representation consisting of the object boundary, which can be
extracted from the α-plane. Three different approaches are used to
replace a missing boundary segment: Bézier interpolation, Bézier
approximation and NURBS approximation. Experimental results on
object shape with different concealment difficulty demonstrate the
performance of the proposed methods. Comparisons with proposed
methods are also presented.
Abstract: Mammographic images and data analysis to
facilitate modelling or computer aided diagnostic (CAD) software development should best be done using a common database that can handle various mammographic image file
formats and relate these to other patient information.
This would optimize the use of the data as both primary
reporting and enhanced information extraction of research data could be performed from the single dataset. One desired
improvement is the integration of DICOM file header information into the database, as an efficient and reliable source of supplementary patient information intrinsically
available in the images.
The purpose of this paper was to design a suitable database to link and integrate different types of image files and gather common information that can be further used for research
purposes. An interface was developed for accessing, adding,
updating, modifying and extracting data from the common
database, enhancing the future possible application of the data in CAD processing.
Technically, future developments envisaged include the creation of an advanced search function to selects image files
based on descriptor combinations. Results can be further used for specific CAD processing and other research. Design of a
user friendly configuration utility for importing of the required fields from the DICOM files must be done.
Abstract: Different agricultural waste peels were assessed for
their suitability to be used as primary substrates for the
bioremediation of free cyanide (CN-) by a cyanide-degrading fungus
Aspergillus awamori isolated from cyanide containing wastewater.
The bioremediated CN- concentration were in the range of 36 to 110
mg CN-/L, with Orange (C. sinensis) > Carrot (D. carota) > Onion
(A. cepa) > Apple (M. pumila), being chosen as suitable substrates
for large scale CN- degradation processes due to: 1) the high
concentration of bioremediated CN-, 2) total reduced sugars released
into solution to sustain the biocatalyst, and 3) minimal residual NH4-
N concentration after fermentation. The bioremediation rate constants
(k) were 0.017h-1 (0h < t < 24h), with improved bioremediation rates
(0.02189h-1) observed after 24h. The averaged nitrilase activity was
~10 U/L.
Abstract: A preliminary evaluation of the feasibility of installing small wind turbines on offshore oil and gas extraction platforms is presented. Some aerodynamic considerations are developed in order to determine the best rotor architecture to exploit the wind potential on such installations, assuming that wind conditions over the platforms are similar to those registered on the roofs of urban buildings. Economical considerations about both advantages and disadvantages of the exploitation of wind energy on offshore extraction platforms with respect to conventional offshore wind plants, is also presented. Finally, wind charts of European offshore winds are presented together with a map of the major offshore installations.
Abstract: In this paper we propose a robust environmental sound classification approach, based on spectrograms features driven from log-Gabor filters. This approach includes two methods. In the first methods, the spectrograms are passed through an appropriate log-Gabor filter banks and the outputs are averaged and underwent an optimal feature selection procedure based on a mutual information criteria. The second method uses the same steps but applied only to three patches extracted from each spectrogram.
To investigate the accuracy of the proposed methods, we conduct experiments using a large database containing 10 environmental sound classes. The classification results based on Multiclass Support Vector Machines show that the second method is the most efficient with an average classification accuracy of 89.62 %.
Abstract: In this paper, we apply the PQ theory with shunt active power filter in an unbalanced and distorted power system voltage to compensate the perturbations generated by non linear load. The power factor is also improved in the current source. The PLL system is used to extract the fundamental component of the even sequence under conditions mentioned of the power system voltage.
Abstract: Classifier fusion may generate more accurate
classification than each of the basic classifiers. Fusion is often based
on fixed combination rules like the product, average etc. This paper
presents decision templates as classifier fusion method for the
recognition of the handwritten English and Farsi numerals (1-9).
The process involves extracting a feature vector on well-known
image databases. The extracted feature vector is fed to multiple
classifier fusion. A set of experiments were conducted to compare
decision templates (DTs) with some combination rules. Results from
decision templates conclude 97.99% and 97.28% for Farsi and
English handwritten digits.
Abstract: We review a knowledge extractor model in
constructing 3G Killer Applications. The success of 3G is essential
for Government as it became part of Telecommunications National
Strategy. The 3G wireless technologies may reach larger area and
increase country-s ICT penetration. In order to understand future
customers needs, the operators require proper information
(knowledge) lying inside. Our work approached future customers as
complex system where the complex knowledge may expose regular
behavior. The hidden information from 3G future customers is
revealed by using fractal-based questionnaires. Afterward, further
statistical analysis is used to match the results with operator-s
strategic plan. The developments of 3G applications also consider its
saturation time and further improvement of the application.
Abstract: The standard investigational method for obstructive
sleep apnea syndrome (OSAS) diagnosis is polysomnography (PSG),
which consists of a simultaneous, usually overnight recording of
multiple electro-physiological signals related to sleep and
wakefulness. This is an expensive, encumbering and not a readily
repeated protocol, and therefore there is need for simpler and easily
implemented screening and detection techniques. Identification of
apnea/hypopnea events in the screening recordings is the key factor
for the diagnosis of OSAS. The analysis of a solely single-lead
electrocardiographic (ECG) signal for OSAS diagnosis, which may
be done with portable devices, at patient-s home, is the challenge of
the last years. A novel artificial neural network (ANN) based
approach for feature extraction and automatic identification of
respiratory events in ECG signals is presented in this paper. A
nonlinear principal component analysis (NLPCA) method was
considered for feature extraction and support vector machine for
classification/recognition. An alternative representation of the
respiratory events by means of Kohonen type neural network is
discussed. Our prospective study was based on OSAS patients of the
Clinical Hospital of Pneumology from Iaşi, Romania, males and
females, as well as on non-OSAS investigated human subjects. Our
computed analysis includes a learning phase based on cross signal
PSG annotation.
Abstract: Discovering new biological knowledge from the highthroughput biological data is a major challenge to bioinformatics today. To address this challenge, we developed a new approach for protein classification. Proteins that are evolutionarily- and thereby functionally- related are said to belong to the same classification. Identifying protein classification is of fundamental importance to document the diversity of the known protein universe. It also provides a means to determine the functional roles of newly discovered protein sequences. Our goal is to predict the functional classification of novel protein sequences based on a set of features extracted from each protein sequence. The proposed technique used datasets extracted from the Structural Classification of Proteins (SCOP) database. A set of spectral domain features based on Fast Fourier Transform (FFT) is used. The proposed classifier uses multilayer back propagation (MLBP) neural network for protein classification. The maximum classification accuracy is about 91% when applying the classifier to the full four levels of the SCOP database. However, it reaches a maximum of 96% when limiting the classification to the family level. The classification results reveal that spectral domain contains information that can be used for classification with high accuracy. In addition, the results emphasize that sequence similarity measures are of great importance especially at the family level.