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: Prediction of bacterial virulent protein sequences can
give assistance to identification and characterization of novel
virulence-associated factors and discover drug/vaccine targets against
proteins indispensable to pathogenicity. Gene Ontology (GO)
annotation which describes functions of genes and gene products as a
controlled vocabulary of terms has been shown effectively for a
variety of tasks such as gene expression study, GO annotation
prediction, protein subcellular localization, etc. In this study, we
propose a sequence-based method Virulent-GO by mining informative
GO terms as features for predicting bacterial virulent proteins.
Each protein in the datasets used by the existing method
VirulentPred is annotated by using BLAST to obtain its homologies
with known accession numbers for retrieving GO terms. After
investigating various popular classifiers using the same five-fold
cross-validation scheme, Virulent-GO using the single kind of GO
term features with an accuracy of 82.5% is slightly better than
VirulentPred with 81.8% using five kinds of sequence-based features.
For the evaluation of independent test, Virulent-GO also yields better
results (82.0%) than VirulentPred (80.7%). When evaluating single
kind of feature with SVM, the GO term feature performs much well,
compared with each of the five kinds of features.
Abstract: This study proposes a conceptual model and
empirically tests the relationships between customers and librarians
(i.e. tangibles, responsiveness, assurance, reliability and empathy)
with a dependent variable (customer satisfaction) regarding library
services. The SERVQUAL instrument was administered to 100
respondents which comprises of staff and students at a public higher
learning institution in the Federal Territory of Labuan, Malaysia.
They were public university library users. Results revealed that all
service quality dimensions tested were significant and influenced
customer satisfaction of visitors to a public university library.
Assurance is the most important factor that influences customer
satisfaction with the services rendered by the librarian. It is
imperative for the library management to take note that the top five
service attributes that gained greatest attention from library visitors-
perspective includes employee willingness to help customers,
availability of customer representatives online for response to
queries, library staff actively and promptly provide services, signs in
the building are clear and library staff are friendly and courteous.
This study provides valuable results concerning the determinants of
the service quality and customer satisfaction of public university
library services from the users' perspective.
Abstract: Recent trends in building constructions in Libya are
more toward tall (high-rise) building projects. As a consequence, a
better estimation of the lateral loading in the design process is
becoming the focal of a safe and cost effective building industry. Byin-
large, Libya is not considered a potential earthquake prone zone,
making wind is the dominant design lateral loads. Current design
practice in the country estimates wind speeds on a mere random
bases by considering certain factor of safety to the chosen wind
speed. Therefore, a need for a more accurate estimation of wind
speeds in Libya was the motivation behind this study. Records of
wind speed data were collected from 22 metrological stations in
Libya, and were statistically analysed. The analysis of more than four
decades of wind speed records suggests that the country can be
divided into four zones of distinct wind speeds. A computer “survey"
program was manipulated to draw design wind speeds contour map
for the state of Libya.
The paper presents the statistical analysis of Libya-s recorded
wind speed data and proposes design wind speed values for a 50-year
return period that covers the entire country.
Abstract: In this paper, we propose a face recognition algorithm
using AAM and Gabor features. Gabor feature vectors which are well
known to be robust with respect to small variations of shape, scaling,
rotation, distortion, illumination and poses in images are popularly
employed for feature vectors for many object detection and
recognition algorithms. EBGM, which is prominent among face
recognition algorithms employing Gabor feature vectors, requires
localization of facial feature points where Gabor feature vectors are
extracted. However, localization method employed in EBGM is based
on Gabor jet similarity and is sensitive to initial values. Wrong
localization of facial feature points affects face recognition rate. AAM
is known to be successfully applied to localization of facial feature
points. In this paper, we devise a facial feature point localization
method which first roughly estimate facial feature points using AAM
and refine facial feature points using Gabor jet similarity-based facial
feature localization method with initial points set by the rough facial
feature points obtained from AAM, and propose a face recognition
algorithm using the devised localization method for facial feature
localization and Gabor feature vectors. It is observed through
experiments that such a cascaded localization method based on both
AAM and Gabor jet similarity is more robust than the localization
method based on only Gabor jet similarity. Also, it is shown that the
proposed face recognition algorithm using this devised localization
method and Gabor feature vectors performs better than the
conventional face recognition algorithm using Gabor jet
similarity-based localization method and Gabor feature vectors like
EBGM.
Abstract: This paper presents a computational methodology
based on matrix operations for a computer based solution to the
problem of performance analysis of software reliability models
(SRMs). A set of seven comparison criteria have been formulated to
rank various non-homogenous Poisson process software reliability
models proposed during the past 30 years to estimate software
reliability measures such as the number of remaining faults, software
failure rate, and software reliability. Selection of optimal SRM for
use in a particular case has been an area of interest for researchers in
the field of software reliability. Tools and techniques for software
reliability model selection found in the literature cannot be used with
high level of confidence as they use a limited number of model
selection criteria. A real data set of middle size software project from
published papers has been used for demonstration of matrix method.
The result of this study will be a ranking of SRMs based on the
Permanent value of the criteria matrix formed for each model based
on the comparison criteria. The software reliability model with
highest value of the Permanent is ranked at number – 1 and so on.
Abstract: A state of the art Speaker Identification (SI) system requires a robust feature extraction unit followed by a speaker modeling scheme for generalized representation of these features. Over the years, Mel-Frequency Cepstral Coefficients (MFCC) modeled on the human auditory system has been used as a standard acoustic feature set for SI applications. However, due to the structure of its filter bank, it captures vocal tract characteristics more effectively in the lower frequency regions. This paper proposes a new set of features using a complementary filter bank structure which improves distinguishability of speaker specific cues present in the higher frequency zone. Unlike high level features that are difficult to extract, the proposed feature set involves little computational burden during the extraction process. When combined with MFCC via a parallel implementation of speaker models, the proposed feature set outperforms baseline MFCC significantly. This proposition is validated by experiments conducted on two different kinds of public databases namely YOHO (microphone speech) and POLYCOST (telephone speech) with Gaussian Mixture Models (GMM) as a Classifier for various model orders.
Abstract: Complex engineering design problems consist of
numerous factors of varying criticalities. Considering fundamental features of design and inferior details alike will result in an extensive
waste of time and effort. Design parameters should be introduced gradually as appropriate based on their significance relevant to the
problem context. This motivates the representation of design parameters at multiple levels of an abstraction hierarchy. However, developing abstraction hierarchies is an area that is not well
understood. Our research proposes a novel hierarchical abstraction methodology to plan effective engineering designs and processes. It
provides a theoretically sound foundation to represent, abstract and stratify engineering design parameters and tasks according to causality and criticality. The methodology creates abstraction
hierarchies in a recursive and bottom-up approach that guarantees no
backtracking across any of the abstraction levels. The methodology consists of three main phases, representation, abstraction, and layering to multiple hierarchical levels. The effectiveness of the
developed methodology is demonstrated by a design problem.
Abstract: Corporate credit rating prediction using statistical and
artificial intelligence (AI) techniques has been one of the attractive
research topics in the literature. In recent years, multiclass
classification models such as artificial neural network (ANN) or
multiclass support vector machine (MSVM) have become a very
appealing machine learning approaches due to their good
performance. However, most of them have only focused on classifying
samples into nominal categories, thus the unique characteristic of the
credit rating - ordinality - has been seldom considered in their
approaches. This study proposes new types of ANN and MSVM
classifiers, which are named OMANN and OMSVM respectively.
OMANN and OMSVM are designed to extend binary ANN or SVM
classifiers by applying ordinal pairwise partitioning (OPP) strategy.
These models can handle ordinal multiple classes efficiently and
effectively. To validate the usefulness of these two models, we applied
them to the real-world bond rating case. We compared the results of
our models to those of conventional approaches. The experimental
results showed that our proposed models improve classification
accuracy in comparison to typical multiclass classification techniques
with the reduced computation resource.
Abstract: In this paper, a new approach based on the extent of
friendship between the nodes is proposed which makes the nodes to
co-operate in an ad hoc environment. The extended DSR protocol is
tested under different scenarios by varying the number of malicious
nodes and node moving speed. It is also tested varying the number of
nodes in simulation used. The result indicates the achieved
throughput by extended DSR is greater than the standard DSR and
indicates the percentage of malicious drops over total drops are less
in the case of extended DSR than the standard DSR.
Abstract: Speedups from mapping four real-life DSP
applications on an embedded system-on-chip that couples coarsegrained
reconfigurable logic with an instruction-set processor are
presented. The reconfigurable logic is realized by a 2-Dimensional
Array of Processing Elements. A design flow for improving
application-s performance is proposed. Critical software parts, called
kernels, are accelerated on the Coarse-Grained Reconfigurable
Array. The kernels are detected by profiling the source code. For
mapping the detected kernels on the reconfigurable logic a prioritybased
mapping algorithm has been developed. Two 4x4 array
architectures, which differ in their interconnection structure among
the Processing Elements, are considered. The experiments for eight
different instances of a generic system show that important overall
application speedups have been reported for the four applications.
The performance improvements range from 1.86 to 3.67, with an
average value of 2.53, compared with an all-software execution.
These speedups are quite close to the maximum theoretical speedups
imposed by Amdahl-s law.
Abstract: The study of human hand morphology reveals that developing an artificial hand with the capabilities of human hand is an extremely challenging task. This paper presents the development of a robotic prosthetic hand focusing on the improvement of a tendon driven mechanism towards a biomimetic prosthetic hand. The design of this prosthesis hand is geared towards achieving high level of dexterity and anthropomorphism by means of a new hybrid mechanism that integrates a miniature motor driven actuation mechanism, a Shape Memory Alloy actuated mechanism and a passive mechanical linkage. The synergy of these actuators enables the flexion-extension movement at each of the finger joints within a limited size, shape and weight constraints. Tactile sensors are integrated on the finger tips and the finger phalanges area. This prosthesis hand is developed with an exact size ratio that mimics a biological hand. Its behavior resembles the human counterpart in terms of working envelope, speed and torque, and thus resembles both the key physical features and the grasping functionality of an adult hand.
Abstract: Facility Layout Problem (FLP) is one of the essential
problems of several types of manufacturing and service sector. It is
an optimization problem on which the main objective is to obtain the
efficient locations, arrangement and order of the facilities. In the
literature, there are numerous facility layout problem research
presented and have used meta-heuristic approaches to achieve
optimal facility layout design. This paper presented genetic algorithm
to solve facility layout problem; to minimize total cost function. The
performance of the proposed approach was verified and compared
using problems in the literature.
Abstract: A lateral trench-gate power metal-oxide-semiconductor on 4H-SiC is proposed. The device consists of two separate trenches in which two gates are placed on both sides of P-body region resulting two parallel channels. Enhanced current conduction and reduced-surface-field effect in the structure provide substantial improvement in the device performance. Using two dimensional simulations, the performance of proposed device is evaluated and compare of with that of the conventional device for same cell pitch. It is demonstrated that the proposed structure provides two times higher output current, 11% decrease in threshold voltage, 70% improvement in transconductance, 70% reduction in specific ON-resistance, 52% increase in breakdown voltage, and nearly eight time improvement in figure-of-merit over the conventional device.
Abstract: In this paper, a new probability density function (pdf)
is proposed to model the statistics of wavelet coefficients, and a
simple Kalman-s filter is derived from the new pdf using Bayesian
estimation theory. Specifically, we decompose the speckled image
into wavelet subbands, we apply the Kalman-s filter to the high
subbands, and reconstruct a despeckled image from the modified
detail coefficients. Experimental results demonstrate that our method
compares favorably to several other despeckling methods on test
synthetic aperture radar (SAR) images.
Abstract: In this work, we study the impact of dynamically changing link slowdowns on the stability properties of packetswitched networks under the Adversarial Queueing Theory framework. Especially, we consider the Adversarial, Quasi-Static Slowdown Queueing Theory model, where each link slowdown may take on values in the two-valued set of integers {1, D} with D > 1 which remain fixed for a long time, under a (w, p)-adversary. In this framework, we present an innovative systematic construction for the estimation of adversarial injection rate lower bounds, which, if exceeded, cause instability in networks that use the LIS (Longest-in- System) protocol for contention-resolution. In addition, we show that a network that uses the LIS protocol for contention-resolution may result in dropping its instability bound at injection rates p > 0 when the network size and the high slowdown D take large values. This is the best ever known instability lower bound for LIS networks.
Abstract: In the recent years multimedia traffic and in particular
VoIP services are growing dramatically. We present a new algorithm
to control the resource utilization and to optimize the voice codec
selection during SIP call setup on behalf of the traffic condition
estimated on the network path.
The most suitable methodologies and the tools that perform realtime
evaluation of the available bandwidth on a network path have
been integrated with our proposed algorithm: this selects the best
codec for a VoIP call in function of the instantaneous available
bandwidth on the path. The algorithm does not require any explicit
feedback from the network, and this makes it easily deployable over
the Internet. We have also performed intensive tests on real network
scenarios with a software prototype, verifying the algorithm
efficiency with different network topologies and traffic patterns
between two SIP PBXs.
The promising results obtained during the experimental validation
of the algorithm are now the basis for the extension towards a larger
set of multimedia services and the integration of our methodology
with existing PBX appliances.
Abstract: Due to growing environmental concerns of the cement
industry, alternative cement technologies have become an area of
increasing interest. It is now believed that new binders are
indispensable for enhanced environmental and durability
performance. Self-compacting Geopolymer concrete is an innovative
method and improved way of concreting operation that does not
require vibration for placing it and is produced by complete
elimination of ordinary Portland cement.
This paper documents the assessment of the compressive strength
and workability characteristics of low-calcium fly ash based selfcompacting
geopolymer concrete. The essential workability
properties of the freshly prepared Self-compacting Geopolymer
concrete such as filling ability, passing ability and segregation
resistance were evaluated by using Slump flow, V-funnel, L-box and
J-ring test methods. The fundamental requirements of high
flowability and segregation resistance as specified by guidelines on
Self Compacting Concrete by EFNARC were satisfied. In addition,
compressive strength was determined and the test results are included
here. This paper also reports the effect of extra water, curing time and
curing temperature on the compressive strength of self-compacting
geopolymer concrete. The test results show that extra water in the
concrete mix plays a significant role. Also, longer curing time and
curing the concrete specimens at higher temperatures will result in
higher compressive strength.
Abstract: An innovative tri-axes micro-power receiver is
proposed. The tri-axes micro-power receiver consists of two sets 3-D
micro-solenoids and one set planar micro-coils in which iron core is
embedded. The three sets of micro-coils are designed to be orthogonal
to each other. Therefore, no matter which direction the flux is present
along, the magnetic energy can be harvested and transformed into
electric power. Not only dead space of receiving power is mostly
reduced, but also transformation efficiency of electromagnetic energy
to electric power can be efficiently raised. By employing commercial
software, Ansoft Maxwell, the preliminary simulation results verify
that the proposed micro-power receiver can efficiently pick up the
energy transmitted by magnetic power source.
As to the fabrication process, the isotropic etching technique is
employed to micro-machine the inverse-trapezoid fillister so that the
copper wire can be successfully electroplated. The adhesion between
micro-coils and fillister is much enhanced.
Abstract: this paper presents a multi-context recurrent network for time series analysis. While simple recurrent network (SRN) are very popular among recurrent neural networks, they still have some shortcomings in terms of learning speed and accuracy that need to be addressed. To solve these problems, we proposed a multi-context recurrent network (MCRN) with three different learning algorithms. The performance of this network is evaluated on some real-world application such as handwriting recognition and energy load forecasting. We study the performance of this network and we compared it to a very well established SRN. The experimental results showed that MCRN is very efficient and very well suited to time series analysis and its applications.