Abstract: The aspiration of this research article is to target and
focus the gains of university-Industry (U-I) collaborations and
exploring those hurdles which are the obstacles for attaining these
gains. University-Industry collaborations have attained great
importance since 1980 in USA due to its application in all fields of
life. U-I collaboration is a bilateral process where academia is a
proactive member to make such alliances. Universities want to
ameliorate their academic-base with the technicalities of technobabbles.
U-I collaboration is becoming an essential lane for achieving
innovative goals in this century. Many developed nations have set
successful examples to prove this phenomenon as a catalyst to reduce
costs, efforts and personnel for R&D projects. This study is exploits
amplitudes of UI collaboration incentives in the light of success
stories of developed countries. Many universities in USA, UK,
Canada and various European Countries have been engaged with
enterprises for numerous collaborative agreements. A long list of
strategic and short term R&D projects has been executed in
developed countries to accomplish their intended purposes. Due to
the lack of intentions, genuine research and research-oriented
environment, the mentioned field could not grow very well in
developing countries. During last decade, a new wave of research
has induced the institutes of developing countries to promote R&D
culture especially in Pakistan. Higher Education Commission (HEC)
has initiated many projects and funding supports for universities
which have collaborative intentions with industry.
Findings show that rapid innovation, overwhelm the technological
complexities and articulated intellectual-base are major incentives
which steer both partners to establish faculty-industry alliances. Everchanging
technologies, concerned about intellectual property,
different research environment and culture, research relevancy (Basic
or applied), exposure differences and diversity of knowledge
(bookish or practical) are main barriers to establish and retain joint
ventures. Findings also concluded that, it is dire need to support and
enhance cooperation among academia and industry to promote highly
coordinated research behaviors. Author has proposed a roadmap for
developing countries to promote R&D clusters among faculty and
industry to deal the technological challenges and innovation
complexities. Based on our research findings, Model for R&D
Collaboration for developing countries also have been proposed to
promote articulated R&D environment. If developing countries
follow this phenomenon, rapid innovations can be achieved with
limited R&D budget heads.
Abstract: Gabor-based face representation has achieved enormous success in face recognition. This paper addresses a novel algorithm for face recognition using neural networks trained by Gabor features. The system is commenced on convolving a face image with a series of Gabor filter coefficients at different scales and orientations. Two novel contributions of this paper are: scaling of rms contrast and introduction of fuzzily skewed filter. The neural network employed for face recognition is based on the multilayer perceptron (MLP) architecture with backpropagation algorithm and incorporates the convolution filter response of Gabor jet. The effectiveness of the algorithm has been justified over a face database with images captured at different illumination conditions.
Abstract: This paper attempts to investigate the factors that influence hotel managers- attitudes towards sustainable tourism practices (STP) in Kuala Lumpur and the state of Selangor in Malaysia. The study distributes 104 questionnaires to hotels ranging from one star to five-star categories including budget hotels. Out of this figure, 60 copies of the questionnaires were returned and analyzed. The finding revealed that of all the seven factors investigated, only the variables measuring incentives and knowledge have significantly influenced sustainable tourism practices in the country. Therefore, government and other green bodies within the country should continue to provide hotels with incentives for sound technologies. Moreover, the government agencies should continue to educate hoteliers on the relevance of environmental protection for the successful implementation of sustainable tourism practices.
Abstract: This work presents the design of an expert system that aims in the procurement of patient medial background and in the search for suitable skin test selections. Skin testing is the tool used most widely to diagnose allergies. The language of expert systems CLIPS is used as a tool of designing. Finally, we present the evaluation of the proposed expert system which was achieved with the import of certain medical cases and the system produced with suitable successful skin tests.
Abstract: The wavelet transform is one of the most important
method used in signal processing. In this study, we have introduced
frequency-energy characteristics of local earthquakes using discrete
wavelet transform. Frequency-energy characteristic was analyzed
depend on difference between P and S wave arrival time and noise
within records. We have found that local earthquakes have similar
characteristics. If frequency-energy characteristics can be found
accurately, this gives us a hint to calculate P and S wave arrival time.
It can be seen that wavelet transform provides successful
approximation for this. In this study, 100 earthquakes with 500
records were analyzed approximately.
Abstract: Classification of electroencephalogram (EEG) signals
extracted during mental tasks is a technique that is actively pursued
for Brain Computer Interfaces (BCI) designs. In this paper, we
compared the classification performances of univariateautoregressive
(AR) and multivariate autoregressive (MAR) models
for representing EEG signals that were extracted during different
mental tasks. Multilayer Perceptron (MLP) neural network (NN)
trained by the backpropagation (BP) algorithm was used to classify
these features into the different categories representing the mental
tasks. Classification performances were also compared across
different mental task combinations and 2 sets of hidden units (HU): 2
to 10 HU in steps of 2 and 20 to 100 HU in steps of 20. Five different
mental tasks from 4 subjects were used in the experimental study and
combinations of 2 different mental tasks were studied for each
subject. Three different feature extraction methods with 6th order
were used to extract features from these EEG signals: AR
coefficients computed with Burg-s algorithm (ARBG), AR
coefficients computed with stepwise least square algorithm (ARLS)
and MAR coefficients computed with stepwise least square
algorithm. The best results were obtained with 20 to 100 HU using
ARBG. It is concluded that i) it is important to choose the suitable
mental tasks for different individuals for a successful BCI design, ii)
higher HU are more suitable and iii) ARBG is the most suitable
feature extraction method.
Abstract: This paper describes an application of a dual satellite
geolocation (DSG) system on identifying and locating the unknown
source of uplink sweeping interference. The geolocation system
integrates the method of joint time difference of arrival (TDOA) and
frequency difference of arrival (FDOA) with ephemeris correction
technique which successfully demonstrated high accuracy in
interference source location. The factors affecting the location error
were also discussed.
Abstract: The use of 3D computer-aided design (CAD) models
to support construction project planning has been increasing in the
previous year. 3D CAD models reveal more planning ideas by
visually showing the construction site environment in different stages
of the construction process. Using 3D CAD models together with
scheduling software to prepare construction plan can identify errors
in process sequence and spatial arrangement, which is vital to the
success of a construction project. A number of 4D (3D plus time)
CAD tools has been developed and utilized in different construction
projects due to the awareness of their importance. Virtual prototyping
extends the idea of 4D CAD by integrating more features for
simulating real construction process. Virtual prototyping originates
from the manufacturing industry where production of products such
as cars and airplanes are virtually simulated in computer before they
are built in the factory. Virtual prototyping integrates 3D CAD,
simulation engine, analysis tools (like structural analysis and
collision detection), and knowledgebase to streamline the whole
product design and production process. In this paper, we present the
application of a virtual prototyping software which has been used in
a few construction projects in Hong Kong to support construction
project planning. Specifically, the paper presents an implementation
of virtual prototyping in a residential building project in Hong Kong.
The applicability, difficulties and benefits of construction virtual
prototyping are examined based on this project.
Abstract: Since the presentation of the backpropagation algorithm, a vast variety of improvements of the technique for training a feed forward neural networks have been proposed. This article focuses on two classes of acceleration techniques, one is known as Local Adaptive Techniques that are based on weightspecific only, such as the temporal behavior of the partial derivative of the current weight. The other, known as Dynamic Adaptation Methods, which dynamically adapts the momentum factors, α, and learning rate, η, with respect to the iteration number or gradient. Some of most popular learning algorithms are described. These techniques have been implemented and tested on several problems and measured in terms of gradient and error function evaluation, and percentage of success. Numerical evidence shows that these techniques improve the convergence of the Backpropagation algorithm.
Abstract: The trend in the world of Information Technology
(IT) is getting increasingly large and difficult projects rather than
smaller and easier. However, the data on large-scale IT project
success rates provide cause for concern. This paper seeks to answer
why large-scale IT projects are different from and more difficult than
other typical engineering projects. Drawing on the industrial
experience, a compilation of the conditions that influence failure is
presented. With a view to improve success rates solutions are
suggested.
Abstract: In this article, models based on quantitative analysis,
physical geometry and regression analysis are established, by using
analytic hierarchy process analysis, fuzzy cluster analysis, fuzzy
photographic and data fitting. The reasons of various leaf shapes
among different species and the differences between the leaf shapes on
same tree have been solved by using software, such as Eviews, VB and
Matlab. We also successfully estimate the leaf mass of a tree and the
correlation with the tree profile.
Abstract: In this paper the effects of top management commitment on knowledge management activities has been analyzed. This research has been conducted as a case study in an academic environment. The data collection was carried out in the form of semi-structured interview with an interview guide. This study shows the effects of knowledge management strategic plan developing in academia strategic plan on knowledge management success. This paper shows the importance top management commitment factors including strategic plan, communication, and training on knowledge management success in academia. In particular the most important role of Strategic planning in knowledge management success is clarified. This study explores one of the necessary organizational infrastructures of successful implementation of knowledge management. The idea of this research could be applied in the other context especially in the industrial organizations.
Abstract: The advent of modern technology shadows its impetus repercussions on successful Legacy systems making them obsolete with time. These systems have evolved the large organizations in major problems in terms of new business requirements, response time, financial depreciation and maintenance. Major difficulty is due to constant system evolution and incomplete, inconsistent and obsolete documents which a legacy system tends to have. The myriad dimensions of these systems can only be explored by incorporating reverse engineering, in this context, is the best method to extract useful artifacts and by exploring these artifacts for reengineering existing legacy systems to meet new requirements of organizations. A case study is conducted on six different type of software systems having source code in different programming languages using the architectural recovery framework.
Abstract: There are various kinds of medical equipment which
requires relatively accurate positional adjustments for successful
treatment. However, patients tend to move without notice during a
certain span of operations. Therefore, it is common practice that
accompanying operators adjust the focus of the equipment. In this
paper, tracking controllers for medical equipment are suggested to
replace the operators. The tracking controllers use AHRS sensor
information to recognize the movements of patients. Sensor fusion is
applied to reducing the error magnitudes through linear Kalman filters.
The image processing of optical markers is included to adjust the
accumulation errors of gyroscope sensor data especially for yaw
angles.
The tracking controller reduces the positional errors between the
current focus of a device and the target position on the body of a
patient. Since the sensing frequencies of AHRS sensors are very high
compared to the physical movements, the control performance is
satisfactory. The typical applications are, for example, ESWT or
rTMS, which have the error ranges of a few centimeters.
Abstract: Implemented 5-bit 125-MS/s successive
approximation register (SAR) analog to digital converter (ADC) on
FPGA is presented in this paper.The design and modeling of a high
performance SAR analog to digital converter are based on monotonic
capacitor switching procedure algorithm .Spartan 3 FPGA is chosen
for implementing SAR analog to digital converter algorithm. SAR
VHDL program writes in Xilinx and modelsim uses for showing
results.
Abstract: In this paper, we propose a fuzzy aggregate
production planning (APP) model for blending problem in a brass
factory which is the problem of computing optimal amounts of raw
materials for the total production of several types of brass in a
period. The model has deterministic and imprecise parameters
which follows triangular possibility distributions. The brass casting
APP model can not always be solved by using common approaches
used in the literature. Therefore a mathematical model is presented
for solving this problem. In the proposed model, the Lai and
Hwang-s fuzzy ranking concept is relaxed by using one constraint
instead of three constraints. An application of the brass casting
APP model in a brass factory shows that the proposed model
successfully solves the multi-blend problem in casting process and
determines the optimal raw material purchasing policies.
Abstract: An alternative approach to the use of Discrete Fourier
Transform (DFT) for Magnetic Resonance Imaging (MRI) reconstruction
is the use of parametric modeling technique. This method
is suitable for problems in which the image can be modeled by
explicit known source functions with a few adjustable parameters.
Despite the success reported in the use of modeling technique as an
alternative MRI reconstruction technique, two important problems
constitutes challenges to the applicability of this method, these are
estimation of Model order and model coefficient determination. In
this paper, five of the suggested method of evaluating the model
order have been evaluated, these are: The Final Prediction Error
(FPE), Akaike Information Criterion (AIC), Residual Variance (RV),
Minimum Description Length (MDL) and Hannan and Quinn (HNQ)
criterion. These criteria were evaluated on MRI data sets based on the
method of Transient Error Reconstruction Algorithm (TERA). The
result for each criterion is compared to result obtained by the use of a
fixed order technique and three measures of similarity were evaluated.
Result obtained shows that the use of MDL gives the highest measure
of similarity to that use by a fixed order technique.
Abstract: The paper is included within the framework of a
complex research program, which was initiated from the hypothesis
arguing on the existence of a correlation between pineal indolic and
peptide hormones and the somatic development rhythm, including
thus the epithalamium-epiphysis complex involvement. At birds,
pineal gland contains a circadian oscillator, playing a main role in the
temporal organization of the cerebral functions. The secretion of
pineal indolic hormones is characterized by a high endogenous
rhythmic alternation, modulated by the light/darkness (L/D)
succession and by temperature as well. The research has been carried
out using 100 chicken broilers - “Ross" commercial hybrid,
randomly allocated in two experimental batches: Lc batch, reared
under a 12L/12D lighting schedule and Lexp batch, which was photic
pinealectomised through continuous exposition to light (150 lux, 24
hours, 56 days). Chemical and physical features of the meat issued
from breast fillet and thighs muscles have been studied, determining
the dry matter, proteins, fat, collagen, salt content and pH value, as
well. Besides the variations of meat chemical composition in relation
with lighting schedule, other parameters have been studied: live
weight dynamics, feed intake and somatic development degree. The
achieved results became significant since chickens have 7 days of
age, some variations of the studied parameters being registered,
revealing that the pineal gland physiologic activity, in relation with
the lighting schedule, could be interpreted through the monitoring of
the somatic development technological parameters, usually studied
within the chicken broilers rearing aviculture practice.
Abstract: In this paper we present an energy efficient match-line
(ML) sensing scheme for high-speed ternary content-addressable
memory (TCAM). The proposed scheme isolates the sensing unit of
the sense amplifier from the large and variable ML capacitance. It
employs feedback in the sense amplifier to successfully detect a
match while keeping the ML voltage swing low. This reduced voltage
swing results in large energy saving. Simulation performed using
130nm 1.2V CMOS logic shows at least 30% total energy saving in
our scheme compared to popular current race (CR) scheme for
similar search speed. In terms of speed, dynamic energy, peak power
consumption and transistor count our scheme also shows better
performance than mismatch-dependant (MD) power allocation
technique which also employs feedback in the sense amplifier.
Additionally, the implementation of our scheme is simpler than CR
or MD scheme because of absence of analog control voltage and
programmable delay circuit as have been used in those schemes.
Abstract: Inspired by the recent experiments [1]-[3] indicating
unusual doubly magic nucleus 24O which lies just at the neutron
drip-line and encouraged by the success of our relativistic mean-field
(RMF) plus state dependent BCS approach for the description of
the ground state properties of the drip-line nuclei [23]-[27], we have
further employed this approach, across the entire periodic table, to
explore the unusual shell closures in exotic nuclei. In our RMF+BCS
approach the single particle continuum corresponding to the RMF is
replaced by a set of discrete positive energy states for the calculations
of pairing energy. Detailed analysis of the single particle spectrum,
pairing energies and densities of the nuclei predict the unusual proton
shell closures at Z = 6, 14, 16, 34, and unusual neutron shell closures
at N = 6, 14, 16, 34, 40, 70, 112.