Abstract: This paper presents preliminary results regarding system-level power awareness for FPGA implementations in wireless sensor networks. Re-configurability of field programmable gate arrays (FPGA) allows for significant flexibility in its applications to embedded systems. However, high power consumption in FPGA becomes a significant factor in design considerations. We present several ideas and their experimental verifications on how to optimize power consumption at high level of designing process while maintaining the same energy per operation (low-level methods can be used additionally). This paper demonstrates that it is possible to estimate feasible power consumption savings even at the high level of designing process. It is envisaged that our results can be also applied to other embedded systems applications, not limited to FPGA-based.
Abstract: Water leakage is a serious problem in the maintenance of a waterworks facility. Monitoring the water flow rate is one way to locate leakage. However, conventional flowmeters such as the wet-type flowmeter and the clamp-on type ultrasonic flowmeter require additional construction for their installation and are therefore quite expensive. This paper proposes a novel estimation system for the flow rate in a water pipeline, which employs a vibration sensor. This assembly can be attached to any water pipeline without the need for additional high-cost construction. The vibration sensor is designed based on a condenser microphone. This sensor detects vibration caused by water flowing through a pipeline. It is possible to estimate the water flow rate by measuring the amplitude of the output signal from the vibration sensor. We confirmed the validity of the proposed sensing system experimentally.
Abstract: In order to alleviate the mental and physical problems
of persons with disabilities, animal-assisted therapy (AAT) is one of
the possible modalities that employs the merit of the human-animal
interaction. Nevertheless, to achieve the purpose of AAT for persons
with severe disabilities (e.g. spinal cord injury, stroke, and
amyotrophic lateral sclerosis), real-time animal language
interpretation is desirable. Since canine behaviors can be visually
notable from its tail, this paper proposes the automatic real-time
interpretation of canine tail language for human-canine interaction in
the case of persons with severe disabilities. Canine tail language is
captured via two 3-axis accelerometers. Directions and frequencies
are selected as our features of interests. The novel fuzzy rules based
on Gaussian-Trapezoidal model and center of gravity (COG)-based
defuzzification method are proposed in order to interpret the features
into four canine emotional behaviors, i.e., agitate, happy, scare and
neutral as well as its blended emotional behaviors. The emotional
behavior model is performed in the simulated dog and has also been
evaluated in the real dog with the perfect recognition rate.
Abstract: Detection of squirrel cage induction motor (SCIM) broken bars has long been an important but difficult job in the detection area of motor faults. Early detection of this abnormality in the motor would help to avoid costly breakdowns. A new detection method based on particle swarm optimization (PSO) is presented in this paper. Stator current in an induction motor will be measured and characteristic frequency components of faylted rotor will be detected by minimizing a fitness function using pso. Supply frequency and side band frequencies and their amplitudes can be estimated by the proposed method. The proposed method is applied to a faulty motor with one and two broken bars in different loading condition. Experimental results prove that the proposed method is effective and applicable.
Abstract: Nowadays, hand vein recognition has attracted more attentions in identification biometrics systems. Generally, hand vein image is acquired with low contrast and irregular illumination. Accordingly, if you have a good preprocessing of hand vein image, we can easy extracted the feature extraction even with simple binarization. In this paper, a proposed approach is processed to improve the quality of hand vein image. First, a brief survey on existing methods of enhancement is investigated. Then a Radon Like features method is applied to preprocessing hand vein image. Finally, experiments results show that the proposed method give the better effective and reliable in improving hand vein images.
Abstract: In this paper we introduce a novel kernel classifier
based on a iterative shrinkage algorithm developed for compressive
sensing. We have adopted Bregman iteration with soft and hard
shrinkage functions and generalized hinge loss for solving l1 norm
minimization problem for classification. Our experimental results
with face recognition and digit classification using SVM as the
benchmark have shown that our method has a close error rate
compared to SVM but do not perform better than SVM. We have
found that the soft shrinkage method give more accuracy and in some
situations more sparseness than hard shrinkage methods.
Abstract: This paper presents an optimal design of linear phase
digital high pass finite impulse response (FIR) filter using Improved
Particle Swarm Optimization (IPSO). In the design process, the filter
length, pass band and stop band frequencies, feasible pass band and
stop band ripple sizes are specified. FIR filter design is a multi-modal
optimization problem. An iterative method is introduced to find the
optimal solution of FIR filter design problem. Evolutionary
algorithms like real code genetic algorithm (RGA), particle swarm
optimization (PSO), improved particle swarm optimization (IPSO)
have been used in this work for the design of linear phase high pass
FIR filter. IPSO is an improved PSO that proposes a new definition
for the velocity vector and swarm updating and hence the solution
quality is improved. A comparison of simulation results reveals the
optimization efficacy of the algorithm over the prevailing
optimization techniques for the solution of the multimodal, nondifferentiable,
highly non-linear, and constrained FIR filter design
problems.
Abstract: Inadequate curriculum for software engineering is considered to be one of the most common software risks. A number of solutions, on improving Software Engineering Education (SEE) have been reported in literature but there is a need to collectively present these solutions at one place. We have performed a mapping study to present a broad view of literature; published on improving the current state of SEE. Our aim is to give academicians, practitioners and researchers an international view of the current state of SEE. Our study has identified 70 primary studies that met our selection criteria, which we further classified and categorized in a well-defined Software Engineering educational framework. We found that the most researched category within the SE educational framework is Innovative Teaching Methods whereas the least amount of research was found in Student Learning and Assessment category. Our future work is to conduct a Systematic Literature Review on SEE.
Abstract: We study how the outcome of evolutionary dynamics on
graphs depends on a randomness on the graph structure. We gradually
change the underlying graph from completely regular (e.g. a square lattice) to completely random. We find that the fixation probability increases as the randomness increases; nevertheless, the increase is
not significant and thus the fixation probability could be estimated by the known formulas for underlying regular graphs.
Abstract: In this paper, we present a novel objective nonreference performance assessment algorithm for image fusion. It takes into account local measurements to estimate how well the important information in the source images is represented by the fused image. The metric is based on the Universal Image Quality Index and uses the similarity between blocks of pixels in the input images and the fused image as the weighting factors for the metrics. Experimental results confirm that the values of the proposed metrics correlate well with the subjective quality of the fused images, giving a significant improvement over standard measures based on mean squared error and mutual information.
Abstract: In this paper we present discretization and decomposition methods for a multi-component transport model of a chemical vapor deposition (CVD) process. CVD processes are used to manufacture deposition layers or bulk materials. In our transport model we simulate the deposition of thin layers. The microscopic model is based on the heavy particles, which are derived by approximately solving a linearized multicomponent Boltzmann equation. For the drift-process of the particles we propose diffusionreaction equations as well as for the effects of heat conduction. We concentrate on solving the diffusion-reaction equation with analytical and numerical methods. For the chemical processes, modelled with reaction equations, we propose decomposition methods and decouple the multi-component models to simpler systems of differential equations. In the numerical experiments we present the computational results of our proposed models.
Abstract: The effect of teaching method on learning
assistance Dunn Review .The study, to compare the effects of
collaboration on teaching mathematics learning courses, including
writing, science, experimental girl students by other methods of
teaching basic first paid and the amount of learning students
methods have been trained to cooperate with other students with
other traditional methods have been trained to compare. The
survey on 100 students in Tehran that using random sampling ¬
cluster of girl students between the first primary selections was
performed. Considering the topic of semi-experimental research
methods used to practice the necessary information by
questionnaire, examination questions by the researcher, in
collaboration with teachers and view authority in this field and
related courses that teach these must have been collected.
Research samples to test and control groups were divided.
Experimental group and control group collaboration using
traditional methods of mathematics courses, including writing and
experimental sciences were trained. Research results using
statistical methods T is obtained in two independent groups show
that, through training assistance will lead to positive results and
student learning in comparison with traditional methods, will
increase also led to collaboration methods increase skills to solve
math lesson practice, better understanding and increased skill
level of students in practical lessons such as science and has been
writing.
Abstract: A special case of floating point data representation is block
floating point format where a block of operands are forced to have a joint
exponent term. This paper deals with the finite wordlength properties of
this data format. The theoretical errors associated with the error model for
block floating point quantization process is investigated with the help of error
distribution functions. A fast and easy approximation formula for calculating
signal-to-noise ratio in quantization to block floating point format is derived.
This representation is found to be a useful compromise between fixed point
and floating point format due to its acceptable numerical error properties over
a wide dynamic range.
Abstract: This paper provides a framework in order to
incorporate reliability issue as a sign of disruption in distribution
systems and partial covering theory as a response to limitation in
coverage radios and economical preferences, simultaneously into the
traditional literatures of capacitated facility location problems. As a
result we develop a bi-objective model based on the discrete
scenarios for expected cost minimization and demands coverage
maximization through a three echelon supply chain network by
facilitating multi-capacity levels for provider side layers and
imposing gradual coverage function for distribution centers (DCs).
Additionally, in spite of objectives aggregation for solving the model
through LINGO software, a branch of LP-Metric method called Min-
Max approach is proposed and different aspects of corresponds
model will be explored.
Abstract: Iranians- imagination of heaven, which is the reward
of a person-s good deeds during their life, has shown itself in
pleasant and green gardens where earthly gardens were made as
representations of paradise. Iranians are also quite interested in
making their earthly gardens and plantations around their buildings.
With Iran-s hot and dry climate with a lack of sufficient water for
plantation coverage, it becomes noticeable how important it is to
Iranians- art in making gardens. This study, with regard to examples,
documents and library studies, investigates the characteristics of
Persian gardens. The result shows that elements such as soil, water,
plants and layout have been used in forming a unique style of Persian
gardens. Bagh-e Shah Zadeh Mahan (Mahan prince garden) is a
typical example and has been carefully studied. In this paper I try to
investigate and evaluate the characteristics of a Persian garden by
means of a descriptive approach.
Abstract: Grey mold on grape is caused by the fungus Botrytis
cinerea Pers. Trichodex WP, a new biofungicide, that contains fungal
spores of Trichoderma harzianum Rifai, was used for biological
control of Grey mold on grape. The efficacy of Trichodex WP has
been reported from many experiments. Experiments were carried out
in the locality – Banatski Karlovac, on grapevine species – talijanski
rizling. The trials were set according to instructions of methods
PP1/152(2) and PP1/17(3) , according to a fully randomized block
design. Phytotoxicity was estimated by PP methods 1/135(2), the
intensity of infection according to Towsend Heuberger , the
efficiency by Abbott, the analysis of variance with Duncan test and
PP/181(2). Application of Trichodex WP is limited to the first two
treatments. Other treatments are performed with the fungicides based
on a.i. procymidone, vinclozoline and iprodione.
Abstract: In this paper usefulness of quasi-Newton iteration
procedure in parameters estimation of the conditional variance
equation within BHHH algorithm is presented. Analytical solution of
maximization of the likelihood function using first and second
derivatives is too complex when the variance is time-varying. The
advantage of BHHH algorithm in comparison to the other
optimization algorithms is that requires no third derivatives with
assured convergence. To simplify optimization procedure BHHH
algorithm uses the approximation of the matrix of second derivatives
according to information identity. However, parameters estimation in
a/symmetric GARCH(1,1) model assuming normal distribution of
returns is not that simple, i.e. it is difficult to solve it analytically.
Maximum of the likelihood function can be founded by iteration
procedure until no further increase can be found. Because the
solutions of the numerical optimization are very sensitive to the
initial values, GARCH(1,1) model starting parameters are defined.
The number of iterations can be reduced using starting values close
to the global maximum. Optimization procedure will be illustrated in
framework of modeling volatility on daily basis of the most liquid
stocks on Croatian capital market: Podravka stocks (food industry),
Petrokemija stocks (fertilizer industry) and Ericsson Nikola Tesla
stocks (information-s-communications industry).
Abstract: The customer satisfaction for textile sector carries
great importance like the customer satisfaction for other sectors
carry. Especially, if it is considered that gaining new customers
create four times more costs than protecting existing customers from
leaving, it can be seen that the customer satisfaction plays a great
role for the firms. In this study the affecting independent variables of
customer satisfaction are chosen as brand image, perceived service
quality and perceived product quality. By these independent
variables, it is investigated that if any differences exist in perception
of customer satisfaction according to the Turkish textile consumers in
the view of gender. In data analysis of this research the SPSS
program is used.
Abstract: A nucleotide sequence can be expressed as a numerical sequence when each nucleotide is assigned its proton number. A resulting gene numerical sequence can be investigated for its fractal dimension in terms of evolution and chemical properties for comparative studies. We have investigated such nucleotide fluctuation in the 16S rRNA gene of archaea thermophiles. The studied archaea thermophiles were archaeoglobus fulgidus, methanothermobacter thermautotrophicus, methanocaldococcus jannaschii, pyrococcus horikoshii, and thermoplasma acidophilum. The studied five archaea-euryarchaeota thermophiles have fractal dimension values ranging from 1.93 to 1.97. Computer simulation shows that random sequences would have an average of about 2 with a standard deviation about 0.015. The fractal dimension was found to correlate (negative correlation) with the thermophile-s optimal growth temperature with R2 value of 0.90 (N =5). The inclusion of two aracheae-crenarchaeota thermophiles reduces the R2 value to 0.66 (N = 7). Further inclusion of two bacterial thermophiles reduces the R2 value to 0.50 (N =9). The fractal dimension is correlated (positive) to the sequence GC content with an R2 value of 0.89 for the five archaea-euryarchaeota thermophiles (and 0.74 for the entire set of N = 9), although computer simulation shows little correlation. The highest correlation (positive) was found to be between the fractal dimension and di-nucleotide Shannon entropy. However Shannon entropy and sequence GC content were observed to correlate with optimal growth temperature having an R2 of 0.8 (negative), and 0.88 (positive), respectively, for the entire set of 9 thermophiles; thus the correlation lacks species specificity. Together with another correlation study of bacterial radiation dosage with RecA repair gene sequence fractal dimension, it is postulated that fractal dimension analysis is a sensitive tool for studying the relationship between genotype and phenotype among closely related sequences.
Abstract: This study proposes a novel recommender system to
provide the advertisements of context-aware services. Our proposed
model is designed to apply a modified collaborative filtering (CF)
algorithm with regard to the several dimensions for the personalization
of mobile devices – location, time and the user-s needs type. In
particular, we employ a classification rule to understand user-s needs
type using a decision tree algorithm. In addition, we collect primary
data from the mobile phone users and apply them to the proposed
model to validate its effectiveness. Experimental results show that the
proposed system makes more accurate and satisfactory advertisements
than comparative systems.