Abstract: Extraction of edge-end-pixels is an important step for the edge linking process to achieve edge-based image segmentation. This paper presents an algorithm to extract edge-end pixels together with their directional sensitivities as an augmentation to the currently available mathematical models. The algorithm is implemented in the Java environment because of its inherent compatibility with web interfaces since its main use is envisaged to be for remote image analysis on a virtual instrumentation platform.
Abstract: Multi User Virtual Worlds are becoming a valuable educational tool. Learning experiences within these worlds focus on discovery and active experiences that both engage students and motivate them to explore new concepts. As educators, we need to explore these environments to determine how they can most effectively be used in our instructional practices. This paper explores the current application of virtual worlds to identify meaningful educational strategies that are being used to engage students and enhance teaching and learning.
Abstract: The belief decision tree (BDT) approach is a decision
tree in an uncertain environment where the uncertainty is represented
through the Transferable Belief Model (TBM), one interpretation
of the belief function theory. The uncertainty can appear either in
the actual class of training objects or attribute values of objects to
classify. In this paper, we develop a post-pruning method of belief
decision trees in order to reduce size and improve classification
accuracy on unseen cases. The pruning of decision tree has a
considerable intention in the areas of machine learning.
Abstract: This paper considers the autonomous navigation
problem of multiple n-link nonholonomic mobile manipulators within
an obstacle-ridden environment. We present a set of nonlinear
acceleration controllers, derived from the Lyapunov-based control
scheme, which generates collision-free trajectories of the mobile
manipulators from initial configurations to final configurations in a
constrained environment cluttered with stationary solid objects of
different shapes and sizes. We demonstrate the efficiency of the
control scheme and the resulting acceleration controllers of the
mobile manipulators with results through computer simulations of an
interesting scenario.
Abstract: A neurofuzzy approach for a given set of input-output training data is proposed in two phases. Firstly, the data set is partitioned automatically into a set of clusters. Then a fuzzy if-then rule is extracted from each cluster to form a fuzzy rule base. Secondly, a fuzzy neural network is constructed accordingly and parameters are tuned to increase the precision of the fuzzy rule base. This network is able to learn and optimize the rule base of a Sugeno like Fuzzy inference system using Hybrid learning algorithm, which combines gradient descent, and least mean square algorithm. This proposed neurofuzzy system has the advantage of determining the number of rules automatically and also reduce the number of rules, decrease computational time, learns faster and consumes less memory. The authors also investigate that how neurofuzzy techniques can be applied in the area of control theory to design a fuzzy controller for linear and nonlinear dynamic systems modelling from a set of input/output data. The simulation analysis on a wide range of processes, to identify nonlinear components on-linely in a control system and a benchmark problem involving the prediction of a chaotic time series is carried out. Furthermore, the well-known examples of linear and nonlinear systems are also simulated under the Matlab/Simulink environment. The above combination is also illustrated in modeling the relationship between automobile trips and demographic factors.
Abstract: Cameras are often mounted on platforms that canmove like rovers, booms, gantries and aircraft. People operate suchplatforms to capture desired views of scene or target. To avoidcollisions with the environment and occlusions, such platforms oftenpossess redundant degrees-of-freedom. As a result, manipulatingsuch platforms demands much skill. Visual-servoing some degrees-of-freedom may reduce operator burden and improve tracking per-formance. This concept, which we call human-in-the-loop visual-servoing, is demonstrated in this paper and applies a Α-β-γ filter and feedforward controller to a broadcast camera boom.
Abstract: The study of the variability of the postural strategies
in low back pain patients, as a criterion in evaluation of the
adaptability of this system to the environmental demands is the
purpose of this study. A cross-sectional case-control study was
performed on 21 recurrent non-specific low back pain patients and 21
healthy volunteers. The electromyography activity of Deltoid,
External Oblique (EO), Transverse Abdominis/Internal Oblique
(TrA/IO) and Erector Spine (ES) muscles of each person was
recorded in 75 rapid arm flexion with maximum acceleration.
Standard deviation of trunk muscles onset relative to deltoid muscle
onset were statistically analyzed by MANOVA . The results show
that chronic low back pain patients exhibit less variability in their
anticipatory postural adjustments (APAs) in comparison with the
control group. There is a decrease in variability of postural control
system of recurrent non-specific low back pain patients that can
result in the persistence of pain and chronicity by decreasing the
adaptability to environmental demands.
Abstract: In this research, a mathematical model for integrated evaluation of green design and green manufacturing processes is presented. To design a product, there can be alternative options to design the detailed components to fulfill the same product requirement. In the design alternative cases, the components of the product can be designed with different materials and detailed specifications. If several design alternative cases are proposed, the different materials and specifications can affect the manufacturing processes. In this paper, a new concept for integrating green design and green manufacturing processes is presented. A green design can be determined based the manufacturing processes of the designed product by evaluating the green criteria including energy usage and environmental impact, in addition to the traditional criteria of manufacturing cost. With this concept, a mathematical model is developed to find the green design and the associated green manufacturing processes. In the mathematical model, the cost items include material cost, manufacturing cost, and green related cost. The green related cost items include energy cost and environmental cost. The objective is to find the decisions of green design and green manufacturing processes to achieve the minimized total cost. In practical applications, the decision-making can be made to select a good green design case and its green manufacturing processes. In this presentation, an example product is illustrated. It shows that the model is practical and useful for integrated evaluation of green design and green manufacturing processes.
Abstract: Drought is a phenomenon caused by
environmental and climatic changes. This phenomenon is
affected by shortage of rainfall and temperature. Dust is one
of important environmental problems caused by climate
change and drought. With recent multi-year drought, many
environmental crises caused by dust in Iran and Middle East.
Dust in the vast areas of the provinces occurs with high
frequency. By dust affecting many problems created in terms
of health, social and economic. In this study, we tried to study
the most important factors causing dust. In this way we have
used the satellite images and meteorological data. Finally,
strategies to deal with the dust will be mentioned.
Abstract: Malware is software which was invented and meant for doing harms on computers. Malware is becoming a significant threat in computer network nowadays. Malware attack is not just only involving financial lost but it can also cause fatal errors which may cost lives in some cases. As new Internet Protocol version 6 (IPv6) emerged, many people believe this protocol could solve most malware propagation issues due to its broader addressing scheme. As IPv6 is still new compares to native IPv4, some transition mechanisms have been introduced to promote smoother migration. Unfortunately, these transition mechanisms allow some malwares to propagate its attack from IPv4 to IPv6 network environment. In this paper, a proof of concept shall be presented in order to show that some existing IPv4 malware detection technique need to be improvised in order to detect malware attack in dual-stack network more efficiently. A testbed of dual-stack network environment has been deployed and some genuine malware have been released to observe their behaviors. The results between these different scenarios will be analyzed and discussed further in term of their behaviors and propagation methods. The results show that malware behave differently on IPv6 from the IPv4 network protocol on the dual-stack network environment. A new detection technique is called for in order to cater this problem in the near future.
Abstract: Estimation of stormwater pollutants is a pre-requisite
for the protection and improvement of the aquatic environment and
for appropriate management options. The usual practice for the
stormwater quality prediction is performed through water quality
modeling. However, the accuracy of the prediction by the models
depends on the proper estimation of model parameters. This paper
presents the estimation of model parameters for a catchment water
quality model developed for the continuous simulation of stormwater
pollutants from a catchment to the catchment outlet. The model is
capable of simulating the accumulation and transportation of the
stormwater pollutants; suspended solids (SS), total nitrogen (TN) and
total phosphorus (TP) from a particular catchment. Rainfall and water
quality data were collected for the Hotham Creek Catchment (HTCC),
Gold Coast, Australia. Runoff calculations from the developed model
were compared with the calculated discharges from the widely used
hydrological models, WBNM and DRAINS. Based on the measured
water quality data, model water quality parameters were calibrated
for the above-mentioned catchment. The calibrated parameters are
expected to be helpful for the best management practices (BMPs)
of the region. Sensitivity analyses of the estimated parameters were
performed to assess the impacts of the model parameters on overall
model estimations of runoff water quality.
Abstract: The purposes of this research were 1) to investigate
behavior of media exposure and participation in environmental
activities of King Mongkut-s University of Technology Thonburi
(KMUTT) dormitory students, 2) to compare the correlation between
faculties and participation in environmental activities of KMUTT
dormitory students, and 3) to compare the correlation between media
exposure and participation in environmental activities of KMUTT
dormitory students. The tool used for collecting data was
questionnaire. The research findings revealed that dormitory students
were mostly exposed to the environmental media via public relations
boards for general media and KMUTT dormitory media. Dormitory
students were daily exposed to media via websites on the internet and
weekly for other media. Dormitory students participation in the
environmental activities was at high level (x = 3.65) on an
individual basis and was at medium level (x = 2.76) on a collective
basis. Faculties did not correlate with the participation in
environmental activities of dormitory students at the .01 statistical
level and media exposure via various media correlated with
participation in environmental activities of dormitory students at the
.01 statistical level.
Abstract: In this paper, the processing of sonar signals has been
carried out using Minimal Resource Allocation Network (MRAN)
and a Probabilistic Neural Network (PNN) in differentiation of
commonly encountered features in indoor environments. The
stability-plasticity behaviors of both networks have been
investigated. The experimental result shows that MRAN possesses
lower network complexity but experiences higher plasticity than
PNN. An enhanced version called parallel MRAN (pMRAN) is
proposed to solve this problem and is proven to be stable in
prediction and also outperformed the original MRAN.
Abstract: In the present paper, we-ll explore how social media tools provide an opportunity for new developments of the e-Learning in the context of managing personal knowledge. There will be a discussion how social media tools provide a possibility for helping knowledge workersand students to gather, organize and manage their personal information as a part of the e-learning process. At the centre of this social software driven approach to e-learning environments are the challenges of personalization and collaboration. We-ll share concepts of how organizations are using social media for e-Learning and believe that integration of these tools into traditional e-Learning is probably not a choice, but inevitability. Students- Survey of use of web technologies and social networking tools is presented. Newly developed framework for semantic blogging capable of organizing results relevant to user requirements is implemented at Varna Free University (VFU) to provide more effective navigation and search.
Abstract: The objective of this work was to examine the changes
in non destructive properties caused by carbonation of CEM II
mortar. Samples of CEM II mortar were prepared and subjected to
accelerated carbonation at 20°C, 65% relative humidity and 20% CO2
concentration. We examined the evolutions of the gas permeability,
the thermal conductivity, the thermal diffusivity, the volume of the
solid phase by helium pycnometry, the longitudinal and transverse
ultrasonic velocities. The principal contribution of this work is that,
apart of the gas permeability, changes in other non destructive
properties have never been studied during the carbonation of cement
materials. These properties are important in predicting/measuring the
durability of reinforced concrete in CO2 environment. The
carbonation depth and the porosity accessible to water were also
reported in order to explain comprehensively the changes in non
destructive parameters.
Abstract: This paper presents an environmental and technoeconomic
evaluation of light duty vehicles in Iran. A comprehensive
well-to-wheel (WTW) analysis is applied to compare different
automotive fuel chains, conventional internal combustion engines and
innovative vehicle powertrains. The study examines the
competitiveness of 15 various pathways in terms of energy
efficiencies, GHG emissions, and levelized cost of different energy
carriers. The results indicate that electric vehicles including battery
electric vehicles (BEV), fuel cell vehicles (FCV) and plug-in hybrid
electric vehicles (PHEV) increase the WTW energy efficiency by
54%, 51% and 46%, respectively, compared to common internal
combustion engines powered by gasoline. On the other hand,
greenhouse gas (GHG) emissions per kilometer of FCV and BEV
would be 48% lower than that of gasoline engines. It is concluded
that BEV has the lowest total cost of energy consumption and
external cost of emission, followed by internal combustion engines
(ICE) fueled by CNG. Conventional internal combustion engines
fueled by gasoline, on the other hand, would have the highest costs.
Abstract: Radio frequency identification (RFID) applications have grown rapidly in many industries, especially in indoor location identification. The advantage of using received signal strength indicator (RSSI) values as an indoor location measurement method is a cost-effective approach without installing extra hardware. Because the accuracy of many positioning schemes using RSSI values is limited by interference factors and the environment, thus it is challenging to use RFID location techniques based on integrating positioning algorithm design. This study proposes the location estimation approach and analyzes a scheme relying on RSSI values to minimize location errors. In addition, this paper examines different factors that affect location accuracy by integrating the backpropagation neural network (BPN) with the LANDMARC algorithm in a training phase and an online phase. First, the training phase computes coordinates obtained from the LANDMARC algorithm, which uses RSSI values and the real coordinates of reference tags as training data for constructing an appropriate BPN architecture and training length. Second, in the online phase, the LANDMARC algorithm calculates the coordinates of tracking tags, which are then used as BPN inputs to obtain location estimates. The results show that the proposed scheme can estimate locations more accurately compared to LANDMARC without extra devices.
Abstract: It is suggested to evaluate environmental performance
of energy sector using Data Envelopment Analysis with nondiscretionary
factors (DEA-ND) with relative indicators as inputs and
outputs. The latter allows for comparison of the objects essentially
different in size. Inclusion of non-discretionary factors serves
separation of the indicators that are beyond the control of the objects.
A virtual perfect object comprised of maximal outputs and minimal
inputs was added to the group of actual ones. In this setting, explicit
solution of the DEA-ND problem was obtained. Energy sector of the
United States was analyzed using suggested approach for the period
of 1980 – 2006 with expected values of economic indicators for 2030
used for forming the perfect object. It was obtained that
environmental performance has been increasing steadily for the
period from 7.7% through 50.0% but still remains well below the
prospected level
Abstract: Nowadays Multilevel inverters are widely using in various applications. Modulation strategy at fundamental switching frequency like, SHEPWM is prominent technique to eliminate lower order of harmonics with less switching losses and better harmonic profile. The equations which are formed by SHE are highly nonlinear transcendental in nature, there may exist single, multiple or even no solutions for a particular MI. However, some loads such as electrical drives, it is required to operate in whole range of MI. In order to solve SHE equations for whole range of MI, intelligent techniques are well suited to solve equations so as to produce lest %THDV. Hence, this paper uses Continuous genetic algorithm for minimising harmonics. This paper also presents wavelet based analysis of harmonics. The developed algorithm is simulated and %THD from FFT analysis and Wavelet analysis are compared. MATLAB programming environment and SIMULINK models are used whenever necessary.
Abstract: The efficient operation of any biological treatment
process requires pre-treatment of incompatible pollutants such as
acids, bases, oil, toxic substances, etc. which hamper the treatment
of other major components which are otherwise degradable. The
pre-treatment of alkaline waste-waters, generated from various
industries like textile, paper & pulp, potato-processing industries,
etc., having a pH of 10 or higher, is essential. The pre-treatment,
i.e., neutralization of such alkaline waste-waters can be achieved by
chemical as well as biological means. However, the biological pretreatment
offers better package over the chemical means by being
safe and economical. The biological pre-treatment can be
accomplished by using a blend of microorganisms able to withstand
such harsh alkaline conditions. In the present study, for the proper
pre-treatment of alkaline waste-waters, a package of alkalophilic
bacteria is formulated to neutralise the alkaline pH of the industrial
waste-waters. The developed microbial package is cost-effective as
well as environmental friendly.