Scholarly

Indoor and Outdoor Concentration of Particulate Matter at Domestic Homes

Year: 2013 Volume: 7 Issue: 6 368 - 375 Pages

Abstract: Particulate matter (PM) in ambient air is responsible for adverse health effects in adults and children. Relatively little is known about the concentrations, sources and health effects of PM in indoor air. A monitoring study was conducted in Ankara by three campaigns in order to measure PM levels in indoor and outdoor environments to identify and quantify associations between sources and concentrations. Approximately 82 homes (1st campaign for 42, 2nd campaign for 12, and 3rd campaign for 28), three rooms (living room, baby-s room and living room used as a baby-s room) and outdoor ambient at each home were sampled with Grimm Environmental Dust Monitoring (EDM) 107, during different seasonal periods of 2011 and 2012. In this study, the relationship between indoor and outdoor PM levels for particulate matter less than 10 micrometer (.m) (PM10), particulate matter less than 2.5.m (PM2.5) and particulate matter less than 1.0.m (PM1) were investigated. The mean concentration of PM10, PM2.5, and PM1.0 at living room used as baby-s room is higher than living and baby-s room (or bedroom) for three sampling campaigns. It is concluded that the household activities and environmental conditions are very important for PM concentrations in the indoor environments during the sampling periods. The amount of smokers, being near a main street and/or construction activities increased the PM concentration. This study is based on the assessment the relationship between indoor and outdoor PM levels and the household activities and environmental conditions

Artificial Intelligence Techniques applied to Biomedical Patterns

Year: 2007 Volume: 1 Issue: 12 633 - 638 Pages

Authors:
Giovanni Luca Masala

Abstract: Pattern recognition is the research area of Artificial Intelligence that studies the operation and design of systems that recognize patterns in the data. Important application areas are image analysis, character recognition, fingerprint classification, speech analysis, DNA sequence identification, man and machine diagnostics, person identification and industrial inspection. The interest in improving the classification systems of data analysis is independent from the context of applications. In fact, in many studies it is often the case to have to recognize and to distinguish groups of various objects, which requires the need for valid instruments capable to perform this task. The objective of this article is to show several methodologies of Artificial Intelligence for data classification applied to biomedical patterns. In particular, this work deals with the realization of a Computer-Aided Detection system (CADe) that is able to assist the radiologist in identifying types of mammary tumor lesions. As an additional biomedical application of the classification systems, we present a study conducted on blood samples which shows how these methods may help to distinguish between carriers of Thalassemia (or Mediterranean Anaemia) and healthy subjects.

Quantification of Heart Rate Variability: A Measure based on Unique Heart Rates

Year: 2008 Volume: 2 Issue: 3 106 - 109 Pages

Abstract: It is established that the instantaneous heart rate (HR) of healthy humans keeps on changing. Analysis of heart rate variability (HRV) has become a popular non invasive tool for assessing the activities of autonomic nervous system. Depressed HRV has been found in several disorders, like diabetes mellitus (DM) and coronary artery disease, characterised by autonomic nervous dysfunction. A new technique, which searches for pattern repeatability in a time series, is proposed specifically for the analysis of heart rate data. These set of indices, which are termed as pattern repeatability measure and pattern repeatability ratio are compared with approximate entropy and sample entropy. In our analysis, based on the method developed, it is observed that heart rate variability is significantly different for DM patients, particularly for patients with diabetic foot ulcer.

Multiple Object Tracking using Particle Swarm Optimization

Year: 2012 Volume: 6 Issue: 8 846 - 849 Pages

Abstract: This paper presents a particle swarm optimization (PSO) based approach for multiple object tracking based on histogram matching. To start with, gray-level histograms are calculated to establish a feature model for each of the target object. The difference between the gray-level histogram corresponding to each particle in the search space and the target object is used as the fitness value. Multiple swarms are created depending on the number of the target objects under tracking. Because of the efficiency and simplicity of the PSO algorithm for global optimization, target objects can be tracked as iterations continue. Experimental results confirm that the proposed PSO algorithm can rapidly converge, allowing real-time tracking of each target object. When the objects being tracked move outside the tracking range, global search capability of the PSO resumes to re-trace the target objects.

Generalized Predictive Control of Batch Polymerization Reactor

Year: 2007 Volume: 1 Issue: 2 302 - 307 Pages

Abstract: This paper describes the application of a model predictive controller to the problem of batch reactor temperature control. Although a great deal of work has been done to improve reactor throughput using batch sequence control, the control of the actual reactor temperature remains a difficult problem for many operators of these processes. Temperature control is important as many chemical reactions are sensitive to temperature for formation of desired products. This controller consist of two part (1) a nonlinear control method GLC (Global Linearizing Control) to create a linear model of system and (2) a Model predictive controller used to obtain optimal input control sequence. The temperature of reactor is tuned to track a predetermined temperature trajectory that applied to the batch reactor. To do so two input signals, electrical powers and the flow of coolant in the coil are used. Simulation results show that the proposed controller has a remarkable performance for tracking reference trajectory while at the same time it is robust against noise imposed to system output.

Design and Implementation of Optimal Winner Determination Algorithm in Combinatorial e- Auctions

Year: 2008 Volume: 2 Issue: 8 2766 - 2769 Pages

Abstract: The one of best robust search technique on large scale search area is heuristic and meta heuristic approaches. Especially in issue that the exploitation of combinatorial status in the large scale search area prevents the solution of the problem via classical calculating methods, so such problems is NP-complete. in this research, the problem of winner determination in combinatorial auctions have been formulated and by assessing older heuristic functions, we solve the problem by using of genetic algorithm and would show that this new method would result in better performance in comparison to other heuristic function such as simulated annealing greedy approach.

Effect of Cladding and Secondary Members on the Elastic Stability of Main Columns

Year: 2013 Volume: 7 Issue: 2 170 - 175 Pages

Abstract: The corrugated steel cladding used to cover most of steel buildings is considered as non-structural element. This research will reflect the effect of cladding as a shear diaphragm in increasing the normal elastic capacity of columns. This study is important because of the lack of information of the behavior of cladding and secondary members in various codes. Mathematical models for six different cases are carried by software. The results extracted from the program have been plotted showing the effects of different variables on the ultimate load of column. The variables considered in our research are the spacing between columns and the thickness of the corrugated sheet representing the sheet stiffness.

Using Morphological and Microsatellite (SSR) Markers to Assess the Genetic Diversity in Alfalfa (Medicago sativa L.)

Year: 2012 Volume: 6 Issue: 9 734 - 740 Pages

Abstract: Utilization of diverse germplasm is needed to enhance the genetic diversity of cultivars. The objective of this study was to evaluate the genetic relationships of 98 alfalfa germplasm accessions using morphological traits and SSR markers. From the 98 tested populations, 81 were locals originating in Europe, 17 were introduced from USA, Australia, New Zealand and Canada. Three primers generated 67 polymorphic bands. The average polymorphic information content (PIC) was very high (> 0.90) over all three used primer combinations. Cluster analysis using Unweighted Pair Group Method with Arithmetic Means (UPGMA) and Jaccard´s coefficient grouped the accessions into 2 major clusters with 4 sub-clusters with no correlation between genetic and morphological diversity. The SSR analysis clearly indicated that even with three polymorphic primers, reliable estimation of genetic diversity could be obtained.

IFS on the Multi-Fuzzy Fractal Space

Year: 2009 Volume: 3 Issue: 5 372 - 376 Pages

Abstract: The IFS is a scheme for describing and manipulating complex fractal attractors using simple mathematical models. More precisely, the most popular “fractal –based" algorithms for both representation and compression of computer images have involved some implementation of the method of Iterated Function Systems (IFS) on complete metric spaces. In this paper a new generalized space called Multi-Fuzzy Fractal Space was constructed. On these spases a distance function is defined, and its completeness is proved. The completeness property of this space ensures the existence of a fixed-point theorem for the family of continuous mappings. This theorem is the fundamental result on which the IFS methods are based and the fractals are built. The defined mappings are proved to satisfy some generalizations of the contraction condition.

Analytical Modelling of Average Bond Stress within the Anchorage of Tensile Reinforcing Bars in Reinforced Concrete Members

Year: 2013 Volume: 7 Issue: 6 482 - 488 Pages

Abstract: A reliable estimate of the average bond stress within the anchorage of steel reinforcing bars in tension is critically important for the design of reinforced concrete member. This paper describes part of a recently completed experimental research program in the Centre for Infrastructure Engineering and Safety (CIES) at the University of New South Wales, Sydney, Australia aimed at assessing the effects of different factors on the anchorage requirements of modern high strength steel reinforcing bars. The study found that an increase in the anchorage length and bar diameter generally leads to a reduction of the average ultimate bond stress. By the extension of a well established analytical model of bond and anchorage, it is shown here that the differences in the average ultimate bond stress for different anchorage lengths is associated with the variable degree of plastic deformation in the tensile zone of the concrete surrounding the bar.

Hybrid Modulation Technique for Fingerprinting

Year: 2007 Volume: 1 Issue: 2 298 - 301 Pages

Abstract: This paper addresses an efficient technique to embed and detect digital fingerprint code. Orthogonal modulation method is a straightforward and widely used approach for digital fingerprinting but shows several limitations in computational cost and signal efficiency. Coded modulation method can solve these limitations in theory. However it is difficult to perform well in practice if host signals are not available during tracing colluders, other kinds of attacks are applied, and the size of fingerprint code becomes large. In this paper, we propose a hybrid modulation method, in which the merits of or-thogonal modulation and coded modulation method are combined so that we can achieve low computational cost and high signal efficiency. To analyze the performance, we design a new fingerprint code based on GD-PBIBD theory and modulate this code into images by our method using spread-spectrum watermarking on frequency domain. The results show that the proposed method can efficiently handle large fingerprint code and trace colluders against averaging attacks.

Dynamic Visualization on Student's Performance, Retention and Transfer of Procedural Learning

Year: 2011 Volume: 5 Issue: 11 1604 - 1608 Pages

Abstract: This study examined the effects of two dynamic visualizations on 60 Malaysian primary school student-s performance (time on task), retention and transference. The independent variables in this study were the two dynamic visualizations, the video and the animated instructions. The dependent variables were the gain score of performance, retention and transference. The results showed that the students in the animation group significantly outperformed the students in the video group in retention. There were no significant differences in terms of gain scores in the performance and transference among the animation and the video groups, although the scores were slightly higher in the animation group compared to the video group. The conclusion of this study is that the animation visualization is superior compared to the video in the retention for a procedural task.

An Embedded System for Artificial Intelligence Applications

Year: 2007 Volume: 1 Issue: 4 1059 - 1073 Pages

Abstract: Conventional approaches in the implementation of logic programming applications on embedded systems are solely of software nature. As a consequence, a compiler is needed that transforms the initial declarative logic program to its equivalent procedural one, to be programmed to the microprocessor. This approach increases the complexity of the final implementation and reduces the overall system's performance. On the contrary, presenting hardware implementations which are only capable of supporting logic programs prevents their use in applications where logic programs need to be intertwined with traditional procedural ones, for a specific application. We exploit HW/SW codesign methods to present a microprocessor, capable of supporting hybrid applications using both programming approaches. We take advantage of the close relationship between attribute grammar (AG) evaluation and knowledge engineering methods to present a programmable hardware parser that performs logic derivations and combine it with an extension of a conventional RISC microprocessor that performs the unification process to report the success or failure of those derivations. The extended RISC microprocessor is still capable of executing conventional procedural programs, thus hybrid applications can be implemented. The presented implementation is programmable, supports the execution of hybrid applications, increases the performance of logic derivations (experimental analysis yields an approximate 1000% increase in performance) and reduces the complexity of the final implemented code. The proposed hardware design is supported by a proposed extended C-language called C-AG.

Local Perspectives on Climate Change Mitigation and Sustainability of Clean Development Mechanism (CDM) Project: A Case Study in Thailand

Year: 2011 Volume: 5 Issue: 11 710 - 714 Pages

Abstract: Global climate change has become the preeminent threat to human security in the 21st century. From mitigation perspective, this study aims to evaluate the performance of biogas renewable project under clean development mechanism activities (namely Korat-Waste-to-Energy) in Thailand and to assess local perceptions towards the significance of climate change mitigation and sustainability of such project in their community. Questionnaire was developed based on the national sustainable development criteria and was distributed among systematically selected households within project boundaries (n=260). Majority of the respondents strongly agreed with the reduction of odor problems (81%) and air pollution (76%). However, they were unsure about greenhouse gas reduction from such project and ignorant about the key issues of climate change. A lesson learned suggested that there is a need to further investigate the possible socio-psychological barriers may significantly shape public perception and understandings of climate change in the local context.

Developing Forecasting Tool for Humanitarian Relief Organizations in Emergency Logistics Planning

Year: 2012 Volume: 6 Issue: 11 3219 - 3225 Pages

Abstract: Despite the availability of natural disaster related time series data for last 110 years, there is no forecasting tool available to humanitarian relief organizations to determine forecasts for emergency logistics planning. This study develops a forecasting tool based on identifying probability distributions. The estimates of the parameters are used to calculate natural disaster forecasts. Further, the determination of aggregate forecasts leads to efficient pre-disaster planning. Based on the research findings, the relief agencies can optimize the various resources allocation in emergency logistics planning.

Affine Radial Basis Function Neural Networks for the Robust Control of Hyperbolic Distributed Parameter Systems

Year: 2008 Volume: 2 Issue: 10 231 - 237 Pages

Abstract: In this work, a radial basis function (RBF) neural network is developed for the identification of hyperbolic distributed parameter systems (DPSs). This empirical model is based only on process input-output data and used for the estimation of the controlled variables at specific locations, without the need of online solution of partial differential equations (PDEs). The nonlinear model that is obtained is suitably transformed to a nonlinear state space formulation that also takes into account the model mismatch. A stable robust control law is implemented for the attenuation of external disturbances. The proposed identification and control methodology is applied on a long duct, a common component of thermal systems, for a flow based control of temperature distribution. The closed loop performance is significantly improved in comparison to existing control methodologies.

Relative Radiometric Correction of Cloudy Multitemporal Satellite Imagery

Year: 2009 Volume: 3 Issue: 3 519 - 523 Pages

Abstract: Repeated observation of a given area over time yields potential for many forms of change detection analysis. These repeated observations are confounded in terms of radiometric consistency due to changes in sensor calibration over time, differences in illumination, observation angles and variation in atmospheric effects. This paper demonstrates applicability of an empirical relative radiometric normalization method to a set of multitemporal cloudy images acquired by Resourcesat1 LISS III sensor. Objective of this study is to detect and remove cloud cover and normalize an image radiometrically. Cloud detection is achieved by using Average Brightness Threshold (ABT) algorithm. The detected cloud is removed and replaced with data from another images of the same area. After cloud removal, the proposed normalization method is applied to reduce the radiometric influence caused by non surface factors. This process identifies landscape elements whose reflectance values are nearly constant over time, i.e. the subset of non-changing pixels are identified using frequency based correlation technique. The quality of radiometric normalization is statistically assessed by R2 value and mean square error (MSE) between each pair of analogous band.

Estimation of the Moisture Diffusivity and Activation Energy in Thin Layer Drying of Ginger Slices

Year: 2013 Volume: 7 Issue: 6 420 - 423 Pages

Abstract: In the present work, the effective moisture diffusivity and activation energy were calculated using an infinite series solution of Fick-s diffusion equation. The results showed that increasing drying temperature accelerated the drying process. All drying experiments had only falling rate period. The average effective moisture diffusivity values varied from 2.807x10-10 to 6.977x10-10m2 s_1 over the temperature and velocity range. The temperature dependence of the effective moisture diffusivity for the thin layer drying of the ginger slices was satisfactorily described by an Arrhenius-type relationship with activation energy values of 19.313- 22.722 kJ.mol-1 within 40–70 °C and 0.8-3 ms-1 temperature range.

Modeling of Heat and Mass Transfer in Soil Plant-Atmosphere. Influence of the Spatial Variability of Soil Hydrodynamic

Year: 2012 Volume: 6 Issue: 4 463 - 466 Pages

Abstract: The modeling of water transfer in the unsaturated zone uses techniques and methods of the soil physics to solve the Richards-s equation. However, there is a disaccord between the size of the measurements provided by the soil physics and the size of the fields of hydrological modeling problem, to which is added the strong spatial variability of soil hydraulic properties. The objective of this work was to develop a methodology to estimate the hydrodynamic parameters for modeling water transfers at different hydrological scales in the soil-plant atmosphere systems.

Robust Adaptive Control of a Robotic Manipulator with Unknown Dead Zone and Friction Torques

Year: 2010 Volume: 4 Issue: 8 681 - 688 Pages

Abstract: The problem of controlling a two link robotic manipulator, consisting of a rotating and a prismatic links, is addressed. The actuations of both links are assumed to have unknown dead zone nonlinearities and friction torques modeled by LuGre friction model. Because of the existence of the unknown dead zone and friction torque at the actuations, unknown parameters and unmeasured states would appear to be part of the overall system dynamics that need for estimation. Unmeasured states observer, unknown parameters estimators, and robust adaptive control laws have been derived such that closed loop global stability is achieved. Simulation results have been performed to show the efficacy of the suggested approach.

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