Search results for: surface runoff network

Authors: K.Ting, K. T. Chen, Y. L. Su, C. J. Chang

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In medical therapy, laser has been widely used to conduct cosmetic, tumor and other treatments. During the process of laser irradiation, there may be thermal damage caused by excessive laser exposure. Thus, the establishment of a complete thermal analysis model is clinically helpful to physicians in reference data. In this study, porcine liver in place of tissue was subjected to laser irradiation to set up the experimental data considering the explored impact on surface thermal field and thermal damage region under different conditions of power, laser irradiation time, and distance between laser and porcine liver. In the experimental process, the surface temperature distribution of the porcine lever was measured by the infrared thermal imager. In the part of simulation, the bio heat transfer Pennes-s equation was solved by software SYSWELD applying in welding process. The double ellipsoid function as a laser source term is firstly considered in the prediction for surface thermal field and internal tissue damage. The simulation results are compared with the experimental data to validate the mathematical model established here in.

Keywords: laser infrared thermal imager, bio-heat transfer, double ellipsoid function.

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Authors: A. Thakur, P. S. Panesar, M. S. Saini

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Extraction of lactic acid by emulsion liquid membrane technology (ELM) using n-trioctyl amine (TOA) in n-heptane as carrier within the organic membrane along with sodium carbonate as acceptor phase was optimized by using response surface methodology (RSM). A three level Box-Behnken design was employed for experimental design, analysis of the results and to depict the combined effect of five independent variables, vizlactic acid concentration in aqueous phase (c_l), sodium carbonate concentration in stripping phase (c_s), carrier concentration in membrane phase (ψ), treat ratio, and batch extraction time (τ)  with equal volume of organic and external aqueous phase on lactic acid extraction efficiency. The maximum lactic acid extraction efficiency (ηext) of 98.21%from aqueous phase in a batch reactor using ELM was found at the optimized values for test variables, cl, cs, ψ,
and τ as 0.06 [M], 0.18 [M], 4.72 (%,v/v), 1.98 (v/v) and 13.36 min respectively. 

Keywords: Emulsion liquid membrane, extraction, lactic acid, n-trioctylamine, response surface methodology.

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Authors: Yi Chao Ma, Cheng Siong Chin, Wai Lok Woo

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Since the last decade, there has been a rapid growth in digital multimedia, such as high-resolution media files and threedimentional movies. Hence, there is a need for large digital storage such as Hard Disk Drive (HDD). As such, users expect to have a quieter HDD in their laptop. In this paper, a jury test has been conducted on a group of 34 people where 17 of them are students who are the potential consumer, and the remaining are engineers who know the HDD. A total 13 HDD sound samples have been selected from over hundred HDD noise recordings. These samples are selected based on an agreed subjective feeling. The samples are played to the participants using head acoustic playback system, which enabled them to experience as similar as possible the same environment as have been recorded. Analysis has been conducted and the obtained results have indicated different group has different perception over the noises. Two neural network-based acoustic annoyance models are established based on back propagation neural network. Four psychoacoustic metrics, loudness, sharpness, roughness and fluctuation strength, are used as the input of the model, and the subjective evaluation results are taken as the output. The developed models are reasonably accurate in simulating both training and test samples.

Keywords: Hard disk drive noise, jury test, neural network model, psychoacoustic annoyance.

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Authors: Amandeep Singh Dhaliwal

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Networking solutions, particularly wireless local area networks have revolutionized the technological advancement. Wireless Local Area Networks (WLANs) have gained a lot of popularity as they provide location-independent network access between computing devices. There are a number of access methods used in Wireless Networks among which DCF and PCF are the fundamental access methods. This paper emphasizes on the impact of DCF and PCF access mechanisms on the performance of the IEEE 802.11a, 802.11b and 802.11g standards. On the basis of various parameters viz. throughput, delay, load etc performance is evaluated between these three standards using above mentioned access mechanisms. Analysis revealed a superior throughput performance with low delays for 802.11g standard as compared to 802.11 a/b standard using both DCF and PCF access methods.

Keywords: DCF, IEEE, PCF, WLAN.

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Authors: Kalyani J. Kulkarni, Bharat S. Chaudhari

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This paper addresses the issue of resource allocation in the emerging cognitive technology. Focusing the Quality of Service (QoS) of Primary Users (PU), a novel method is proposed for the resource allocation of Secondary Users (SU). In this paper, we propose the unique Utility Function in the game theoretic model of Cognitive Radio which can be maximized to increase the capacity of the Cognitive Radio Network (CRN) and to minimize the interference scenario. Utility function is formulated to cater the need of PUs by observing Signal to Noise ratio. Existence of Nash Equilibrium for the postulated game is established.

Keywords: Cognitive Networks, Game Theory, Nash Equilibrium, Resource Allocation.

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Authors: Asaad Moosa

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In recent years with the rapid development of Internet and the Web, more and more web applications have been deployed in many fields and organizations such as finance, military, and government. Together with that, hackers have found more subtle ways to attack web applications. According to international statistics, SQL Injection is one of the most popular vulnerabilities of web applications. The consequences of this type of attacks are quite dangerous, such as sensitive information could be stolen or authentication systems might be by-passed. To mitigate the situation, several techniques have been adopted. In this research, a security solution is proposed using Artificial Neural Network to protect web applications against this type of attacks. The solution has been experimented on sample datasets and has given promising result. The solution has also been developed in a prototypic web application firewall called ANNbWAF.

Keywords: Artificial Neural Networks ANN, SQL Injection, Web Application Firewall WAF, Web Application Scanner WAS.

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Authors: M.R.Shabgard, R.M.Shotorbani

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FW4 is a newly developed hot die material widely used in Forging Dies manufacturing. The right selection of the machining conditions is one of the most important aspects to take into consideration in the Electrical Discharge Machining (EDM) of FW4. In this paper an attempt has been made to develop mathematical models for relating the Material Removal Rate (MRR), Tool Wear Ratio (TWR) and surface roughness (Ra) to machining parameters (current, pulse-on time and voltage). Furthermore, a study was carried out to analyze the effects of machining parameters in respect of listed technological characteristics. The results of analysis of variance (ANOVA) indicate that the proposed mathematical models, can adequately describe the performance within the limits of the factors being studied.

Keywords: Electrical Discharge Machining (EDM), linearregression technique, Response Surface Methodology (RSM)

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Authors: Sakir Tasdemir, Mustafa Altin, Gamze Fahriye Pehlivan, Ismail Saritas, Sadiye Didem Boztepe Erkis, Selma Tasdemir

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Artificial intelligence applications are commonly used in industry in many fields in parallel with the developments in the computer technology. In this study, a fire room was prepared for the resistance of wooden construction elements and with the mechanism here, the experiments of polished materials were carried out. By utilizing from the experimental data, an artificial neural network (ANN) was modelled in order to evaluate the final cross sections of the wooden samples remaining from the fire. In modelling, experimental data obtained from the fire room were used. In the developed system, the first weight of samples (ws-gr), preliminary cross-section (pcs-mm2), fire time (ft-minute), and fire temperature (t-oC) as input parameters and final cross-section (fcs-mm2) as output parameter were taken. When the results obtained from ANN and experimental data are compared after making statistical analyses, the data of two groups are determined to be coherent and seen to have no meaning difference between them. As a result, it is seen that ANN can be safely used in determining cross sections of wooden materials after fire and it prevents many disadvantages.

Keywords: Artificial neural network, final cross-section, fire retardant polishes, fire safety, wood resistance.

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Authors: Abdullah Gokhan Yilmaz, Monzur Alam Imteaz, Shirley Gato-Trinidad, Iqbal Hossain

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Climate change leading to global warming affects the earth through many different ways such as weather (temperature, precipitation, humidity and the other parameters of weather), snow coverage and ice melting, sea level rise, hydrological cycles, quality of water, agriculture, forests, ecosystems and health. One of the most affected areas by climate change is hydrology and water resources. Regions where majority of runoff consists of snow melt are more sensitive to climate change. The first step of climate change studies is to establish trends of significant climate variables including precipitation, temperature and flow data to detect any potential climate change impacts already happened. Two popular non-parametric trend analysis methods, Mann-Kendal and Spearman-s Rho were applied to Upper Euphrates Basin (Turkey) to detect trends of precipitation, temperatures (maximum, minimum and average) and streamflow.

Keywords: Climate change, precipitation, snow hydrology, trend analysis and Upper Euphrates Basin

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Authors: Donna M. G. Comissiong, Tyrone D. Dass, Harold Ramkissoon, Alana R. Sankar

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The B'enard-Marangoni thermal instability problem for a viscoelastic Jeffreys- fluid layer with internal heat generation is investigated. The fluid layer is bounded above by a realistic free deformable surface and by a plane surface below. Our analysis shows that while the internal heat generation and the relaxation time both destabilize the fluid layer, its stability may be enhanced by an increased retardation time.

Keywords: Viscoelastic fluid, Jeffreys' model, Maxwell model, internal heat generation, retardation time, relaxation time.

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Authors: Samit Ari, Goutam Saha

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Artificial Neural Network (ANN) has been extensively used for classification of heart sounds for its discriminative training ability and easy implementation. However, it suffers from overparameterization if the number of nodes is not chosen properly. In such cases, when the dataset has redundancy within it, ANN is trained along with this redundant information that results in poor validation. Also a larger network means more computational expense resulting more hardware and time related cost. Therefore, an optimum design of neural network is needed towards real-time detection of pathological patterns, if any from heart sound signal. The aims of this work are to (i) select a set of input features that are effective for identification of heart sound signals and (ii) make certain optimum selection of nodes in the hidden layer for a more effective ANN structure. Here, we present an optimization technique that involves Singular Value Decomposition (SVD) and QR factorization with column pivoting (QRcp) methodology to optimize empirically chosen over-parameterized ANN structure. Input nodes present in ANN structure is optimized by SVD followed by QRcp while only SVD is required to prune undesirable hidden nodes. The result is presented for classifying 12 common pathological cases and normal heart sound.

Keywords: ANN, Classification of heart diseases, murmurs, optimization, Phonocardiogram, QRcp, SVD.

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Authors: Omer F. Cansiz, Said M. Easa

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The purpose of this study is mainly to predict collision frequency on the horizontal tangents combined with vertical curves using artificial neural network methods. The proposed ANN models are compared with existing regression models. First, the variables that affect collision frequency were investigated. It was found that only the annual average daily traffic, section length, access density, the rate of vertical curvature, smaller curve radius before and after the tangent were statistically significant according to related combinations. Second, three statistical models (negative binomial, zero inflated Poisson and zero inflated negative binomial) were developed using the significant variables for three alignment combinations. Third, ANN models are developed by applying the same variables for each combination. The results clearly show that the ANN models have the lowest mean square error value than those of the statistical models. Similarly, the AIC values of the ANN models are smaller to those of the regression models for all the combinations. Consequently, the ANN models have better statistical performances than statistical models for estimating collision frequency. The ANN models presented in this paper are recommended for evaluating the safety impacts 3D alignment elements on horizontal tangents.

Keywords: Collision frequency, horizontal tangent, 3D two-lane highway, negative binomial, zero inflated Poisson, artificial neural network.

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Authors: A. Briseno, A, Zorrilla

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Firms are increasingly responding to social and environmental claims from society. Practices oriented to attend issues such as poverty, work equality, or renewable energy, are being implemented more frequently by firms to address impacts on sustainability. However, questions remain on how the responses of firms vary across industries and regions between the social and the economic objectives. Using concepts from organizational theory and social network theory, this paper aims to create a theoretical framework that explains the internal and external influences that make a firm establish its objective. The framework explains why firms might have a different objective orientation in terms of its economic and social prioritization.

Keywords: Organizational identity, social network analysis, firm objective, value maximization, social responsibility.

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Authors: Gitimoni Deka, Krishna G Bhattacharyya

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The textile industry produces highly coloured effluents containing polar and non-polar compounds. The textile mill run by the Assam Polyester Co-operative Society Limited (APOL) is situated at Rangia, about 55 km from Guwahati (26011' N, 91047' E) in the northern bank of the river Brahmaputra, Assam (India). This unit was commissioned in June 1988 and started commercial production in November 1988. The installed capacity of the weaving unit was 8000 m/day and that of the processing unit was 20,000 m/day. The mill has its own dyeing unit with a capacity of 1500-2000 kg/day. The western side of the mill consists of vast agricultural land and the far northern and southern side of the mill has scattered human population. The eastern side of the mill has a major road for thoroughfare. The mill releases its effluents into the agricultural land in the western side of the mill. The present study was undertaken to assess the impact of the textile mill on surface soil quality in and around the mill with particular reference to Cr, Mn, Ni and Zn. Surface soil samples, collected along different directions at 200, 500 and 1000 m were digested and the metals were estimated with Atomic Absorption Spectrophotometer. The metals were found in the range of: Cr 50.9 – 105.0 mg kg-1, Mn 19.2- 78.6 mg kg-1, Ni 41.9 – 50.6 mg kg-1 and Zn 187.8 – 1095.8 mg kg-1. The study reveals enrichment of Cr, Mn, Ni and Zn in the soil near the textile mill.

Keywords: Cr, Mn, Ni, Zn, soil quality, heavy metal enrichment.

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Authors: Mohsen Hayati, Yazdan Shirvany

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In this paper, the application of neural networks to study the design of short-term load forecasting (STLF) Systems for Illam state located in west of Iran was explored. One important architecture of neural networks named Multi-Layer Perceptron (MLP) to model STLF systems was used. Our study based on MLP was trained and tested using three years (2004-2006) data. The results show that MLP network has the minimum forecasting error and can be considered as a good method to model the STLF systems.

Keywords: Artificial neural networks, Forecasting, Multi-layer perceptron.

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Authors: N. R. Harris, J. Curry

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This paper describes the construction and operation of an experimental LoRaWAN network surrounding the University of Southampton in the United Kingdom. Following successful installation, an experimental node design is built and characterised, with particular emphasis on radio range. Several configurations are investigated, including different data rates, and varying heights of node. It is concluded that although range can be great (over 8 km in this case), environmental topology is critical. However, shorter range implementations, up to about 2 km in an urban environment, are relatively insensitive although care is still needed. The example node and the relatively simple base station reported demonstrate that LoraWan can be a very low cost and practical solution to Internet of Things type applications for distributed monitoring systems with sensors spread over distances of several km.

Keywords: Wireless sensor network, LoRa, internet of things, propagation.

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Authors: Elena Carcano, Mirzi Betasolo

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This study aims to provide sub-hourly streamflow predictions and associated rating curves for small catchments of intermittent and torrential flow regime characterized by flash floods occurring especially during April and November. The methodology entails two lumped conceptual hydrological models which work in series. The total model is based upon eleven parameters and shows good flexibility in handling different input sets. Runoff Coefficient has contributed to improving the model’s performances and has been treated as an additional parameter; while Sensitivity Analysis has highlighted how slight changes in the model’s input can lead to changes in model’s output. The adopted procedure is steady and useful to give very practical engineering information at the expense of a parsimonious request both in input data and in the number of adopted parameters. According to the obtained results, the authors encourage the test of this combined procedure on different hydrological scenarios in order to provide information for poorly monitored catchments and not updated sites.

Keywords: Streamflow rating curve, chronological data, streamflow sequences, conceptual models.

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Authors: H. Shayeghi, H. A. Shayanfar, G. Azimi

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The quality of short term load forecasting can improve the efficiency of planning and operation of electric utilities. Artificial Neural Networks (ANNs) are employed for nonlinear short term load forecasting owing to their powerful nonlinear mapping capabilities. At present, there is no systematic methodology for optimal design and training of an artificial neural network. One has often to resort to the trial and error approach. This paper describes the process of developing three layer feed-forward large neural networks for short-term load forecasting and then presents a heuristic search algorithm for performing an important task of this process, i.e. optimal networks structure design. Particle Swarm Optimization (PSO) is used to develop the optimum large neural network structure and connecting weights for one-day ahead electric load forecasting problem. PSO is a novel random optimization method based on swarm intelligence, which has more powerful ability of global optimization. Employing PSO algorithms on the design and training of ANNs allows the ANN architecture and parameters to be easily optimized. The proposed method is applied to STLF of the local utility. Data are clustered due to the differences in their characteristics. Special days are extracted from the normal training sets and handled separately. In this way, a solution is provided for all load types, including working days and weekends and special days. The experimental results show that the proposed method optimized by PSO can quicken the learning speed of the network and improve the forecasting precision compared with the conventional Back Propagation (BP) method. Moreover, it is not only simple to calculate, but also practical and effective. Also, it provides a greater degree of accuracy in many cases and gives lower percent errors all the time for STLF problem compared to BP method. Thus, it can be applied to automatically design an optimal load forecaster based on historical data.

Keywords: Large Neural Network, Short-Term Load Forecasting, Particle Swarm Optimization.

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Authors: Chockpisit Thepsithar, Aree Thongpukdee

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Two species of Physalis, P.angulataL. and P. peruviana L. were used as models for comparative study to understand the values of micro-morphological, -anatomical and architectural characteristics of leaf for taxonomic purposes and possibly breeding and commercial applications. Both speciespossess amphistomaticleaves with 1-layer epidermis, 3-4-layer spongy mesophyll andbicollateral bundle midrib. Palisade parenchyma cells of P. angulatawere almost twice longer (65-75 μm) than the other one. Type of stomata was similar as anomocyticbut stomatal index(SI) at adaxial surface and abaxial surface of P. angulata were less than of P. peruvianaas 3.57, 4.00 and6.25, 6.66 respectively. Some leaf architectural characteristics such as leaf shape, order of venationalsoprovided information of taxonomic significance

Keywords: Physalis, Solanaceae, micromorphology, anatomy, leaf architecture.

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Authors: R.Muthuvelayudham, T.Viruthagiri

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Response Surface Methodology (RSM) is a powerful and efficient mathematical approach widely applied in the optimization of cultivation process. Cellulase enzyme production by Trichoderma reesei RutC30 using agricultural waste rice straw and banana fiber as carbon source were investigated. In this work, sequential optimization strategy based statistical design was employed to enhance the production of cellulase enzyme through submerged cultivation. A fractional factorial design (26-2) was applied to elucidate the process parameters that significantly affect cellulase production. Temperature, Substrate concentration, Inducer concentration, pH, inoculum age and agitation speed were identified as important process parameters effecting cellulase enzyme synthesis. The concentration of lignocelluloses and lactose (inducer) in the cultivation medium were found to be most significant factors. The steepest ascent method was used to locate the optimal domain and a Central Composite Design (CCD) was used to estimate the quadratic response surface from which the factor levels for maximum production of cellulase were determined.

Keywords: Banana fiber, Cellulase, Optimization, Rice straw

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Authors: H. Bonakdari, I. Ebtehaj

Abstract:

The prediction of scour depth around bridge piers is frequently considered in river engineering. One of the key aspects in efficient and optimum bridge structure design is considered to be scour depth estimation around bridge piers. In this study, scour depth around bridge piers is estimated using two methods, namely the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN). Therefore, the effective parameters in scour depth prediction are determined using the ANN and ANFIS methods via dimensional analysis, and subsequently, the parameters are predicted. In the current study, the methods’ performances are compared with the nonlinear regression (NLR) method. The results show that both methods presented in this study outperform existing methods. Moreover, using the ratio of pier length to flow depth, ratio of median diameter of particles to flow depth, ratio of pier width to flow depth, the Froude number and standard deviation of bed grain size parameters leads to optimal performance in scour depth estimation.

Keywords: Adaptive neuro-fuzzy inference system, ANFIS, artificial neural network, ANN, bridge pier, scour depth, nonlinear regression, NLR.

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Authors: D. Zare, H. Naderi, A. A. Jafari

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Drying characteristics of rough rice (variety of lenjan) with an initial moisture content of 25% dry basis (db) was studied in a hot air dryer assisted by infrared heating. Three arrival air temperatures (30, 40 and 500C) and four infrared radiation intensities (0, 0.2 , 0.4 and 0.6 W/cm2) and three arrival air speeds (0.1, 0.15 and 0.2 m.s-1) were studied. Bending strength of brown rice kernel, percentage of cracked kernels and time of drying were measured and evaluated. The results showed that increasing the drying arrival air temperature and radiation intensity of infrared resulted decrease in drying time. High bending strength and low percentage of cracked kernel was obtained when paddy was dried by hot air assisted infrared dryer. Between this factors and their interactive effect were a significant difference (p<0.01). An intensity level of 0.2 W/cm2 was found to be optimum for radiation drying. Furthermore, in the present study, the application of Artificial Neural Network (ANN) for predicting the moisture content during drying (output parameter for ANN modeling) was investigated. Infrared Radiation intensity, drying air temperature, arrival air speed and drying time were considered as input parameters for the model. An ANN model with two hidden layers with 8 and 14 neurons were selected for studying the influence of transfer functions and training algorithms. The results revealed that a network with the Tansig (hyperbolic tangent sigmoid) transfer function and trainlm (Levenberg-Marquardt) back propagation algorithm made the most accurate predictions for the paddy drying system. Mean square error (MSE) was calculated and found that the random errors were within and acceptable range of ±5% with coefficient of determination (R2) of 99%.

Keywords: Rough rice, Infrared-hot air, Artificial Neural Network

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Authors: W. Thongwat, B. Terakulsatit

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The study area is Kham Sakaesaeng District in Nakhon Ratchasima Province, the south section of Northeastern Thailand, located in the Lower Khorat-Ubol Basin. This region is the one of saline soil area, located in a dry plateau and regularly experience standing with periods of floods and alternating with periods of drought. Especially, the drought in the summer season causes the major saline soil and saline water problems of this region. The general cause of dry land salting resulted from salting on irrigated land, and an excess of water leading to the rising water table in the aquifer. The purpose of this study is to determine the relationship of physical and chemical properties between the soil and groundwater. The soil and groundwater samples were collected in both rainy and summer seasons. The content of pH, electrical conductivity (EC), total dissolved solids (TDS), chloride and salinity were investigated. The experimental result of soil and groundwater samples show the slightly pH less than 7, EC (186 to 8,156 us/cm and 960 to 10,712 us/cm), TDS (93 to 3,940 ppm and 480 to 5,356 ppm), chloride content (45.58 to 4,177,015 mg/l and 227.90 to 9,216,736 mg/l), and salinity (0.07 to 4.82 ppt and 0.24 to 14.46 ppt) in the rainy and summer seasons, respectively. The distribution of chloride content and salinity content were interpolated and displayed as a map by using ArcMap 10.3 program, according to the season. The result of saline soil and brined groundwater in the study area were related to the low-lying topography, drought area, and salt-source exposure. Especially, the Rock Salt Member of Maha Sarakham Formation was exposed or lies near the ground surface in this study area. During the rainy season, salt was eroded or weathered from the salt-source rock formation and transported by surface flow or leached into the groundwater. In the dry season, the ground surface is dry enough resulting salt precipitates from the brined surface water or rises from the brined groundwater influencing the increasing content of chloride and salinity in the ground surface and groundwater.

Keywords: Environmental geology, soil salinity, geochemistry, groundwater hydrology.

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Authors: Goh Han Keat, Azman Samsudin Zurinahni Zainol

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The increase popularity of multimedia application especially in image processing places a great demand on efficient data storage and transmission techniques. Network communication such as wireless network can easily be intercepted and cause of confidential information leaked. Unfortunately, conventional compression and encryption methods are too slow; it is impossible to carry out real time secure image processing. In this research, Embedded Zerotree Wavelet (EZW) encoder which specially designs for wavelet compression is examined. With this algorithm, three methods are proposed to reduce the processing time, space and security protection that will be secured enough to protect the data.

Keywords: Embedded Zerotree Wavelet (EZW), Imagecompression, Wavelet encoder, Entropy encoder, Encryption.

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Authors: Parvinder S. Sandhu, Roma Jaswal, Sandeep Khimta, Shailendra Singh

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As the majority of faults are found in a few of its modules so there is a need to investigate the modules that are affected severely as compared to other modules and proper maintenance need to be done in time especially for the critical applications. As, Neural networks, which have been already applied in software engineering applications to build reliability growth models predict the gross change or reusability metrics. Neural networks are non-linear sophisticated modeling techniques that are able to model complex functions. Neural network techniques are used when exact nature of input and outputs is not known. A key feature is that they learn the relationship between input and output through training. In this present work, various Neural Network Based techniques are explored and comparative analysis is performed for the prediction of level of need of maintenance by predicting level severity of faults present in NASA-s public domain defect dataset. The comparison of different algorithms is made on the basis of Mean Absolute Error, Root Mean Square Error and Accuracy Values. It is concluded that Generalized Regression Networks is the best algorithm for classification of the software components into different level of severity of impact of the faults. The algorithm can be used to develop model that can be used for identifying modules that are heavily affected by the faults.

Keywords: Neural Network, Software faults, Software Metric.

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Authors: Samee Ullah Khan, C. Ardil

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In this paper, a mathematical model for data object replication in ad hoc networks is formulated. The derived model is general, flexible and adaptable to cater for various applications in ad hoc networks. We propose a game theoretical technique in which players (mobile hosts) continuously compete in a non-cooperative environment to improve data accessibility by replicating data objects. The technique incorporates the access frequency from mobile hosts to each data object, the status of the network connectivity, and communication costs. The proposed technique is extensively evaluated against four well-known ad hoc network replica allocation methods. The experimental results reveal that the proposed approach outperforms the four techniques in both the execution time and solution quality

Keywords: Data replication, auctions, static allocation.

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Authors: Shunya Wakayama, Kazuya Okubo, Toru Fujii, Daisuke Sakata, Noriyuki Kado, Hiroshi Furutachi

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The purpose of this study is to propose an effective method to improve frictional coefficient between shoe rubber soles with added glass fibers and the surfaces of icy and snowy road in order to prevent slip-and-fall accidents by the users. The additional fibers into the rubber were uniformly tilted to the perpendicular direction of the frictional surface, where tilting angles were -60, -30, +30, +60, 90 degrees and 0 (as normal specimen), respectively. It was found that parallel arraignment was effective to improve the frictional coefficient when glass fibers were embedded in the shoe rubber, while perpendicular to normal direction of the embedded glass fibers on the shoe surface was also effective to do that once after they were exposed from the shoe rubber with its abrasion. These improvements were explained by the increase of stiffness against the shear deformation of the rubber at critical frictional state and adequate scratching of fibers when fibers were protruded in perpendicular to frictional direction, respectively. Most effective angle of tilting of frictional coefficient between rubber specimens and a stone was perpendicular (= 0 degree) to frictional direction. Combinative modified rubber specimen having 2 layers was fabricated where tilting angle of protruded fibers was 0 degree near the contact surface and tilting angle of embedded fibers was 90 degrees near back surface in thickness direction to further improve the frictional coefficient. Current study suggested that effective arraignments in tilting angle of the added fibers should be applied in designing rubber shoe soles to keep the safeties for users in regions of cold climates.

Keywords: Frictional coefficient, icy and snowy road, shoe rubber soles, tilting angle.

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Authors: T. N. Janakiraman, J. Janet Lourds Rani

Abstract:

In practice, wireless networks has the property that the signal strength attenuates with respect to the distance from the base station, it could be better if the nodes at two hop away are considered for better quality of service. In this paper, we propose a procedure to identify delay preserving substructures for a given wireless ad-hoc network using a new graph operation G 2 – E (G) = G* (Edge difference of square graph of a given graph and the original graph). This operation helps to analyze some induced substructures, which preserve delay in communication among them. This operation G* on a given graph will induce a graph, in which 1- hop neighbors of any node are at 2-hop distance in the original network. In this paper, we also identify some delay preserving substructures in G*, which are (i) set of all nodes, which are mutually at 2-hop distance in G that will form a clique in G*, (ii) set of nodes which forms an odd cycle C2k+1 in G, will form an odd cycle in G* and the set of nodes which form a even cycle C2k in G that will form two disjoint companion cycles ( of same parity odd/even) of length k in G*, (iii) every path of length 2k+1 or 2k in G will induce two disjoint paths of length k in G*, and (iv) set of nodes in G*, which induces a maximal connected sub graph with radius 1 (which identifies a substructure with radius equal 2 and diameter at most 4 in G). The above delay preserving sub structures will behave as good clusters in the original network.

Keywords: Clique, cycles, delay preserving substructures, maximal connected sub graph.

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Authors: Al-Amin Bhuiyan, Chang Hong Liu

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.

Keywords: Fuzzily skewed filter, Gabor filter, rms contrast, neural network.

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Authors: Soungwan Kim

Abstract:

This paper discusses an analysis of how the characteristics of resources at vocational rehabilitation facilities for the disabled affect the improvement of independency skills among people with severe disabilities. The analysis results indicate that more internal financial resources and more connections to local communities among network resources had greater effects on improving the independency of people with severe disabilities. Based on this result, this paper presents strategies for mobilizing resources to improve the independency of people with severe disabilities at vocational rehabilitation facilities.

Keywords: Vocational rehabilitation facility for people with disabilities, types of resources, independency, network resources.

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