Search results for: multi metal remediation

Authors: A. D. Parekh, P. R. Tailor

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Capacity and efficiency of any refrigerating system diminish rapidly as the difference between the evaporating and condensing temperature is increased by reduction in the evaporator temperature. The single stage vapour compression refrigeration system is limited to an evaporator temperature of -40 0C. Below temperature of -40 0C the either cascade refrigeration system or multi stage vapour compression system is employed. Present work describes thermal design of main three heat exchangers namely condenser (HTS), cascade condenser and evaporator (LTS) of R404A-R508B and R410A-R23 cascade refrigeration system. Heat transfer area of condenser (HTS), cascade condenser and evaporator (LTS) for both systems have been compared and the effect of condensing and evaporating temperature on heat-transfer area for both systems have been studied under same operating condition. The results shows that the required heat-transfer area of condenser and cascade condenser for R410A-R23 cascade system is lower than the R404A-R508B cascade system but heat transfer area of evaporator is similar for both the system. The heat transfer area of condenser and cascade condenser decreases with increase in condensing temperature (Tc), whereas the heat transfer area of cascade condenser and evaporator increases with increase in evaporating temperature (Te).

Keywords: Heat-transfer area, R410A, R404A, R508B, R23, Refrigeration system, Thermal design

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Authors: Fatma El-zahraa El-taher, Ayman Taha, Jane Courtney, Susan Mckeever

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Understanding road networks plays an important role in navigation applications such as self-driving vehicles and route planning for individual journeys. Intersections of roads are essential components of road networks. Understanding the features of an intersection, from a simple T-junction to larger multi-road junctions is critical to decisions such as crossing roads or selecting safest routes. The identification and profiling of intersections from satellite images is a challenging task. While deep learning approaches offer state-of-the-art in image classification and detection, the availability of training datasets is a bottleneck in this approach. In this paper, a labelled satellite image dataset for the intersection recognition  problem is presented. It consists of 14,692 satellite images of Washington DC, USA. To support other users of the dataset, an automated download and labelling script is provided for dataset replication. The challenges of construction and fine-grained feature labelling of a satellite image dataset are examined, including the issue of how to address features that are spread across multiple images. Finally, the accuracy of detection of intersections in satellite images is evaluated.

Keywords: Satellite images, remote sensing images, data acquisition, autonomous vehicles, robot navigation, route planning, road intersections.

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Authors: Victor Breux, Jérôme Boutet, Alain Goret, Viviane Cattin

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Early detection of anomalies in data centers is important to reduce downtimes and the costs of periodic maintenance. However, there is little research on this topic and even fewer on the fusion of sensor data for the detection of abnormal events. The goal of this paper is to propose a method for anomaly detection in data centers by combining sensor data (temperature, humidity, power) and deep learning models. The model described in the paper uses one autoencoder per sensor to reconstruct the inputs. The auto-encoders contain Long-Short Term Memory (LSTM) layers and are trained using the normal samples of the relevant sensors selected by correlation analysis. The difference signal between the input and its reconstruction is then used to classify the samples using feature extraction and a random forest classifier. The data measured by the sensors of a data center between January 2019 and May 2020 are used to train the model, while the data between June 2020 and May 2021 are used to assess it. Performances of the model are assessed a posteriori through F1-score by comparing detected anomalies with the data center’s history. The proposed model outperforms the state-of-the-art reconstruction method, which uses only one autoencoder taking multivariate sequences and detects an anomaly with a threshold on the reconstruction error, with an F1-score of 83.60% compared to 24.16%.

Keywords: Anomaly detection, autoencoder, data centers, deep learning.

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Authors: J. P. Dubois, H. M. Chiu

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In this paper, we study statistical multiplexing of VBR video in ATM networks. ATM promises to provide high speed realtime multi-point to central video transmission for telemedicine applications in rural hospitals and in emergency medical services. Video coders are known to produce variable bit rate (VBR) signals and the effects of aggregating these VBR signals need to be determined in order to design a telemedicine network infrastructure capable of carrying these signals. We first model the VBR video signal and simulate it using a generic continuous-data autoregressive (AR) scheme. We carry out the queueing analysis by the Fluid Approximation Model (FAM) and the Markov Modulated Poisson Process (MMPP). The study has shown a trade off: multiplexing VBR signals reduces burstiness and improves resource utilization, however, the buffer size needs to be increased with an associated economic cost. We also show that the MMPP model and the Fluid Approximation model fit best, respectively, the cell region and the burst region. Therefore, a hybrid MMPP and FAM completely characterizes the overall performance of the ATM statistical multiplexer. The ramifications of this technology are clear: speed, reliability (lower loss rate and jitter), and increased capacity in video transmission for telemedicine. With migration to full IP-based networks still a long way to achieving both high speed and high quality of service, the proposed ATM architecture will remain of significant use for telemedicine.

Keywords: ATM, multiplexing, queueing, telemedicine, VBR.

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Authors: Atena Sajedin, Shokoufeh Zakernejad, Soheil Faridi, Mehrdad Javadi, Reza Ebrahimpour

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This paper illustrates the use of a combined neural network model for classification of electrocardiogram (ECG) beats. We present a trainable neural network ensemble approach to develop customized electrocardiogram beat classifier in an effort to further improve the performance of ECG processing and to offer individualized health care. We process a three stage technique for detection of premature ventricular contraction (PVC) from normal beats and other heart diseases. This method includes a denoising, a feature extraction and a classification. At first we investigate the application of stationary wavelet transform (SWT) for noise reduction of the electrocardiogram (ECG) signals. Then feature extraction module extracts 10 ECG morphological features and one timing interval feature. Then a number of multilayer perceptrons (MLPs) neural networks with different topologies are designed. The performance of the different combination methods as well as the efficiency of the whole system is presented. Among them, Stacked Generalization as a proposed trainable combined neural network model possesses the highest recognition rate of around 95%. Therefore, this network proves to be a suitable candidate in ECG signal diagnosis systems. ECG samples attributing to the different ECG beat types were extracted from the MIT-BIH arrhythmia database for the study.

Keywords: ECG beat Classification; Combining Classifiers;Premature Ventricular Contraction (PVC); Multi Layer Perceptrons;Wavelet Transform

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Authors: A. Chaouachi, R. M. Kamel, K. Nagasaka

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This paper presents a novel methodology for Maximum Power Point Tracking (MPPT) of a grid-connected 20 kW Photovoltaic (PV) system using neuro-fuzzy network. The proposed method predicts the reference PV voltage guarantying optimal power transfer between the PV generator and the main utility grid. The neuro-fuzzy network is composed of a fuzzy rule-based classifier and three Radial Basis Function Neural Networks (RBFNN). Inputs of the network (irradiance and temperature) are classified before they are fed into the appropriated RBFNN for either training or estimation process while the output is the reference voltage. The main advantage of the proposed methodology, comparing to a conventional single neural network-based approach, is the distinct generalization ability regarding to the nonlinear and dynamic behavior of a PV generator. In fact, the neuro-fuzzy network is a neural network based multi-model machine learning that defines a set of local models emulating the complex and non-linear behavior of a PV generator under a wide range of operating conditions. Simulation results under several rapid irradiance variations proved that the proposed MPPT method fulfilled the highest efficiency comparing to a conventional single neural network.

Keywords: MPPT, neuro-fuzzy, RBFN, grid-connected, photovoltaic.

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Authors: Ekesa B. Nakhauka, De Lange M., Macharia I., Garming H., Ouma E., Birachi E., Van Asten P., Van-Lauwe B., Blomme G.

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Impact of adopting products promoted by the Consortium for Improving Agriculture-based livelihoods in Central Africa (CIALCA) on food and nutrition security was tested. Multi-stage sampling was used to select 7 project mandate areas, 5 villages/mandate area (stratified into action, satellite and control sites) and 913 households. Structured questionnaires were administered; analysis of impact based on comparison between stratums, differences in means tested by ANOVA and significance of difference obtained by Tukey's HSD multiple rank tests. Perception of adequate food sufficiency received a higher rating in action and satellite sites compared to control sites reason being improved agricultural technologies. For >60% of households, worsened food security was due to climatic conditions. Although a higher proportion of households in action and satellite was meeting calorie RDIs in DRC and Burundi the difference was insignificant from control sites. 53% of respondents in control sites indicated a decrease in intake of protein rich foods, this was significantly higher than the proportion in the action (46%) and satellite (41%) sites.

Keywords: Food security, Farmer-households, Nutrition security

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Authors: Pradyumna Kumar Sahoo, Prasanta Kumar Satpathy

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This paper aims at finding a suitable neural network for monitoring congestion level in electrical power systems. In this paper, the input data has been framed properly to meet the target objective through supervised learning mechanism by defining normal and abnormal operating conditions for the system under study. The congestion level, expressed as line congestion index (LCI), is evaluated for each operating condition and is presented to the NN along with the bus voltages to represent the input and target data. Once, the training goes successful, the NN learns how to deal with a set of newly presented data through validation and testing mechanism. The crux of the results presented in this paper rests on performance comparison of a multi-layered feed forward neural network with eleven types of back propagation techniques so as to evolve the best training criteria. The proposed methodology has been tested on the standard IEEE-14 bus test system with the support of MATLAB based NN toolbox. The results presented in this paper signify that the Levenberg-Marquardt backpropagation algorithm gives best training performance of all the eleven cases considered in this paper, thus validating the proposed methodology.

Keywords: Line congestion index, critical bus, contingency, neural network.

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Authors: Axay J Mehta, Hema A Mehta, T.C.Manjunath, C. Ardil

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In this paper, the modelling and design of artificial neural network architecture for load forecasting purposes is investigated. The primary pre-requisite for power system planning is to arrive at realistic estimates of future demand of power, which is known as Load Forecasting. Short Term Load Forecasting (STLF) helps in determining the economic, reliable and secure operating strategies for power system. The dependence of load on several factors makes the load forecasting a very challenging job. An over estimation of the load may cause premature investment and unnecessary blocking of the capital where as under estimation of load may result in shortage of equipment and circuits. It is always better to plan the system for the load slightly higher than expected one so that no exigency may arise. In this paper, a load-forecasting model is proposed using a multilayer neural network with an appropriately modified back propagation learning algorithm. Once the neural network model is designed and trained, it can forecast the load of the power system 24 hours ahead on daily basis and can also forecast the cumulative load on daily basis. The real load data that is used for the Artificial Neural Network training was taken from LDC, Gujarat Electricity Board, Jambuva, Gujarat, India. The results show that the load forecasting of the ANN model follows the actual load pattern more accurately throughout the forecasted period.

Keywords: Power system, Load forecasting, Neural Network, Neuron, Stabilization, Network structure, Load.

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Authors: Z. El Felsoufi, L. Azrar

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This paper studies a mathematical model based on the integral equations for dynamic analyzes numerical investigations of a non-uniform or multi-material composite beam. The beam is subjected to a sub-tangential follower force and elastic foundation. The boundary conditions are represented by generalized parameterized fixations by the linear and rotary springs. A mathematical formula based on Euler-Bernoulli beam theory is presented for beams with variable cross-sections. The non-uniform section introduces non-uniformity in the rigidity and inertia of beams and consequently, more complicated equilibrium who governs the equation. Using the boundary element method and radial basis functions, the equation of motion is reduced to an algebro-differential system related to internal and boundary unknowns. A generalized formula for the deflection, the slope, the moment and the shear force are presented. The free vibration of non-uniform loaded beams is formulated in a compact matrix form and all needed matrices are explicitly given. The dynamic stability analysis of slender beam is illustrated numerically based on the coalescence criterion. A realistic case related to an industrial chimney is investigated.

Keywords: Chimney, BEM and integral equation formulation, non uniform cross section, vibration and Flutter.

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Authors: Fiona Curran-Cournane

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Soil quality monitoring is a science-based soil management tool that assesses soil ecosystem health. A soil monitoring program in Auckland, New Zealand’s largest city extends from 1995 to the present. The objective of this study was to firstly determine changes in soil parameters (basic soil properties and heavy metals) that were assessed from rural land in 1995-2000 and repeated in 2008-2012. The second objective was to determine differences in soil parameters across various land uses including native bush, rural (horticulture, pasture and plantation forestry) and urban land uses using soil data collected in more recent years (2009- 2013). Across rural land, mean concentrations of Olsen P had significantly increased in the second sampling period and was identified as the indicator of most concern, followed by soil macroporosity, particularly for horticultural and pastoral land. Mean concentrations of Cd were also greatest for pastoral and horticultural land and a positive correlation existed between these two parameters, which highlights the importance of analysing basic soil parameters in conjunction with heavy metals. In contrast, mean concentrations of As, Cr, Pb, Ni and Zn were greatest for urban sites. Native bush sites had the lowest concentrations of heavy metals and were used to calculate a ‘pollution index’ (PI). The mean PI was classified as high (PI > 3) for Cd and Ni and moderate for Pb, Zn, Cr, Cu, As and Hg, indicating high levels of heavy metal pollution across both rural and urban soils. From a land use perspective, the mean ‘integrated pollution index’ was highest for urban sites at 2.9 followed by pasture, horticulture and plantation forests at 2.7, 2.6 and 0.9, respectively. It is recommended that soil sampling continues over time because a longer spanning record will allow further identification of where soil problems exist and where resources need to be targeted in the future. Findings from this study will also inform policy and science direction in regional councils.

Keywords: Heavy metals, Pollution Index, Rural and Urban land use.

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Authors: Sohrab Khanmohammadi, Raana Soltani Zarrin

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In this paper, a heuristic method for simultaneous rescue robot path-planning and mission scheduling is introduced based on project management techniques, multi criteria decision making and artificial potential fields path-planning. Groups of injured people are trapped in a disastrous situation. These people are categorized into several groups based on the severity of their situation. A rescue robot, whose ultimate objective is reaching injured groups and providing preliminary aid for them through a path with minimum risk, has to perform certain tasks on its way towards targets before the arrival of rescue team. A decision value is assigned to each target based on the whole degree of satisfaction of the criteria and duties of the robot toward the target and the importance of rescuing each target based on their category and the number of injured people. The resulted decision value defines the strength of the attractive potential field of each target. Dangerous environmental parameters are defined as obstacles whose risk determines the strength of the repulsive potential field of each obstacle. Moreover, negative and positive energies are assigned to the targets and obstacles, which are variable with respects to the factors involved. The simulation results show that the generated path for two cases studies with certain differences in environmental conditions and other risk factors differ considerably.

Keywords: Artificial potential field, GERT, path planning

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Authors: Wenhao Wang, Xinzhi Xu, Limin Feng, Wei Cong

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This paper comprehensively investigates current development status of global power grids and fossil energy pipelines (oil and natural gas), proposes a standard visual platform of global power and fossil energy based on Geographic Information System (GIS) technology. In this visual platform, a series of systematic visual models is proposed with global spatial data, systematic energy and power parameters. Under this visual platform, the current Global Power Grids Map and Global Fossil Energy Pipelines Map are plotted within more than 140 countries and regions across the world. Using the multi-scale fusion data processing and modeling methods, the world’s global fossil energy pipelines and power grids information system basic database is established, which provides important data supporting global fossil energy and electricity research. Finally, through the systematic and comparative study of global fossil energy pipelines and global power grids, the general status of global fossil energy and electricity development are reviewed, and energy transition in key areas are evaluated and analyzed. Through the comparison analysis of fossil energy and clean energy, the direction of relevant research is pointed out for clean development and energy transition.

Keywords: Energy Transition, geographic information system, fossil energy, power systems.

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Authors: R.Balamurugan, S.Subramanian

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This paper presents the solution of power economic dispatch (PED) problem of generating units with valve point effects and multiple fuel options using Self-Adaptive Differential Evolution (SDE) algorithm. The global optimal solution by mathematical approaches becomes difficult for the realistic PED problem in power systems. The Differential Evolution (DE) algorithm is found to be a powerful evolutionary algorithm for global optimization in many real problems. In this paper the key parameters of control in DE algorithm such as the crossover constant CR and weight applied to random differential F are self-adapted. The PED problem formulation takes into consideration of nonsmooth fuel cost function due to valve point effects and multi fuel options of generator. The proposed approach has been examined and tested with the numerical results of PED problems with thirteen-generation units including valve-point effects, ten-generation units with multiple fuel options neglecting valve-point effects and ten-generation units including valve-point effects and multiple fuel options. The test results are promising and show the effectiveness of proposed approach for solving PED problems.

Keywords: Multiple fuels, power economic dispatch, selfadaptivedifferential evolution and valve-point effects.

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Authors: M. Ishak, K. Yamasaki, K. Maekawa

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Magnesium alloys have gained increased attention in recent years in automotive, electronics, and medical industry. This because of magnesium alloys have better properties than aluminum alloys and steels in respects of their low density and high strength to weight ratio. However, the main problems of magnesium alloy welding are the crack formation and the appearance of porosity during the solidification. This paper proposes a unique technique to weld two thin sheets of AZ31B magnesium alloy using a paste containing Ag nanoparticles. The paste containing Ag nanoparticles of 5 nm in average diameter and an organic solvent was used to coat the surface of AZ31B thin sheet. The coated sheet was heated at 100 °C for 60 s to evaporate the solvent. The dried sheet was set as a lower AZ31B sheet on the jig, and then lap fillet welding was carried out by using a pulsed Nd:YAG laser in a closed box filled with argon gas. The characteristics of the microstructure and the corrosion behavior of the joints were analyzed by opticalmicroscopy (OM), energy dispersive spectrometry (EDS), electron probe micro-analyzer (EPMA), scanning electron microscopy (SEM), and immersion corrosion test. The experimental results show that the wrought AZ31B magnesium alloy can be joined successfully using Ag nanoparticles. Ag nanoparticles insert promote grain refinement, narrower the HAZ width and wider bond width compared to weld without and insert. Corrosion rate of welded AZ31B with Ag nanoparticles reduced up to 44 % compared to base metal. The improvement of corrosion resistance of welded AZ31B with Ag nanoparticles due to finer grains and large grain boundaries area which consist of high Al content. β-phase Mg17Al12 could serve as effective barrier and suppressed further propagation of corrosion. Furthermore, Ag distribution in fusion zone provide much more finer grains and may stabilize the magnesium solid solution making it less soluble or less anodic in aqueous

Keywords: Laser welding, magnesium alloys, nanoparticles, mechanical property

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Authors: J. Modu, J. F. Georgin, L. Briançon, E. Antoinet

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Wind turbine gravity foundations are designed to resist overturning failure through gravitational forces resulting from their masses. Owing to the relatively high volume of the cementitious material present, the foundations tend to suffer thermal strains and internal cracking due to high temperatures and temperature gradients depending on factors such as geometry, mix design and level of restraint. This is a result of a fully coupled mechanism commonly known as THMC (Thermo- Hygro - Mechanical - Chemical) coupling whose kinetics peak during the early age of concrete. The focus of this paper is therefore to present and offer a discussion on the temperature and humidity evolutions occurring in mass pours such as wind turbine gravity foundations based on sensor results obtained from the monitoring of an actual wind turbine foundation. To offer prediction of the evolutions, the formulation of a 3D Thermal-Hydro-Chemical (THC) model that is mainly derived from classical fundamental physical laws is also presented and discussed. The THC model can be mathematically fully coupled in Finite Element analyses. In the current study, COMSOL Multi-physics software was used to simulate the 3D THC coupling that occurred in the monitored wind turbine foundation to predict the temperature evolution at five different points within the foundation from time of casting.

Keywords: Early age behavior, reinforced concrete, THC 3D models, wind turbines.

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Authors: Alisha Khanal, Gokhan Saygili

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It is commonly observed that aftershocks follow the mainshock. Aftershocks continue over a period of time with a decreasing frequency and typically there is not sufficient time for repair and retrofit between a mainshock–aftershock sequence. Usually, aftershocks are smaller in magnitude; however, aftershock ground motion characteristics such as the intensity and duration can be greater than the mainshock due to the changes in the earthquake mechanism and location with respect to the site. The seismic performance of slopes is typically evaluated based on the sliding displacement predicted to occur along a critical sliding surface. Various empirical models are available that predict sliding displacement as a function of seismic loading parameters, ground motion parameters, and site parameters but these models do not include the aftershocks. The seismic risks associated with the post-mainshock slopes ('damaged slopes') subjected to aftershocks is significant. This paper extends the empirical sliding displacement models for flexible slopes subjected to earthquake mainshock-aftershock sequences (a multi hazard approach). A dataset was developed using 144 pairs of as-recorded mainshock-aftershock sequences using the Pacific Earthquake Engineering Research Center (PEER) database. The results reveal that the combination of mainshock and aftershock increases the seismic demand on slopes relative to the mainshock alone; thus, seismic risks are underestimated if aftershocks are neglected.

Keywords: Seismic slope stability, sliding displacement, mainshock, aftershock, landslide, earthquake.

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Authors: Pavel Pořízka, David Prochazka, Karel Novotný, Ota Samek, ZdeněkPilát, Klára Procházková, and Jozef Kaiser

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Themain goal of this article is to find efficient methods for elemental and molecular analysis of living microorganisms (algae) under defined environmental conditions and cultivation processes. The overall knowledge of chemical composition is obtained utilizing laser-based techniques, Laser- Induced Breakdown Spectroscopy (LIBS) for acquiring information about elemental composition and Raman Spectroscopy for gaining molecular information, respectively. Algal cells were suspended in liquid media and characterized using their spectra. Results obtained employing LIBS and Raman Spectroscopy techniques will help to elucidate algae biology (nutrition dynamics depending on cultivation conditions) and to identify algal strains, which have the potential for applications in metal-ion absorption (bioremediation) and biofuel industry. Moreover, bioremediation can be readily combined with production of 3rd generation biofuels. In order to use algae for efficient fuel production, the optimal cultivation parameters have to be determinedleading to high production of oil in selected cellswithout significant inhibition of the photosynthetic activity and the culture growth rate, e.g. it is necessary to distinguish conditions for algal strain containing high amount of higher unsaturated fatty acids. Measurements employing LIBS and Raman Spectroscopy were utilized in order to give information about alga Trachydiscusminutus with emphasis on the amount of the lipid content inside the algal cell and the ability of algae to withdraw nutrients from its environment and bioremediation (elemental composition), respectively. This article can serve as the reference for further efforts in describing complete chemical composition of algal samples employing laserablation techniques.

Keywords: Laser-Induced Breakdown Spectroscopy, Raman Spectroscopy, Algae, Algal strains, Bioremediation, Biofuels.

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Authors: Md. Ashikur Rahman Khan, M. M. Rahman, K. Kadirgama

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Artificial neural networks (ANN) are used in distinct researching fields and professions, and are prepared by cooperation of scientists in different fields such as computer engineering, electronic, structure, biology and so many different branches of science. Many models are built correlating the parameters and the outputs in electrical discharge machining (EDM) concern for different types of materials. Up till now model for Ti-5Al-2.5Sn alloy in the case of electrical discharge machining performance characteristics has not been developed. Therefore, in the present work, it is attempted to generate a model of material removal rate (MRR) for Ti-5Al-2.5Sn material by means of Artificial Neural Network. The experimentation is performed according to the design of experiment (DOE) of response surface methodology (RSM). To generate the DOE four parameters such as peak current, pulse on time, pulse off time and servo voltage and one output as MRR are considered. Ti-5Al-2.5Sn alloy is machined with positive polarity of copper electrode. Finally the developed model is tested with confirmation test. The confirmation test yields an error as within the agreeable limit. To investigate the effect of the parameters on performance sensitivity analysis is also carried out which reveals that the peak current having more effect on EDM performance.

Keywords: Ti-5Al-2.5Sn, material removal rate, copper tungsten, positive polarity, artificial neural network, multi-layer perceptron.

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Authors: Rajneesh Sharma, M. Gopal

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Markov games are a generalization of Markov decision process to a multi-agent setting. Two-player zero-sum Markov game framework offers an effective platform for designing robust controllers. This paper presents two novel controller design algorithms that use ideas from game-theory literature to produce reliable controllers that are able to maintain performance in presence of noise and parameter variations. A more widely used approach for controller design is the H∞ optimal control, which suffers from high computational demand and at times, may be infeasible. Our approach generates an optimal control policy for the agent (controller) via a simple Linear Program enabling the controller to learn about the unknown environment. The controller is facing an unknown environment, and in our formulation this environment corresponds to the behavior rules of the noise modeled as the opponent. Proposed controller architectures attempt to improve controller reliability by a gradual mixing of algorithmic approaches drawn from the game theory literature and the Minimax-Q Markov game solution approach, in a reinforcement-learning framework. We test the proposed algorithms on a simulated Inverted Pendulum Swing-up task and compare its performance against standard Q learning.

Keywords: Reinforcement learning, Markov Decision Process, Matrix Games, Markov Games, Smooth Fictitious play, Controller, Inverted Pendulum.

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Authors: Mrs.K.Kavitha, S.Arivazhagan

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A spatial classification technique incorporating a State of Art Feature Extraction algorithm is proposed in this paper for classifying a heterogeneous classes present in hyper spectral images. The classification accuracy can be improved if and only if both the feature extraction and classifier selection are proper. As the classes in the hyper spectral images are assumed to have different textures, textural classification is entertained. Run Length feature extraction is entailed along with the Principal Components and Independent Components. A Hyperspectral Image of Indiana Site taken by AVIRIS is inducted for the experiment. Among the original 220 bands, a subset of 120 bands is selected. Gray Level Run Length Matrix (GLRLM) is calculated for the selected forty bands. From GLRLMs the Run Length features for individual pixels are calculated. The Principle Components are calculated for other forty bands. Independent Components are calculated for next forty bands. As Principal & Independent Components have the ability to represent the textural content of pixels, they are treated as features. The summation of Run Length features, Principal Components, and Independent Components forms the Combined Features which are used for classification. SVM with Binary Hierarchical Tree is used to classify the hyper spectral image. Results are validated with ground truth and accuracies are calculated.

Keywords: Multi-class, Run Length features, PCA, ICA, classification and Support Vector Machines.

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Authors: R. Bremananth, A. Chitra

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Cryptography provides the secure manner of information transmission over the insecure channel. It authenticates messages based on the key but not on the user. It requires a lengthy key to encrypt and decrypt the sending and receiving the messages, respectively. But these keys can be guessed or cracked. Moreover, Maintaining and sharing lengthy, random keys in enciphering and deciphering process is the critical problem in the cryptography system. A new approach is described for generating a crypto key, which is acquired from a person-s iris pattern. In the biometric field, template created by the biometric algorithm can only be authenticated with the same person. Among the biometric templates, iris features can efficiently be distinguished with individuals and produces less false positives in the larger population. This type of iris code distribution provides merely less intra-class variability that aids the cryptosystem to confidently decrypt messages with an exact matching of iris pattern. In this proposed approach, the iris features are extracted using multi resolution wavelets. It produces 135-bit iris codes from each subject and is used for encrypting/decrypting the messages. The autocorrelators are used to recall original messages from the partially corrupted data produced by the decryption process. It intends to resolve the repudiation and key management problems. Results were analyzed in both conventional iris cryptography system (CIC) and non-repudiation iris cryptography system (NRIC). It shows that this new approach provides considerably high authentication in enciphering and deciphering processes.

Keywords: Autocorrelators, biometrics cryptography, irispatterns, wavelets.

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Authors: A. Abdulkadir, A. A. Okhimamhe, Y. M. Bello, H. Ibrahim, D. H. Makun, M. T. Usman

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Household waste form a larger proportion of waste generated across the state, accumulation of organic waste is an apparent problem and the existing dump sites could be overstress. Niger state has abundant arable land and water resources thus should be one of the highest producers of agricultural crops in the country. However, the major challenge to agricultural sector today is loss of soil nutrient coupled with high cost of fertilizer. These have continued to increase the use of fertilizer and decomposed solid waste for enhance agricultural yield, which have varying effects on the soil as well a threat to human livelihood. Consequently, vegetable yield samples from poultry droppings, decomposed household waste manure, NPK treatments and control from each replication were subjected to proximate analysis to determine the nutritional and antinutritional component as well as heavy metal concentration. Data collected was analyzed using SPSS software and Randomized complete Block Design means were compared. The result shows that the treatments do not devoid the concentrations of any nutritional components while the anti-nutritional analysis proved that NPK had higher oxalate content than control and organic treats. The concentration of lead and cadmium are within safe permissible level while the mercury level exceeded the FAO/WHO maximum permissible limit for the entire treatments depicts the need for urgent intervention to minimize mercury levels in soil and manure in order to mitigate its toxic effect. Thus, eco-agriculture should be widely accepted and promoted by the stakeholders for soil amendment, higher yield, strategies for sustainable environmental protection, food security, poverty eradication, attainment of sustainable development and healthy livelihood.

Keywords: Anti-nutritional, healthy livelihood, nutritional waste, organic waste.

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Authors: Mingxin Zhang, Zhaogan Lu, Junyi Shen

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In current common research reports, salient regions are usually defined as those regions that could present the main meaningful or semantic contents. However, there are no uniform saliency metrics that could describe the saliency of implicit image regions. Most common metrics take those regions as salient regions, which have many abrupt changes or some unpredictable characteristics. But, this metric will fail to detect those salient useful regions with flat textures. In fact, according to human semantic perceptions, color and texture distinctions are the main characteristics that could distinct different regions. Thus, we present a novel saliency metric coupled with color and texture features, and its corresponding salient region extraction methods. In order to evaluate the corresponding saliency values of implicit regions in one image, three main colors and multi-resolution Gabor features are respectively used for color and texture features. For each region, its saliency value is actually to evaluate the total sum of its Euclidean distances for other regions in the color and texture spaces. A special synthesized image and several practical images with main salient regions are used to evaluate the performance of the proposed saliency metric and other several common metrics, i.e., scale saliency, wavelet transform modulus maxima point density, and important index based metrics. Experiment results verified that the proposed saliency metric could achieve more robust performance than those common saliency metrics.

Keywords: salient regions, color and texture features, image segmentation, saliency metric

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Authors: Justinian Anatory, Nelson Theethayi

Abstract:

The paper compares different channel models used for modeling Broadband Power-Line Communication (BPLC) system. The models compared are Zimmermann and Dostert, Philipps, Anatory et al and Anatory et al generalized Transmission Line (TL) model. The validity of each model was compared in time domain with ATP-EMTP software which uses transmission line approach. It is found that for a power-line network with minimum number of branches all the models give similar signal/pulse time responses compared with ATP-EMTP software; however, Zimmermann and Dostert model indicates the same amplitude but different time delay. It is observed that when the numbers of branches are increased only generalized TL theory approach results are comparable with ATPEMTP results. Also the Multi-Carrier Spread Spectrum (MC-SS) system was applied to check the implication of such behavior on the modulation schemes. It is observed that using Philipps on the underground cable can predict the performance up to 25dB better than other channel models which can misread the actual performance of the system. Also modified Zimmermann and Dostert under multipath can predict a better performance of about 5dB better than the actual predicted by Generalized TL theory. It is therefore suggested for a realistic BPLC system design and analyses the model based on generalized TL theory be used.

Keywords: Broadband Power line Channel Models, loadimpedance, Branched network.

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Authors: A. Gagat-Matuła

Abstract:

The article is concerned in the issue of social support and quality of life of youth suffering from cerebral palsy, who are temporarily orphaned due to the emigration of a parent. Migration causes multi-aspect consequences in various spheres of life. They are particularly severe for the functioning of families. Temporal parting of parents and children, especially the disabled, is a difficult situation. In this case, the family structure is changed, as well as the quality of life of its members. Children can handle migration parting in a better or worse way; these can be divided into properly functioning and manifesting behaviour disorders. In conditions of the progressing phenomenon of labour migration of Poles and a wide spectrum of consequences for the whole social life, it is essential to undertake actions aimed at support of migrants and their families. This article focuses mainly on social support and quality of families members, of which, are the labour migrants perceived by youth suffering from cerebral palsy. The quantitative method was used in this study. In the study, the Satisfaction with Life Scale (SWLS) by Diener, was used. The analysed group consisted of 50 persons (37 girls and 13 boys), aged 16 years to 18 years, whose parents are labour migrants. The results indicate that the quality of life and social support for youth suffering from cerebral palsy who are temporarily orphaned is at a low and average level.

Keywords: Social support, quality of life, migration, cerebral palsy.

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Authors: G. Renuka Devi

Abstract:

This paper analyzes the experimental investigation of indirect field oriented control of Field Programmable Gate Array (FPGA) based five-phase induction motor drive. A detailed d-q modeling and Space Vector Pulse Width Modulation (SVPWM) technique of 5-phase drive is elaborated in this paper. In the proposed work, the prototype model of 1 hp 5-phase Voltage Source Inverter (VSI) fed drive is implemented in hardware. SVPWM pulses are generated in FPGA platform through Very High Speed Integrated Circuit Hardware Description Language (VHDL) coding. The experimental results are observed under different loading conditions and compared with simulation results to validate the simulation model.

Keywords: Five-phase induction motor drive, field programmable gate array, indirect field oriented control, multi-phase, space vector pulse width modulation, voltage source inverter, very high speed integrated circuit hardware description language.

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Authors: Mounira Taileb, Sid Lamrous, Sami Touati

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In order to accelerate the similarity search in highdimensional database, we propose a new hierarchical indexing method. It is composed of offline and online phases. Our contribution concerns both phases. In the offline phase, after gathering the whole of the data in clusters and constructing a hierarchical index, the main originality of our contribution consists to develop a method to construct bounding forms of clusters to avoid overlapping. For the online phase, our idea improves considerably performances of similarity search. However, for this second phase, we have also developed an adapted search algorithm. Our method baptized NOHIS (Non-Overlapping Hierarchical Index Structure) use the Principal Direction Divisive Partitioning (PDDP) as algorithm of clustering. The principle of the PDDP is to divide data recursively into two sub-clusters; division is done by using the hyper-plane orthogonal to the principal direction derived from the covariance matrix and passing through the centroid of the cluster to divide. Data of each two sub-clusters obtained are including by a minimum bounding rectangle (MBR). The two MBRs are directed according to the principal direction. Consequently, the nonoverlapping between the two forms is assured. Experiments use databases containing image descriptors. Results show that the proposed method outperforms sequential scan and SRtree in processing k-nearest neighbors.

Keywords: K-nearest neighbour search, multi-dimensional indexing, multimedia databases, similarity search.

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Authors: Akmaluddin, Koichiro Watanabe, Akihiro Kano, Wartono Rahardjo

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Oxygen and carbon isotopes records of multi-species planktonic, benthic foraminifera and bulk carbonate sample from Central Java Indonesia demonstrate that warm sea surface temperature occurred during the Miocene. Planktonic δ18O values from this study consistently lighter (-4 to -3 ‰PDB) than previous studies that indicate sea surface temperature during Miocene in this area was warm than tropical/equatorial localities. A surprising decrease of oxygen isotopic composition was recorded at ±14 Ma where the maximum of δ18O values is -4.87 ‰PDB for Orbulina universa, -5.02 ‰PDB for Globigerinoides sacculifer and -4.30 ‰PDB for Globoquadrina dehiscens, this event we predict as Middle Miocene Optimum. Warming of sea surface temperature we interpret as related to the development of Western Pacific Warm Pool where warm water from Pacific Ocean through the Indonesian seaway appears to remain during Miocene. Our result also show increasing suddenly of oxygen isotope values of planktic, benthic and bulk carbonate sample from ± 12 Ma, the increasing cooled surface water relatively high degree with Late Miocene global cooling climate or we predict that due to closing of Indonesian Gateway.

Keywords: Oxygen isotope, Foraminifera, Miocene, Paleoclimate, Indonesian.

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Authors: W. Assaad, D. Wilmink, H. R. Pasaribu, H. J. M. Geijselaers

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Shell has developed a mono-diameter well concept for oil and gas wells as opposed to the traditional telescopic well design. A Mono-diameter well design allows well to have a single inner diameter from the surface all the way down to reservoir to increase production capacity, reduce material cost and reduce environmental footprint. This is achieved by expansion of liners (casing string) concerned using an expansion tool (e.g. a cone). Since the well is drilled in stages and liners are inserted to support the borehole, overlap sections between consecutive liners exist which should be expanded. At overlap, the previously inserted casing which can be expanded or unexpanded is called the host casing and the newly inserted casing is called the expandable casing. When the cone enters the overlap section, an expandable casing is expanded against a host casing, a cured cement layer and formation. In overlap expansion, ironing or lengthening may appear instead of shortening in the expandable casing when the pressure exerted by the host casing, cured cement layer and formation exceeds a certain limit. This pressure is related to cement strength, thickness of cement layer, host casing material mechanical properties, host casing thickness, formation type and formation strength. Ironing can cause implications that hinder the deployment of the technology. Therefore, the understanding of ironing becomes essential. A physical model is built in-house to calculate expansion forces, stresses, strains and post expansion casing dimensions under different conditions. In this study, only free casing and overlap expansion of two casings are addressed while the cement and formation will be incorporated in future study. Since the axial strain can be predicted by the physical model, the onset of ironing can be confirmed. In addition, this model helps in understanding ironing and the parameters influencing it. Finally, the physical model is validated with Finite Element (FE) simulations and small-scale experiments. The results of the study confirm that high pressure leads to ironing when the casing is expanded in tension mode.

Keywords: Casing expansion, cement, formation, metal forming, plasticity, well design.

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