Search results for: Background Subtraction.

Authors: Bouttout Abdelouahab, Amara Mohamed, Djakabe Saad, Remram Youcef

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This paper presents the results of an evaluation of acoustic comfort such as background noise and reverberation time in teaching rooms in Height National School of Civil Engineering, Algeria. Four teaching rooms are evaluated: conference room, two classroom and amphitheatre. The acoustic quality of the classrooms has been analyzed based on measurements of sound pressure level inside room and reverberations time. The measurement results show that impulse decays dependent on the position of the microphone inside room and the background noise is with agreement of National Official Journal of Algeria published in July 1993. Therefore there exists a discrepancy between the obtained reverberation time value and recommended reverberation time in some classrooms. Three methods have been proposed to reduce the reverberation time values in such room. We developed a program with FORTRAN 6.0 language based on the absorption acoustic values of the Technical Document Regulation (DTR C3.1.1). The important results of this paper can be used to regulate the construction and execute the acoustic rehabilitations of teaching room in Algeria, especially the classrooms of the pupils in primary and secondary schools.

Keywords: Room acoustic, reverberation time, background noise, absorptions materials.

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Authors: Agnes Achs

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The basic aim of our study is to give a possible model for handling uncertain information. This model is worked out in the framework of DATALOG. The concept of multivalued knowledgebase will be defined as a quadruple of any background knowledge; a deduction mechanism; a connecting algorithm, and a function set of the program, which help us to determine the uncertainty levels of the results. At first the concept of fuzzy Datalog will be summarized, then its extensions for intuitionistic- and interval-valued fuzzy logic is given and the concept of bipolar fuzzy Datalog is introduced. Based on these extensions the concept of multivalued knowledge-base will be defined. This knowledge-base can be a possible background of a future agent-model.

Keywords: Fuzzy-, intuitionistic-, bipolar datalog, multivalued knowledge-base

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Authors: Kolis Karel, Kubicek Ales

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The purpose of this paper is to explore the relationship between the customers- issues in company corporate governance and the financial performance. At the beginning theoretical background consisting stakeholder theory and corporate governance is presented. On this theoretical background, the empirical research is built, collecting data of 60 Czech joint stock companies- boards considering their relationships with customers. Correlation analysis and multivariate regression analysis were employed to test the sample on two hypotheses. The weak positive correlation between stakeholder approach and the company size was identified. But both hypotheses were not supported, because there was no significant relation of independent variables to financial performance.

Keywords: customers, stakeholder theory, corporate governance, financial performance

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Authors: Nermin Ozgulbas, Ali Serhan Koyuncugil

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One of the biggest problems of SMEs is their tendencies to financial distress because of insufficient finance background. In this study, an Early Warning System (EWS) model based on data mining for financial risk detection is presented. CHAID algorithm has been used for development of the EWS. Developed EWS can be served like a tailor made financial advisor in decision making process of the firms with its automated nature to the ones who have inadequate financial background. Besides, an application of the model implemented which covered 7,853 SMEs based on Turkish Central Bank (TCB) 2007 data. By using EWS model, 31 risk profiles, 15 risk indicators, 2 early warning signals, and 4 financial road maps has been determined for financial risk mitigation.

Keywords: Early Warning Systems, Data Mining, Financial Risk, SMEs.

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Authors: Ruchika, Munish Vashisht, S. Qamar

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Detection of small ship is crucial task in many automatic surveillance systems which are employed for security of maritime boundaries of a country. To address this problem, image de-noising is technique to identify the target ship in between many other ships in the sea. Image de-noising technique needs to extract the ship’s image from sea background for the analysis as the ship’s image may submerge in the background and flooding waves. In this paper, a noise filter is presented that is based on fuzzy linguistic ‘most’ quantifier. Ordered weighted averaging (OWA) function is used to remove salt-pepper noise of ship’s image. Results obtained are in line with the results available by other well-known median filters and OWA based approach shows better performance.

Keywords: Linguistic quantifier, impulse noise, OWA filter, median filter.

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Authors: Soo- Young Ye, Ki-Gon Nam, Ki-Won Byun

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In this paper, we were introduces a skin detection method using a histogram approximation based on the mean shift algorithm. The proposed method applies the mean shift procedure to a histogram of a skin map of the input image, generated by comparison with standard skin colors in the CbCr color space, and divides the background from the skin region by selecting the maximum value according to brightness level. The proposed method detects the skin region using the mean shift procedure to determine a maximum value that becomes the dividing point, rather than using a manually selected threshold value, as in existing techniques. Even when skin color is contaminated by illumination, the procedure can accurately segment the skin region and the background region. The proposed method may be useful in detecting facial regions as a pretreatment for face recognition in various types of illumination.

Keywords: Skin region detection, mean shift, histogram approximation.

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Authors: Richard Lewis

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The following assumptions of the Big Bang theory are challenged and found to be false: the cosmological principle, the assumption that all matter formed at the same time and the assumption regarding the cause of the cosmic microwave background radiation. The evolution of the universe is described based on the conclusion that the universe is finite with a space boundary. This conclusion is reached by ruling out the possibility of an infinite universe or a universe which is finite with no boundary. In a finite universe, the centre of the universe can be located with reference to our home galaxy (The Milky Way) using the speed relative to the Cosmic Microwave Background (CMB) rest frame and Hubble's law. This places our home galaxy at a distance of approximately 26 million light years from the centre of the universe. Because we are making observations from a point relatively close to the centre of the universe, the universe appears to be isotropic and homogeneous but this is not the case. The CMB is coming from a source located within the event horizon of the universe. There is sufficient mass in the universe to create an event horizon at the Schwarzschild radius. Galaxies form over time due to the energy released by the expansion of space. Conservation of energy must consider total energy which is mass (+ve) plus energy (+ve) plus spacetime curvature (-ve) so that the total energy of the universe is always zero. The predominant position of galaxy formation moves over time from the centre of the universe towards the boundary so that today the majority of new galaxy formation is taking place beyond our horizon of observation at 14 billion light years.

Keywords: Cosmic microwave background, dark energy, dark matter, evolution of the universe.

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Authors: N. Gigauri, V. Kukhalashvili, A. Surmava, L. Intskirveli, L. Gverdtsiteli

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Tbilisi, a large city of the South Caucasus, is a junction point connecting Asia and Europe, Russia and republics of the Asia Minor. Over the last years, its atmosphere has been experienced an increasing anthropogenic load. Numerical modeling method is used for study of Tbilisi atmospheric air pollution. By means of 3D non-linear non-steady numerical model a peculiarity of city atmosphere pollution is investigated during background western light air. Dust concentration spatial and time changes are determined. There are identified the zones of high, average and less pollution, dust accumulation areas, transfer directions etc. By numerical modeling, there is shown that the process of air pollution by the dust proceeds in four stages, and they depend on the intensity of motor traffic, the micro-relief of the city, and the location of city mains. In the interval of time 06:00-09:00 the intensive growth, 09:00-15:00 a constancy or weak decrease, 18:00-21:00 an increase, and from 21:00 to 06:00 a reduction of the dust concentrations take place. The highly polluted areas are located in the vicinity of the city center and at some peripherical territories of the city, where the maximum dust concentration at 9PM is equal to 2 maximum allowable concentrations. The similar investigations conducted in case of various meteorological situations will enable us to compile the map of background urban pollution and to elaborate practical measures for ambient air protection.

Keywords: Numerical modelling, source of pollution, dust propagation, western light air.

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Authors: Songtao Wu, Yuesheng Zhu, Ziqiang Sun

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Region covariance (RC) descriptor is an effective and efficient feature for visual tracking. Current RC-based tracking algorithms use the whole RC matrix to track the target in video directly. However, there exist some issues for these whole RCbased algorithms. If some features are contaminated, the whole RC will become unreliable, which results in lost object-tracking. In addition, if some features are very discriminative to the background, other features are still processed and thus reduce the efficiency. In this paper a new robust tracking method is proposed, in which the whole RC matrix is decomposed into several low rank matrices. Those matrices are dynamically chosen and processed so as to achieve a good tradeoff between discriminability and complexity. Experimental results have shown that our method is more robust to complex environment changes, especially either when occlusion happens or when the background is similar to the target compared to other RC-based methods.

Keywords: Visual tracking, region covariance descriptor, lowrankregion covariance

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Authors: W. Devapriya, C. Nelson Kennedy Babu, T. Srihari

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Automatic License plate recognition (ALPR) is a technology which recognizes the registration plate or number plate or License plate of a vehicle. In this paper, an Indian vehicle number plate is mined and the characters are predicted in efficient manner. ALPR involves four major technique i) Pre-processing ii) License Plate Location Identification iii) Individual Character Segmentation iv) Character Recognition. The opening phase, named pre-processing helps to remove noises and enhances the quality of the image using the conception of Morphological Operation and Image subtraction. The second phase, the most puzzling stage ascertain the location of license plate using the protocol Canny Edge detection, dilation and erosion. In the third phase, each characters characterized by Connected Component Approach (CCA) and in the ending phase, each segmented characters are conceptualized using cross correlation template matching- a scheme specifically appropriate for fixed format. Major application of ALPR is Tolling collection, Border Control, Parking, Stolen cars, Enforcement, Access Control, Traffic control. The database consists of 500 car images taken under dissimilar lighting condition is used. The efficiency of the system is 97%. Our future focus is Indian Vehicle License Plate Validation (Whether License plate of a vehicle is as per Road transport and highway standard).

Keywords: Automatic License plate recognition, Character recognition, Number plate Recognition, Template matching, morphological operation, canny edge detection.

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Authors: K. Ahmad, M. Suhaimi, Mohamad N. Azmi, K. Awang, Mohd A. Nafiah

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The conceptually construction of axially chiral indolostilbenesi.eN-(2-{(E)-2-[2'-(1-Acetyl-1H-indol-2-yl)-3'chloro-4,4',6,6'-tetramethoxy[1,1'-biphenyl]-2yl]ethenyl}phenyl)acetamide and N-(2-{(E)-2-[2'-(1-Acetyl-1H-indol-2-yl)-3'-chloro-2,4',6,6'-tetramethoxy[1,1'-biphenyl]-4-yl]ethenyl}phenyl) acetamide are described in this paper. These structure, were obtained by the tactical combination of palladium-catalyzed coupling which produced 10-acetamido-3,5-dimethoxystilbene, follow by FeCl₃-induced oxidative cyclization/dimerisation. All structures were unambiguously confirmed by 1D (¹H, ¹³C) and 2D NMR experiment, (COSY, HMQC, HMBC) and mass spectrometry.

Keywords: Indolostilbene, FeCl3-oxidative cyclization, Oligostilbenooids, Oxidative cyclization/dimerization.

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Authors: SP. Chokkalingam, K. Komathy

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Advances in the field of image processing envision a new era of evaluation techniques and application of procedures in various different fields. One such field being considered is the biomedical field for prognosis as well as diagnosis of diseases. This plethora of methods though provides a wide range of options to select from, it also proves confusion in selecting the apt process and also in finding which one is more suitable. Our objective is to use a series of techniques on bone scans, so as to detect the occurrence of rheumatoid arthritis (RA) as accurately as possible. Amongst other techniques existing in the field our proposed system tends to be more effective as it depends on new methodologies that have been proved to be better and more consistent than others. Computer aided diagnosis will provide more accurate and infallible rate of consistency that will help to improve the efficiency of the system. The image first undergoes histogram smoothing and specification, morphing operation, boundary detection by edge following algorithm and finally image subtraction to determine the presence of rheumatoid arthritis in a more efficient and effective way. Using preprocessing noises are removed from images and using segmentation, region of interest is found and Histogram smoothing is applied for a specific portion of the images. Gray level co-occurrence matrix (GLCM) features like Mean, Median, Energy, Correlation, Bone Mineral Density (BMD) and etc. After finding all the features it stores in the database. This dataset is trained with inflamed and noninflamed values and with the help of neural network all the new images are checked properly for their status and Rough set is implemented for further reduction.

Keywords: Computer Aided Diagnosis, Edge Detection, Histogram Smoothing, Rheumatoid Arthritis.

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Authors: C. Ardil

Abstract:

This article presents a multiple criteria evaluation for a trainer aircraft selection problem using "preference analysis for reference ideal solution (PARIS)” approach. The available relevant literature points to the use of multiple criteria decision making analysis (MCDMA) methods for the problem of trainer aircraft selection, which often involves conflicting multiple criteria. Therefore, this MCDMA study aims to propose a robust systematic integrated framework focusing on the trainer aircraft selection problem. For this purpose, an integrated preference analysis approach based the mean weight and entropy weight procedures with PARIS, and TOPSIS was used for a MCDMA compensating solution. In this study, six trainer aircraft alternatives were evaluated according to six technical decision criteria, and data were collected from the current relevant literature. As a result, the King Air C90GTi alternative was identified as the most suitable trainer aircraft alternative. In order to verify the stability and accuracy of the results obtained, comparisons were made with existing MCDMA methods during the sensitivity and validity analysis process.The results of the application were further validated by applying the comparative analysis-based PARIS, and TOPSIS method. The proposed integrated MCDMA systematic structure is also expected to address the issues encountered in the aircraft selection process. Finally, the analysis results obtained show that the proposed MCDMA method is an effective and accurate tool that can help analysts make better decisions.

Keywords: aircraft, trainer aircraft selection, multiple criteria decision making, multiple criteria decision making analysis, mean weight, entropy weight, MCDMA, PARIS, TOPSIS

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Authors: M. Mohammad, Y-C Lan

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Australian government agencies have a natural desire to provide migrants a wide range of opportunities. Consequently, government online services should be equally available to migrants with a non-English speaking background (NESB). Despite the commendable efforts of governments and local agencies in Australia to provide such services, in reality, many NESB communities are not taking advantage of these services. This article–based on an extensive case study regarding the use of online government services by the Arabic NESB community in Australia–reports on the possible reasons for this issue, as well as suggestions for improvement. The conclusion is that Australia should implement ICT-based or e-government policies, programmes, and services that more accurately reflect migrant cultures and languages so that migrant integration can be more fully accomplished. Specifically, this article presents an NESB Model that adopts the value of usercentricity or a more individual-focused approach to government online services in Australia.

Keywords: Barriers to use, e-government, ICT, NESB community, online services.

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Authors: Zonglin Yang, Tatsuya Akiyama, Kerry S. Williamson, Michael J. Franklin, Thiruvarangan Ramaraj

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Pseudomonas aeruginosa is an opportunistic pathogen that forms surface-associated microbial communities (biofilms) on artificial implant devices and on human tissue. Biofilm infections are difficult to treat with antibiotics, in part, because the bacteria in biofilms are physiologically heterogeneous. One measure of biological heterogeneity in a population of cells is to quantify the cellular concentrations of ribosomes, which can be probed with fluorescently labeled nucleic acids. The fluorescent signal intensity following fluorescence in situ hybridization (FISH) analysis correlates to the cellular level of ribosomes. The goals here are to provide computationally and statistically robust approaches to automatically quantify cellular heterogeneity in biofilms from a large library of epifluorescent microscopy FISH images. In this work, the initial steps were developed toward these goals by developing an automated biofilm detection approach for use with FISH images. The approach allows rapid identification of biofilm regions from FISH images that are counterstained with fluorescent dyes. This methodology provides advances over other computational methods, allowing subtraction of spurious signals and non-biological fluorescent substrata. This method will be a robust and user-friendly approach which will enable users to semi-automatically detect biofilm boundaries and extract intensity values from fluorescent images for quantitative analysis of biofilm heterogeneity.

Keywords: Image informatics, Pseudomonas aeruginosa, biofilm, FISH, computer vision, data visualization.

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Authors: N. Gigauri, A. Surmava

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According to observational data, experimental measurements and numerical modelling, the pollution of one of the industrial centers of Georgia, Rustavi City’s atmosphere with micro aerosols are assessed. Monthly, daily and hourly changes of the concentrations of PM2.5 and PM10 in the city atmosphere are analyzed. It is accepted that PM2.5 concentrations are always lower than PM10 concentrations, but their change curve is the same. In addition, it has been noted that the maximum concentrations of particles in the atmosphere of Rustavi city will be reached at any part of the day, which is determined by the total impact of the traffic flow and industrial facilities. Through numerical modelling, the influence of background western light air, gentle and fresh breeze on the distribution of particulate matter in the atmosphere was calculated. Calculations showed that background light air and gentle breeze lead to an increase the concentrations of microaerosols in the city's atmosphere, while fresh breeze contributes to the dispersion of dusty clouds. As a result, the level of dust in the city is decreasing, but the distribution area is expanding.

Keywords: Air pollution, numerical modeling, experimental measurement, PM2.5, PM10.

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Authors: C. Ardil

Abstract:

Fuzzy set theory and its extensions (intuitionistic fuzzy sets, picture fuzzy sets, and neutrosophic sets) have been widely used to address imprecision and uncertainty in complex decision-making. However, they may struggle with inherent indeterminacy and inconsistency in real-world situations. This study introduces uncertainty sets as a promising alternative, offering a structured framework for incorporating both types of uncertainty into decision-making processes.This work explores the theoretical foundations and applications of uncertainty sets. A novel decision-making algorithm based on uncertainty set-based proximity measures is developed and demonstrated through a practical application: selecting the most suitable stealth combat aircraft.

The results highlight the effectiveness of uncertainty sets in ranking alternatives under uncertainty. Uncertainty sets offer several advantages, including structured uncertainty representation, robust ranking mechanisms, and enhanced decision-making capabilities due to their ability to account for ambiguity.Future research directions are also outlined, including comparative analysis with existing MCDM methods under uncertainty, sensitivity analysis to assess the robustness of rankings,and broader application to various MCDM problems with diverse complexities. By exploring these avenues, uncertainty sets can be further established as a valuable tool for navigating uncertainty in complex decision-making scenarios.

Keywords: Uncertainty set, stealth combat aircraft selection multiple criteria decision-making analysis, MCDM, uncertainty proximity analysis

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Authors: К. R. Aida–Zade, C. Ardil, S. S. Rustamov

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Statement of the automatic speech recognition problem, the assignment of speech recognition and the application fields are shown in the paper. At the same time as Azerbaijan speech, the establishment principles of speech recognition system and the problems arising in the system are investigated. The computing algorithms of speech features, being the main part of speech recognition system, are analyzed. From this point of view, the determination algorithms of Mel Frequency Cepstral Coefficients (MFCC) and Linear Predictive Coding (LPC) coefficients expressing the basic speech features are developed. Combined use of cepstrals of MFCC and LPC in speech recognition system is suggested to improve the reliability of speech recognition system. To this end, the recognition system is divided into MFCC and LPC-based recognition subsystems. The training and recognition processes are realized in both subsystems separately, and recognition system gets the decision being the same results of each subsystems. This results in decrease of error rate during recognition. The training and recognition processes are realized by artificial neural networks in the automatic speech recognition system. The neural networks are trained by the conjugate gradient method. In the paper the problems observed by the number of speech features at training the neural networks of MFCC and LPC-based speech recognition subsystems are investigated. The variety of results of neural networks trained from different initial points in training process is analyzed. Methodology of combined use of neural networks trained from different initial points in speech recognition system is suggested to improve the reliability of recognition system and increase the recognition quality, and obtained practical results are shown.

Keywords: Speech recognition, cepstral analysis, Voice activation detection algorithm, Mel Frequency Cepstral Coefficients, features of speech, Cepstral Mean Subtraction, neural networks, Linear Predictive Coding.

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Authors: Hiren Mewada, Suprava Patnaik

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Object identification and segmentation application requires extraction of object in foreground from the background. In this paper the Bhattacharya distance based probabilistic approach is utilized with an active contour model (ACM) to segment an object from the background. In the proposed approach, the Bhattacharya histogram is calculated on non-linear structure tensor space. Based on the histogram, new formulation of active contour model is proposed to segment images. The results are tested on both color and gray images from the Berkeley image database. The experimental results show that the proposed model is applicable to both color and gray images as well as both texture images and natural images. Again in comparing to the Bhattacharya based ACM in ICA space, the proposed model is able to segment multiple object too.

Keywords: Active Contour, Bhattacharya Histogram, Structure tensor, Image segmentation.

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Authors: Abbas Nasir, Xiong Zhang, Sohail Ahmad, Yiping Cui

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Non-polar a-plane AlGaN epilayers of high structural quality have been grown on r-sapphire substrate by using metalorganic chemical vapor deposition (MOCVD). A graded non-polar AlGaN buffer layer with variable aluminium concentration was used to improve the structural quality of the non-polar a-plane AlGaN epilayer. The characterisations were carried out by high-resolution X-ray diffraction (HR-XRD), atomic force microscopy (AFM) and Hall effect measurement. The XRD and AFM results demonstrate that the Al-composition-graded non-polar AlGaN buffer layer significantly improved the crystalline quality and the surface morphology of the top layer. A low root mean square roughness 1.52 nm is obtained from AFM, and relatively low background carrier concentration down to 3.9×  cm^-3 is obtained from Hall effect measurement.

Keywords: Non-polar AlGaN epilayer, Al composition-graded AlGaN layer, root mean square, background carrier concentration.

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Authors: M.H. Ahmad Fadzil, Esa Prakasa, Hurriyatul Fitriyah, Hermawan Nugroho, Azura Mohd Affandi, S.H. Hussein

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Psoriasis is a widespread skin disease affecting up to 2% population with plaque psoriasis accounting to about 80%. It can be identified as a red lesion and for the higher severity the lesion is usually covered with rough scale. Psoriasis Area Severity Index (PASI) scoring is the gold standard method for measuring psoriasis severity. Scaliness is one of PASI parameter that needs to be quantified in PASI scoring. Surface roughness of lesion can be used as a scaliness feature, since existing scale on lesion surface makes the lesion rougher. The dermatologist usually assesses the severity through their tactile sense, therefore direct contact between doctor and patient is required. The problem is the doctor may not assess the lesion objectively. In this paper, a digital image analysis technique is developed to objectively determine the scaliness of the psoriasis lesion and provide the PASI scaliness score. Psoriasis lesion is modelled by a rough surface. The rough surface is created by superimposing a smooth average (curve) surface with a triangular waveform. For roughness determination, a polynomial surface fitting is used to estimate average surface followed by a subtraction between rough and average surface to give elevation surface (surface deviations). Roughness index is calculated by using average roughness equation to the height map matrix. The roughness algorithm has been tested to 444 lesion models. From roughness validation result, only 6 models can not be accepted (percentage error is greater than 10%). These errors occur due the scanned image quality. Roughness algorithm is validated for roughness measurement on abrasive papers at flat surface. The Pearson-s correlation coefficient of grade value (G) of abrasive paper and Ra is -0.9488, its shows there is a strong relation between G and Ra. The algorithm needs to be improved by surface filtering, especially to overcome a problem with noisy data.

Keywords: psoriasis, roughness algorithm, polynomial surfacefitting.

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Authors: Musa'ed N. Almarshad, Saleh A. Alshebeili, Mourad Barkat

Abstract:

A challenging problem in radar signal processing is to achieve reliable target detection in the presence of interferences. In this paper, we propose a novel algorithm for automatic censoring of radar interfering targets in log-normal clutter. The proposed algorithm, termed the forward automatic censored cell averaging detector (F-ACCAD), consists of two steps: removing the corrupted reference cells (censoring) and the actual detection. Both steps are performed dynamically by using a suitable set of ranked cells to estimate the unknown background level and set the adaptive thresholds accordingly. The F-ACCAD algorithm does not require any prior information about the clutter parameters nor does it require the number of interfering targets. The effectiveness of the F-ACCAD algorithm is assessed by computing, using Monte Carlo simulations, the probability of censoring and the probability of detection in different background environments.

Keywords: CFAR, Log-normal clutter, Censoring, Probabilityof detection, Probability of false alarm, Probability of falsecensoring.

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Authors: Pascal Mwenge, Tumisang Seodigeng

Abstract:

The combination of world population and the third industrial revolution led to high demand for fuels. On the other hand, the decrease of global fossil 8fuels deposits and the environmental air pollution caused by these fuels has compounded the challenges the world faces due to its need for energy. Therefore, new forms of environmentally friendly and renewable fuels such as biodiesel are needed. The primary analytical techniques for methanolysis yield monitoring have been chromatography and spectroscopy, these methods have been proven reliable but are more demanding, costly and do not provide real-time monitoring. In this work, the in situ monitoring of biodiesel from sunflower oil using FTIR (Fourier Transform Infrared) has been studied; the study was performed using EasyMax Mettler Toledo reactor equipped with a DiComp (Diamond) probe. The quantitative monitoring of methanolysis was performed by building a quantitative model with multivariate calibration using iC Quant module from iC IR 7.0 software. 15 samples of known concentrations were used for the modelling which were taken in duplicate for model calibration and cross-validation, data were pre-processed using mean centering and variance scale, spectrum math square root and solvent subtraction. These pre-processing methods improved the performance indexes from 7.98 to 0.0096, 11.2 to 3.41, 6.32 to 2.72, 0.9416 to 0.9999, RMSEC, RMSECV, RMSEP and R2Cum, respectively. The R² value of 1 (training), 0.9918 (test), 0.9946 (cross-validation) indicated the fitness of the model built. The model was tested against univariate model; small discrepancies were observed at low concentration due to unmodelled intermediates but were quite close at concentrations above 18%. The software eliminated the complexity of the Partial Least Square (PLS) chemometrics. It was concluded that the model obtained could be used to monitor methanol of sunflower oil at industrial and lab scale.

Keywords: Biodiesel, calibration, chemometrics, FTIR, methanolysis, multivariate analysis, transesterification.

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Authors: Olesya Bolkhovskaya, Alexander Maltsev

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Antenna arrays are widely used in modern radio systems in sonar and communications. The solving of the detection problems of a useful signal on the background of noise is based on the GLRT method. There is a large number of problem which depends on the known a priori information. In this work, in contrast to the majority of already solved problems, it is used only difference  spatial properties of the signal and noise for detection. We are analyzing the influence of the degree of non-coherence of signal and noise unhomogeneity on the performance characteristics of different GLRT statistics. The description of the signal and noise is carried out by means of the spatial covariance matrices C in the cases of different number of known information. The partially coherent signalis is simulated as a plane wave with a random angle of incidence of the wave concerning a normal. Background noise is simulated as random process with uniform distribution function in each element. The results of investigation of degradation of performance characteristics for different cases are represented in this work.

Keywords: GLRT, Neumann-Pearson’s criterion, test-statistics, degradation, spatial processing, multielement antenna array

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Authors: Sajjad A. Memon, Sadaf T. Qureshi, Naeem A. Laghari, Noor M. Khuhro

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The present study represents the occupational radiation doses received by selected workers of Nuclear Institute of Medicine and Radiotherapy (NIMRA) Jamshoro Pakistan and conducted to discuss about how we be careful and try to avoid make ourselves overconscious. Film badges with unique identification number were issued to radiation worker to detect occupational radiation doses. In this study, only 08 workers with high radiation doses were assessed amongst 35 radiation workers during the period of January 2012 to December 2012. The selected radiation workers’ occupational doses were according to designated work areas and in the range of 1.21 to 7.78 mSv (mili Sieveret) out of the annual dose limit of 20 mSv. By the comparison of different studies and earth’s HNBR (High Natural Background Radiation) locations’ doses, it is concluded that the worker’s high doses are of magnitude of HNBR Regions and were in the acceptable range of National and International regulatory bodies so we must not to show any type of overconsciousness but be careful in handling the radioactive sources.

Keywords: Natural background radiation, Occupational dose, Overconscious, Personal monitoring.

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Authors: C. Ardil

Abstract:

This paper aims to select the best military attack helicopter to purchase by the Armed Forces and provide greater reconnaissance and offensive combat capability in military operations. For this purpose, a multiple criteria decision analysis method integrated with the variance weight procedure was applied to the military attack helicopter selection problem. A real military aviation case problem is conducted to support the Armed Forces decision-making process and contributes to the better performance of the Armed Forces. Application of the methodology resulted in ranking lists for ordering and prioritizing attack helicopters, providing transparency and simplicity to the decision-making process. Nine military attack helicopter models were analyzed in the light of strategic, tactical, and operational criteria, considering attack helicopters. The selected military attack helicopter would be used for fire support and reconnaissance activities required by the Armed Forces operation. This study makes a valuable contribution to the problem of military attack helicopter selection, as it represents a state-of-the-art application of the MCDMA method to contribute to the solution of a real problem of the Armed Forces. The methodology presented in this paper can be used to solve real problems of a wide variety, especially strategic, tactical and operational, and is, therefore, a very useful method for decision making.

Keywords: aircraft selection, military attack helicopter selection, attack helicopter fleet planning, MCDMA, multiple criteria analysis, multiple criteria decision making analysis, distance function measure

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Authors: C. Ardil

Abstract:

In this paper, the comparison of composite programming and compromise programming for the aircraft selection problem is discussed using the multiple criteria decision analysis method. The decision making process requires the prior definition and fulfillment of certain factors, especially when it comes to complex areas such as aircraft selection problems. The proposed technique gives more efficient results by extending the composite programming and compromise programming, which are widely used in modeling multiple criteria decisions. The proposed model is applied to a practical decision problem for evaluating and selecting aircraft problems.A selection of aircraft was made based on the proposed approach developed in the field of multiple criteria decision making. The model presented is solved by using the following methods: composite programming, and compromise programming. The importance values of the weight coefficients of the criteria are calculated using the mean weight method. The evaluation and ranking of aircraft are carried out using the composite programming and compromise programming methods. In order to determine the stability of the model and the ability to apply the developed composite programming and compromise programming approach, the paper analyzes its sensitivity, which involves changing the value of the coefficient λ and q in the first part. The second part of the sensitivity analysis relates to the application of different multiple criteria decision making methods, composite programming and compromise programming. In addition, in the third part of the sensitivity analysis, the Spearman correlation coefficient of the ranks obtained was calculated which confirms the applicability of all the proposed approaches.

Keywords: composite programming, compromise programming, additive weighted model, multiplicative weighted model, multiple criteria decision making analysis, MCDMA, aircraft selection

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Authors: Parisa Shooshtari, Gelareh Mohamadi, Behnam Molaee Ardekani, Mohammad Bagher Shamsollahi

Abstract:

EEG signal is one of the oldest measures of brain activity that has been used vastly for clinical diagnoses and biomedical researches. However, EEG signals are highly contaminated with various artifacts, both from the subject and from equipment interferences. Among these various kinds of artifacts, ocular noise is the most important one. Since many applications such as BCI require online and real-time processing of EEG signal, it is ideal if the removal of artifacts is performed in an online fashion. Recently, some methods for online ocular artifact removing have been proposed. One of these methods is ARMAX modeling of EEG signal. This method assumes that the recorded EEG signal is a combination of EOG artifacts and the background EEG. Then the background EEG is estimated via estimation of ARMAX parameters. The other recently proposed method is based on adaptive filtering. This method uses EOG signal as the reference input and subtracts EOG artifacts from recorded EEG signals. In this paper we investigate the efficiency of each method for removing of EOG artifacts. A comparison is made between these two methods. Our undertaken conclusion from this comparison is that adaptive filtering method has better results compared with the results achieved by ARMAX modeling.

Keywords: Ocular Artifacts, EEG, Adaptive Filtering, ARMAX

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Authors: Daya Weerasinghe

Abstract:

Parental expectations often differ to that of their children and the influence and involvement of parents, at home, may affect the student performance in the classroom. This paper presents results from a survey of Asian and European background secondary school mathematics students (N=128) in Melbourne, Australia. Student responses to survey questions were analysed using confirmatory factor analysis, followed by t-tests and ANOVA. The aim of the analysis was to identify similarities and differences in parental expectations in relation to ethnicity, gender, and the year level of the students. The notable findings from the analysis showed no significant difference (at 0.05 level) in parental expectations and student performance, in relation to ethnicity or gender. Conversely, there was a significant difference in both parental expectations and student performance between year 7 and year 12 students. Further, whilst there was a significant difference in parental expectations between year 7 and year 11 students, the students’ performances were not significantly different. The results suggest further research may be needed to understand the parental expectations and student performance between the lower and upper secondary school mathematics students.

Keywords: Ethnic background, gender, parental expectations, student performance, year level.

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Authors: Gyo Woo Lee

Abstract:

In this study, the phase transition characteristics of flame-synthesized γ-Al₂O₃ nanoparticles to α-Al₂O₃ have been investigated. The nanoparticles were synthesized by using a coflow hydrogen diffusion flame. The phase transition and particle characteristics of the Al₂O₃ nanoparticles were determined by examining the crystalline structure and the shape of the collected nanoparticles before and after the heat treatment. The morphology and crystal structure of the Al₂O₃ nanoparticles were determined from SEM images and XRD analyses, respectively. The measured specific surface area and averaged particle size were 63.44m²/g and 23.94nm, respectively. Based on the scanning electron microscope images and x-ray diffraction patterns, it is believed that the onset temperature of the phase transition to α-Al₂O₃ was existed near 1200^oC. The averaged diameters of the sintered particles heat treated at 1,260^oC were approximately 80nm.

Keywords: BET Specific Surface Area, Gamma-Al2O3 Nanoparticles, Flame Synthesis, Phase Transition, X-ray Diffraction.

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