Search results for: supervised dimension reduction.

Authors: Marius Gheju, Rodica Pode

Abstract:

Hexavalent chromium is highly toxic to most living organisms and a known human carcinogen by the inhalation route of exposure. Therefore, treatment of Cr(VI) contaminated wastewater is essential before their discharge to the natural water bodies. Cr(VI) reduction to Cr(III) can be beneficial because a more mobile and more toxic chromium species is converted to a less mobile and less toxic form. Zero-valence-state metals, such as scrap iron, can serve as electron donors for reducing Cr(VI) to Cr(III). The influence of pH on scrap iron capacity to reduce Cr(VI) was investigated in this study. Maximum reduction capacity of scrap iron was observed at the beginning of the column experiments; the lower the pH, the greater the experiment duration with maximum scrap iron reduction capacity. The experimental results showed that highest maximum reduction capacity of scrap iron was 12.5 mg Cr(VI)/g scrap iron, at pH 2.0, and decreased with increasing pH up to 1.9 mg Cr(VI)/g scrap iron at pH = 7.3.

Keywords: hexavalent chromium, heavy metals, scrap iron, reduction capacity, wastewater treatment.

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Authors: Donghyun Hwang, Soo-Hun Lee, Moon G. Lee

Abstract:

In the various working field, vibration may cause injurious to human body. Especially, in case of the vibration which is constantly and repeatedly transferred to the human. That gives serious physical problem, so called, Reynaud phenomenon. In this paper, we propose a vibration transmissibility reduction module with flexure mechanism for personal tools. At first, we select a target personal tool, grass cutter, and measure the level of vibration transmissibility on the hand. And then, we develop the concept design of the module that has stiffness for reduction the vibration transmissibility more than 20%, where the vibration transmissibility is measured with an accelerometer. In addition, the vibration reduction can be enhanced when the interior gap between inner and outer body is filled with silicone gel. This will be verified by the further experiment.

Keywords: Flexure spring, tool engineering, vibration damping.

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Authors: Winai Mankhatitham

Abstract:

This research investigated groundcover on the roof (green roof) which can reduce the temperature and carbon monoxide. This study is divided into 3 main aspects: 1. Types of groundcover affecting heat reduction 2. The efficiency on heat reduction of 3 types of groundcover, i.e. lawn, arachis pintoi, and purslane 3. Database for designing green roof. This study has been designed as an experimental research by simulating the 3 types of groundcover in 3 trays placed in the green house for recording the temperature change for 24 hours. The results showed that the groundcover with the highest heat reduction efficiency was lawn. The dense of the lawn can protect the heat transfer to the soil. For the further study, there should be a comparative study of the thickness and the types of soil to get more information for the suitable types of groundcover and the soil for designing the energy saving green roof.

Keywords: Groundcover, Green Roof, Heat Reduction, Energy Saving.

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Authors: Rajasekhar Mamilla, Janardhana G., Anjan Babu G.

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The present research study analyses the students’ satisfaction with university performance regarding the reliability dimension, ability of professors and staff to perform the promised services with quality to students in the post-graduate courses offered by Sri Venkateswara University in India. The research is done with the notion that the student compares the perceived performance with prior expectations. Customer satisfaction is seen as the outcome of this comparison. The sample respondents were administered with schedule based on stratified random technique for this study. Statistical techniques such as factor analysis, t-test and correlation analysis were used to accomplish the respective objectives of the study.

Keywords: Satisfaction, Reliability, Service Quality.

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Authors: M.Radmehr, S.M.Anisheh, I.Yousefian

Abstract:

In this work a new offline signature recognition system based on Radon Transform, Fractal Dimension (FD) and Support Vector Machine (SVM) is presented. In the first step, projections of original signatures along four specified directions have been performed using radon transform. Then, FDs of four obtained vectors are calculated to construct a feature vector for each signature. These vectors are then fed into SVM classifier for recognition of signatures. In order to evaluate the effectiveness of the system several experiments are carried out. Offline signature database from signature verification competition (SVC) 2004 is used during all of the tests. Experimental result indicates that the proposed method achieved high accuracy rate in signature recognition.

Keywords: Fractal Dimension, Offline Signature Recognition, Radon Transform, Support Vector Machine

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Authors: K. Puren, H. Coetzee, V. Roos

Abstract:

Uniqueness and distinctiveness of localities (referred to as genius loci or sense of place) are important to ensure people-s identification with their locality. Existing frameworks reveals that the affective dimension of environments is rarely mentioned or explored and limited public participation was used in constructing the frameworks. This research argues that the complexity of sense of place would be recognised and appropriate planning guidelines formulated by exploring and integrating the affective dimension of a site. Aims of the research therefore are to (i) explore relational dimensions between people and a natural rural landscape, (ii) to implement a participatory approach to obtain insight into different relational dimensions, and (ii) to concretise socio-affective relational dimensions into site planning guidelines. A qualitative, interdisciplinary research approach was followed and conducted on the farm Kromdraai, Vredefort Dome World Heritage Site. In essence the first phase of the study reveals various affective responses and projections of personal meanings. The findings in phase 1 informed the second phase, to involve people from various disciplines and different involvement with the area to make visual presentations of appropriate planning and design of the site in order to capture meanings of the interactions between people and their environment. Final site planning and design guidelines were formulated, based on these. This research contributed to provide planners with new possibilities of exploring the dimensions between people and places as well as to develop appropriate methods for participation to obtain insight into the underlying meanings of sites.

Keywords: Affective dimension, Sense of place, spatialplanning, Vredefort Dome World Heritage Site.

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Authors: Elena Ezhova, Vadim Mottl, Olga Krasotkina

Abstract:

The problem of estimating time-varying regression is inevitably concerned with the necessity to choose the appropriate level of model volatility - ranging from the full stationarity of instant regression models to their absolute independence of each other. In the stationary case the number of regression coefficients to be estimated equals that of regressors, whereas the absence of any smoothness assumptions augments the dimension of the unknown vector by the factor of the time-series length. The Akaike Information Criterion is a commonly adopted means of adjusting a model to the given data set within a succession of nested parametric model classes, but its crucial restriction is that the classes are rigidly defined by the growing integer-valued dimension of the unknown vector. To make the Kullback information maximization principle underlying the classical AIC applicable to the problem of time-varying regression estimation, we extend it onto a wider class of data models in which the dimension of the parameter is fixed, but the freedom of its values is softly constrained by a family of continuously nested a priori probability distributions.

Keywords: Time varying regression, time-volatility of regression coefficients, Akaike Information Criterion (AIC), Kullback information maximization principle.

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Authors: Tsuyoshi Okita

Abstract:

For a given specific problem an efficient algorithm has been the matter of study. However, an alternative approach orthogonal to this approach comes out, which is called a reduction. In general for a given specific problem this reduction approach studies how to convert an original problem into subproblems. This paper proposes a formal modeling language to support this reduction approach in order to make a solver quickly. We show three examples from the wide area of learning problems. The benefit is a fast prototyping of algorithms for a given new problem. It is noted that our formal modeling language is not intend for providing an efficient notation for data mining application, but for facilitating a designer who develops solvers in machine learning.

Keywords: Formal language, statistical inference problem, reduction.

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Authors: Marius Gheju, Laura Cocheci

Abstract:

In this study, the reduction of Cr(VI) by use of scrap iron, a cheap and locally available industrial waste, was investigated in continuous system. The greater scrap iron efficiency observed for the first two sections of the column filling indicate that most of the reduction process was carried out in the bottom half of the column filling. This was ascribed to a constant decrease of Cr(VI) concentration inside the filling, as the water front passes from the bottom to the top end of the column. While the bottom section of the column filling was heavily passivated with secondary mineral phases, the top section was less affected by the passivation process; therefore the column filling would likely ensure the reduction of Cr(VI) for time periods longer than 216 hours. The experimental results indicate that fixed beds columns packed with scrap iron could be successfully used for the first step of Cr(VI) polluted wastewater treatment. However, the mass of scrap iron filling should be carefully estimated since it significantly affects the Cr(VI) reduction efficiency.

Keywords: hexavalent chromium, heavy metals, scrap iron, reduction capacity, wastewater treatment

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Authors: Gökhan Polat, Dicle Kocaoğlu Yılmazer, Muhlis Nezihi Sarıdede

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Iron oxides are the main input to produce iron in integrated iron and steel plants. During production of iron from iron oxides, some wastes with high iron content occur. These main wastes can be classified as basic oxygen furnace (BOF) sludge, flue dust and rolling scale. Recycling of these wastes has a great importance for both environmental effects and reduction of production costs. In this study, recycling experiments were performed on basic oxygen furnace sludge, flue dust and rolling scale which contain 53.8%, 54.3% and 70.2% iron respectively. These wastes were mixed together with coke as reducer and these mixtures are pressed to obtain cylindrical briquettes. These briquettes were pressed under various compacting forces from 1 ton to 6 tons. Also, both stoichiometric and twice the stoichiometric cokes were added to investigate effect of coke amount on reduction properties of the waste mixtures. Then, these briquettes were reduced at 1000°C and 1100°C during 30, 60, 90, 120 and 150 min in a muffle furnace. According to the results of reduction experiments, the effect of compacting force, temperature and time on reduction ratio of the wastes were determined. It is found that 1 ton compacting force, 150 min reduction time and 1100°C are the optimum conditions to obtain reduction ratio higher than 75%.

Keywords: Iron oxide wastes, reduction, coke, recycling.

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Authors: Jiann-Ming Wu, Hsiao-Chang Chen, Chun-Chang Wu, Pei-Hsun Hsu

Abstract:

This work explores blind image deconvolution by recursive function approximation based on supervised learning of neural networks, under the assumption that a degraded image is linear convolution of an original source image through a linear shift-invariant (LSI) blurring matrix. Supervised learning of neural networks of radial basis functions (RBF) is employed to construct an embedded recursive function within a blurring image, try to extract non-deterministic component of an original source image, and use them to estimate hyper parameters of a linear image degradation model. Based on the estimated blurring matrix, reconstruction of an original source image from a blurred image is further resolved by an annealed Hopfield neural network. By numerical simulations, the proposed novel method is shown effective for faithful estimation of an unknown blurring matrix and restoration of an original source image.

Keywords: Blind image deconvolution, linear shift-invariant(LSI), linear image degradation model, radial basis functions (rbf), recursive function, annealed Hopfield neural networks.

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Authors: Zahra Ramezanpanah, Joachim Carvallo, Aurelien Rodriguez

Abstract:

This paper aims to detect anomalies in streaming data using machine learning algorithms. In this regard, we designed two separate pipelines and evaluated the effectiveness of each separately. The first pipeline, based on supervised machine learning methods, consists of two phases. In the first phase, we trained several supervised models using the UNSW-NB15 data set. We measured the efficiency of each using different performance metrics and selected the best model for the second phase. At the beginning of the second phase, we first, using Argus Server, sniffed a local area network. Several types of attacks were simulated and then sent the sniffed data to a running algorithm at short intervals. This algorithm can display the results of each packet of received data in real-time using the trained model. The second pipeline presented in this paper is based on unsupervised algorithms, in which a Temporal Graph Network (TGN) is used to monitor a local network. The TGN is trained to predict the probability of future states of the network based on its past behavior. Our contribution in this section is introducing an indicator to identify anomalies from these predicted probabilities.

Keywords: Cyber-security, Intrusion Detection Systems, Temporal Graph Network, Anomaly Detection.

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Authors: Eric P. Jiang

Abstract:

As the Internet continues to grow at a rapid pace as the primary medium for communications and commerce and as telecommunication networks and systems continue to expand their global reach, digital information has become the most popular and important information resource and our dependence upon the underlying cyber infrastructure has been increasing significantly. Unfortunately, as our dependency has grown, so has the threat to the cyber infrastructure from spammers, attackers and criminal enterprises. In this paper, we propose a new machine learning based network intrusion detection framework for cyber security. The detection process of the framework consists of two stages: model construction and intrusion detection. In the model construction stage, a semi-supervised machine learning algorithm is applied to a collected set of network audit data to generate a profile of normal network behavior and in the intrusion detection stage, input network events are analyzed and compared with the patterns gathered in the profile, and some of them are then flagged as anomalies should these events are sufficiently far from the expected normal behavior. The proposed framework is particularly applicable to the situations where there is only a small amount of labeled network training data available, which is very typical in real world network environments.

Keywords: classification, data analysis and mining, network intrusion detection, semi-supervised learning.

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Authors: A. Hossein Sahoolizadeh, B. Zargham Heidari, C. Hamid Dehghani

Abstract:

In this paper, a new face recognition method based on PCA (principal Component Analysis), LDA (Linear Discriminant Analysis) and neural networks is proposed. This method consists of four steps: i) Preprocessing, ii) Dimension reduction using PCA, iii) feature extraction using LDA and iv) classification using neural network. Combination of PCA and LDA is used for improving the capability of LDA when a few samples of images are available and neural classifier is used to reduce number misclassification caused by not-linearly separable classes. The proposed method was tested on Yale face database. Experimental results on this database demonstrated the effectiveness of the proposed method for face recognition with less misclassification in comparison with previous methods.

Keywords: Face recognition Principal component analysis, Linear discriminant analysis, Neural networks.

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Authors: Abhishek Chandra, Man Singh

Abstract:

Highly stable and homogeneously dispersed amino acid coated silver nanoparticles (ANP) of ≈ 10 nm diameter, ranging from 420 to 430 nm are prepared on AgNO3 solution addition to gum of Azadirachta indica solution at 373.15 K. The amino acids were selected based on their polarity. The synthesized nanoparticles were characterized by UV-Vis, FTIR spectroscopy, HR-TEM, XRD, SEM and 1H-NMR. The coated nanoparticles were used as catalyst for the reduction of methylene blue dye in presence of Sn(II) in aqueous, anionic and cationic micellar media. The rate of reduction of dye was determined by measuring the absorbance at 660 nm, spectrophotometrically and followed the order: Kcationic > Kanionic > Kwater. After 12 min and in absence of the ANP, only 2%, 3% and 6% of the dye reduction was completed in aqueous, anionic and cationic micellar media respectively while, in presence of ANP coated by polar neutral amino acid with non-polar -R group, the reduction completed to 84%, 95% and 98% respectively. The ANP coated with polar neutral amino acid having non-polar -R group, increased the rate of reduction of the dye by 94, 3205 and 6370 folds in aqueous, anionic and cationic micellar media respectively. Also, the rate of reduction of the dye increased by three folds when the micellar media was changed from anionic to cationic when the ANP is coated by a polar neutral amino acid having a non-polar -R group.

Keywords: Silver nanoparticle, surfactant, methylene blue, amino acid.

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Authors: Jan Doutreloigne

Abstract:

This paper describes two methods for the reduction of the peak input current during the boosting of Dickson charge pumps. Both methods are implemented in the fully integrated Dickson charge pumps of a high-voltage display driver chip for smart-card applications. Experimental results reveal good correspondence with Spice simulations and show a reduction of the peak input current by a factor of 6 during boosting.

Keywords: Bi-stable display driver, Dickson charge pump, highvoltage generator, peak current reduction, sub-pump boosting, variable frequency boosting.

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Authors: D. Bala Bhaskar

Abstract:

An algorithm is proposed for the order reduction of large scale linear dynamic multi variable systems where the reduced order model denominator is obtained by using Stability equation method and numerator coefficients are obtained by using SRAM. The proposed algorithm produces a lower order model for an original stable high order multivariable system. The reduction procedure is easy to understand, efficient and computer oriented. To highlight the advantages of the approach, the algorithm is illustrated with the help of a numerical example and the results are compared with the other existing techniques in literature.

Keywords: Multi variable systems, order reduction, stability equation method, SRAM, time domain characteristics, ISE.

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Authors: Ayman A. AbuBaker, Rami S.Qahwaji, Musbah J. Aqel, Mohmmad H. Saleh

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Two algorithms are proposed to reduce the storage requirements for mammogram images. The input image goes through a shrinking process that converts the 16-bit images to 8-bits by using pixel-depth conversion algorithm followed by enhancement process. The performance of the algorithms is evaluated objectively and subjectively. A 50% reduction in size is obtained with no loss of significant data at the breast region.

Keywords: Breast cancer, Image processing, Image reduction, Mammograms, Image enhancement

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Authors: Siyuan Jing, Kun She

Abstract:

Neighborhood Rough Sets (NRS) has been proven to be an efficient tool for heterogeneous attribute reduction. However, most of researches are focused on dealing with complete and noiseless data. Factually, most of the information systems are noisy, namely, filled with incomplete data and inconsistent data. In this paper, we introduce a generalized neighborhood rough sets model, called VPTNRS, to deal with the problem of heterogeneous attribute reduction in noisy system. We generalize classical NRS model with tolerance neighborhood relation and the probabilistic theory. Furthermore, we use the neighborhood dependency to evaluate the significance of a subset of heterogeneous attributes and construct a forward greedy algorithm for attribute reduction based on it. Experimental results show that the model is efficient to deal with noisy data.

Keywords: attribute reduction, incomplete data, inconsistent data, tolerance neighborhood relation, rough sets

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Authors: J. Šindler, A. Suleng, T. Jelstad Olsen, P. Bárta

Abstract:

A subsea hydrocarbon production system can undergo planned and unplanned shutdowns during the life of the field. The thermal FEA is used to simulate the cool down to verify the insulation design of the subsea equipment, but it is also used to derive an acceptable insulation design for the cold spots. The driving factors of subsea analyses require fast responding and accurate models of the equipment cool down. This paper presents cool down analysis carried out by a Krylov subspace reduction method, and compares this approach to the commonly used FEA solvers. The model considered represents a typical component of a subsea production system, a closed valve on a dead leg. The results from the Krylov reduction method exhibits the least error and requires the shortest computational time to reach the solution. These findings make the Krylov model order reduction method very suitable for the above mentioned subsea applications.

Keywords: Model order reduction, Krylov subspace, subsea production system, finite element.

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Authors: A. Akın, H. Çoban

Abstract:

This study was carried out to determine the effects of Control (C), 1/3 Cluster Tip Reduction (1/3 CTR), 1/6 Cluster Tip Reduction (1/6 CTR), 1/9 Cluster Tip Reduction (1/9 CTR), 1/3 CTR + Boric Acid (BA), 1/6 CTR + BA, 1/9 CTR + BA applications on yield and yield components of four years old Alphonse Lavallee grape variety (Vitis vinifera L.) grown on grafted 110 Paulsen rootstock in Konya province in Turkey in the vegetation period in 2015. According to the results, the highest maturity index 21.46 with 1/9 CTR application; the highest grape juice yields 736.67 ml with 1/3 CTR + BA application; the highest L* color value 32.07 with 1/9 CTR application; the highest a* color value 1.74 with 1/9 CTR application; the highest b* color value 3.72 with 1/9 CTR application were obtained. The effects of applications on grape fresh yield, cluster weight and berry weight were not found statistically significant.

Keywords: Alphonse Lavallee grape cultivar, different cluster tip reduction (1/3, 1/6, 1/9), foliar boric acid application, yield, quality.

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Authors: Jérôme Azé, Mathieu Roche, Yves Kodratoff, Michèle Sebag

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Term Extraction, a key data preparation step in Text Mining, extracts the terms, i.e. relevant collocation of words, attached to specific concepts (e.g. genetic-algorithms and decisiontrees are terms associated to the concept “Machine Learning" ). In this paper, the task of extracting interesting collocations is achieved through a supervised learning algorithm, exploiting a few collocations manually labelled as interesting/not interesting. From these examples, the ROGER algorithm learns a numerical function, inducing some ranking on the collocations. This ranking is optimized using genetic algorithms, maximizing the trade-off between the false positive and true positive rates (Area Under the ROC curve). This approach uses a particular representation for the word collocations, namely the vector of values corresponding to the standard statistical interestingness measures attached to this collocation. As this representation is general (over corpora and natural languages), generality tests were performed by experimenting the ranking function learned from an English corpus in Biology, onto a French corpus of Curriculum Vitae, and vice versa, showing a good robustness of the approaches compared to the state-of-the-art Support Vector Machine (SVM).

Keywords: Text-mining, Terminology Extraction, Evolutionary algorithm, ROC Curve.

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Authors: Weng Ming Chu

Abstract:

When faced with stochastic networks with an uncertain duration for their activities, the securing of network completion time becomes problematical, not only because of the non-identical pdf of duration for each node, but also because of the interdependence of network paths. As evidenced by Adlakha & Kulkarni [1], many methods and algorithms have been put forward in attempt to resolve this issue, but most have encountered this same large-size network problem. Therefore, in this research, we focus on network reduction through a Series/Parallel combined mechanism. Our suggested algorithm, named the Activity Network Reduction Algorithm (ANRA), can efficiently transfer a large-size network into an S/P Irreducible Network (SPIN). SPIN can enhance stochastic network analysis, as well as serve as the judgment of symmetry for the Graph Theory.

Keywords: Series/Parallel network, Stochastic network, Network reduction, Interdictive Graph, Complexity Index.

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Authors: Mukesh Doble, Sunil K Narayan

Abstract:

Diffuse viral encephalitis may lack fever and other cardinal signs of infection and hence its distinction from other acute encephalopathic illnesses is challenging. Often, the EEG changes seen routinely are nonspecific and reflect diffuse encephalopathic changes only. The aim of this study was to use nonlinear dynamic mathematical techniques for analyzing the EEG data in order to look for any characteristic diagnostic patterns in diffuse forms of encephalitis.It was diagnosed on clinical, imaging and cerebrospinal fluid criteria in three young male patients. Metabolic and toxic encephalopathies were ruled out through appropriate investigations. Digital EEGs were done on the 3rd to 5th day of onset. The digital EEGs of 5 male and 5 female age and sex matched healthy volunteers served as controls.Two sample t-test indicated that there was no statistically significant difference between the average values in amplitude between the two groups. However, the standard deviation (or variance) of the EEG signals at FP1-F7 and FP2-F8 are significantly higher for the patients than the normal subjects. The regularisation dimension is significantly less for the patients (average between 1.24-1.43) when compared to the normal persons (average between 1.41-1.63) for the EEG signals from all locations except for the Fz-Cz signal. Similarly the wavelet dimension is significantly less (P = 0.05*) for the patients (1.122) when compared to the normal person (1.458). EEGs are subdued in the case of the patients with presence of uniform patterns, manifested in the values of regularisation and wavelet dimensions, when compared to the normal person, indicating a decrease in chaotic nature.

Keywords: Chaos, Diffuse encephalitis, Electroencephalogram, Fractal dimension, Fourier spectrum.

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Authors: Mahtab Davari, Charles Edward Okon, Somayeh Aghanavesi

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Every institute of learning is usually interested in the performance of enrolled students. The level of these performances determines the approach an institute of study may adopt in rendering academic services. The focus of this paper is to evaluate students' academic performance in given courses of study using machine learning methods. This study evaluated various supervised machine learning classification algorithms such as Logistic Regression (LR), Support Vector Machine (SVM), Random Forest, Decision Tree, K-Nearest Neighbors, Linear Discriminant Analysis (LDA), and Quadratic Discriminant Analysis, using selected features to predict study performance. The accuracy, precision, recall, and F1 score obtained from a 5-Fold Cross-Validation were used to determine the best classification algorithm to predict students’ performances. SVM (using a linear kernel), LDA, and LR were identified as the best-performing machine learning methods. Also, using the LR model, this study identified students' educational habits such as reading and paying attention in class as strong determinants for a student to have an above-average performance. Other important features include the academic history of the student and work. Demographic factors such as age, gender, high school graduation, etc., had no significant effect on a student's performance.

Keywords: Student performance, supervised machine learning, prediction, classification, cross-validation.

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Authors: Joseph C. Chen, Joshua Cox

Abstract:

This research focuses on the use of the Taguchi method to reduce the surface roughness and improve dimensional accuracy of parts machined by Wire Electrical Discharge Machining (EDM) with S7 heat treated steel material. Due to its high impact toughness, the material is a candidate for a wide variety of tooling applications which require high precision in dimension and desired surface roughness. This paper demonstrates that Taguchi Parameter Design methodology is able to optimize both dimensioning and surface roughness successfully by investigating seven wire-EDM controllable parameters: pulse on time (ON), pulse off time (OFF), servo voltage (SV), voltage (V), servo feed (SF), wire tension (WT), and wire speed (WS). The temperature of the water in the Wire EDM process is investigated as the noise factor in this research. Experimental design and analysis based on L₁₈ Taguchi orthogonal arrays are conducted. This paper demonstrates that the Taguchi-based system enables the wire EDM process to produce (1) high precision parts with an average of 0.6601 inches dimension, while the desired dimension is 0.6600 inches; and (2) surface roughness of 1.7322 microns which is significantly improved from 2.8160 microns.

Keywords: Taguchi parameter design, surface roughness, dimensional accuracy, Wire EDM.

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Authors: Michele Sinico

Abstract:

The psychological present has an actual extension. When a sequence of instantaneous stimuli falls in this short interval of time, observers perceive a compresence of events in succession and the temporal order depends on the qualitative relationships between the perceptual properties of the events. Two experiments were carried out to study the influence of perceptual grouping, with and without temporal displacement, on the duration of auditory sequences. The psychophysical method of adjustment was adopted. The first experiment investigated the effect of temporal displacement of a white noise on sequence duration. The second experiment investigated the effect of temporal displacement, along the pitch dimension, on temporal shortening of sequence. The results suggest that the temporal order of sounds, in the case of temporal displacement, is organized along the pitch dimension.

Keywords: Time perception, perceptual present, temporal displacement, gestalt laws of perceptual organization

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Authors: Khaled Harrar, Rachid Jennane

Abstract:

The purpose of this study is the discrimination of 28 postmenopausal with osteoporotic femoral fractures from an agematched control group of 28 women using texture analysis based on fractals. Two pre-processing approaches are applied on radiographic images; these techniques are compared to highlight the choice of the pre-processing method. Furthermore, the values of the fractal dimension are compared to those of the fractal signature in terms of the classification of the two populations. In a second analysis, the BMD measure at proximal femur was compared to the fractal analysis, the latter, which is a non-invasive technique, allowed a better discrimination; the results confirm that the fractal analysis of texture on calcaneus radiographs is able to discriminate osteoporotic patients with femoral fracture from controls. This discrimination was efficient compared to that obtained by BMD alone. It was also present in comparing subgroups with overlapping values of BMD.

Keywords: Osteoporosis, fractal dimension, fractal signature, bone mineral density.

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Authors: Hamad Hussain Balhareth, Kecheng Liu, Sharm Manwani

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Business and IT alignment has continued as a top concern for business and IT executives for almost three decades. Many researchers have conducted empirical studies on the relationship between business-IT alignment and performance. Yet, these approaches, lacking a social perspective, have had little impact on sustaining performance and competitive advantage. In addition to the limited alignment literature that explores organisational learning that is represented in shared understanding, communication, cognitive maps and experiences. Hence, this paper proposes an integrated process that enables social and intellectual dimensions through the concept of organisational learning. In particular, the feedback and feedforward process which provide a value creation across dynamic multilevel of learning. This mechanism enables on-going effectiveness through development of individuals, groups and organisations, which improves the quality of business and IT strategies and drives to performance.

Keywords: business-IT alignment, social dimension, intellectual dimension, organisational learning

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Authors: Jay Singh, C. B. Vishwakarma, Kalyan Chatterjee

Abstract:

A mixed method by combining modified pole clustering technique and modified cauer continued fraction is proposed for reducing the order of the large-scale dynamic systems. The denominator polynomial of the reduced order model is obtained by using modified pole clustering technique while the coefficients of the numerator are obtained by modified cauer continued fraction. This method generated 'k' number of reduced order models for kth order reduction. The superiority of the proposed method has been elaborated through numerical example taken from the literature and compared with few existing order reduction methods.

Keywords: Modified Pole Clustering, Modified Cauer Continued Fraction, Order Reduction, Stability, Transfer Function.

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