Search results for: weighted morphing

Authors: Noor F. Ilias, Mazlifah Omar, Hashbullah Ismail

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Exercise training has been shown to improve functional capacity and is recommended as a therapy for management of blood pressure. Our purpose was to establish whether different exercise capacity produces different effect size for Cardiorespiratory Fitness (CRF), systolic (SBP) and diastolic (DBP) blood pressure in patients with hypertension. Exercise characteristic is required in order to have optimal benefit from the training, but optimal exercise capacity is still unwarranted. A MEDLINE search (1985 to 2015) was conducted for exercise based rehabilitation trials in hypertensive patients. Thirty-seven studies met the selection criteria. Of these, 31 (83.7%) were aerobic exercise and 6 (16.3%) aerobic with additional resistance exercise, providing a total of 1318 exercise subjects and 819 control, the total of subjects was 2137. We calculated exercise volume and energy expenditure through the description of exercise characteristics. 4 studies (18.2%) were 451kcal - 900 kcal, 12 (54.5%) were 900 kcal – 1350 kcal and 6 (27.3%) >1351kcal per week. Peak oxygen consumption (peak VO2) increased by mean difference of 1.44 ml/kg/min (95% confidence interval [CI]: 1.08 to 1.79 ml/kg/min; p = 0.00001) with weighted mean 21.2% for aerobic exercise compare to aerobic with additional resistance exercise 4.50 ml/kg/min (95% confidence interval [CI]: 3.57 to 5.42 ml/kg/min; p = 0.00001) with weighted mean 14.5%. SBP was clinically reduce for both aerobic and aerobic with resistance training by mean difference of -4.66 mmHg (95% confidence interval [CI]: -5.68 to -3.63 mmHg; p = 0.00001) weighted mean 6% reduction and -5.06 mmHg (95% confidence interval [CI]: -7.32 to -2.8 mmHg; p = 0.0001) weighted mean 5% reduction respectively. Result for DBP was clinically reduce for aerobic by mean difference of -1.62 mmHg (95% confidence interval [CI]: -2.09 to -1.15 mmHg; p = 0.00001) weighted mean 4% reduction and aerobic with resistance training reduce by mean difference of -3.26 mmHg (95% confidence interval [CI]: -4.87 to -1.65 mmHg; p = 0.0001) weighted mean 6% reduction. Optimum exercise capacity for 451 kcal – 900 kcal showed greater improvement in peak VO2 and SBP by 2.76 ml/kg/min (95% confidence interval [CI]: 1.47 to 4.05 ml/kg/min; p = 0.0001) with weighted mean 40.6% and -16.66 mmHg (95% confidence interval [CI]: -21.72 to -11.60 mmHg; p = 0.00001) weighted mean 9.8% respectively. Our data demonstrated that aerobic exercise with total volume of 451 kcal – 900 kcal/ week energy expenditure may elicit greater changes in cardiorespiratory fitness and blood pressure in hypertensive patients. Higher exercise capacity weekly does not seem better result in management hypertensive patients.

Keywords: blood Pressure, exercise, hypertension, peak VO2

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Authors: Noha Seddik, Sherine Youssef, Mohamed Kholeif

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The automated epileptic seizure detection research field has emerged in the recent years; this involves analyzing the Electroencephalogram (EEG) signals instead of the traditional visual inspection performed by expert neurologists. In this study, a Support Vector Machine (SVM) that uses Weighted Permutation Entropy (WPE) as the input feature is proposed for classifying normal and seizure EEG records. WPE is a modified statistical parameter of the permutation entropy (PE) that measures the complexity and irregularity of a time series. It incorporates both the mapped ordinal pattern of the time series and the information contained in the amplitude of its sample points. The proposed system utilizes the fact that entropy based measures for the EEG segments during epileptic seizure are lower than in normal EEG.

Keywords: electroencephalogram (EEG), epileptic seizure detection, weighted permutation entropy (WPE), support vector machine (SVM)

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Authors: Ariful Islam, Showkat Ahmad Lone

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The Mukherjee-Islam Model is commonly used as a simple life time distribution to assess system reliability. The model exhibits a better fit for failure information and provides more appropriate information about hazard rate and other reliability measures as shown by various authors. It is possible to introduce a location parameter at a time (i.e., a time before which failure cannot occur) which makes it a more useful failure distribution than the existing ones. Even after shifting the location of the distribution, it represents a decreasing, constant and increasing failure rate. It has been shown to represent the appropriate lower tail of the distribution of random variables having fixed lower bound. This study presents the reliability computations and probability weighted moment estimation of three parameter model. A comparative analysis is carried out between three parameters finite range model and some existing bathtub shaped curve fitting models. Since probability weighted moment method is used, the results obtained can also be applied on small sample cases. Maximum likelihood estimation method is also applied in this study.

Keywords: comparative analysis, maximum likelihood estimation, Mukherjee-Islam failure model, probability weighted moment estimation, reliability

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Authors: M. Ciminello, I. Dimino, S. Ameduri, A. Concilio

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Improving aircraft's efficiency is one of the key elements of Aeronautics. Modern aircraft possess many advanced functions, such as good transportation capability, high Mach number, high flight altitude, and increasing rate of climb. However, no aircraft has a possibility to reach all of this optimized performance in a single airframe configuration. The aircraft aerodynamic efficiency varies considerably depending on the specific mission and on environmental conditions within which the aircraft must operate. Structures that morph their shape in response to their surroundings may at first seem like the stuff of science fiction, but take a look at nature and lots of examples of plants and animals that adapt to their environment would arise. In order to ensure both the controllable and the static robustness of such complex structural systems, a monitoring network is aimed at verifying the effectiveness of the given control commands together with the elastic response. In order to achieve this kind of information, the use of FBG sensors network is, in this project, proposed. The sensor network is able to measure morphing structures shape which may show large, global displacements due to non-standard architectures and materials adopted. Chord -wise variations may allow setting and chasing the best layout as a function of the particular and transforming reference state, always targeting best aerodynamic performance. The reason why an optical sensor solution has been selected is that while keeping a few of the contraindication of the classical systems (like cabling, continuous deployment, and so on), fibre optic sensors may lead to a dramatic reduction of the wires mass and weight thanks to an extreme multiplexing capability. Furthermore, the use of the ‘light’ as ‘information carrier’, permits dealing with nimbler, non-shielded wires, and avoids any kind of interference with the on-board instrumentation. The FBG-based transducers, herein presented, aim at monitoring the actual shape of adaptive trailing edge. Compared to conventional systems, these transducers allow more fail-safe measurements, by taking advantage of a supporting structure, hosting FBG, whose properties may be tailored depending on the architectural requirements and structural constraints, acting as strain modulator. The direct strain may, in fact, be difficult because of the large deformations occurring in morphing elements. A modulation transducer is then necessary to keep the measured strain inside the allowed range. In this application, chord-wise transducer device is a cantilevered beam sliding trough the spars and copying the camber line of the ATE ribs. FBG sensors array position are dimensioned and integrated along the path. A theoretical model describing the system behavior is implemented. To validate the design, experiments are then carried out with the purpose of estimating the functions between rib rotation and measured strain.

Keywords: fiber optic sensor, morphing structures, strain sensor, shape reconstruction

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Authors: Wanatchapong Kongkaew

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This paper proposes an application of probabilistic technique, namely Gaussian process regression, for estimating an optimal sequence of the single machine with total weighted tardiness (SMTWT) scheduling problem. In this work, the Gaussian process regression (GPR) model is utilized to predict an optimal sequence of the SMTWT problem, and its solution is improved by using an iterated local search based on simulated annealing scheme, called GPRISA algorithm. The results show that the proposed GPRISA method achieves a very good performance and a reasonable trade-off between solution quality and time consumption. Moreover, in the comparison of deviation from the best-known solution, the proposed mechanism noticeably outperforms the recently existing approaches.

Keywords: Gaussian process regression, iterated local search, simulated annealing, single machine total weighted tardiness

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Authors: Yao-Hong Tsai

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This paper presents a weighted approach to unconstrained iris recognition. Nowadays, commercial systems are usually characterized by strong acquisition constraints based on the subject’s cooperation. However, it is not always achievable for real scenarios in our daily life. Researchers have been focused on reducing these constraints and maintaining the performance of the system by new techniques at the same time. With large variation in the environment, there are two main improvements to develop the proposed iris recognition system. For solving extremely uneven lighting condition, statistic based illumination normalization is first used on eye region to increase the accuracy of iris feature. The detection of the iris image is based on Adaboost algorithm. Secondly, the weighted approach is designed by Gaussian functions according to the distance to the center of the iris. Furthermore, local binary pattern (LBP) histogram is then applied to texture classification with the weight. Experiment showed that the proposed system provided users a more flexible and feasible way to interact with the verification system through iris recognition.

Keywords: authentication, iris recognition, adaboost, local binary pattern

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Authors: Derya Karagöz

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A Shewhart chart based on normality assumption is not appropriate for skewed distributions since its Type-I error rate is inflated. This study presents X̄ and S control charts for monitoring the process variability for skewed distributions. We propose Weighted Standard Deviation (WSD) X̄ and S control charts. Standard deviation estimator is applied to monitor the process variability for estimating the process standard deviation, in the case of the W SD X̄ and S control charts as this estimator is simple and easy to compute. Unlike the Shewhart control chart, the proposed charts provide asymmetric limits in accordance with the direction and degree of skewness to construct the upper and lower limits. The performances of the proposed charts are compared with other heuristic charts for skewed distributions by using Simulation study. The Simulation studies show that the proposed control charts have good properties for skewed distributions and large sample sizes.

Keywords: weighted standard deviation, MAD, skewed distributions, S control charts

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Authors: Soha A. Bahanshal, Byung G. Kim

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Identification of patients at high risk for hospital readmission is of crucial importance for quality health care and cost reduction. Predicting hospital readmissions among diabetic patients has been of great interest to many researchers and health decision makers. We build a prediction model to predict hospital readmission for diabetic patients within 30 days of discharge. The core of the prediction model is a modified k Nearest Neighbor called Hybrid Fuzzy Weighted k Nearest Neighbor algorithm. The prediction is performed on a patient dataset which consists of more than 70,000 patients with 50 attributes. We applied data preprocessing using different techniques in order to handle data imbalance and to fuzzify the data to suit the prediction algorithm. The model so far achieved classification accuracy of 80% compared to other models that only use k Nearest Neighbor.

Keywords: machine learning, prediction, classification, hybrid fuzzy weighted k-nearest neighbor, diabetic hospital readmission

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Authors: Doyin Afolabi, Phillip Adewole, Oladipupo Sennaike

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Most well-known classifiers, including the decision tree algorithm, can make predictions on balanced datasets efficiently. However, the decision tree algorithm tends to be biased towards imbalanced datasets because of the skewness of the distribution of such datasets. To overcome this problem, this study proposes a weighted decision tree algorithm that aims to remove the bias toward the majority class and prevents the reduction of majority observations in imbalance datasets classification. The proposed weighted decision tree algorithm was tested on three imbalanced datasets- cancer dataset, german credit dataset, and banknote dataset. The specificity, sensitivity, and accuracy metrics were used to evaluate the performance of the proposed decision tree algorithm on the datasets. The evaluation results show that for some of the weights of our proposed decision tree, the specificity, sensitivity, and accuracy metrics gave better results compared to that of the ID3 decision tree and decision tree induced with minority entropy for all three datasets.

Keywords: data mining, decision tree, classification, imbalance dataset

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Authors: Muhammad Sheraz, Vasile Preda, Silvia Dedu

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Concepts of econophysics are usually used to solve problems related to uncertainty and nonlinear dynamics. In the theory of option pricing the risk neutral probabilities play very important role. The application of entropy in finance can be regarded as the extension of both information entropy and the probability entropy. It can be an important tool in various financial methods such as measure of risk, portfolio selection, option pricing and asset pricing. Gulko applied Entropy Pricing Theory (EPT) for pricing stock options and introduced an alternative framework of Black-Scholes model for pricing European stock option. In this article, we present solutions to maximum entropy problems based on Tsallis, Weighted-Tsallis, Kaniadakis, Weighted-Kaniadakies entropies, to obtain risk-neutral densities. We have also obtained the value of European call and put in this framework.

Keywords: option pricing, Black-Scholes model, Tsallis entropy, Kaniadakis entropy, weighted entropy, risk-neutral density

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Authors: Ayhan Kucukmanisa, Orhan Akbulut, Oguzhan Urhan

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Nowadays, advanced driver assistance systems (ADAS) have become popular, since they enable safe driving. Lane detection is a vital step for ADAS. The performance of the lane detection process is critical to obtain a high accuracy lane departure warning system (LDWS). Challenging factors such as road cracks, erosion of lane markings, weather conditions might affect the performance of a lane detection system. In this paper, 1-D weighted filter based on row filtering to detect lane marking is proposed. 2-D input image is filtered by 1-D weighted filter considering four-pixel values located symmetrically around the center of candidate pixel. Performance evaluation is carried out by two metrics which are true positive rate (TPR) and false positive rate (FPR). Experimental results demonstrate that the proposed approach provides better lane marking detection accuracy compared to the previous methods while providing real-time processing performance.

Keywords: lane marking filter, lane detection, ADAS, LDWS

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Authors: Ju-Hong Lee, Ding-Chen Chung

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This paper deals with the optimal design of recursive doubly complementary (DC) digital filter design using a metaheuristic based optimization technique. Based on the theory of DC digital filters using two recursive digital all-pass filters (DAFs), the design problem is appropriately formulated to result in an objective function which is a weighted sum of the phase response errors of the designed DAFs. To deal with the stability of the recursive DC filters during the design process, we can either impose some necessary constraints on the phases of the recursive DAFs. Through a frequency sampling and a weighted least squares approach, the optimization problem of the objective function can be solved by utilizing a population based stochastic optimization approach. The resulting DC digital filters can possess satisfactory frequency response. Simulation results are presented for illustration and comparison.

Keywords: doubly complementary, digital all-pass filter, weighted least squares algorithm, particle swarm optimization

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Authors: Chen-Fang Tsai, Shin-Li Lu

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Distribution-free control chart is an oncoming area from the statistical process control charts in recent years. Some researchers have developed various nonparametric control charts and investigated the detection capability of these charts. The major advantage of nonparametric control charts is that the underlying process is not specifically considered the assumption of normality or any parametric distribution. In this paper, two nonparametric exponentially weighted moving average (EWMA) control charts based on nonparametric tests, namely NE-S and NE-M control charts, are proposed for monitoring process variability. Generally, weighted moving average (GWMA) control charts are extended by utilizing design and adjustment parameters for monitoring the changes in the process variability, namely NG-S and NG-M control charts. Statistical performance is also investigated on NG-S and NG-M control charts with run rules. Moreover, sensitivity analysis is performed to show the effects of design parameters under the nonparametric NG-S and NG-M control charts.

Keywords: Distribution-free control chart, EWMA control charts, GWMA control charts

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Authors: Ju-Hong Lee, Ding-Chen Chung

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The paper deals with the optimal design of two-channel linear-phase (LP) quadrature mirror filter (QMF) banks using a metaheuristic based optimization technique. Based on the theory of two-channel QMF banks using two recursive digital all-pass filters (DAFs), the design problem is appropriately formulated to result in an objective function which is a weighted sum of the group delay error of the designed QMF bank and the magnitude response error of the designed low-pass analysis filter. Through a frequency sampling and a weighted least squares approach, the optimization problem of the objective function can be solved by utilizing a particle swarm optimization algorithm. The resulting two-channel QMF banks can possess approximately LP response without magnitude distortion. Simulation results are presented for illustration and comparison.

Keywords: quadrature mirror filter bank, digital all-pass filter, weighted least squares algorithm, particle swarm optimization

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Authors: Marine Segui, Matthieu Mantilla, Ruxandra Mihaela Botez

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This research is the part of a major project at the Research Laboratory in Active Controls, Avionics and Aeroservoelasticity (LARCASE) aiming to improve a Cessna Citation X aircraft cruise performance with an application of the morphing wing technology on its horizontal tail. However, the horizontal stabilizer of the Cessna Citation X turns around its span axis with an angle between -8 and 2 degrees. Within this range, the horizontal stabilizer generates certainly some unwanted drag. To cancel this drag, the LARCASE proposes to trim the aircraft with a horizontal stabilizer equipped by a morphing wing technology. This technology aims to optimize aerodynamic performances by changing the conventional horizontal tail shape during the flight. As a consequence, this technology will be able to generate enough lift on the horizontal tail to balance the aircraft without an unwanted drag generation. To conduct this project, an accurate aerodynamic model of the horizontal tail is firstly required. This aerodynamic model will finally allow precise comparison between a conventional horizontal tail and a morphed horizontal tail results. This paper presents how this aerodynamic model was designed. In this way, it shows how the 2D geometry of the horizontal tail was collected and how the unknown airfoil’s shape of the horizontal tail has been recovered. Finally, the complete horizontal tail airfoil shape was found and a comparison between aerodynamic polar of the real horizontal tail and the horizontal tail found in this paper shows a maximum difference of 0.04 on the lift or the drag coefficient which is very good. Aerodynamic polar data of the aircraft horizontal tail are obtained from the CAE Inc. level D research aircraft flight simulator of the Cessna Citation X.

Keywords: aerodynamic, Cessna, citation, coefficient, Datcom, drag, lift, longitudinal, model, OpenVSP

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Authors: Ana Paula Camelo, Keila Sanches

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The Geographically Weighted Regression (GWR) was proposed in geography literature to allow relationship in a regression model to vary over space. In Brazil, the agricultural exploitation of the Cerrado Biome is the main cause of deforestation. In this study, we propose a methodology using geostatistical methods to characterize the spatial dependence of deforestation in the Cerrado based on agricultural production indicators. Therefore, it was used the set of exploratory spatial data analysis tools (ESDA) and confirmatory analysis using GWR. It was made the calibration a non-spatial model, evaluation the nature of the regression curve, election of the variables by stepwise process and multicollinearity analysis. After the evaluation of the non-spatial model was processed the spatial-regression model, statistic evaluation of the intercept and verification of its effect on calibration. In an analysis of Spearman’s correlation the results between deforestation and livestock was +0.783 and with soybeans +0.405. The model presented R²=0.936 and showed a strong spatial dependence of agricultural activity of soybeans associated to maize and cotton crops. The GWR is a very effective tool presenting results closer to the reality of deforestation in the Cerrado when compared with other analysis.

Keywords: deforestation, geographically weighted regression, land use, spatial analysis

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Authors: Sifriyani Sifriyani, I Nyoman Budiantara, Sri Haryatmi, Gunardi Gunardi

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East Java Province has a first rank as a province that has the most counties and cities in Indonesia and has the largest population. In 2015, the population reached 38.847.561 million, this figure showed a very high population growth. High population growth is feared to lead to increase the levels of unemployment. In this study, the researchers mapped and modeled the unemployment rate with 6 variables that were supposed to influence. Modeling was done by nonparametric geographically weighted regression methods with truncated spline approach. This method was chosen because spline method is a flexible method, these models tend to look for its own estimation. In this modeling, there were point knots, the point that showed the changes of data. The selection of the optimum point knots was done by selecting the most minimun value of Generalized Cross Validation (GCV). Based on the research, 6 variables were declared to affect the level of unemployment in eastern Java. They were the percentage of population that is educated above high school, the rate of economic growth, the population density, the investment ratio of total labor force, the regional minimum wage and the ratio of the number of big industry and medium scale industry from the work force. The nonparametric geographically weighted regression models with truncated spline approach had a coefficient of determination 98.95% and the value of MSE equal to 0.0047.

Keywords: East Java, nonparametric geographically weighted regression, spatial, spline approach, unemployed rate

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Authors: Meetty Tomy, Arxhana G Thosar

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This paper provides the implementation of optimal control for an armature-controlled DC motor. The selection of error weighted Matrix and control weighted matrix in order to implement optimal control theory for improving the dynamic behavior of DC motor is presented. The closed loop performance of Armature controlled DC motor with derived linear optimal controller is then evaluated for the transient operating condition (starting). The result obtained from MATLAB is compared with that of PID controller and simple closed loop response of the motor.

Keywords: optimal control, DC motor, performance index, MATLAB

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Authors: Farhad Asadi, Mohammad Javad Mollakazemi, Aref Ghafouri

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Recent research in neural networks science and neuroscience for modeling complex time series data and statistical learning has focused mostly on learning from high input space and signals. Local linear models are a strong choice for modeling local nonlinearity in data series. Locally weighted projection regression is a flexible and powerful algorithm for nonlinear approximation in high dimensional signal spaces. In this paper, different learning scenario of one and two dimensional data series with different distributions are investigated for simulation and further noise is inputted to data distribution for making different disordered distribution in time series data and for evaluation of algorithm in locality prediction of nonlinearity. Then, the performance of this algorithm is simulated and also when the distribution of data is high or when the number of data is less the sensitivity of this approach to data distribution and influence of important parameter of local validity in this algorithm with different data distribution is explained.

Keywords: local nonlinear estimation, LWPR algorithm, online training method, locally weighted projection regression method

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Authors: Ali A. Abdollahi

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During the last several decades, scientists have consistently applied Multiple Criteria Decision-Making methods in making decisions about multi-faceted, complicated subjects. While making such decisions and in order to achieve more accurate evaluations, they have regularly used a variety of criteria instead of applying just one Optimum Evaluation Criterion. The method presented here utilizes both ‘quantity’ and ‘quality’ to assess the function of the Multiple-Criteria method. Applying Data envelopment analysis (DEA), weighted aggregated sum product assessment (WASPAS), Weighted Sum Approach (WSA), Analytic Network Process (ANP), and Charnes, Cooper, Rhodes (CCR) methods, we have analyzed thirteen parks in Kerman city. It further indicates that the functions of WASPAS and WSA are compatible with each other, but also that their deviation from DEA is extensive. Finally, the results for the CCR technique do not match the results of the DEA technique. Our study indicates that the ANP method, with the average rate of 1/51, ranks closest to the DEA method, which has an average rate of 1/49.

Keywords: multiple criteria decision making, Data envelopment analysis (DEA), Charnes Cooper Rhodes (CCR), Weighted Sum Approach (WSA)

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Authors: Gunasekaran Raja, Ramkumar Jayaraman

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In this paper, we consider CCL-N (Cooperative Cross Layer Network) topology based on the cross layer (both centralized and distributed) environment to form network communities. Various performance metrics related to the IEEE 802.16 networks are discussed to design CCL-N Topology. In CCL-N topology, nodes are classified as master nodes (Master Base Station [MBS]) and serving nodes (Relay Station [RS]). Nodes communities are organized based on the networking terminologies. Based on CCL-N Topology, various simulation analyses for both transparent and non-transparent relays are tabulated and throughput efficiency is calculated. Weighted load balancing problem plays a challenging role in IEEE 802.16 network. CoTS (Concurrent Transmission Scheduling) Scheme is formulated in terms of three aspects – transmission mechanism based on identical communities, different communities and identical node communities. CoTS scheme helps in identifying the weighted load balancing problem. Based on the analytical results, modularity value is inversely proportional to that of the error value. The modularity value plays a key role in solving the CoTS problem based on hop count. The transmission mechanism for identical node community has no impact since modularity value is same for all the network groups. In this paper three aspects of communities based on the modularity value which helps in solving the problem of weighted load balancing and CoTS are discussed.

Keywords: cross layer network topology, concurrent scheduling, modularity value, network communities and weighted load balancing

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Authors: Kang-Mo Jung

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Least squares support vector machine (LS-SVM) is a penalized regression which considers both fitting and generalization ability of a model. However, the squared loss function is very sensitive to even single outlier. We proposed a weighted absolute deviation loss function for the robustness of the estimates in least absolute deviation support vector machine. The proposed estimates can be obtained by a quadratic programming algorithm. Numerical experiments on simulated datasets show that the proposed algorithm is competitive in view of robustness to outliers.

Keywords: least absolute deviation, quadratic programming, robustness, support vector machine, weight

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Authors: Aleksander Mendoza

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Glushkov's construction has many interesting properties, and they become even more evident when applied to transducers. This article strives to show the vast range of possible extensions and optimisations for this algorithm. Special flavour of regular expressions is introduced, which can be efficiently converted to e-free functional subsequential weighted finite state transducers. Produced automata are very compact, as they contain only one state for each symbol (from input alphabet) of original expression and only one transition for each range of symbols, no matter how large. Such compactified ranges of transitions allow for efficient binary search lookup during automaton evaluation. All the methods and algorithms presented here were used to implement open-source compiler of regular expressions for multitape transducers.

Keywords: weighted automata, transducers, Glushkov, follow automata, regular expressions

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Authors: Attallah Bilal, Hendel Fatiha

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This paper proposes the bimodal biometrics system for identity verification iris and fingerprint, at matching score level architecture using weighted sum of score technique. The features are extracted from the pre processed images of iris and fingerprint. These features of a query image are compared with those of a database image to obtain matching scores. The individual scores generated after matching are passed to the fusion module. This module consists of three major steps i.e., normalization, generation of similarity score and fusion of weighted scores. The final score is then used to declare the person as genuine or an impostor. The system is tested on CASIA database and gives an overall accuracy of 91.04% with FAR of 2.58% and FRR of 8.34%.

Keywords: iris, fingerprint, sum rule, fusion

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Authors: Dongxu Chen, Yipeng Li

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This paper presents an information geometry NonlocalMeans(NLM) algorithm for Poisson denoising. NLM estimates a noise-free pixel as a weighted average of image pixels, where each pixel is weighted according to the similarity between image patches in Euclidean space. In this work, every pixel is a Poisson distribution locally estimated by Maximum Likelihood (ML), all distributions consist of a statistical manifold. A NLM denoising algorithm is conducted on the statistical manifold where Fisher information matrix can be used for computing distribution geodesics referenced as the similarity between patches. This approach was demonstrated to be competitive with related state-of-the-art methods.

Keywords: image denoising, Poisson noise, information geometry, nonlocal-means

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Authors: Anastasiia Yu. Timofeeva

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Two new algorithms for nonparametric estimation of errors-in-variables models are proposed. The first algorithm is based on penalized regression spline. The spline is represented as a piecewise-linear function and for each linear portion orthogonal regression is estimated. This algorithm is iterative. The second algorithm involves locally weighted regression estimation. When the independent variable is measured with error such estimation is a complex nonlinear optimization problem. The simulation results have shown the advantage of the second algorithm under the assumption that true smoothing parameters values are known. Nevertheless the use of some indexes of fit to smoothing parameters selection gives the similar results and has an oversmoothing effect.

Keywords: grade point average, orthogonal regression, penalized regression spline, locally weighted regression

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Authors: O. Poleshchuk, E. Komarov

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This paper presents a regression model for interval type-2 fuzzy sets based on the least squares estimation technique. Unknown coefficients are assumed to be triangular fuzzy numbers. The basic idea is to determine aggregation intervals for type-1 fuzzy sets, membership functions of whose are low membership function and upper membership function of interval type-2 fuzzy set. These aggregation intervals were called weighted intervals. Low and upper membership functions of input and output interval type-2 fuzzy sets for developed regression models are considered as piecewise linear functions.

Keywords: interval type-2 fuzzy sets, fuzzy regression, weighted interval

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Authors: Faramarz Khosravi, Gokhan Izbirak

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A weighted statistical stochastic based Analytical Hierarchy Process (AHP) model for modeling the potential barriers and enablers of sustainability for measuring and assessing the sustainability level is proposed. For context-dependent potential barriers and enablers, the proposed model takes the basis of the properties of the variables describing the sustainability functions and was developed into a realistic analytical model for the sustainable behavior of an organization. This thus serves as a means for measuring the sustainability of the organization. The main focus of this paper was the application of the AHP tool in a statistically-based model for measuring sustainability. Hence a strong weighted stochastic AHP based procedure was achieved. A case study scenario of a widely reported major Canadian electric utility was adopted to demonstrate the applicability of the developed model and comparatively examined its results with those of an equal-weighted model method. Variations in the sustainability of a company, as fluctuations, were figured out during the time. In the results obtained, sustainability index for successive years changed form 73.12%, 79.02%, 74.31%, 76.65%, 80.49%, 79.81%, 79.83% to more exact values 73.32%, 77.72%, 76.76%, 79.41%, 81.93%, 79.72%, and 80,45% according to priorities of factors that have found by expert views, respectively. By obtaining relatively necessary informative measurement indicators, the model can practically and effectively evaluate the sustainability extent of any organization and also to determine fluctuations in the organization over time.

Keywords: AHP, sustainability fluctuation, environmental indicators, performance measurement

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Authors: Susanne Fridsten, Kristina Hellman, Anders Sundin, Lennart Blomqvist

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Background: Patients diagnosed with locally advanced cervical carcinoma are treated with definitive concomitant chemo-radiotherapy. Treatment failure occurs in 30-50% of patients with very poor prognoses. The treatment is standardized with risk for both over-and undertreatment. Consequently, there is a great need for biomarkers able to predict therapy outcomes to allow for individualized treatment. Aim: To explore the role of T2- and diffusion-weighted magnetic resonance imaging (MRI) for early prediction of therapy outcome and the optimal time point for assessment. Methods: A pilot study including 15 patients with cervical carcinoma stage IIB-IIIB (FIGO 2009) undergoing definitive chemoradiotherapy. All patients underwent MRI four times, at baseline, 3 weeks, 5 weeks, and 12 weeks after treatment started. Tumour size, size change (∆size), visibility on diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) and change of ADC (∆ADC) at the different time points were recorded. Results: 7/15 patients relapsed during the study period, referred to as "poor prognosis", PP, and the remaining eight patients are referred to "good prognosis", GP. The tumor size was larger at all time points for PP than for GP. The ∆size between any of the four-time points was the same for PP and GP patients. The sensitivity and specificity to predict prognostic group depending on a remaining tumor on DWI were highest at 5 weeks and 83% (5/6) and 63% (5/8), respectively. The combination of tumor size at baseline and remaining tumor on DWI at 5 weeks in ROC analysis reached an area under the curve (AUC) of 0.83. After 12 weeks, no remaining tumor was seen on DWI among patients with GP, as opposed to 2/7 PP patients. Adding ADC to the tumor size measurements did not improve the predictive value at any time point. Conclusion: A large tumor at baseline MRI combined with a remaining tumor on DWI at 5 weeks predicted a poor prognosis.

Keywords: chemoradiotherapy, diffusion-weighted imaging, magnetic resonance imaging, uterine cervical carcinoma

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Authors: Azza Gaber Farag, Yasser Atta Shehata, Sara Elsayed Elghazouly, Mustafa Elsayed Elshaib, Nesreen Gamal Elden Elhelbawy

Abstract:

Background: Androgen receptor (AR) polymorphism in cytosine adenineguanine (CAG) repeat has an effect on the functional capacity of AR in males. However, little researches in this field are available regarding female sexual function. Aim: To investigate the possible link between polymorphism in the CAG repeat of AR gene and female sexual function in a sample of the Egyptian population. Materials and methods: 500 Egyptian married females completed a questionnaire regarding sociodemographic, reproductive, and sexual data. AR CAG repeat length was analyzed for those having female sexual dysfunctions (FSD) using real-time PCR. Results: The most sensitive domain to AR CAG repeat length was the orgasm domain that showed significant positive correlations with short allele (p=0.001), long allele (p=.015), biallellic mean (p=.000), and X weighted biallelic mean (p=.000). The satisfaction domain had significant positive correlations with the biallelic mean (p=.035), and the X weighted biallelic mean (p=. 032). However, the pain domain was of significant negative correlations with AR polymorphism of short allele (p=.002), biallelic mean (p=.013), and X weighted biallelic mean (p = . 011). Conclusions: AR polymorphism could represent a non-negligible aspect in female sexual function. The lower AR CAG repeat polymorphism was of significant impact on FSD, affecting mainly female orgasm followed by pain disorders that finally reflected On her sexual satisfaction.

Keywords: female sexual dysfunction, androgen receptor, CAG repeat polymorphism, androgen

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