Sharif Digital Repository

In this paper, we propose a two-phase approach to find the optimal solutions of a class of fuzzy linear programming problems called fully fuzzified linear programming (FFLP), where all decision parameters and variables are fuzzy numbers. Our approach is constructed on the basis of comparison of mean and standard deviation of fuzzy numbers. In this approach, the first phase maximizes the possibilistic mean value of fuzzy objective function and obtains a set of feasible solutions. The second phase minimizes the standard deviation of the original fuzzy objective function, by considering all basic feasible solutions obtained at the end of the first phase. The advantage of the proposed approach... 

Biomechanical modeling approaches require body posture to evaluate the risk of spine injury during manual material handling. The procedure to measure body posture via motion-analysis techniques as well as the subsequent calculations of lumbosacral moments and spine loads by, respectively, inverse-dynamic and musculoskeletal models are complex and time-consuming. We aim to develop easy-to-use yet accurate artificial neural networks (ANNs) that predict 3D whole-body posture (ANNposture), segmental orientations (ANNangle), and lumbosacral moments (ANNmoment) based on our measurements during load-handling activities. Fifteen individuals each performed 135 load-handling activities by reaching (0... 

Effective connectivity is one of the most important considerations in brain functional mapping via EEG. It demonstrates the effects of a particular active brain region on others. In this paper, a new method is proposed which is based on dual Kalman filter. In this method, firstly by using a brain active localization method (standardized low resolution brain electromagnetic tomography) and applying it to EEG signal, active regions are extracted, and appropriate time model (multivariate autoregressive model) is fitted to extracted brain active sources for evaluating the activity and time dependence between sources. Then, dual Kalman filter is used to estimate model parameters or effective... 

Common similarity measures of time domain signals such as cross-correlation and Symbolic Aggregate approximation (SAX) are not appropriate for nonlinear signal analysis. This is because of the high sensitivity of nonlinear systems to initial points. Therefore, a similarity measure for nonlinear signal analysis must be invariant to initial points and quantify the similarity by considering the main dynamics of signals. The statistical behavior of local extrema (SBLE) method was previously proposed to address this problem. The SBLE similarity index uses quantized amplitudes of local extrema to quantify the dynamical similarity of signals by considering patterns of sequential local extrema. By... 

In this research, the interaction between electroencephalogram (EEG) and, a cardiac parameter, photoplethysmogram (PPG), using connectivity measures to emphasize the importance of autonomic nervous system over the central nervous system during a deception is investigated. In this survey, connectivity analysis was applied, since it can provide information flow of brain regions; moreover, lying and truth appear to be cohered with the flow of information in the brain. Initially, a new wavelet-based approach for EEG/PPG effective connectivity fusion was introduced; then, it was validated for 41 subjects. For each subject, after extracting specific wavelet component of EEG and PPG signals, an... 

Deception is mentioned as an expression or action which hides the truth and deception detection as a concept to uncover the truth. In this research, a connectivity analysis of Electro Encephalography study is presented regarding cognitive processes of an instructed liar/truth-teller about identity during an interview. In this survey, connectivity analysis is applied because it can provide unique information about brain activity patterns of lying and interaction among brain regions. The novelty of this paper lies in applying an open-ended questions interview protocol during EEG recording. We recruited 40 healthy participants to record EEG signal during the interview. For each subject,... 

Finding binary sequences with low autocorrelation is very important in many applications and their construction is a hard computational problem. Here a new exhaustive search algorithm is developed to find all optimal aperiodic binary sequences which are faster than simple one and it achieves its efficiency through a combination of the following four devices: (1) A branch-and-bound search strategy; (2) Search logic that avoids codes redundant relative to two PSL-preserving operations; (3) A fast recursive method for computing autocorrelation functions of binary sequences; (4) A simple scheme for partitioning and parallelizing, made possible by the fixed upper bound on psl  

Distinguishing reflected waveforms from two separated targets which are very close to each other is an important challenge in radar signal processing. Pulse compression is a technique used for accounting for this problem. There are several methods for compressing such as phase coding waveform and the goal of this paper is finding these optimal codes. In this paper, by combining several contents, a new optimum method based on Genetic Algorithm is suggested. This method has low computational operation and its speed is faster than the other ordinary algorithms. This method is belonged to local or partial search methods and has following advantages: 1. It uses branch-and-bound strategy; 2. It's... 

Most of previous studies on fuzzy regression analysis have a common characteristic of increasing spreads for the estimated fuzzy responses as the independent variable increases its magnitude, which is not suitable for general cases. In this paper, fuzzy linear regression models with fuzzy/crisp output, fuzzy/crisp input are considered, and an estimated method along with a mathematical-programming-based approach is proposed. The advantages of the proposed approach are simplicity in programming and computation, and minimum difference of total spread between observed and estimated values. © 2003 Elsevier Inc. All rights reserved  

The debate over innovativeness of large firms and SMEs, which was bolded by Schumpeter, still continues under mixed empirical evidences. There are several implications for this debate including policy orientation in support of large firms or SMEs. But there is a scarce of studies in developing countries and no such study in Iran yet. The present study has explored the proportionality of increase of innovation activity versus firm size within 522 Iranian knowledge-based firms categorized in 9 industries. Innovation activity was measured by R&D expenditure while firm size stood for number of employees. Using log-log regression in the first phase, it was found that R&D expenditure confirms a... 

The debate over innovativeness of large firms and SMEs, which was bolded by Schumpeter, still continues under mixed empirical evidences. There are several implications for this debate including policy orientation in support of large firms or SMEs. But there is a scarce of studies in developing countries and no such study in Iran yet. The present study has explored the proportionality of increase of innovation activity versus firm size within 522 Iranian knowledge-based firms categorized in 9 industries. Innovation activity was measured by R&D expenditure while firm size stood for number of employees. Using log–log regression in the first phase, it was found that R&D expenditure confirms a... 

Brain connectivity estimation is a useful method to study brain functions and diagnose neuroscience disorders. Effective connectivity is a subdivision of brain connectivity which discusses the causal relationship between different parts of the brain. In this study, a dual Kalman-based method is used for effective connectivity estimation. Because of connectivity changes in autism, the method is applied to autistic signals for effective connectivity estimation. For method validation, the dual Kalman based method is compared with other connectivity estimation methods by estimation error and the dual Kalman-based method gives acceptable results with less estimation errors. Then, connectivities... 

To estimate the parameters of fuzzy linear regression models with fuzzy output and crisp inputs, we develop a mathematical programming model in this paper. The method is constructed on the basis of minimizing the square of the total difference between observed and estimated spread values or in other words minimizing the least square errors. The advantage of the proposed approach is its simplicity in programming and computation as well as its performance. To compare the performance of the proposed approach with the other methods, two examples are presented. © 2004 Elsevier Inc. All rights reserved  

In this paper, fuzzy linear regression models with fuzzy/crisp output, fuzzy/crisp input are considered. In this regard, we define risk-neutral, risk-averse and risk-seeking fuzzy linear regression models. In order to do that, two equality indices are applied to express the degree of equality between a pair of fuzzy numbers. We also develop three mathematical models to obtain the parameters of fuzzy linear regression models. Minimizing the difference between the total spread of the observed and estimated values is the objective of these models. The advantage of our proposed models is the simplicity in programming and computation  

This paper develops a new evolutionary algorithm for generating low autocorrelation binary sequences. These sequences are of interest in pulse compression technique. The proposed algorithm is fast enough to yield optimum or near optimum codes. The generated sequences were compared to the best literature and were seen that its results are better than the others. This suggested method could change 11 rows of the previous best known PSLs table, whereas the previous literature could change only one record. These records were combined with the best results reported in the papers to produce a new best minimal-PSL binary sequence table for bit lengths 71 through 100. ©2008 IEEE  

Functional magnetic resonance imaging (fMRI) is an imaging tool that is used to analyze the brain’s functions. Brain functional connectivity analysis based on fMRI signals often calculated correlations among time series in different areas of the brain. For FC analysis most prior research works generate the brain graphs based on linear correlations, however, the nonlinear behavior of the brain can lower the accuracy of such graphs. Usually, the Pearson correlation coefficient is used which has limitations in revealing nonlinear relationships. One of the proper methods for nonlinear analysis is the Kernel trick. This method maps the data into a high dimensional space and calculates the linear... 

Alzheimer's Disease (AD) begins several years before the symptoms develop. It starts with Mild Cognitive Impairment (MCI) which can be separated into Early MCI and Late MCI (EMCI and LMCI). Functional connectivity analysis and classification are done among the different stages of illness with Functional Magnetic Resonance Imaging (fMRI). In this study, in addition to the four stages including healthy, EMCI, LMCI, and AD, the patients have been tracked for a year. Indeed, the classification has been done among 7 groups to analyze the functional connectivity changes in one year in different stages. After generating the functional connectivity graphs for eliminating the weak links, three... 

Purpose: Alzheimer's disease (AD) starts years before its signs and symptoms including the dementia become apparent. Diagnosis of the AD in the early stages is important to reduce the speed of brain decline. Aim of the study: Identifying the alterations in the functional connectivity of the brain during the disease stages is among the main important issues in this regard. Therefore, in this study, the changes in the functional connectivity during the AD stages were analyzed. Materials and methods: By employing the functional magnetic resonance imaging (fMRI) data and graph theory, weighted undirected graphs of the whole-brain and default mode network (DMN) network were investigated... 

Functional magnetic resonance imaging (fMRI) is a non-invasive method that helps to analyze brain function based on BOLD signal fluctuations. Functional Connectivity (FC) catches the transient relationship between various brain regions usually measured by correlation analysis. The elements of the correlation matrix are between -1 to 1. Some of them are very small values usually related to weak and spurious correlations due to noises and artifacts. They can not be concluded as real strong correlations between brain regions and their existence could make a misconception and leads to fake results. It is crucial to make a conclusion based on reliable and informative correlations. In order to... 

Functional magnetic resonance imaging (fMRI) is an imaging tool that is used to analyze the brain’s functions. Brain functional connectivity analysis based on fMRI signals often calculated correlations among time series in different areas of the brain. For FC analysis most prior research works generate the brain graphs based on linear correlations, however, the nonlinear behavior of the brain can lower the accuracy of such graphs. Usually, the Pearson correlation coefficient is used which has limitations in revealing nonlinear relationships. One of the proper methods for nonlinear analysis is the Kernel trick. This method maps the data into a high dimensional space and calculates the linear...