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... 

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... 

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  

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  

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  

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... 

This paper addresses a real-world problem faced by the public healthcare sector. The problem consists of both the patients’ and service provider's requirements (i.e., accessibility vs. costs) for locating healthcare facilities, allocating service units to those facilities, and determining the facilities’ capacities. The main contribution of this study is capturing both short-term and long-term uncertainties at the modelling stage. The queuing theory is incorporated to consider stochastic demand and service time as a short-term uncertainty, as well as a service level measurement. The developed nonlinear model is then converted into a linear model after introducing a new set of decision... 

In this paper, the nonlinear transversal vibration of an axially moving viscoelastic string on a viscoelastic guide subjected to a mono-frequency excitation is considered. The model of the viscoelastic guide is a parallel combination of springs and viscous dampers. The governing equation of motion is developed using Hamilton's principle. Applying the method of multiple scales to the governing partial differential equation, the solvability condition and approximate solutions are derived. Three cases, namely primary, subharmonic and superharmonic resonances are studied and appropriate analytical solutions are obtained. The effect of mean value velocity, force amplitude, guide stiffness and... 

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 paper, a fuzzy comparison of fuzzy numbers is defined and a slack-based measure (SBM model) in data envelopment analysis (DEA) is extended to be a fuzzy DEA model, using it. Proposed measure is employed for evaluation and ranking of all decision making units, using a fuzzy concept called fuzzy profit. Also, it is shown that the introduced model is convenient for using weights restrictions. Furthermore, we compare the results of proposed model with Guo and Tanaka's results [Fuzzy Sets Syst. 119 (2001) 149] by representing a numerical example introduced by them. © 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... 

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,... 

This article summarizes the first and second Iranian brain-computer interface competitions held in 2017 and 2018 by the National Brain Mapping Lab. Two 64-channel electroencephalography (EEG) datasets were contributed, including motor imagery as well as motor execution by three limbs. The competitors were asked to classify the type of motor imagination or execution based on EEG signals in the first competition and the type of executed motion as well as the movement onset in the second competition. Here, we provide an overview of the datasets, the tasks, the evaluation criteria, and the methods proposed by the top-ranked teams. We also report the results achieved with the submitted algorithms... 

Wearable human activity recognition systems (HAR) using inertial measurement units (IMU) play a key role in the development of smart rehabilitation systems. Training of a HAR system with patient data is costly, time-consuming, and difficult for the patients. This study proposes a new scheme for the optimal design of HARs with minimal involvement of the patients. It uses healthy subject data for optimal design for a set of activities used in the rehabilitation of PD1 patients. It maintains its performance for individual PD subjects using a single session data collection and an adaptation procedure. In the optimal design, several classifiers (i.e. NM, k-NN, MLP with RBF as a hidden layer, and...