Sharif Digital Repository

The problem of fuzzy data association for target tracking in a cluttered environment is discussed in this paper. In data association filters based on fuzzy clustering, the association probabilities of tracking filters are reconstructed by utilizing the fuzzy membership degree of the measurement belonging to the target. Clearly in these filters, the fuzzy clustering method has an important role; better approach causes better precision in target tracking. Recently, by using the information theory, the maximum entropy fuzzy data association filter (MEF-DAF), as a fast and efficient algorithm, is introduced in literature. In this paper, by modification of a fuzzy clustering objective function,... 

This paper introduces a new dimension reduction (DR) method, called minimum change rate deviation (MCRD), which is applicable to the DR of remote sensing images. As the main shortcoming of the well-known principal component analysis (PCA) method is that it does not consider the spatial relation among image points, our proposed approach takes into account the spatial relation among neighboring image pixels while preserving all useful properties of PCA. These include uncorrelatedness property in resulted components and the decrease of error with the increasing of the number of selected components. Our proposed method can be considered as a generalization of PCA and, under certain conditions,... 

Traffic density estimation is one of the most challenging problems in Intelligent Transportation Systems. In this paper, we estimate the traffic flow density based on classification. Various new efficient features are introduced for distinguishing between different traffic states, including number of key-points, edges of difference-image and moving edges. These features describe the traffic flow without any need to individual vehicles detection and tracking. We experiment our proposed approach on a standard database and some real videos from Tehran roads. The results show high accuracy performance of our method, even in changes of environmental conditions (e.g., lighting), by using efficient... 

The proper modeling of radiation is of great importance in numerical simulation of combustion processes. A simple gray weighted-sum-of-gray-gases (WSGG) model has been intensively used in CFD simulations of industrial combustion applications, mainly because of its low computational costs. However, the gray assumption is far from the real behavior of non-gray combustion gases. This could consequently affect the accuracy of temperature field and heat flux distribution predictions. On the other hand, modern non-gray global radiation models such as the standard spectral line-based weighted-sum-od-gray-gases (SLW) model are still computationally expensive for many engineering purposes. To... 

Discriminative subspace analysis is a popular approach for a variety of applications. There is a growing interest in subspace learning techniques for face recognition. Principal component analysis (PCA) and eigenfaces are two important subspace analysis methods have been widely applied in a variety of areas. However, the excessive dimension of data space often causes the curse of dimensionality dilemma, expensive computational cost, and sometimes the singularity problem. In this paper, a new supervised discriminative subspace analysis is presented by encoding face image as a high order general tensor. As face space can be considered as a nonlinear submanifold embedded in the tensor space, a... 

Different efforts have been done to address the problem of information overload on the Internet. Recommender systems aim at directing users through this information space, toward the resources that best meet their needs and interests by extracting knowledge from the previous users' interactions. In this paper, we propose an algorithm to solve the web page recommendation problem. In our algorithm, we use distributed learning automata to learn the behavior of previous users' and recommend pages to the current user based on learned pattern. Our experiments on real data set show that the proposed algorithm performs better than the other algorithms that we compared to and, at the same time, it is... 

Active Learning Method (ALM) is one of the powerful tools in soft computing and it is inspired by the human brain capabilities in approaching complicated problems. ALM, which is in essence an adaptive fuzzy learning algorithm, tries to model a Multi-Input Single-Output system with several single-input single-output subsystems. Each of these subsystems is then modeled by an ink drop spread (IDS) plane. IDS operator, which is the main processing engine of ALM, extracts two kinds of informative features, Narrow Path and Spread, from each IDS plane without complicated computations. These features from all IDS planes are then aggregated in the inference engine. Despite the great performance of... 

Concrete is widely used for many practices in Civil Engineering. Therefore, an understanding of its behavior helps engineers and researchers to perform more accurate and cost-effective analyses and designs. In this respect, several models have been proposed to predict the behaviors of concrete most of which are satisfactorily accurate. However, by increasing the accuracy of the models, their computational cost increases, too. In this study, a model with the least computational cost is proposed to predict the behaviors of various concretes and confinement conditions. This model does not require any experimental tests to determine its parameters. It was proved to be able to predict the... 

Active Learning Method (ALM) is one of the powerful tools in soft computing and it is inspired by the human brain capabilities in approaching complicated problems. ALM, which is in essence an adaptive fuzzy learning algorithm, tries to model a Multi-Input Single-Output system with several single-input single-output subsystems. Each of these subsystems is then modeled by an ink drop spread (IDS) plane. IDS operator, which is the main processing engine of ALM, extracts two kinds of informative features, Narrow Path and Spread, from each IDS plane without complicated computations. These features from all IDS planes are then aggregated in the inference engine. Despite the great performance of... 

This paper proposes an inverse isogeometric analysis to estimate the blank and predict the strain distribution in sheet metal forming processes. In this study, the same NURBS basis functions are used for drawing a final part and analysis of the forming process. In other words, this approach requires only one modeling and analysis representation, in contrast to inverse FEM. This model deals with minimization of potential energy, deformation theory of plasticity, and infinitesimal deformation relations with considering a new non-uniform friction model. One advantage of the presented methodology is that the governing equations are solved in two-dimensional space without concerning about... 

In this paper, a multiscale homogenization approach is developed for fully coupled saturated porous media to represent the idealized sugar cube model, which is generally employed in fractured porous media on the basis of dual porosity models. In this manner, an extended version of the Hill-Mandel theory that incorporates the microdynamic effects into the multiscale analysis is presented, and the concept of the deformable dual porosity model is demonstrated. Numerical simulations are performed employing the multiscale analysis and dual porosity model, and the results are compared with the direct numerical simulation through 2 numerical examples. Finally, a combined multiscale-dual porosity... 

Numerical simulation of dam-foundation-fault system, considering the earthquake source, propagation path, and local site effects, was carried out for realistic and reasonable seismic safety analysis of concrete dams. The Domain Reduction Method (DRM) was used for seismic analysis of Dam-Foundation-Fault (DFF) system, in which a modular two-step methodology for reducing the computational costs in large domain analysis was introduced. In this method, seismic excitation is directly applied to the computational domain such that assigning artificial boundary to the finite element models is more comfortable. In order to verify the implementation of the DRM in Finite Element Method (FEM), a simple... 

In this paper, a multiscale homogenization approach is developed for fully coupled saturated porous media to represent the idealized sugar cube model, which is generally employed in fractured porous media on the basis of dual porosity models. In this manner, an extended version of the Hill-Mandel theory that incorporates the microdynamic effects into the multiscale analysis is presented, and the concept of the deformable dual porosity model is demonstrated. Numerical simulations are performed employing the multiscale analysis and dual porosity model, and the results are compared with the direct numerical simulation through 2 numerical examples. Finally, a combined multiscale-dual porosity... 

Numerical simulation of dam-foundation-fault system, considering the earthquake source, propagation path, and local site effects, was carried out for realistic and reasonable seismic safety analysis of concrete dams. The Domain Reduction Method (DRM) was used for seismic analysis of Dam-Foundation-Fault (DFF) system, in which a modular two-step methodology for reducing the computational costs in large domain analysis was introduced. In this method, seismic excitation is directly applied to the computational domain such that assigning artificial boundary to the finite element models is more comfortable. In order to verify the implementation of the DRM in Finite Element Method (FEM), a simple... 

Isogeny-based cryptography is one of the promising post-quantum candidates mainly because of its smaller public key length. Due to its high computational cost, efficient implementations are significantly important. In this paper, we have proposed a high-speed FPGA implementation of the supersingular isogeny Diffie-Hellman (SIDH) and key encapsulation (SIKE). To this end, we have adapted the algorithm of finding optimal large-degree isogeny computation strategy for hardware implementations. Using this algorithm, hardware-suited strategies (HSSs) can be devised. We have also developed a tool to schedule field arithmetic operations efficiently using constraint programming. This tool enables... 

Attitude control of a satellite having only two reaction wheels is a challenging issue. To address this problem, previously published researches considered some simplifying assumptions on the satellites such as diagonality of the moment of the inertia matrix. On the other hand, in some works, the total angular momentum of the satellite is assumed to be zero. In this paper, a linear time-variant model predictive control (LTV MPC) is designed to control a satellite with two reaction wheels. This control method can be applied to a satellite with a non-diagonal inertial matrix in the presence of external torques, to rotate the satellite toward the desired directions in the space and orbit. The... 

Sparse representation can be extended to high dimensions and can be used in many applications, including three-dimensional (3D) Inverse synthetic aperture radar (ISAR) imaging. In this study, the high-dimensional sparse representation problem and a recovery method called high-dimensional smoothed least zero-norm (HDSL0) are formulated. In this method, the theory and computation of tensors and approximating L0 norm using Gaussian functions are used for sparse recovery of high-dimensional data. To enhance the performance of HDSL0, modified regularised high-dimensional SL0 (MRe-HDSL0) algorithm, which benefits from the regularised form of SL0 and an additional hard thresholding step, is... 

The main concern in Molecular Dynamics (MD) simulations is the computational cost, and coarse-graining methods accelerate simulations by reducing the degrees of freedom in the system. Yet, the utilization of these methods should be carefully followed. In this paper, we presented an energy-based coarse-graining method for Tersoff and Stillinger-Weber potential functions. The presented coarse-graining method is based on the domain mapping and modification of potential function. The focus of this paper is on Carbon and Silicon materials; however, this method can be applied to model other materials for which Tersoff and Stillinger-Weber potentials are defined. This method has been validated by... 

Proton exchange membrane fuel cell (PEMFC) is a rich source of renewable energy. A non-destructive prediction method is needed to determine the content of water in the PEMFC. In the gas channel of a transparent PEMFC, water is detected with image processing. This method has a high computational cost and is sensitive to the initial position of the camera and ambient lighting. In this paper, the deep neural network (DNN) has been trained to learn the transparent PEMFC's labeled images as a way to determine the content of water, limit human interference and employed in a real-time process. This DNN model is a virtual sensor for measuring the water coverage ratio. To produce the label of images,... 

In this paper, a novel moving object modeling suitable for multicamera correspondence is introduced. Taking into consideration the color and motion features of foreground objects in each independent video stream, our method segments the existing moving objects and constructs a graph-based structure to maintain the relational information of each segment. Using such graph structures reduces our correspondence problem to a subgraph optimal isomorphism problem. The proposed method is robust against various resolutions and orientations of objects at each view. Our system uses the fuzzy logic to employ a human-like color perception in its decision making stage in order to handle color inconstancy...