Sharif Digital Repository

Real networks often exhibit nontrivial topological features that do not occur in random graphs. The need for synthesizing realistic networks has resulted in development of various network models. In this paper, we address the problem of selecting and calibrating the model that best fits a given target network. The existing model fitting approaches mostly suffer from sensitivity to network perturbations, lack of the parameter estimation component, dependency on the size of the networks, and low accuracy. To overcome these limitations, we considered a broad range of network features and employed machine learning techniques such as genetic algorithms, distance metric learning, nearest neighbor... 

The nonlinear dynamics of a bistable micro/nano-electro-mechanical system resonator composed of an arch-shaped microbeam is investigated. The initially curved microbeam is actuated through a combined DC and AC electrostatic parallel plate field. A single degree of freedom model obtained using the Galerkin's decomposition method with distributed electrostatic force is implemented in order to investigate the resonator dynamics near its primary resonance. According to the shape of the potential energy function which depends on the system parameters, the nonlinear dynamics of the system are classified into certain categories. The appearance of various nonlinear phenomena including dynamic... 

This study presents a passive model to detect islanding conditions of synchronous distributed generation resources in a distribution network or a microgrid. The proposed approach uses the classification and regression tree algorithm for distinguishing between islanding and non-islanding situations. It utilises the rate of change of frequency (ROCOF) and harmonic content of the equivalent reactance seen at the location of distributed generation as input features for decision tree construction. Indeed the thresholds of the proposed input features are extracted by the decision tree algorithm. The output if-then rules of the decision tree algorithm are then utilised to make a new relay logic for... 

The main core of HMM-based speech recognition systems is Viterbi algorithm. Viterbi algorithm uses dynamic programming to find out the best alignment between the input speech and a given speech model. In this paper, dynamic programming is replaced by a search method which is based on particle swarm optimization algorithm. The major idea is focused on generating an initial population of segmentation vectors in the solution search space and improving the location of segments by an updating algorithm. Several methods are introduced and evaluated for the representation of particles and their corresponding movement structures. In addition, two segmentation strategies are explored. The first... 

In this study, we are proposing a novel nonlinear classification approach to discriminate between Alzheimer's Disease (AD) and a control group using T1-weighted and T2- weighted Magnetic Resonance Images (MRI's) of brain. Since T1-weighted images and T2-weighted images have inherent physical differences, obviously each of them has its own particular medical data and hence, we extracted some specific features from each. Then the variations of the relevant eigenvalues of the extracted features were tracked to pick up the most informative ones. The final features were assigned to two parallel systems to be nonlinearly categorized. Considering the fact that AD defects the white and gray regions... 

"Web 2.0" refers to a second generation of web development and design, that facilitates communication, secure information sharing, interoperability, and collaboration on the World Wide Web. The truth is that Web 2.0 is a difficult term to define, even for web experts. Usually phrases like "the web as platform" and "architecture of participation" are used to describe this term. Examples of Web 2.0 include web-based communities, hosted services, web applications, social-networking sites, video-sharing sites, wikis, blogs, mashups and folksonomies. The Internet is changing medicine and Web 2.0 is the current buzz word in the World Wide Web dictionary. Radiology in the Web 2.0 probably refers to... 

Temporal relation classification is one of contemporary demanding tasks of natural language processing. This task can be used in various applications such as question answering, summarization, and language specific information retrieval. In this paper, we propose an improved algorithm for classifying temporal relations, between events or between events and time, using support vector machines (SVM). Along with gold-standard corpus features, the proposed method aims at exploiting some useful automatically generated syntactic features to improve the accuracy of classification. Accordingly, a number of novel kernel functions are introduced and evaluated. Our evaluations clearly demonstrate that... 

Quasi-ultrafine (quasi-UF) particulate matter (PM0.25) and its components were measured in indoor and outdoor environments at four retirement communities in Los Angeles Basin, California, as part of the Cardiovascular Health and Air Pollution Study (CHAPS). The present paper focuses on the characterization of the sources, organic constituents and indoor and outdoor relationships of quasi-UF PM. The average indoor/outdoor ratios of most of the measured polycyclic aromatic hydrocarbons (PAHs), hopanes, and steranes were close to or slightly lower than 1, and the corresponding indoor-outdoor correlation coefficients (R) were always positive and, for the most part, moderately strong (median R... 

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,... 

3D scene understanding is one of the most important problems in the field of computer vision. Although, in the past decades, considerable attention has been devoted on the 2D scene understanding problem, now with the development of the depth sensors (like Microsoft Kinect), the 3D scene understanding has become a very challenging task. Traditionally, the scene understanding problem was considered as the semantic labeling of each image pixel. Semantic labeling of RGB-D images has not attained a comparable success, as the RGB semantic labeling, due to the lack of a challenging dataset. With the introduction of an RGB-D dataset, called NYU-V2, it became possible to propose a novel method to... 

In this paper, we present an optical computing method for string data alignment applicable to genome information analysis. By applying moire technique to spatial encoding patterns of deoxyribonucleic acid (DNA) sequences, association information of the genome and the expressed phenotypes could more effectively be extracted. Such moire fringes reveal occurrence of matching, deletion and insertion between DNA sequences providing useful visualized information for prediction of gene function and classification of species. Furthermore, by applying a cylindrical lens, a new technique is proposed to map two-dimensional (2D) association information to a one-dimensional (1D) column of pixels, where... 

An advanced optimization system for Vehicle Routing and Scheduling Problems (VRSP), which is one of the Supply Chain Planning modules, is introduced. An object oriented system, Computer Aided Routing and Scheduling (CARS) can handle complicated distribution models using advanced heuristic optimization algorithms. To classify various types of routing and scheduling problems in a structured manner, a classification scheme is introduced based on the main objects of VRSP. Also, the modeling and solution approach in the CARS optimization engine has been elaborated. Main static and dynamic objects of the system as well as their relationships and interactions have been explained. The user interface... 

A combined texture- and color-based skin detection is proposed in this paper. Nonsubsampled contourlet transform is used to represent texture of the whole image. Local neighbor contourlet coefficients of a pixel are used as feature vectors to classify each pixel. Dimensionality reduction is addressed through principal component analysis (PCA) to remedy the curse of dimensionality in the training phase. Before texture classification, the pixel is tested to determine whether it is skin-colored. Therefore, the classifier is learned to discriminate skin and non-skin texture for skin colored regions. A multi-layer perceptron is then trained using the feature vectors in the PCA reduced space. The... 

This work presents a method to increase the face recognition accuracy using a combination of Wavelet, PCA, KPCA, and RBF Neural Networks. Preprocessing, feature extraction and classification rules are three crucial issues for face recognition. This paper presents a hybrid approach to employ these issues. For preprocessing and feature extraction steps, we apply a combination of wavelet transform, PCA and KPCA. During the classification stage, the Neural Network (RBF) is explored to achieve a robust decision in presence of wide facial variations. At first derives a feature vector from a set of downsampled wavelet representation of face images, then the resulting PCA-based linear features and... 

This work presents a method to increase the face recognition accuracy using a combination of Wavelet, PCA, and SVM. Pre-processing, feature extraction and classification rules are three crucial issues for face recognition. This paper presents a hybrid approach to employ these issues. For pre-processing and feature extraction steps, we apply a combination of wavelet transform and PCA. During the classification stage, SVMs incorporated with a binary tree recognition strategy are applied to tackle the multi-class face recognition problem to achieve a robust decision in presence of wide facial variations. The binary trees extend naturally, the pairwise discrimination capability of the SVMs to... 

Recently, a wide variety of research works have focused on carbon nanotube (CNT)-ceramic matrix nanocomposites. In many cases, these novel materials are produced through conventional powder metallurgy methods including hot pressing, conventional sintering, and hot isostatic pressing. However, spark plasma sintering (SPS) as a novel and efficient consolidation technique is exploited for the full densification of high-temperature ceramic systems. In these binary nanocomposites, CNTs are added to ceramic matrices to noticeably modify their inferior properties and SPS is employed to produce fully dense compacts. In this review, a broad overview of these systems is provided and the potential... 

Causal Heart Rate (HR) monitoring using photoplethysmographic (PPG) signals recorded from wrist during physical exercise is a challenging task because the PPG signals in this scenario are highly contaminated by artifacts caused by hand movements of the subject. This paper proposes a novel algorithm for this problem, which consists of two main blocks of Noise Suppression and Peak Selection. The Noise Suppression block removes Motion Artifacts (MAs) from the PPG signals utilizing simultaneously recorded 3D acceleration data. The Peak Selection block applies some decision mechanisms to correctly select the spectral peak corresponding to HR in PPG spectra. Experimental results on benchmark... 

Fuzzy techniques can be used for accurate and high-speed modeling as well as for the control of complex systems, but various challenging problems are usually encountered during their actual implementation. For example, the variable parameters need to be optimized iteratively during the training phase, where this process is inspired by crisp domain algorithms. However, in recent years, memristor-based structures have emerged as another promising method for implementing neural network structures and fuzzy algorithms. In this study, we propose a novel adaptive and real-time fuzzy modeling algorithm, which employs the active learning method concept to mimic the functionality of the brain's right... 

Unfortunately, energy demands and destruction of the environment from uncontrolled manipulation of fossil fuels have increased. Climate change concerns have resulted in the rapid use of new, alternative combustion technologies. In this study, reactivity controlled compression ignition (RCCI) combustion, which can simply be exploited in internal combustion (IC) engines, is investigated. To introduce and identify extra insightful information, an exergy-based study was conducted to classify various irreversibility and loss sources. Multidimensional models were combined with the primary kinetics mechanism to investigate RCCI combustion, incorporating the second law of thermodynamics. The... 

Many problems in applied machine learning deal with graphs (also called networks), including social networks, security, web data mining, protein function prediction, and genome informatics. The kernel paradigm beautifully decouples the learning algorithm from the underlying geometric space, which renders graph kernels important for the aforementioned applications. In this paper, we give a new graph kernel which we call graph traversal edit distance (GTED). We introduce the GTED problem and give the first polynomial time algorithm for it. Informally, the graph traversal edit distance is the minimum edit distance between two strings formed by the edge labels of respective Eulerian traversals...