Sharif Digital Repository

The hierarchical multi-scale approach is one of the most powerful techniques that takes the advantage of different scales and succeeds the limitations of each method in a way that the large systems in coarse-scale can be simulated with atomic precision. In this thesis, the hierarchical atomistic-continuum multi-scale method is developed for modeling the phenomena with non-homogenous deformation, large deformation and plastic behavior. In this regard at first, an atomistic-based higher-order continuum model is formulated in the framework of nonlinear finite element method to present the geometrically nonlinear behavior of nano-structures. The efficiency of higher-order Cauchy-Born hypothesis... 

Usually the structures are designed based on the fixed-base assumption; however this assumption is not always correct and the bed flexibility should be considered. This effect which is called soil-structure interaction, change the response of structure on both elastic and inelastic behavior. In the regulations for the design of structures on flexible base, generallysoil-structure system is replaced with an equivalent fixed base structure; in the way that the design force of soil-structure system with reasonable accuracy can be estimated by equivalent fixed-base structure. In some regulations, dynamic characteristics of equivalent structure have been determined on elastic state and the... 

Today, with the increasing expansion and development of computer networks and information technology, network security has become an important concern for experts and researchers in this field. One of the main elements in the field of information and network security are intrusion detection systems. To maintain the accuracy and quality of these systems, we need to test and evaluate them frequently. The datasets of intrusion detection systems are one of the main tools for evaluating these systems. The quality and accuracy of these systems in detecting anomalies and attacks in the network largely rely on rich and complete data. Also, the main component of this datasets is the traffic data,... 

Ray-tracing method is extensively used for wave propagation analysis of indoor and outdoor environment for narrowband systems. The main goal of this research is to extend ray-tracing method for site-specific modeling of wideband electromagnetic propagation. In this thesis, Time-domain reflection and transmission coefficients have been computed for dielectric half-space and dielectric slab with frequency dependent permittivity. First, analytical form of Fresnel reflection coefficient for vertical and parallel polarizations in time-domain have been computed for dispersive half-spaces with lossy, extended Debye, two-pole Debye and multi-pole Debye models. By using these time-domain... 

This mixed-method study explored the intercultural competence of a group of engineering university students in Sharif University of Technology enrolling in a “Listening and Speaking” class, minoring in English. The aims of this study were first to assess the initial ICC level of the participants, second to evaluate the effect of a culture-based syllabus on ICC development of the learners, third to measure the variation of their L1 cultural identity of the students after the course, fourth to discover recursive themes in the assignments and recorded interviews with the participants and fifth to measure the learners’ satisfaction with the developed syllabus. Quantitative data was derived from... 

Due to its extensive applications in imaging, data security, medication, etc., Terahertz has attaracted an ever-inceasing attention. Design and implementation of Terahertz sources and circuits as waveguide, filters, etc. in this band have attracted attention. The unique elec-tric characteristics of graphene, like showing plasmonic characteristics in Terahertz frequency band, made this recently recognized matter a promising one to build super-integrated plas-monic circuits in Terahertz band. In this text, the first section represents an introduction for a number of graphene waveguides as well as studying the effective refractive index method to analyze graphene waveguides. Due to the small... 

Emission of the greenhouse gases is a great concern and use of lightweight materials in transport vehicles has found a major importance in this respect. Metal/polymer laminated composites are of great interest due to their special mechanical properties and thoughness. Application of metal/polymer sheets is limited because of low bonding strength of metals and polymers in these sheets, which is not sufficient, to endure the stresses and strains during deformation processes such as deep drawing and bending. In this research, roll bonding of AA5052/Polypropylene sheets is investigated and the effects of bonding parameters on strength are evaluated and finally the bonding mechanisms during... 

Language model is one of the most important parsts of an automated speech recognition system whiche incorporates the knowledge of Natural Language to the system to improve its accuracy. Conventionally used language model in recognition systems is ngram which usually is extracted from a large corpus using related frequency method. ngram model approximates the probability of a word sequence by multiplying its ngram probabilities and thus does not take into account the long distance dependencies. So, syntactic language models could be of interest. In this research after probing different syntactic language models, a mehtod for re-estimating ngram model, introduced by Stolcke in 1994, was... 

Applying spoken dialogue systems (SDS's) is growing in the real life more rapidly because of the advances in the design and management of these systems. The traditional touch tone computer telephony systems are being substituted by the SDS's. In a typical SDS, the user speaks naturally to the system through a phone line and the system provides the required information or performs the required action. Banking and ticket reservation are typical examples of the prevalent SDS's. A spoken dialogue system has four units: automatic speech recognition (ASR), natural language understanding (NLU), dialogue management (DM), and spoken language generation (SLG). In this work, the first spoken dialogue... 

The utilizing language model is one of the most effective methods for improving speech recognition performance. For speech recognition applications, several types of language models have been proposed for speech recognition applications that try to model some parts of language information, such as n-gram models, syntactic models, and semantic models. Although n-gram, syntactic and semantic models are able to model different structures that exist in natural language, they each only capture specific linguistic phenomena. None of them can simultaneously take into account all of language phenomena in a unified probabilistic framework. Recently, a number of semantic models called "latent topic... 

Automatic speech recognition performance degrades significantly when speech is affected by environmental noise. Nowadays, the major challenge is to achieve good robustness in adverse noisy conditions so that automatic speech recognizers can be used in real situations. Spectral subtraction (SS) is a well-known and effective approach; it was originally designed for improving the quality of speech signal judged by human listeners. SS techniques usually improve the quality and intelligibility of speech signal while speech recognition systems need compensation techniques to reduce mismatch between noisy speech features and clean trained acoustic model. Nevertheless, correlation can be expected... 

Signle-channel speech enhancement using hidden Markov model (HMM) based on minimum mean square error (MMSE) estimator is focused on and an HMM-based speech enhancement in Mel-frequency domain is proposed. The MMSE estimator results in a weighted sum filtering of the noisy signal in which accurate estimation of the filter values and filter weights comprise the main challenges. The cepstral domain modeling for speech enhancement is motivated by accurate filter selection in this domain. In the propsed framework, Mel-frequency spectral (MFS) and Mel-frequency cepstral (MFC) features are studied and experimented. In addition to the spectrum estimator, magnitude spectrum, log-magnitude spectrum... 

Semantic classification of words based on unsupervised learning methods is a challenging issue in computational lexical semantics. The goal of this field of study is to recognize the words that are in the same semantic classes; i.e., can have the same set of arguments. Among all word categories, verb is known as one the most important and is assumed as the central part of the sentence in certain linguistic theories such as case grammar and dependency grammar. Based on Levin’s idea, diathesis alternations and the similarity between these alternations are the clues for the semantic classification of verbs. This idea is verified in languages such as English and German with promising results.... 

The human speech conveys much information such as semantic contents, emotion and even speaker identity. Our goal in this thesis is the task of text-independent speaker identification (SI) in large population applications. Identification (test) time has become one of the most important issues in recent real time systems. Identification time depends on the cost of likelihood computation between test features and registered speaker models. For real time application of SI, system must identify an unknown speaker quickly. Hence the conventional SI methods cannot be used. The main goal in this thesis is to propose several methods that reduced identification time without any loss of identification... 

Measuring emotions of music is one of the methods to determine music content. Music emotion detection is applicable in music retrieval, recognition of music genre and also music data management softwares. Music emotion is considered in different sciences such as physiology, psychology, musicology and engineering. First, we collected a database of different types of music with various emotions. These data have been labeled according to their emotions. In this project, four emotions (Angry, happy, relax and sad) have been used as labels based on Thayer’s two dimension emotion model. There are two basic steps for music emotion recognition similar to other recognition systems: Feature extraction... 

Spoken language understanding unit is one of the most important parts of a spoken dialogue system. The input of this system is the output of speech recognition unit. The main function of this unit is to extract the semantic information from the input utterances. There are two main types of approaches to do this task: rule-based approaches, and data-driven approaches. Today data-driven approaches are of more interest because they are more flexible and robust compared to the rule-based approaches. The main drawback of these methods is that they need a large amount of fully annotated or in some cases Treebank data. Preparing such data is time consuming and expensive. The goal of this thesis is... 

Scattered and little research in the field of Persian speech synthesis systems has been performed during the last ten years. Comprehensive framework that properly implements and adapts statistical speech synthesis methods for Persian has not been conducted yet. In this thesis, recent statistical parametric speech synthesis methods including CLUSTERGEN, traditional HMM-based speech synthesis and its STRAIGHT version, are implemented and adapted for Persian language. CCR test is carried out to compare these methods with each other and with unit selection method. Listeners Score samples based on CMOS. The methods were ranked by averaging the CCR scores. The results show that STRAIGHT-based... 

Recently various speaker adaptation methods in HMM-based speech synthesis are proposed. The importance of adaptation techniques is that we can design a system in which speech is generated with high quality and target speaker characteristics through limited adaptation data sets.
In this research, we focus on adaptation based on clustering and develop a new and novel method using eigenvoices in order to adapt a new speaker. We employ this approach for the first time in HSMM-based speech synthesis systems and its goal is to reduce the parameters and adaptation data of the system. In our proposed method, first some speaker dependent models are trained. For each model we combine the... 

Given two speech segments, the task of speaker verification is defined as determining whether or not both of them have been uttered by the same person. Most of the new approaches in speaker verification are based on Total Variability Space which is the result of applying a factor analysis on GMM mean supervector space. The representation of speech with arbitrary duration in this space is called i-vector.
In this thesis, first the basics of speaker verification is described and i-vector approaches are explained in more details. Then, a method for improving accuracy of Cosine Similarity Scoring is proposed which normalize the raw score using the score of test utterance against a model- and...