Sharif Digital Repository

In this thesis, a reduced model of a distributed one-dimensional Solid Oxide Fuel Cell (SOFC) system is presented. Consequently the reduced model is used to estimate the states of the exact model. The reduced model is derived using the exact solution of the distributed model and implementing the Karhunen-Loève-Galerkin procedure. To achieve an exact solution of the distributed model, method of lines is used. In the method of lines, accuracy of the solution depends on the number of grid points. To increase the accuracy, the number of grid points should be increased which will lead to a high order dynamic model. Such a model is not suitable for state estimation, optimization or control... 

With prominent features such as lower power consumption, shock resistance, and smaller size, SSDs are widely being used in portable applications. In addition, SSDs are becoming more popular in data storage systems and servers due to their even access and fast response time. Several efforts have been made in the past few years to improve the performance of SSDs. One common technique to improve performance is designing an Input/Output (IO) scheduler based on SSD organization.
In this thesis, we propose an IO scheduling algorithm to improve both performance and endurance of SSDs. Since in-place update is not allowed in flash chips, erasure before any data overwriting is required. As... 

Methodology has always one of important issues in philosophy of science; especially in social sciences lots of challenges arise from this field. Each of positivistic and relativistic different schools, on their own ontology about human and society, had suggested different methods of social studies. Roy Bhaskar, as the others, offers a model for scientific method, on the base of his own critical realistic ontological principles; and tries to secure his model from the objections on previous models of scientific method. In this model, hi suppose empirical survey as an obvious and undoubted activity in science. Then, he transcendentally derives his ontological principles from this antecedent,... 

The philosophers of social sciences, in addition to epistemological questions-the questions about our knowledge of the social world- implicitly or explicitly, consider ontological issues: the questions about the nature of social world (society, structures and relations, people and actions). In Critical Realism, ontology and epistemology are linked. It means that what it is awarded, it is crucial that knowledge can be gained from it. This school, with a critique of Positivism and Hermeneutics, tries to authenticate ontological arguments and by reconstruction of concepts like causality, necessity, structure and stratification reality, forms the realist ontology and offers a novel solution to... 

Memory and recalling process has always been a basic question. Decoding the Long-Term_Memory is one of the first steps in answering this question. Since various experiments in the field of human long-term memory, was conducted. This research is motivated by a trial that in which, the Mgntvansfalvgram (MEG) has been recorded while recalling the color and orientation of a grading which is associated with an object, after the object has been shown. High accuracy in Decoding the mentioned color and direction, will be decoding the long-term memory. In order to enhance memory decoding, the research studies different classifiers such as sparse based classifiers and other popular one. It has also... 

In recent years there has been a great effort to enhance the computer-aided diagnosis systems, Since expertise elicited from past resolved cases plays an important role in medical applications, and images acquired from various cases have a great contribution to diagnosis of the abnormalities, Content based medical image retrieval has become an active research area for many scientists. In this project we proposed a new framework to retrieve visually similar images from a large database, in which visual similarity is regarded as much as the semantic category relevance, we used optimized wavelet transform as the multi-resolution analysis of the images and extracted various statistical SGLDM... 

Breast cancer is considered as the most common type of cancer in women worldwide and mammography is currently utilized as the principal method for screening the breast cancer. Breast Magnetic resonance imaging (MRI) can be used as a complementary imaging technique besides mammography. MRI technique involves scanning a patient before and repeatedly after the injection of the contrast agent (DCE-BMRI). This examination often takes 7-10 minutes and any movement of the patient’s breasts due to breath, heartbeat or deliberate movement, made in this relatively long acquisition period, leads to a distortion in images called motion artifact. This problem makes the quantitative analysis of the images... 

Image registration is an important preprocessing step in analysis of medical images. Detection, Treatment plan, disease grows process analysis and assistance in surgical applications are some of medical images applications. We need to be able to compare different modalities in medical images such as X-ray, PET, MRI, and CT... , or sometimes doctors need to take images of a patient in a same modality but in different times and directions. In which in order to be able to do theses comparisons we need to first align these images by using image registration methods. Image registration is an image processing method in which tries to find a geometrical transformation that would map different... 

Nowadays studying the brain's function in different Mental States like Resting-State or performing cognitive tasks is a very important component of research areas such as Biomedical Engineering, Neuroscience and Cognitive Sciences. The applications of studying the brain's function can be divided into two principal groups. In the first group of applications, the goal is understanding how the brain processes and response to external stimuli (like visual or audio stimuli) and internal states (like emotions). In these kinds of applications, particularly healthy subjects participate since the goal of these studies is finding healthy brain function in different states and stimuli. However, in the... 

The definition of human action recognition is classification of input visual elements based on the action which is done by a person in the scene. One of the most important topics in the filed which has lots of applications is human action recognition in videos. Some of these applications are surveillance, video retrieval, human computer interaction and smart houses. Due to increments in number of alone elderly people, surveillance of them is one of the important applications of human action recognition. The challenges of the task are, camera movement, differences of environment and differences in acting by different actors.The goal of the project is proposing a deep convolutional neural... 

Autism spectrum disorders (ASD) have been defined as developmental disorders characterized by abnormalities in social interaction, communication skills, and behavioral flexibility. Over the past decades, studies using various genetic, neurobiological, cognitive and behavioral approaches have sought a single explanation for the heterogeneous manifestations of ASD, but no consensus on the etiology of ASD has emerged. Further studies aim to clarify the mechanism of disease.
Functional Magnetic Resonance Imaging (fMRI) is a new way of imaging which evaluates activity of brain by measuring magnetic difference caused by oscillation in blood oxygen level. fMRI has been widely used in recent... 

In this study pain level estimation using facial expression is investigated. To do this, there are two approaches, one approach is sequence level pain estimation and the other one is frame level pain estimation. In sequence level, after feature extraction from all frames of sequence, each sequence is represented by a fixed length feature vector, this feature vector is constructed by concatenating min, max and mean of frame features of that specific sequence, then KLPP is applied in order to reduce feature vector dimension and in the end a linear regression is implemented to predict the pain labels of the sequence. In the frame level, two approaches are introduced, the first one is based on... 

Advancements in medical imaging, especially 3D imaging, leads to progressive growth of image processing for diagnosis, studying behavior of organs and development of disease. Therefore many researches have been done on segmentation, registration, modeling and 3D image refinement. In this thesis, we want to parameterize anatomic surfaces, using volume parameterization model.
Hippocampus is one of the most important components in brain that plays significant role in learning, memory, stress management and etc. There is been a theory that, shape and structure of hippocampus may change in diseases like Alzheimer, schizophrenia, chronic depression and epilepsy.
The aim of this research is... 

Medical imaging is of interest because of information that will provide for doctors and registration is inevitable when we need to compare two or more images, taken from a subject at different times or with different sensors or when comparing two or more subjects together. Registration methods can be categorized in two major groups; methods based on feature and methods based on intensity. Methods in first group have three steps in common: feature extraction, finding matches and transform estimation. In second group it’s important to define a similarity measure and find the transform that minimizes this measure.
Manifold learning algorithms are mostly used as a dimensionality reduction... 

Development of DNA sequencing technologies in the recent years magnifies the need for computational tools in genomic data processing, and thus has attracted inten- sive research interest to this area. Among them, Genome-Wide Association Study (GWAS) refers to discovering of causal relationships among genetic sequences of living organisms and the macroscopic phenotypes present in their physiological structure. Chosen phenotypes for genomic association studies are mostly vulnerability or im- munity to common genetic diseases. Conventional methods in GWAS consists of statistical hypothesis testing algorithms in case/control approaches; Most of which are based upon single-locus analysis and... 

Lorentz reciprocity theorem is one of the most fundamental concepts in electromagnetism. Although, in specific conditions, for example in nonlinear systems or systems with broken time-reversal symmetry it can be violated. In recent years, many interests have been attracted towards such systems. With a proper design, they can support nonreciprocal or at specific conditions unidirectional propagation behavior. For example, despite ordinary dielectrics, in ferrite layers, the propagation constant of surface waves on ferrite edge, depends on the direction of propagation with respect to magnetization direction. Furthermore, in the specific interval of frequencies, ferrite structures allow... 

Sparse representation has attracted a lot of attention during the last few years. This adaptive representation has been used as an alternative of the classic transforms. In this kind of representation, signals are decomposed in terms of some basis functions. This basis functions are called “atoms” and their collection is called a “dictionary”. Dictionary learning should be such that signals have a sparse representation. Specified dictionary could apply some other properties except sparsity for the transform domain representation.In this thesis after study of methods convert the dictionary discriminative, we propose KLDA method for learning a discriminative dictionary. Also a new algorithm... 

For radiotherapy and removal of cancerous tissues, it is necessary to determine the location of the tumor and the vulnerable structures around the tumor before treating and irradiating the high-energy beam. To do this, the images received from the patient need to be segmented. This is usually done manually, which is not only time consuming but also very expensive.Various methods for segmenting these images are presented automatically and semi-automatically, among which methods based on machine learning and deep learning have shown much higher accuracy than other methods. Despite this superiority, these methods have problems such as high computational costs, inability to learn the shape and...