Sharif Digital Repository

One of the problems in signal processing is supervised classification. In supervised classification, the goal is to learn the structures and patterns of a dataset using a set of labeled data called the training dataset to correctly classify data samples that are not used in the training data but follow the same pattern and structure. One approach to this problem that has recently received attention is neural networks. Although this approach has good performance in applications, in order to perform well, they require a large amount of data and many trainable parameters, which result in high computational complexity. Another approach to this problem is dictionary learning-based classification.... 

Brain computer interfaces (BCI) are systems designed to understand the brain function from activation patterns and dynamics recorded from the brain activity and use this knowledge to give disabled people the ability to communicate with their surroundings. Features are extracted from recorded signals from the brain while occupied in a mental task and classified into categories related to the task given. These classifiers are then used for the estimation of user anticipation. Usually, the tasks defined are meant to evoke or induce a potential in the pattern of the brain. Awareness of error responses is one of the cognitive functions of the brain which occurs when a response is in conflict with... 

Electroencephalogram (EEG) brain source localization carries many potential applications in systems and cognitive neuroscience, and for treatment of various neurological problems such as epilepsy. According to some recent studies, determining the spatial extent of sources and estimating their true time courses have proved challenging. This master's thesis proposes a method for localizing extended brain sources. Cortical surface parcellation has been used to reduce the dimension of the inverse problem without losing much information. The active regions are assumed to be sparse and the time course of the sources exhibits a correlation structure. The reduced dimension problem was then solved by... 

Principal component analysis (PCA) is a conventional tool for dynamic system analysis. In this paper, a new damage diagnosis method is presented based on sensitivities of principal components obtained from PCA of Frequency response functions (FRFs). Damage identification, determination of its location and estimation of the damage severity are conducted by an innovative well established sensitivity relation of PCA results. Then, sensitivity matrix is obtained by using measured frequency of damaged structure, and comparison of PCA of the intact structure with PCA from FRFs which are measured at sensors locations. Damage location and its severity are calculated by model updating procedure. This... 

Damage identification and localization using dynamic data and its Principal Components The objective of this study is to demonstrate the application of SVD-based principal component analysis performed on moving windows of transfer function. It uses the sensitivities of measured responses in frequency domain, its singular values and right eigenvectors for FE model updating in an efficient way, by developing a quasi-linear sensitivity equation of structural response. The benefit of applying PCA for dynamical systems comes from its ability to detect and rank the dominant coherent spatial and frequency-dependent information of dynamic response. The challenge of using modal parameters of... 

Steganalysis is the science of detecting covert communication. It is called blind (universal) if designed to detect stego images steganographied by a wide range of embedding methods. In this method, statistical properties of the image are explored, regardless the embedding procedure employed. The main problem for image steganalysis is to find sensitive features and characteristics of the image which make a statistically significant difference between the clean and stego images. In this thesis we propose a blind image steganalysis method based on the singular value decomposition (SVD) of the discrete cosine transform (DCT) coefficients that are revisited in this work in order to enhance the... 

The use of wind energy and wind generators as an inexpensive type of renewable energy sources is increasing. Wind farms and distributed wind generators connected to the power transmission and distribution systems and microgrids, and wind generators in standalone mode of operation affect the stability and power quality of the grid. In this thesis, the performance of a matrix converter in grid connection of variable speed PMSG–driven wind turbine is improved. In this way, by transferring the matrix converter modulation matrix into the synchronous reference frame and decomposing it into the singular values, a new general method is proposed for analysis, modelling and modulation of a matrix... 

The field of digital image processing refers to processing digital images by means of a digital computer. One of the principal applications used in digital image processing is image compression. Image compression is the problem of reducing the amount of data required to represent a digital image. The basis of the reduction process is the removal of redundant data. One of the other principal applications of image processing is noise reduction (filtering) of images corrupted with additive noise. Filtering techniques are oriented toward modeling the degradation and applying the inverse process in order to recover the original image. Image watermarking is another application of image processing.... 

Reduced-order modeling (ROM) is a novel approach in all realms of computational science including reservoir simulation. Among various ROM methods, Trajectory Piecewise Linearization (TPWL) is on its evolution for reservoir applications. Previous investigations reflect promising future for incorporating TPWL into the next generations of enhanced reservoir simulators. In this work, we employ TPWL to investigate the claimed efficiency, robustness and accuracy of this method as a surrogate simulator for a developed reservoir simulator. The self construction of the used simulator gives us the opportunity to explore this method and to examine previous assertions on the subject. The efficiency of... 

Blind image steganalysis is a technique used to, which require no prior information about the steganographic method applied to the stego im- age, determine whether the image contains an embedded message or not. The basic idea of blind steganalysis is to extract some features sensitive to information hiding, and then exploit classifiers for judging whether a given test image contains a secret message.The main focus of this research is to design an choose features sen-sitive to the embedding changes. In fact, we use high order moments in different domains, such as spatial, DCT and multi-resolution do-main, in order to improve the performance of existing steganalyzers.Accordingly, First, we...