Sharif Digital Repository

In low energy limit, some string theory models are described with an effective lagrangian which consist of two term. first is Eistein-Hilbert term and second is chern-simons. Adding the chern-simons term to Eistein-Hilbert lagrangian leads to the new modified field equations which schuwarzshild, Reissner-Nordstrom and FRW metrics satisfy these new modified equations while kerr metric is not a solution for these new fields. Since the gravitational field of spinning objects are similar to electromagnetic field, we expect to observe the chern-simons effects in gravitomagnetic component of the gravitational field. Here, in order to better understanding of subject, we investigate the Maxwell... 

There is an ever increasing trend to apply AdS/CFT to explain physical phenomena and this endeavor has recently begun concerning Superconductivity. Using black holes with differing structures and gravitational content has proved to be uitful in this explanation. In this thesis we try to explain superconductivity for a charged ro-tating black hole with Kerr-Newman-Ads metric in four dimensions. Having calcu-lated condensation for a rotating black hole we obtained diagrams of phase transition in superconductivity with respect to temperature. In addition a brief discussion on calculating the conductivity has been presented  

AdS/CFT Correspondence since it origination has found numerous applications in various aspects of Physics such as ion collision in Accelerators, Condensed matter Physics and etc. this orrespondence is a consequence of String Theory. In this project we have tried to analyze some of its applications in Condensed matter Physics and in the controversial phenomenon of superconductivity without resorting to the complexities of String Theory. Having applied this approach we have found a dual for it in Gravity and having calculated some common physical quantities and equations in these two sets such as Energy gap, Conductivity, London Equations and etc, we have tried to show the correspondence,... 

Project scheduling problems can be divided into different categories which one of the most important of them is resource investment problem. In this problem we are faced with a time limit for completion but resources are available unlimitedly and can be allocated to the project by specific cost. One of the most important constraints in real problems is the times that resources are available. In this research, resource investment problem considering calendar for resources has been investigated. Since resource investment problem is NP-Hard, after a brief overview of the different solutions, we used genetic algorithm to solve this problem. For this purpose, after determining chromosome... 

The aim of this thesis was creating Titania/RGO coating with electrophoretic method to evaluate the corrosion resistance of stainless steel 316. At first Graphene Oxide were synthesized with modified Hammers method and their physical, chemical and morphological properties have been investigated using XRD, FTIR, EDX, TGA, UV-Vis and Raman analysis. then Graphene Oxide was added to the bath of titanium and was deposited on 316 stainless steel substrates. Moreover, corrosion protection of coating has been examined using Poarization test and Electrochemical Impedance Spectrometry. The effect of deposition parameters on corrosion properties of composite coatings were examined. The key variables... 

Deep neural networks have not progresses comparative until last decade due to computational complexity and principal challenges as gradient vanishing. Thanks to newly designed hardware architecture and great breakthroughs in 2000s leading to the solution of principal challenges, we currently face a tsunami of deep architecture utilization in various machine learning applications. Sparsity of a representation as a feature to make it more descriptive has been considered in different deep learning architectures leading to different formulations where sparsity is impose on specific representations. Due to the gradient based optimization methods for training deep architecture, smooth regularizers... 

Data hiding in digital media has been widely investigated over the past decade because of covering different crucial applications. Amongst digital multimedia signals, video has been received a special attention and data hiding in video signals has reached a significant improvement in recent years. This thesis aims at introducing an information theoretic based analysis method for calculating the capacity of data hiding in video signals, as one of most challenging issues in this area. This analysis is expected to establish a reasonable basis for the design and analysis of data hiding algorithms. We study and investigate the data hiding problems that could be specific to video signals and its... 

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... 

Steganography is the art and science of writing hidden messages in such a way that no one, apart from the sender and intended recipient, suspects the existence of the message, a form of security through obscurity. In this thesis, we focus on entropic issue of multimedia signal in the two branches of Information hiding namely Steganography and Watermarking. How to choose the block and noise estimation in the watermarking, and analysis of the singular values decomposition in steganography are examples of using entopic issue which we use in our thesis. The two new designs for video signals AVI are presented in Watermarking. For the both proposed method ,first AVI video signal will be divided... 

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... 

The rapid increase in the size of digital multimedia data collections has resulted in wide availability of multimedia contents to the general users. Effective and efficient management of these collections is an important task that has become a focus in the research of multimedia signal processing and pattern recognition. In this thesis, we address the problem of automatic classification of music, as one of the main multimedia signals. In this context, music genres are crucial descriptors that are widely used to organize the large music collections. The two main components of automatic music genre classification systems are feature extraction and classification. While features are a compact... 

This project aims at forgery detection in digital videos. Most of existing methods are based on similar methods for image forgery detection. Therefore, they do not have sufficient accuracy in case of video forgery detection. In this project, we focus on local copy/move attacks in digital videos and propose 3 solutions for 3 problems in this field: 1) detection of copy/move along time axis (temporal copy/move), 2) detection of copy/move along x and y axes (spatial copy/move) and 3) detection of original and fake part in case of finding a duplication. For each of these 3 problems a feature extraction algorithm and a forgery detection algorithm are proposed. Feature extraction algorithms are... 

In this project we proposed a new method for improving the detection abality of a steganalyser with a pre-processing on contents of an image. Steganalysis, using machine learning, is designing a classifier with two classes: Stego or Cover. This classifier should be trained with extracted features from signal. The result of the training procedure is a machine that decides a signal belongs to stego or cover class. The first step of steganalysis process is extraction of proper features from signal. Proper feature is a variable that represents all of the useful properties of signal. Second step of this process is classifying data to two class of stego and cover. Many algorithms are proposed for... 

This thesis explored and described a new steganalysis system modeling. Image steganalysis systems are divided into two parts: feature extraction from images and classification of images. Many researches have been done in both parts and satisfactory results have also been reported. There are acceptable steganalysis methods which work accurately in high rates of steganography; however steganography in low rates is still undetectable. By lowering the rate of steganography in images, the difference between stego images and clean images would be reduced which accordingly led to the reduction of the difference between the corresponding extracted features. Thus, the ability of classification... 

Availability of video recording instruments and ease of working with video editing tools have made contents of this digital signal unreliable in general. The goal of this thesis is to present a method to detect tampering of compressed videos in H.264/AVC format and restoring an approximate version of its original contents using watermarking. In the proposed scheme, a low resolution image from a number of video frames in certain time slots are embedded into the DCT coefficients of the other parts of the video which are adequately far from the reference frames. For detecting temporal and spatial tampering, the index of each frame and macroblock is embedded into itself as an authentication... 

Recognition of complex visual events has attracted much interest in recent years. Compared to somehow similar tasks like action recognition, event recognition is much more complex, primarily because of huge intra-class variation of events, variable video durations, lack of pre-imposed video structures, and severe preprocessing noises. To deal with these complexities and improve the state of the art approaches to the problem of video understanding, this thesis is focused on video event recognition based on frame level CNN descriptors. Using transfer learning, the image trained descriptors are applied to the video domain to make event recognition feasible in scenarios with limited...