Sharif Digital Repository

AC/DC/AC converters are widely being used in a variety of power applications. Continuity of service of these systems, as well as their reliability and performance are now of the major concerns. Indeed, the failure of the converter can lead to the total or partial loss of the control of the phase currents and can cause serious system malfunction or even shutdown. Thus, uncompensated faults can quickly endanger the system. Therefore, to prevent the spread of the fault to the other system components and to ensure continuity of service, fault tolerant converter topologies associated with quick and effective fault detection and compensation methods must be implemented. In this thesis, the... 

In the recent years, there is a worldwide wave of considerable changes in power industries, including the operation of distribution networks. Deregulation, open market, alternative and local energy sources, new energy conversion technologies and other future development of electrical power systems must pursue different goals. Also growth in the demand and change in load patterns may create major bottlenecks in the delivery of electric energy. This would cause distribution system stress.Furthermore, in competitive electricity markets, operators determine the electricity price for specific intervals during a day, taking into account various economical and operational factors. Traditionally, a... 

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... 

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... 

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... 

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... 

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... 

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... 

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... 

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... 

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... 

Among all biometric approaches, gait analysis is one of the most practical methods for human identity recognition. Gait has a lot of advantages over other biometrics like face recognition, iris recognition, fingerprint, etc. First and foremost, the gait data can be collected from a distance, and there is no need for subject’s cooperation. Another advantage of this biometric method is its cost-effectiveness and the fact that it does not need high-resolution images. But there are significant challenges in detecting and analyzing this feature. One of the most important challenges is decreased recognition accuracy caused by identity-irrelevant factors like camera viewpoint and changes in walking... 

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... 

Speech is the most natural and widely used approach to communication between people and the fastest communication between humans and computers. Advances have been made in this area to achieve complete success in the natural human-computer relationship. The big challenge in this way is the inability of the computer to recognize the user's feelings; Therefore, in speech processing, one of the things that should be studied and considered is; detection of emotion from a speech by a computer. This is because Emotion recognition of speech can help extract meanings and improve the functioning of the speech recognition system. This study first defined the emotions and materials needed to build... 

The purpose of this project is to improve current speaker verification techniques with short utterance using phonetic information extraction. I-vector technique is widely used in speaker verification systems. Different speakers span a subspace of universal acoustic space, which is usually modeled by “Universal Background model”. Speaker-specific subspace depends on the voice of speaker. In state-of-the-art speaker verification systems i-vectors are extracted by a factor analysis technique to represent speaker characteristics. Studies demonstrate that voiced phonemes contain more speaker-specific information than unvoiced. In this thesis we have classified voiced frames in order to exploit... 

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... 

The goal of this thesis is using sparse decomposition to design secure steganography algorithms to insert secret data into the different cover signals. We propose data embedding in higher semantic levels of the cover signal to reach this goal. Sparse decomposition represents a signal as a linear combination of its structural elements. Data insertion into the sparse coefficients slightly changes the effect of structural elements in the signal representation. So, quality of the stego signal is preserved and such a steganography method leads to higher imperceptibility. In addition, data insertion into the higher semantic levels leads to the higher undetectability. Steganalyzers use statistical...