Sharif Digital Repository

Sparse signal processing (SSP), as a powerful tool and an efficient alternative to traditional complete transforms, has become a focus of attention during the last decade. In this ap-proach, we want to approximate a given signal as a linear combination of as few as possible basis signals. Each basis signal is called an atom and their collection is called a dictionary. This problem is generally difficult and belongs to the NP-hard problems; since it requires a combinatorial search. In recent years however, it has been shown both theoretically and experimentally that the sparset possible representation of a signal in an overcomplete dictio-nary is unique under some conditions and can be found in... 

Sparse decomposition has recently attracted the attention of many researchers in dierent areas of signal processing. In mathematical viewpoint, sparse decomposition is nding a sparse solution of an Underdetermined System of Linear Equations (USLE). This concept has many applications in dierent signal processing elds including blind sourse separation, optical character recognition and image processing. In this thesis, we investigate the application of sparse decomposition in image denoising. One of the image denoising methods which is related to sparse decomposition concepts is the \transfrom domain method". In this method, the noisy image is rst transformed to another domain, and then noise... 

One of problemsin an inverse problem like history matching is there is no unique solution. This means that maybe several diferent permeability maps can correctly reproduced the history of fieldt but there is no garanttee that these maps accurately predidct reservoir production behavior. One way to face and deal with this problem, except manipulation of solution and optimization algorithm (inverse modeling) is to use other data sources like seismic data, Variogram, pore volume, and fracture density or any other parameter obtained from geostatistical investigations.Multi-resolution wavelet analysis can be an appropriate tool to gather the necessary information to characterize the inverse model... 

Transformers are one of the most important equipments in transmission and distribution networks. Transformer unplanned outages have severe impacts on the continuity of power system operation. To improve the reliability of transformers and to achieve an optimum operation cost, online condition monitoring of transformers is inevitable. Information about the quality of the transformers insulation system is known as the best parameter to be monitored in a transformer. Since partial discharge signals are initiated long before the beginning of a severe damage, partial discharge monitoring and its evaluation canbe employed to warn the operator.Data mining on the partial discharge signals extracts... 

Over-complete transforms due to their maneuverability in signal representation have been under focus during the last decade. Different properties for the representation can be useful in different applications. These properties includes minimum ℓ2 representation, minimum ℓ1 representation, minimum ℓ0 representation and so on. Among these properties, minimum ℓ0 representation (also known as sparse representation) has been shown to be efficient in many applications including image denoising, data compression, blind source separation and so on, and create a new approach in signal processing area named sparse signal processing. Sparse signal processing is based on two principles, the first one is... 

In recent years, sparse representation has had a variety of applications in computer vision such as noise reduction, image reconstruction, classification and dimension reduction. In this project, we aim to provide a method of matching the keypoints obtained from the Scale Invariant feature Transform (SIFT) algorithm. In this algorithm is used descriptor instead of intensity . The proposed method, first, extracts the salient points from the images and learns a dictionary-based descriptors corresponding to the points. Then, using the dictionary, it obtains the sparse coefficients for each salient point by which, it determines the correspondence of the salient points in the two images using SVD... 

The goal of the research presented in this thesis is removing noise from electrocardiogram (ECG) signals. The electrocardiogram is a test that measures the electrical activity of the heart. The information obtained from an electrocardiogram can be used to diagnose different types of heart disease. It may be useful for seeing how well the patient is responding to treatment. The extraction of high resolution ECG signals from noisy measurements is among the most tempting open problems of biomedical signal processing. Extracting useful clinical information from the real (noisy) ECG requires reliable signal processing techniques. Numerous methods have been reported to denoise ECG signals based on... 

The sparse decomposition problem or nding sparse solutions of underdetermined linear systems of equations is one of the fundamental issues in signal processing and statistics. In recent years, this issue has been of great interest to researches in various elds of signal processing and accordingly found to be greatly benecial in those elds. This thesis aims at the investigation of the applications of the sparse decomposition problem in image processing. Among dierent applications such as compression, reconstruction, separation and image denoising, this thesis mainly focuses on the last one. One of the methods of image denoising which is closely tied to the sparse decomposition, is the method... 

Interictal EEG signals are very critical in diagnosis of epilepsy. Analysis of interictal EEG signals is very challenging due to contamination by various undesired signals like background EEG, muscular activity, noise, etc. Thus denoising of interictal signals has been an active research field in recent years. Primary purpose of this thesis is to denoise interictal EEG signals by using different combinations of ICA-based and wavelet denoising approaches. Then a new direction is pursued by using Morphological Component Analysis (MCA) which is a method for solving source separation problems based on morphological diversity of sources. Afterward MCA is modified by considering more prior... 

Noise contamination is inevitable in biomedical recordings. In some cases biomedical recordings are highly contaminated with artifacts which make the effective recovering process hard to achieve. Many different methods have been proposed for artifact removal from biomedical signals but introducing an effective method which can present valuable data for medical analysis, is still an ongoing process.
This dissertation focuses on inter-ictal EEG denoising approaches including ICA-based and EMD-based methods and different combination of these methods. These methods are tested on simulated epileptic recordings which are contaminated with real muscle artifact and EEG signal. The denoised...