Loading...
Search for: thresholding
0.069 seconds

    Seismic Image Denoising by Thresholding Neural Network in Curvelet Domain

    , M.Sc. Thesis Sharif University of Technology Haghighatgoo, Leila (Author) ; Haj Sadeghi, Khosro (Supervisor)
    Abstract
    Predicting the location of oil and gas recourses is the first challenge in the petroleum industry. One of the most popular and acceptable ways which can guide an explorer to the position of the resources is the seismic survey. By this kind of survey, geologists can observe inside of solid matter by using the ultrasound waves. The process works by sending sound waves to the surface and measuring the length it takes to be reflected from rocks underneath, then with recording these echoes by arrays of sensors, they can obtain a seismic image which has too noise, including ghosting, multiples (multiples are the waves that has been reflected more than once between the energy source and the... 

    Epileptic Seizure Detetion by use of Accelerometer

    , M.Sc. Thesis Sharif University of Technology Ghaderi, Nasser (Author) ; Ahmadian, Mohammad-Taghi (Supervisor)
    Abstract
    After Alzheimer and brain attack, the most common neurological disorder is epilepsy, which often involves seizures. In two-thirds of patients with epilepsy, the seizures can be controlled by antiepileptic drugs, and about 8% of patients can use epilepsy surgery; but unfortunately there is no acceptable treatment for the other 25% of these patients. Therefore preventing from epilepsy losses is a very important topic.
    The gold standard for the diagnosis of the epilepsy is EEG monitoring. In this method, electrodes are placed on the scalp. Electrodes are uncomfortable to wear, and cause invasion to the patient, hence long-term monitoring and home monitoring is not feasible. In some... 

    ECG Denoising by Deterministic Approaches

    , M.Sc. Thesis Sharif University of Technology Taghavi Razavizadeh, Marjaneh (Author) ; Shamsollahi, Mohammad Bagher (Supervisor)
    Abstract
    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... 

    High-Performance Keyword Spotting System for Persian Language

    , M.Sc. Thesis Sharif University of Technology Ghorbani, Shahram (Author) ; Sameti, Hossein (Supervisor)
    Abstract
    Keyword spotting with high speed and accuracy is an important subject whithin speech processing domain especially when we are dealing with various transmission channels. In this research discriminative keyword spotting methods are compared with HMM-based approaches. We have employed the discriminative approaches as our baseline methods due to their higher accuracy. The drawback of the conventional discriminative methods is their high computation cost and long execution time. The discriminative approach consists of two steps: feature extraction and classification. We have proposed four ideas to improve the performance of the baseline method. To improve the speed of the process, in feature...