Blind Curvelet based Denoising of Seismic Surveys in Coherent and Incoherent Noise Environments

The localized nature of curvelet functions, together with their frequency and dip characteristics, makes the curvelet transform an excellent choice for processing seismic data. In this work, a denoising method is proposed based on a combination of the curvelet transform and a whitening filter along with procedure for noise variance estimation. The whitening filter is added to get the best performance of the curvelet transform under coherent and incoherent correlated noise cases, and furthermore, it simplifies the noise estimation method and makes it easy to use the standard threshold methodology without digging into the curvelet domain. The proposed method is tested on pseudo-synthetic data by adding noise to real noise-less data set of the Netherlands offshore F3 block and on the field data set from east Texas, USA, containing ground roll noise. Our experimental results show that the proposed algorithm can achieve the best results under all types of noises (incoherent or uncorrelated or random, and coherent noise)

An Approach for ECG Feature Extraction using Daubechies 4 (DB4) Wavelet

An Electrocardiogram (ECG) signal describes the electrical activity of the heart recorded by electrodes placed on the surface of human body. It summarizes an important electrical activity used for the primary diagnosis of heart abnormalities such as Tachycardia, Bradycardia, Normalcy, Regularity and Heart Rate Variation. The most clinically useful information of the ECG signal is found in the time intervals between its consecutive waves and amplitudes defined by its features. In this paper, an ECG feature extraction algorithm based on Daubechies Wavelet Transform is presented. DB4 Wavelet is selected due to the similarity of its scaling function to the shape of the ECG signal. R peaks detection is the core of this algorithm’s feature extraction. All other primary peaks are extracted with respect to the location of R peaks through creating windows proportional to their normal intervals. The proposed extraction algorithm is evaluated on MIT-BIH Arrhythmia Database. Experimental results indicate that the algorithm can successfully detect and extract all the primary features with a deviation error of less than 10%

A reduced reference image quality metric based on feature fusion and neural networks

A Global Reduced Reference Image Quality Metric (IQM) based on feature fusion using neural networks is proposed. The main idea is the introduction of a Reduced Reference degradation-dependent IQM (RRIQM/D) across a set of common distortions. The first stage consists of extracting a set of features from the wavelet-based edge map. Such features are then used to identify the type of degradation using Linear Discriminant Analysis (LDA). The second stage consists of fusing the extracted features into a single measure using Artificial Neural Networks (ANN). The result is a degradation- dependent IQM measure called the RRIQM/D. The performance of the proposed method is evaluated using the TID 2008 database and compared to some existing IQMs. The experimental results obtained using the proposed method demonstrate an improved performance even when compared to some Full Reference IQMs

Automatic Detection of High Temperature Hydrogen Attack Defects from Ultrasonic A-scan Signals.

Successful application of the rich collection of classification algorithms to nondestructive testing signals depends heavily on the availability of adequate and representative sets of training examples, whose acquisition can often be very expensive and time consuming. In this paper, an out-of-service pressure vessel known to have lots of high temperature hydrogen attach (HTHA) defects is used to develop in a cost effective manner a database of ultrasonic A-scan signals. To test how adequate and representative these sets of A-scan signals are, a basic feature extraction method, coupled with a primitive classifier is shown to distinguish accurately the hydrogen attack from geometrically similar defects

On the Performance of Time Frequency Distributions in A-Scan Signals Classification

In this paper we discuss the performance of different time frequency distributions in characterizing AScan signals in NDT applications. We then introduce a new set of time frequency features that we extract from such distributions. In particular, we propose to extract four signals representing energy and frequency parameters of Ascan signals. We show that the means and spreads extracted from such signals can be used as robust features for classification of A-scans signals. We also show the best discrimination among different classes of A-scans can be obtained using the Gabor transform

A New Approach for Recognizing Saudi Arabian License Plates using Neural Networks

In this paper, a neural networks (NN) based automatic license plate recognition system (ALPR) is proposed for Saudi Arabian license plates with Arabic characters. The license plate region is rst localized by rst tracing the exterior and the interior close boundaries of objects in the car image and then separating the license plate by determining the rectangularity characteristic of these close objects. Character segmentation is performed via vertical and horizontal projection proles. Finally, a Multilayer Feedforward Neural Network (MFNN) with a backpropagation (BP) algorithm is used for character recognition. We discuss new features from the characters for training the NN. The results obtained from a medium size data base are very promising, i.e., 98%. The alogoritms discussed here were tested at the entrance of a praking lot to mimic a real life situation