Hashm Mustafa

Salahaddin University - Erbil | SUH · Department of Arabic

In this paper we propose a feature to distinguish frequency from amplitude-phase digital modulations. We compare the performance of the feature where every symbol is sampled more than once to that where every symbol is sampled only once. The feature is based on the product of two consecutive signal values and on time averaging of the imaginary part...

This paper proposes an expansion of the cumulant-based classifier of digital modulations to frequency shift keying (FSK) modulations. Cumulant estimates are calculated when the FSK modulation is present. The features obtained from the cumulant estimators are used in a support vector machine (SVM) classifier. The performance of the SVM classifier is...

In this paper we study the effect of carrier offset on a feature distinguishing binary phase shift keying and quadrature phase shift keying. The feature is based on the subtraction of two consecutive signal values and the kurtosis of the absolute value of this subtraction. The paper compares performance of the proposed feature to a cumulant based f...

In this paper we study the effect of carrier offset on a feature distinguishing different digital modulation signals. The feature is based on the multiplication of two consecutive signal values and the kurtosis of this multiplication. The feature distinguishes amplitude shift keying with two and four levels (ASK2 and ASK4), phase shift keying (PSK)...

In this paper we propose a feature to distinguish frequency shift keying modulation from amplitude shift keying and phase shift keying modulations in the presence of carrier offset. The feature is based on the product of two consecutive signal values and on time averaging of the imaginary part of the product. First, the conditional probability dens...

The paper considers features based on the multiplication of two consecutive signal values. Furthermore, three new classifiers using the features are proposed: fixed threshold tree classifier, dynamic threshold tree classifier and support vector machine (SVM) classifier. It is shown that the multiplication produces dependence of the features on the...

We propose four features to classify amplitude shift keying with two levels and four levels, binary phase shift keying, quadrature phase keying, frequency shift keying with two carriers and four carriers. After that we present a new method of classification based on support vector machine (SVM) that uses the four proposed features. We study the per...

In this paper, we propose a simple, effective and robust method based on the statistical moments of instantaneous features to classify digital modulation signals. This method adopts a tree structure scheme and uses different features in each branch to make full use of distinguishing modulation type's characteristics. The proposed method is capable...

This paper describes three algorithms for automatic radio station detection based on the shape of power spectrum. The objective is to find the number of active stations and for each of them to estimate the bandwidth and carrier frequency. All three algorithms are grouping spectrum components into clusters that are assumed to correspond to active ra...

This paper describes the benefit for potentially rectifying the multiple nonlinear Phonocardiography measurements so that we can apply the linear independent component analysis (ICA) to separate blindly the sources of heart valve murmuring in order to find a noninvasive way to discover abnormalities in the Bioprosthetic heart valves. At the beginni...