Higher order statistics

Speckle is a granular noise that inherently exists in all types of coherent imaging systems. The presence of speckle in an image reduces the resolution of the image and the detectability of the target. Many speckle reduction algorithms assume speckle noise is multiplicative. We instead model the speckle according to the exact physical process of coherent image formation. Thus, the

We present algorithms for automated event identification, pre-location, and P- and S-arrival time determination. Based on STA/LTA-detections of the single station trigger, theoretical relative travel times of predefined master events are compared to observed relative travel times using a grid search algorithm. The determined STA/LTA-values are weighted for epicentral distance. If a defined number of stations satisfy the theoretical conditions

Background Electrocardiography (ECG) signal is a primary criterion for medical practitioners to diagnose heart diseases. The development of a reliable, accurate, non-invasive and robust method for arrhythmia detection could assists cardiologists in the study of patients with heart diseases. This paper provides a method for morphological heart arrhythmia detection which might have different shapes in one category and also different morphologies in relation to the patients. The distinctive property of this method in addition to accuracy is the robustness of that, in presence of Gaussian noise, time and amplitude shift. Methods In this work 2nd, 3rd and 4th order cumulants of the ECG beat are calculated and modeled by linear combinations of Hermitian basis functions. Then, the parameters of each cumulant model are used as feature vectors to classify five different ECG beats namely as Normal, PVC, APC, RBBB and LBBB using 1-Nearest Neighborhood (1-NN) classifier. Finally, after classifying each model, a final decision making rule apply to these specified classes and the type of ECG beat is defined. Results The experiment was applied for a set of ECG beats consist of 9367 samples in 5 different categories from MIT/BIH heart arrhythmia database. The specificity of 99.67% and the sensitivity of 98.66% in arrhythmia detection are achieved which indicates the power of the algorithm. Also, the accuracy of the system remained almost intact in the presence of Gaussian noise, time shift and amplitude shift of ECG signals. Conclusions This paper presents a novel and robust methodology in morphological heart arrhythmia detection. The methodology based on the Hermite model of the Higher-Order Statistics (HOS). The ability of HOS in suppressing morphological variations of different class-specific arrhythmias and also reducing the effects of Gaussian noise, made HOS, suitable for detection morphological heart arrhythmias. The proposed method exploits these properties in conjunction with Hermitian model to perform an efficient and reliable classification approach to detect five morphological heart arrhythmias. And the time consumption of this method for each beat is less than the period of a normal beat.

Over the last ten years blind source separation (BSS) has become a prominent processing tool in the study of human electroencephalography (EEG). Without relying on head modeling BSS aims at estimating both the waveform and the scalp spatial pattern of the intracranial dipolar current responsible of the observed EEG. In this review we begin by placing the BSS linear instantaneous model of EEG within the framework of brain volume conduction theory. We then review the concept and current practice of BSS based on second-order statistics (SOS) and on higher-order statistics (HOS), the latter better known as independent component analysis (ICA). Using neurophysiological knowledge we consider the fitness of SOS-based and HOS-based methods for the extraction of spontaneous and induced EEG and their separation from extra-cranial artifacts. We then illustrate a general BSS scheme operating in the time-frequency domain using SOS only. The scheme readily extends to further data expansions in order to capture experimental source of variations as well. A simple and efficient implementation based on the approximate joint diagonalization of Fourier cospectral matrices is described (AJDC). We conclude discussing useful aspects of BSS analysis of EEG, including its assumptions and limitations.

Since microcalcification clusters are primary indicators of malignant types of breast cancer, its detection is important to prevent and treat the disease. This paper proposes a method for detection of microcalcification clusters in mammograms using sequential Difference of Gaussian filters (DoG). In a first stage, fifteen DoG filters are applied sequentially to extract the potential regions, and later, these regions are classified using the following features: absolute contrast, standard deviation of the gray level of the microcalcification and a moment of contour sequence (asymmetry coefficient). Once the microcalcifications are detected, two approaches for clustering are compared. In the first one, several microcalcification clusters are detected in each mammogram. In the other, all microcalcifications are considered in a single cluster. We demonstrate that the diagnosis based on the detection of several microcalcification clusters in a mammogram is more efficient than considering a single cluster including all the microcalcifications in the image.

Abstract: The paper presents different methods of combining many neural classifiers into one ensemble system for recognition and classification of arrhythmia. Majority and weighted voting, Kullback-Leibler divergence and modified Bayes methods will be presented and ...

This paper presents a method based on higher order statistics (HOS), namely the normalized third-order moment (skewness), for blind estimation of the inverse filter of the room impulse response (RIR). Skewness is used as a measure of asymmetry, and a comprehensive comparison with the commonly used metric (kurtosis) is presented. It is shown that a sufficiently long linear predictive (LP) residual of the speech signal has an asymmetric pdf with sufficient skewness to be used as a score function for the HOS-based approach. The proposed algorithm is optimized for the inverse filter estimation problem. This optimization includes an efficient initialization for high reverberation intensities, enabling the method to be employed in highly reverberant rooms. The direct-to-reverberation ratio (DRR) as well as the equalized impulse response clearly show that our method can estimate the inverse filter even in highly reverberant environments. In addition, performance results using recorded background noise and in time-varying environments illustrate that our approach is applicable in real world situations. The proposed method is shown to be superior to the method by Wu and Wang, particularly in terms of reducing the coloration effect. Experiments under different acoustic conditions confirm the effectiveness of the proposed method for time delay estimation (TDE). Finally, the proposed algorithm is used as the first-stage of monaural segregation, and it is shown to improve the performance under different conditions.

We consider the problem of training a discriminative classifier given a set of labelled multivariate time series (a.k.a. multichannel signals or vector processes). We propose a novel kernel function that exploits the spectral information of tensors of fourth-order cross-cumulants associated to each multichannel signal. Contrary to existing approaches the arising procedure does not require an (often nontrivial) blind identification step. Nonetheless, insightful connections with the dynamics of the generating systems can be drawn under specific modeling assumptions. The method is illustrated on both synthetic examples as well as on a brain decoding task where the direction, either left of right, towards where the subject modulates attention is predicted from magnetoencephalography (MEG) signals. Kernel functions for unstructured data do not leverage the underlying dynamics of multichannel signals. A comparison with these kernels as well as with state-of-the-art approaches, including generative methods, shows the merits of the proposed technique.

Synthetic Aperture Sonar (SAS) imagery is largely used in de- tection, location and classification of underwater mines laying or buried in the sea bed. This paper proposes a detection method us- ing Higher Order Statistics (HOS) on SAS images. The proposed method can be divided into two steps. Firstly, the HOS (Skewness and Kurtosis) are locally estimated using a square sliding compu- tation window. In a second step, the results are focused by a cor- relation process. This enables the precise location of the objects. This method is tested on real SASdata containing both underwater mines laying on the sea bed and buried objects.