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17 pages, 2113 KB

Open AccessArticle

Direction of Arrival Estimation with Nested Arrays in Presence of Impulsive Noise: A Correlation Entropy-Based Infinite Norm Strategy

by Jun Zhao, Renzhou Gui, Xudong Dong, Meng Sun and Yide Wang

Remote Sens. 2023, 15(22), 5345; https://doi.org/10.3390/rs15225345 - 13 Nov 2023

Cited by 5 | Viewed by 2300

Abstract

Direction of arrival (DOA) estimation with nested arrays has been widely investigated in the field of array signal processing, but most studies assume that the noise is Gaussian white noise. In practical situations, there may exist impulsive noise (a kind of heavy-tailed noise), wherein the performance of traditional subspace-based DOA estimation algorithms deteriorates significantly. In this paper, we propose a correlation entropy-based infinite norm preprocessing algorithm, which can be applicable to any type of impulsive noise. Each snapshot of the sensor array data is processed by an exponential kernel function with the infinite norm, which can effectively combat the outliers. Furthermore, we construct the equivalent second-order covariance matrix and perform DOA estimation using classical subspace methods. Simulation results demonstrate the effectiveness of the proposed method for both symmetric

α

-stable distribution and the Gaussian mixture model. Full article

(This article belongs to the Special Issue Advanced Radar Signal Processing and Applications)

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15 pages, 658 KB

Open AccessArticle

Online Learning of Finite and Infinite Gamma Mixture Models for COVID-19 Detection in Medical Images

by Hassen Sallay, Sami Bourouis and Nizar Bouguila

Computers 2021, 10(1), 6; https://doi.org/10.3390/computers10010006 - 27 Dec 2020

Cited by 22 | Viewed by 4786

Abstract

The accurate detection of abnormalities in medical images (like X-ray and CT scans) is a challenging problem due to images’ blurred boundary contours, different sizes, variable shapes, and uneven density. In this paper, we tackle this problem via a new effective online variational learning model for both mixtures of finite and infinite Gamma distributions. The proposed approach takes advantage of the Gamma distribution flexibility, the online learning scalability, and the variational inference efficiency. Three different batch and online learning methods based on robust texture-based feature extraction are proposed. Our work is evaluated and validated on several real challenging data sets for different kinds of pneumonia infection detection. The obtained results are very promising given that we approach the classification problem in an unsupervised manner. They also confirm the superiority of the Gamma mixture model compared to the Gaussian mixture model for medical images’ classification. Full article

(This article belongs to the Special Issue Artificial Intelligence for Health)

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29 pages, 798 KB

Open AccessArticle

Risk, Return and Volatility Feedback: A Bayesian Nonparametric Analysis

by Mark J. Jensen and John M. Maheu

J. Risk Financial Manag. 2018, 11(3), 52; https://doi.org/10.3390/jrfm11030052 - 5 Sep 2018

Cited by 10 | Viewed by 5235

Abstract

In this paper, we let the data speak for itself about the existence of volatility feedback and the often debated risk–return relationship. We do this by modeling the contemporaneous relationship between market excess returns and log-realized variances with a nonparametric, infinitely-ordered, mixture representation of the observables’ joint distribution. Our nonparametric estimator allows for deviation from conditional Gaussianity through non-zero, higher ordered, moments, like asymmetric, fat-tailed behavior, along with smooth, nonlinear, risk–return relationships. We use the parsimonious and relatively uninformative Bayesian Dirichlet process prior to overcoming the problem of having too many unknowns and not enough observations. Applying our Bayesian nonparametric model to more than a century’s worth of monthly US stock market returns and realized variances, we find strong, robust evidence of volatility feedback. Once volatility feedback is accounted for, we find an unambiguous positive, nonlinear, relationship between expected excess returns and expected log-realized variance. In addition to the conditional mean, volatility feedback impacts the entire joint distribution. Full article

(This article belongs to the Special Issue Nonparametric Econometric Methods and Application)

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27 pages, 464 KB

Open AccessArticle

Non-Parametric Bayesian Human Motion Recognition Using a Single MEMS Tri-Axial Accelerometer

by M. Ejaz Ahmed and Ju Bin Song

Sensors 2012, 12(10), 13185-13211; https://doi.org/10.3390/s121013185 - 27 Sep 2012

Cited by 6 | Viewed by 6431

Abstract

In this paper, we propose a non-parametric clustering method to recognize the number of human motions using features which are obtained from a single microelectromechanical system (MEMS) accelerometer. Since the number of human motions under consideration is not known a priori and because of the unsupervised nature of the proposed technique, there is no need to collect training data for the human motions. The infinite Gaussian mixture model (IGMM) and collapsed Gibbs sampler are adopted to cluster the human motions using extracted features. From the experimental results, we show that the unanticipated human motions are detected and recognized with significant accuracy, as compared with the parametric Fuzzy C-Mean (FCM) technique, the unsupervised K-means algorithm, and the non-parametric mean-shift method. Full article

(This article belongs to the Section Physical Sensors)

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