Visual versus Statistical Features Selection Applied to Mammography Mass Detection

2014 ◽  
Vol 4 (2) ◽  
pp. 237-244
Author(s):  
Ibrahim Mohamed Ibrahim ◽  
Manal Abdel Wahed
Entropy ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. 1235
Author(s):  
Gianmarco Baldini ◽  
Irene Amerini

Research findings have shown that microphones can be uniquely identified by audio recordings since physical features of the microphone components leave repeatable and distinguishable traces on the audio stream. This property can be exploited in security applications to perform the identification of a mobile phone through the built-in microphone. The problem is to determine an accurate but also efficient representation of the physical characteristics, which is not known a priori. Usually there is a trade-off between the identification accuracy and the time requested to perform the classification. Various approaches have been used in literature to deal with it, ranging from the application of handcrafted statistical features to the recent application of deep learning techniques. This paper evaluates the application of different entropy measures (Shannon Entropy, Permutation Entropy, Dispersion Entropy, Approximate Entropy, Sample Entropy, and Fuzzy Entropy) and their suitability for microphone classification. The analysis is validated against an experimental dataset of built-in microphones of 34 mobile phones, stimulated by three different audio signals. The findings show that selected entropy measures can provide a very high identification accuracy in comparison to other statistical features and that they can be robust against the presence of noise. This paper performs an extensive analysis based on filter features selection methods to identify the most discriminating entropy measures and the related hyper-parameters (e.g., embedding dimension). Results on the trade-off between accuracy and classification time are also presented.


2020 ◽  
Vol 500 (3) ◽  
pp. 3920-3925
Author(s):  
Wolfgang Brandner ◽  
Hans Zinnecker ◽  
Taisiya Kopytova

ABSTRACT Only a small number of exoplanets have been identified in stellar cluster environments. We initiated a high angular resolution direct imaging search using the Hubble Space Telescope (HST) and its Near-Infrared Camera and Multi-Object Spectrometer (NICMOS) instrument for self-luminous giant planets in orbit around seven white dwarfs in the 625 Myr old nearby (≈45 pc) Hyades cluster. The observations were obtained with Near-Infrared Camera 1 (NIC1) in the F110W and F160W filters, and encompass two HST roll angles to facilitate angular differential imaging. The difference images were searched for companion candidates, and radially averaged contrast curves were computed. Though we achieve the lowest mass detection limits yet for angular separations ≥0.5 arcsec, no planetary mass companion to any of the seven white dwarfs, whose initial main-sequence masses were >2.8 M⊙, was found. Comparison with evolutionary models yields detection limits of ≈5–7 Jupiter masses (MJup) according to one model, and between 9 and ≈12 MJup according to another model, at physical separations corresponding to initial semimajor axis of ≥5–8 au (i.e. before the mass-loss events associated with the red and asymptotic giant branch phase of the host star). The study provides further evidence that initially dense cluster environments, which included O- and B-type stars, might not be highly conducive to the formation of massive circumstellar discs, and their transformation into giant planets (with m ≥ 6 MJup and a ≥6 au). This is in agreement with radial velocity surveys for exoplanets around G- and K-type giants, which did not find any planets around stars more massive than ≈3 M⊙.


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