A comparative analysis of the special shapes (patterns, profiles) of the eclipses applied for the phenomenological modeling of the light curves of eclipsing binary stars is conducted. Families of functions are considered, generalizing local approximations (Andronov, 2010(Andronov, , 2012 and the functions theoretically unlimited in a width, based on a Gaussian (Mikulášek, 2015). For an analysis, the light curve of the star V0882 Car = 2MASS J11080308 -6145589 of the classic Algolsubtype ( Persei) is used. By analyzing dozens of modified functions with additional parameters, it was chosen the 14 best ones according to the criterion of the least sum of squares of deviations. The best are the functions with an additional parameter, describing profiles, which are limited in phase.
During processing the observations of the intermediate polar 1RXS J180340.0+401214, obtained 26.05.2012 at the 60-cm telescope of the Mt. Suhora observatory (Krakow, Poland), variability of 2MASS J18024395 +4003309 was discovered. As this object was not listed in the General Catalogue of Variale Stars or Variable Stars Index, we registered it as VSX J180243.9+400331. Additionally we used 189 separate observations from the Catalina Sky Survey spread over 7 years. The periodogram analysis yields the period of 0 d .3348837±0 d .0000002.The object was classified as the Algol-type eclipsing binary with a strong effect of ellipticity. The depths of the primary and secondary minima are nearly identical, which corresponds to a brightness (and maybe) mass ratio close to 1. The statistically optimal degree of the trigonometric polynomial n=4. The most recent minimum occurred at HJD 2456074.4904. The brightness range from our data is 16.56-17.52 (V), 16.18-17.08 (R).The NAV (New Algol Variable) algorithm was applied for statistically optimal phenomenological modeling and determination of corresponding parameters.
The phenomenological parameters of eclipsing binary stars, which are the prototypes of the EA, EB and EW systems are determined using the expert complex of computer programs, which realizes the NAV ("New Algol Variable") algorithm (Andronov , 2012 and its possible modifications are discussed, as well as constrains for estimates of some physical parameters of the systems in a case of photometric observations only, such as the degree of eclipse, ratio of the mean surface brightnesses of the components. The half-duration of the eclipse is 0.0617 (7), 0.1092(18) and 0.1015 (7) for Algol, β Lyrae and W UMa, respectively. The brightness ratio is 6.8±1.0, 4.9±1.0 and 1.15±0.13. These results show that the eclipses have distinct begin and end not only in EA (as generally assumed), but also in EB and EW -type systems as well. The algorithm may be applied to classification and study of the newly discovered (or poorly studied) eclipsing variables based on own observations or that obtained using photometric surveys.
Currently, one of the most common causes of death worldwide is cancer. The development of innovative methods to support the early and accurate detection of cancers is required to increase the recovery rate of patients. Several studies have shown that medical Hyperspectral Imaging (HSI) combined with artificial intelligence algorithms is a powerful tool for cancer detection. Various preprocessing methods are commonly applied to hyperspectral data to improve the performance of the algorithms. However, there is currently no standard for these methods, and no studies have compared them so far in the medical field. In this work, we evaluated different combinations of preprocessing steps, including spatial and spectral smoothing, Min-Max scaling, Standard Normal Variate normalization, and a median spatial smoothing technique, with the goal of improving tumor detection in three different HSI databases concerning colorectal, esophagogastric, and brain cancers. Two machine learning and deep learning models were used to perform the pixel-wise classification. The results showed that the choice of preprocessing method affects the performance of tumor identification. The method that showed slightly better results with respect to identifing colorectal tumors was Median Filter preprocessing (0.94 of area under the curve). On the other hand, esophagogastric and brain tumors were more accurately identified using Min-Max scaling preprocessing (0.93 and 0.92 of area under the curve, respectively). However, it is observed that the Median Filter method smooths sharp spectral features, resulting in high variability in the classification performance. Therefore, based on these results, obtained with different databases acquired by different HSI instrumentation, the most relevant preprocessing technique identified in this work is Min-Max scaling.
Phenomenological characteristics of the sample of the Algol -type stars are revised using a recently developed NAV ("New Algol Variable") algorithm (2012Ap.....55..536A, 2012arXiv 1212.6707A) and compared to that obtained using common methods of Trigonometric Polynomial Fit (TP) or local Algebraic Polynomial (A) fit of a fixed or (alternately) statistically optimal degree (1994OAP.....7...49A, 2003ASPC..292..391A).The computer program NAV is introduced, which allows to determine the best fit with 7 "linear" and 5 "nonlinear" parameters and their error estimates. The number of parameters is much smaller than for the TP fit (typically 20 -40, depending on the width of the eclipse, and is much smaller (5 -20) for the W UMa and Lyrae -type stars. This causes more smooth approximation taking into account the reflection and ellipsoidal effects (TP2) and generally different shapes of the primary and secondary eclipses. An application of the method to two -color CCD photometry to the recently discovered eclipsing variable 2MASS J18024395 + 4003309 = VSX J180243.9 +400331 (2015JASS...32..101A) allowed to make estimates of the physical parameters of the binary system based on the phenomenological parameters of the light curve. The phenomenological parameters of the light curves were determined for the sample of newly discovered EA and EW -type stars (VSX J223429.3+552903, VSX J223421.4+553013, VSX J223416.2+553424, USNO-B1.0 1347-0483658, UCAC3-191-085589, VSX J180755.6+074711= UCAC3 196-166827). Despite we have used original observations published by the discoverers, the accuracy estimates of the period using the NAV method are typically better than the original ones.
Background: Recent studies have shown that hyperspectral imaging (HSI) combined with neural networks can detect colorectal cancer. Usually, different pre-processing techniques (e.g., wavelength selection and scaling, smoothing, denoising) are analyzed in detail to achieve a well-trained network. The impact of post-processing was studied less. Methods: We tested the following methods: (1) Two pre-processing techniques (Standardization and Normalization), with (2) Two 3D-CNN models: Inception-based and RemoteSensing (RS)-based, with (3) Two post-processing algorithms based on median filter: one applies a median filter to a raw predictions map, the other applies the filter to the predictions map after adopting a discrimination threshold. These approaches were evaluated on a dataset that contains ex vivo hyperspectral (HS) colorectal cancer records of 56 patients. Results: (1) Inception-based models perform better than RS-based, with the best results being 92% sensitivity and 94% specificity; (2) Inception-based models perform better with Normalization, RS-based with Standardization; (3) Our outcomes show that the post-processing step improves sensitivity and specificity by 6.6% in total. It was also found that both post-processing algorithms have the same effect, and this behavior was explained. Conclusion: HSI combined with tissue classification algorithms is a promising diagnostic approach whose performance can be additionally improved by the application of the right combination of pre- and post-processing.
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