In this paper, a Fast-parametric method for ISAR autofocus is proposed which is based on the minimum Entropy method and eigenvalue decomposition, and has less computational complexity than that of conventional autofocus methods. In this technique, the covariance matrix of the range compressed and aligned data is formed and by utilizing eigenvalue decomposition, signal and noise are separated. Then, the Fourier transform of the signal eigenvectors which are much smaller than the total eigenvectors is taken. Finally, by applying the conventional autofocus approaches to the image of eigenvectors, the phase error is estimated. In this paper, a parametric method based on Entropy is utilized. The simulation results show that although the computational complexity is decreased, the performance of the algorithm is maintained.

The traditional method for studying non-stationary signals is spectrogram based on the short-time Fourier transform (STFT). The well known limitation of the STFT is the inherent trade-off between time and frequency resolution. The Wigner-Ville (WV) distribution has the best time-frequency resolution, but its draw back is generating cross-terms. The matching pursuit (MP) distribution based on using the Gaussian atom is always positive, does not include crossterm, and has convenient resolution. In this paper, we have shown in addition to the known properties, the MP distribution can also remove the additive noise inherently. On the other words, we are able to remove the noise just by limiting the algorithm iterations and without paying any additional cost. Although the MP distribution based on using the Gaussian atoms is always positive and it has convenient resolution, according to the MP the time marginal and the frequency marginal will not be obtained accurately. In this paper, it has been shown that by implementing the minimum cross Entropy (MCE) technique according to the MP distribution as a priory positive distribution, the new extracted distribution has the most similarity to the MP distribution and it also satisfies the correct time and frequency marginal.

Purpose: To develop a Fast algorithm for automatic accurate extraction of blood vessels from retinal images in order to facilitate image analysis and minimize the rate of diagnosis error.Methods: We proposed an efficient algorithm using adaptive filter and thresholding technique which are based on fuzzy Entropy. The data included retinal images from 60 index normals and patients (including 38 men and 22 women) obtained from STARE and DRIVE databases. The mean subject age was 47.6±9.1 years. In images from index patients, the presence of abnormal tissue was confirmed by two specialists. Initially, image blurring caused by vibrations during image capturing was eliminated using designed Wiener filter. The impact of optical reflection in each image was minimized through transferring images into the HSV space and selecting the spatial mask for retina. Extraction of retinal blood vessels was done using adaptive filtering. Finally, to separate the main artery of the retina from the rest of the tissues, the optimal threshold was obtained which was defined by the fuzzy Entropy technique.Results: By using combined methods in pre-processing step of retinal images, the optic disc border and radiant reflections of ophthalmoscope disappeared in the output image. Therefore, the accuracy (0.9475±0.08) and F-measure (0.7426±0.05) increased significantly.Conclusion: Fast and accurate extraction of blood vessels in the retinal images enables the ophthalmologist to predict disease progression, hence suggesting appropriate treatment modality.

Clouds, because of their significant effects on energy balance on the ground surface and in the atmosphere, has always been considered by different meteorologists and climatologists. The ability of remote sensing in the assessment of cloud characteristics has been proven by in the study of their changes in different locations and times. One of the important aspects of the study regarding the clouds, is to determine the pressure at their top, which the aim of the current study is to present a Fast method for its determine by using MODIS images.Therefore, by using Radiosonde data in Mehrabad and Kermanshah stations, the atmospheric temperature profile was extracted. Then using a five stages technique, the pixels of the considered image were grouped into 4 different categories including cloudy, possibly cloudy, possibly non- cloud and non- cloudy. Then by using LSE model on channels 31 and 32 of MODIS, the emissivity of cloud in cloudy pixels was determined.Then by using this temperature and the temperature profile extracted from Radiosonde data, the pressure at the top of the clouds were extracted. To review the accuracy of the obtained results, the results obtained from Fast CTP method with those of standard CTP method were compared. Based on this comparison, it is found that the presented method for the clouds with optical depth of more than 10 is optimal and the results obtained from CTP model, shows a very small difference. But the difference regarding the thin clouds with low optical depth (lower than 10) is considerably great in comparing with the clouds of high optical depth.

The purpose of this study is to define the concepts of Tsallis Entropy and conditional Tsallis Entropy of fuzzy partitions and to obtain some results concerning this kind Entropy. We show that the Tsallis Entropy of fuzzy partitions has the subadditivity and concavity properties. We study this information measure under the refinement and zero mode subset relations. We check the chain rules for this information measure and prove some properties about the Entropy of independent fuzzy partitions. Some results of the relationship between the Tsallis Entropy and conditional Tsallis Entropy of fuzzy partitions are obtained and, by using conditional Tsallis Entropy of fuzzy partitions, we show that the subadditivity property for Tsallis Entropy of fuzzy partitions is not established in the case that the parameter of this Entropy is smaller than one. In general, the Tsallis Entropy of fuzzy partitions has similar properties to the shannon Entropy, where the parameter of this Entropy is larger than one, and therefore can be used in addition to the Shannon Entropy, to measure the amount of information to be extracted from a fuzzy experiment.

Background and purpose: Trauma is the fourth leading cause of death in some developing countries and the second cause of death among young people in Iran. Focused assessment with sonography for trauma (Fast) as part of the preliminary examination is invaluable in emergency care. The aim of this study was to evaluate the accuracy of Fast in determining free abdominal fluid in traumatic patients admitted in an emergency department.Materials and methods: A cross-sectional study was carried out in 150 trauma patients in emergency department (Imam Teaching Hospital, Sari, Iran). Fast reports for presence of free fluid in the abdomen by emergency residents were compared with those of abdominal and pelvic CT scan (oral and intravenous contrast). Sensitivity, specificity, positive predictive value and negative predictive value were evaluated.Results: The mean age of patients was 37.02±14.46. There were 26 (84%) males and 24 (16%) females. In Fast and CT scan of the abdomen with oral and IV contrast 13 (8.7%) and 10 patients (6.7%) were reported with free fluid in the abdomen.|Conclusion: Ultrasound is a reliable tool for assessing trauma patients that can be performed with reasonable accuracy. Emergency physicians are constantly available upon admission, therefore, ultrasound plays an important role in decisions made for trauma patients in emergency departments.

In real life, occurrences of a series of things are supposed to come in an order. Therefore, it is necessary to regard sequence as a crucial factor in managing different kinds of things in fuzzy environment. However, few related researches have been made to provide a reasonable solution to this demand. Therefore, how to measure degree of uncertainty of ordinal fuzzy sets is still an open issue. To address this issue, a novel ordinal fuzzy Entropy is proposed in this paper taking orders of propositions into consideration in measuring level of uncertainty in fuzzy environment. Compared with previously proposed entropies, effects on degrees of fuzzy uncertainty brought by sequences of sequential propositions are embodied in values of measurement using proposed method in this article. Moreover, some numerical examples are offered to verify the correctness and validity of the proposed Entropy.

The present paper is devoted to the study of Renyi Entropy in algebraic structures. We define Renyi Entropy of order q and  its conditional version for a partition of an algebraic structure, and we study their properties. In particular, we show that the  Renyi Entropy of a partition is monotonically decreasing.

Emergence of electronic resources on the web has caused some problems for librarians and information specialists. The problems are how to describe these resources and how to provide subject access for facilitating their discovery and retrieval. Passage of time has revealed that the use made of LCSH (Library of Congress Subject Headings) in the electronic environment is limited and to some extent impossible. So it may be useful to design a rich, simple and understandable subject heading scheme which is compatible with the electronic environment while introducing Fast subject headings, this article examines its compatibility with widespread metadata schemes, such as Dublin Core and MARC 21. Fast is developed for information organization, discovery and retrieval in the 21st century. The scheme is based on LCSH's controlled vocabulary but it is more faceted and post-coordinated than it.