Geomorphologic Features and processes related to it, is origin of many of the hazards and environmental resources. Also, due to the formation of industry and science geomorphotorism and finally be necessary of the study these Features for civil projects, will be more important Cater these maps. With widespread use of geospatial sciences Such as remote sensing, geographic information systems, and its application in the study of Earth Sciences in the study of OLI sensor images for mapping Valley, Blade, Alluvial Fans and Debris Fans were used. And using visual interpretation Features were extracted. Results were compared with images of Esri's World Imagery of ArcGIS Online, The parameters of correctness, completeness, quality and Kappa were calculated. And the results thus obtained, the accuracy of 80 percent, overall accuracy 62.01 percent, quality 53.87 percent and kappa coefficient was 49.74 percent. The results showed that successfully detects all Features except Debris Fans has been satisfactory. According to the Kappa coefficient is determined that segmentation done for Features extraction is appropriate.

Economic growth is considered as a major component of economic development. Besides trade Globalization with the countries joining the global economy over time Issue is that different countries are considered by economists. Therefore, this study examines the factors affecting economic growth, including trade Globalization can be addressed. For the purpose of statistical time series and by the year 1360 to 1393 and using Auto Regressive Distributed Lag Method (ARDL) was used. Also check the causality relationship between trade Globalization and economic growth in the long term and short term error correction model was used to test causality. Results from this study showed positive and significant impact variables, the natural logarithm of total investment, Natural logarithm of the total number of labor and trade Globalization on growth rate economy in the short term and long term. The Wald test on coefficients using error correction model, this result was achieved always in the short term and long-term causality of trade Globalization to economic growth.

Hyperspectral sensors provide a large number of spectral bands. This massive and complex data structure of hyperspectral images presents a challenge to traditional data processing techniques. Therefore, reducing the dimensionality of hyperspectral images without losing important information is a very important issue for the remote sensing community. We propose to use overlap-based Feature weighting (OFW) for supervised Feature extraction of hyperspectral data. In the OFW method, the Feature vector of each pixel of hyperspectral image is divided to some segments. The weighted mean of adjacent spectral bands in each segment is calculated as an extracted Feature. The less the overlap between classes is, the more the class discrimination ability will be. Therefore, the inverse of overlap between classes in each band (Feature) is considered as a weight for that band. The superiority of OFW, in terms of classification accuracy and computation time, over other supervised Feature extraction methods is established on three real hyperspectral images in the small sample size situation.

Opinion mining deals with an analysis of user reviews for extracting their opinions, sentiments and demands in a specific area, which plays an important role in making major decisions in such areas. In general, opinion mining extracts user reviews at three levels of document, sentence and Feature. Opinion mining at the Feature level is taken into consideration more than the other two levels due to orientation analysis of different aspects of an area. In this paper, two methods are introduced for a Feature extraction. The recommended methods consist of four main stages. First, opinion-mining lexicon for Persian is created. This lexicon is used to determine the orientation of users’ reviews. Second, the preprocessing stage includes unification of writing, tokenization, creating parts-of-speech tagging and syntactic dependency parsing for documents. Third, the extraction of Features uses two methods including frequency-based Feature extraction and dependency grammar based Feature extraction. Fourth, the Features and polarities of the word reviews extracted in the previous stage are modified and the final Features' polarity is determined. To assess the suggested techniques, a set of user reviews in both scopes of university and cell phone areas were collected and the results of the two methods were compared.

In the past twenty-five years, structural health monitoring (SHM) has become an increasingly significant topic of investigation in the civil and structural engineering research community. An SHM schema involves three main steps: (a) measurement and acquisition of signals related to the structural response, (b) signal processing consisting of pre-processing and Feature extraction employing nonlinear measurements, and (c) interpretation using machine learning. This article presents a review of recent journal articles on nonlinear measurements used for Feature extraction in SHM of building and bridge structures. It also reviews three recently-developed nonlinear indexes with potential applications in SHM.

There are many methods for Feature extraction from texture images. Local Binary Pattern (LBP) is one of the most important of these methods. It is a simple method for implementation and can extracttexture Features efficiently. LBP can be combined with local variance (VAR) to provide higher classification rate. In this paper, a new method is proposedwhich is named Local Entropy Pattern (LEP). The equation of this method is similar to Entropy literally, butit is differ from Entropy in some issues. The proposed method is more robust to noise than LBP and VAR. In addition, by combiningit's Features with LBP Features the classification rate increases significantly and it provides higher accuracy than LBP/VAR. Local Entropy Pattern shows dissimilarity of a local neighborhood. This approach has all positive points of LBP and some state-of-art similar methods. It is not only rotation and grayscale invariant but also noise robust.

Files are the most important sources of information presentation in various formats such as texts, audio, video, images, web pages, etc. … ; (in-depth) analysis of files for the purpose of recognition and investigating their unique properties (or characteristics) is one of the most significant issues in the field of personal security safety, information security, file-type identification, codes structuration analysis etc… . Statistical analytic methodology of working on the binary files contents based on the n-gram model has been opted for in the present paper in order to full investigate all the different aspects of a file’ s range of characteristics. Moreover, to reduce down the calculations volume and the n-gram model peculiar to the needed amount of memory, use has been made of word clustering. Later on analysis has been conducted on both files’ contents in two states of “ blocking” and “ full” : it is to be noted that in the “ full” case such characteristics as Chi-square, Auto-correlation, Weighted term frequency-Inverse document frequency (TF-IDF), Fractal dimension etc … have been brought under comprehensive study; while in the “ blocking” case, other properties like the entropy rate, the distance, etc … have been delved into. The gained results indicate that the extracted characteristics in the first method could well easily reflect the unique properties belonging to jpg, mp3, swf and html files; and in the second method, are able to clearly well reflect doc, html and pdf files properties

The movement state of contaminated oil in the pipeline is of great significance to the safe operation of oil-using equipment. The dynamic motion characteristics of the oil can be characterized by signals. However, the pressure signal of the oil is time-varying and complex, hence it is a typically non-stationary nonlinear signal. Therefore, the traditional linear analysis method used for the analysis of the oil signal is not suitable. For this reason, the Hilbert-Huang Transform (HHT) method is used to process and analyze the differential pressure signals of oils with different degrees of pollution, to obtain the characteristic frequencies of oil pressure signals, to explore the intrinsic connection of the characteristic frequencies and oils with different degrees of pollution, and to reveal the dynamic movement characteristics of oil in the pipeline. The results show that the characteristic frequencies corresponding to the five groups of oil samples with a pollution degree of 17/12, 18/12, 19/13, 19/13, 20/16 (ISO4406 standard) are 20. 29 Hz, 10. 22 Hz, 6. 94 Hz, 17. 01 Hz, and 6. 81 Hz, respectively, Each Intrinsic Mode Function (IMF) component of the oil signal has obvious frequency modulation characteristics, As the pollution degree increases, the oil frequency of the IMF2-4 component mainly shifts toward the middle of the interval, and the oil frequency of the IMF5-7 component mainly shifts toward the direction of 5. 00 Hz, 3. 00 Hz, and 1. 60 Hz respectively.