Acquiring up-to-date information about stands is necessary for scientific management and planning of forest. Remote sensing by its great capabilities in this way can be very useful. This study was done to investigate the capability of satellite data for gathering information about Beech diameter specification.21 plots of one hectare dimension were selected at experimental forest of Kheyroud in Noshahr. In each plot, diameters of all trees above 12.5 cm were measured and recorded. Then geographic specifications of centennial portion and four corners of each plot were recorded and their digital map was prepared. Geometric correction was performed up to orthorectification level with affine transformation and resampling of nearest neighbour with less than 0.6 pixel error. For surveying of synthetic bands and various indices, Principle component analysis, Ratio and Fusion were used. Then digital numbers matching with plots from principle and synthetic bands were extracted and used in regression analysis test. Results showed that maximum correlation of diameter was with G/R, GRVI, [(G+NIR) -R] / (G+R+NIR), R+G+NIR with correlation coefficients of -69%, -67%, -54% and -53%, respectively. After extraction of correlation coefficients, equations of relationship between diameter and digital numbers of images were produced. Results of this study supported the hypothesis of possibility of predicting diameter of Beech stands using satellite images.

The sub-pixel level mineral information with reasonable accuracy could be a valuable guide to geological and exploration community for expensive ground and/or lab experiments to discover economic deposits. Thus, several studies demonstrate the feasibility of hyperSpectral images for sub-pixel mineral mapping.Target detection is one of the most important applications in hyperSpectral remote sensing image analysis. Relative to multiSpectral sensing, hyperSpectral sensing can increase the detectability of pixel and subpixel size targets by exploiting finer detail in the Spectral Signatures of targets and natural backgrounds. Over the past several years, different algorithms for the detection subpixel targets with known Spectral Signature have been developed.Using derivative spectra in analytical chemistry is an established technique which is renowned as derivative spectroscopy. On the other hand, hyperSpectral data nature allows the implementation of derivative spectroscopy, in hyperSpectral images. Application of this technique in hyperSpectral remote sensing is due to its ability for resolving complex spectra of several target species within individual pixels. Several researches have shown that the application of derivative spectrum in target detection, prepare better detection results in each individual pixel.However, one of the points that we have to pay special attention is that spectrum differentiation eliminates low frequency components of the spectrum. Hence, having a spectra and its best derivative order in a unified approach cause an increase in the information content of a Spectral curve. Thus, an ensemble approach was proposed in this research.To justify the above-mentioned framework, this study was carried out with an airborne hyperSpectral data in comparison to the previous works whereby the standard laboratory images were applied. Both proposed detection algorithms were used for identification of four mineral targets including alunite, kaolinite, epidote and hematite which were located in a hydrothermally altered mineral region (Gonabad county) in Iran east (Lat. AWT IMAGE and Lon. AWT IMAGE ). The Hymap images were acquired on September 11th 2006 with a spatial resolution of AWT IMAGE.Gonabad is structurally a part of Iran’s central desert (Lūt Desert). The regional rock units show the presence of altered mineralized rocks consisting of volcanic and subvolcanic tracit, agglomera, tuff, riolite, riodacite, dacite, etc. These mass rocks were altered due to the effect of hydrothermal solutions. The importance of geological researches in this region comes from above-mentioned hydrothermally altered zones.Ground sample data were collected for four dominant targets in the study area, including alunite, kaolinite, hematite and epidote. Stratified random sampling strategy was used for collecting the minerals in the field based on geological experts’ opinion. The precise target’s coordinates were determined by using GPS sensors.After the completion of field sampling procedure, samples were transformed to laboratory. Samples were grained in lab for Spectral measurements. Then, reflectance measurements were done using a SVC HR 1024 lab spectrometer covering a full Spectral range of VIS/SWIR (350 to 2500 nm). After Spectral measurements were recorded, all obtained spectra were resampled to Hymap Spectral response function. Finally, the resampled spectra was applied in the proposed ensemble approach for subpixel mapping of above-mentioned mineral targets.Experimental results show that the proposed method had clearly better detection results in entire understudy samples. The best performance upgrade was about 28, 24, 26 and 16 percent respectively for Alunite, Kaolinite, Hematite and Epidote targets.

In the recent years, the use of ASTER and Landsat data have become prevalent for mapping different types of rock formations. Specifically, this study utilizes ASTER (L1B) and Landsat 8 (AOL) images to map outcrops of various gypsum facies in Ras Malaab area of west-central Sinai. These gypsum facies are part of a lithostratigraphic group called Ras Malaab, estimated to have been formed during the Miocene period. A range of image processing techniques was employed to create the final facies map including quartz and sulphate indices, composite image band combinations, band ratios, principal component analyses, decorrelation stretching, and SAM mapping followed by supervised classification. By using band combinations, mineral indices, and principal component analyses, sulphate minerals were distinguished from their surroundings. Additionally, decorrelation stretches and band ratios were used to differentiate between primary, secondary, faulted gypsum, anhydrite, and carbonates. The SAM rapid mapping algorithm was also an effective tool to distinguish between the main facies in the studied area and to differentiate between primary massive and bedded gypsum. The results of this study were summarized by creating a facies map of the area using supervised classification, which, in addition to petrographic studies, greatly aided in understanding the distribution of the different gypsum facies.

One of the important matters about the reviewing the conclusion of the Electronic Contracts, which are concluded through different Electronic devices like telephone, telegram, fax, in the cyber space and through Email, is the matter of the Electronic Signature. Indeed the Electronic documents and specially the Electronic Contracts will be completed by Signature and will be attributable to the relevant party. The comparison between the Electronic and traditional Signature, will be helpful for achieving a better conception of the issue.The importance of this discussion is for supporting the contractual right both for the sender of data massages and also the receiver. In the other words conclusion or non-conclusion of the Contract formatted via Electronic devices and subsequently forming the rights and the obligations of the parties, is substantially focused on this key question that whether the relevance of data massage to the sender is attributable? The idea of insured data massage or insured Electronic Signature and also suggesting the certification authority is based on possible avoidance of any kind of doubt or ambiguity about the attribution of data massage.

the study area in Fars province and the range maps 1.100000 Sargedar and 1.200000 Darab is located. Set during the paper map 1.100000 Srgdar still not printing stage and in the study area map 1.200000 Darab supplied by oil companies and no information has not been traced. Aim of identifying potential iron ore mining is based on satellite image processing study has been done. Methods used include FCC, split band, filtering, calculation of OIF, PCA, DPCA, classified satellite images and Spectral Signatures of the desired range and comparison with standard Spectral Signature of iron ore and Spectral Signatures observed in the mining goals Gohar Sirjan, leading to new mineral potential was identified.

Gastric cancer (GC) is one of the most common types of cancer and the second leading cause of cancer-associated mortality. Identification of novel biomarkers is critical to prolonging patient survival. MicroRNAs (miRNAs) proved to play diverse roles in the physiological and pathological state in cancers including GC. Herein we aimed at performing a meta-analysis on miRNA profiling studies that used microarray platforms. Relevant studies were retrieved from PubMed and GEO databases. We used the robust rank aggregation to perform the meta-analysis. Moreover, for meta-Signature miRNAs target genes, we performed pathway enrichment and GO molecular function enrichment analysis. A total of 19 upregulated miRNAs and seven downregulated miRNAs in GC samples were identified. However, only three upregulated and one downregulated miRNA reached statistical significance after multiple test correction. Here we showed that hsa-miR-21-5p, hsa-miR-93-5p, hsa-miR-25-3p, and hsa-miR-375 are differentially expressed in GC samples.

Signature is one of the personal identification methods. In this paper we have proposed a Signature identification approach which is based on using dynamic information of Signature video. It composes of four stages. First, the foreground image is extracted. Second the Signature shape is obtained by detecting and tracking the pen tip in each frame. The pen tip detection error rate is decreased using a proposed modification. Third, some features based on energy motion image are calculated. At the last stage, the Signature model is formed which is used in Signature identification procedure.We have generated a Signature video database of 50 persons to evaluate the proposed method. The number of 13 Signatures of each person are used for training. In addition 8 genuine and 8 fake Signatures are used for testing purposes. The Accuracy and Equal Error Rate measures areobtained as 95.02% and 3.8% respectively.