A special X-radiography,used in obtaining the deformed specimen grating in in-plane moire method is introduced.The density of gridline is up to 50 lines / mm.This method is suitable not only for the copy of grating in...A special X-radiography,used in obtaining the deformed specimen grating in in-plane moire method is introduced.The density of gridline is up to 50 lines / mm.This method is suitable not only for the copy of grating in fiat surface but also for that in curved surface.From unfolding the copy and superposing it with a reference grating,the moire effect arises.By processing this moire fringes with the digital image processing unit, the strain distribution would be obtained.Furthermore,the u-and v-displacement fields could be separated by the optical processing of this copy.The measurment sensitivity and accuracy become better simultaneously because of the fringe multiplication.Thus,this method makes it possible to measure the elastic-plastic strain precisely in any developable curved surfaces,for example, the surface of shaft,rotary-wing,cylindrical shell and cone shell etc.展开更多
Copy-move offense is considerably used to conceal or hide several data in the digital image for specific aim, and onto this offense some portion of the genuine image is reduplicated and pasted in the same image. There...Copy-move offense is considerably used to conceal or hide several data in the digital image for specific aim, and onto this offense some portion of the genuine image is reduplicated and pasted in the same image. Therefore, Copy-Move forgery is a very significant problem and active research area to check the confirmation of the image. In this paper, a system for Copy Move Forgery detection is proposed. The proposed system is composed of two stages: one is called the detection stages and the second is called the refine detection stage. The detection stage is executed using Speeded-Up Robust Feature (SURF) and Binary Robust Invariant Scalable Keypoints (BRISK) for feature detection and in the refine detection stage, image registration using non-linear transformation is used to enhance detection efficiency. Initially, the genuine image is picked, and then both SURF and BRISK feature extractions are used in parallel to detect the interest keypoints. This gives an appropriate number of interest points and gives the assurance for finding the majority of the manipulated regions. RANSAC is employed to find the superior group of matches to differentiate the manipulated parts. Then, non-linear transformation between the best-matched sets from both extraction features is used as an optimization to get the best-matched set and detect the copied regions. A number of numerical experiments performed using many benchmark datasets such as, the CASIA v2.0, MICC-220, MICC-F600 and MICC-F2000 datasets. With the proposed algorithm, an overall average detection accuracy of 95.33% is obtained for evaluation carried out with the aforementioned databases. Forgery detection achieved True Positive Rate of 97.4% for tampered images with object translation, different degree of rotation and enlargement. Thus, results from different datasets have been set, proving that the proposed algorithm can individuate the altered areas, with high reliability and dealing with multiple cloning.展开更多
<strong>Objective:</strong> <span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">DNA copy number alterati...<strong>Objective:</strong> <span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">DNA copy number alterations and difference expression are frequently observed in ovarian cancer. The purpose of this way was to pinpoint gene expression change that w</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">as</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> associated with alterations in DNA copy number and could therefore enlighten some potential oncogenes and stability genes with functional roles in cancers, and investigated the bioinformatics significance for those correlated genes</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">. </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><b><span style="font-family:Verdana;">Method: </span></b></span></span><span><span><span style="font-family:""><span style="font-family:Verdana;">We obtained the DNA copy </span><span style="font-family:Verdana;">number and mRNA expression data from the Cancer Genomic Atlas and</span><span style="font-family:Verdana;"> identified the most statistically significant copy number alteration regions using the GISTIC. Then identified the significance genes between the tumor samples within the copy number alteration regions and analyzed the correlation using a binary matrix. The selected genes were subjected to bio</span><span><span style="font-family:Verdana;">informatics analysis using GSEA tool. </span><b><span style="font-family:Verdana;">Results:</span></b><span style="font-family:Verdana;"> GISTIC analysis results</span></span><span style="font-family:Verdana;"> showed there were 45 significance copy number amplification regions in the ovarian cancer, SAM and Fisher’s exact test found there have 40 genes can affect the expression level, which located in the amplification regions. That means we obtained 40 genes which have a correlation between copy number amplification and drastic up- and down-expression, which p-value < 0.05 (Fisher’s exact test) and an FDR < 0.05. GSEA enrichment analysis found these genes w</span></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">ere</span></span></span><span><span><span style="font-family:""><span style="font-family:Verdana;"> overlapped with the several published studies which were focused on the gene study of tumorigenesis. </span><b><span style="font-family:Verdana;">Conclusion:</span></b><span style="font-family:Verdana;"> The use of statistics and bioinformatics to analyze the microarray data can found an interaction network involved.</span></span></span></span><span style="font-family:""> <a name="OLE_LINK16"></a><a name="OLE_LINK10"></a><span><span style="font-family:Verdana;">The combination of the copy number data and expression has pro</span><span style="font-family:Verdana;">vided a short list of candidate genes that are consistent with tumor</span><span style="font-family:Verdana;"> driving roles. These would offer new ideas for early diagnosis and treat target of ovarian cancer.</span></span></span>展开更多
文摘A special X-radiography,used in obtaining the deformed specimen grating in in-plane moire method is introduced.The density of gridline is up to 50 lines / mm.This method is suitable not only for the copy of grating in fiat surface but also for that in curved surface.From unfolding the copy and superposing it with a reference grating,the moire effect arises.By processing this moire fringes with the digital image processing unit, the strain distribution would be obtained.Furthermore,the u-and v-displacement fields could be separated by the optical processing of this copy.The measurment sensitivity and accuracy become better simultaneously because of the fringe multiplication.Thus,this method makes it possible to measure the elastic-plastic strain precisely in any developable curved surfaces,for example, the surface of shaft,rotary-wing,cylindrical shell and cone shell etc.
文摘Copy-move offense is considerably used to conceal or hide several data in the digital image for specific aim, and onto this offense some portion of the genuine image is reduplicated and pasted in the same image. Therefore, Copy-Move forgery is a very significant problem and active research area to check the confirmation of the image. In this paper, a system for Copy Move Forgery detection is proposed. The proposed system is composed of two stages: one is called the detection stages and the second is called the refine detection stage. The detection stage is executed using Speeded-Up Robust Feature (SURF) and Binary Robust Invariant Scalable Keypoints (BRISK) for feature detection and in the refine detection stage, image registration using non-linear transformation is used to enhance detection efficiency. Initially, the genuine image is picked, and then both SURF and BRISK feature extractions are used in parallel to detect the interest keypoints. This gives an appropriate number of interest points and gives the assurance for finding the majority of the manipulated regions. RANSAC is employed to find the superior group of matches to differentiate the manipulated parts. Then, non-linear transformation between the best-matched sets from both extraction features is used as an optimization to get the best-matched set and detect the copied regions. A number of numerical experiments performed using many benchmark datasets such as, the CASIA v2.0, MICC-220, MICC-F600 and MICC-F2000 datasets. With the proposed algorithm, an overall average detection accuracy of 95.33% is obtained for evaluation carried out with the aforementioned databases. Forgery detection achieved True Positive Rate of 97.4% for tampered images with object translation, different degree of rotation and enlargement. Thus, results from different datasets have been set, proving that the proposed algorithm can individuate the altered areas, with high reliability and dealing with multiple cloning.
文摘<strong>Objective:</strong> <span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">DNA copy number alterations and difference expression are frequently observed in ovarian cancer. The purpose of this way was to pinpoint gene expression change that w</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">as</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> associated with alterations in DNA copy number and could therefore enlighten some potential oncogenes and stability genes with functional roles in cancers, and investigated the bioinformatics significance for those correlated genes</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">. </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><b><span style="font-family:Verdana;">Method: </span></b></span></span><span><span><span style="font-family:""><span style="font-family:Verdana;">We obtained the DNA copy </span><span style="font-family:Verdana;">number and mRNA expression data from the Cancer Genomic Atlas and</span><span style="font-family:Verdana;"> identified the most statistically significant copy number alteration regions using the GISTIC. Then identified the significance genes between the tumor samples within the copy number alteration regions and analyzed the correlation using a binary matrix. The selected genes were subjected to bio</span><span><span style="font-family:Verdana;">informatics analysis using GSEA tool. </span><b><span style="font-family:Verdana;">Results:</span></b><span style="font-family:Verdana;"> GISTIC analysis results</span></span><span style="font-family:Verdana;"> showed there were 45 significance copy number amplification regions in the ovarian cancer, SAM and Fisher’s exact test found there have 40 genes can affect the expression level, which located in the amplification regions. That means we obtained 40 genes which have a correlation between copy number amplification and drastic up- and down-expression, which p-value < 0.05 (Fisher’s exact test) and an FDR < 0.05. GSEA enrichment analysis found these genes w</span></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">ere</span></span></span><span><span><span style="font-family:""><span style="font-family:Verdana;"> overlapped with the several published studies which were focused on the gene study of tumorigenesis. </span><b><span style="font-family:Verdana;">Conclusion:</span></b><span style="font-family:Verdana;"> The use of statistics and bioinformatics to analyze the microarray data can found an interaction network involved.</span></span></span></span><span style="font-family:""> <a name="OLE_LINK16"></a><a name="OLE_LINK10"></a><span><span style="font-family:Verdana;">The combination of the copy number data and expression has pro</span><span style="font-family:Verdana;">vided a short list of candidate genes that are consistent with tumor</span><span style="font-family:Verdana;"> driving roles. These would offer new ideas for early diagnosis and treat target of ovarian cancer.</span></span></span>