Efficient Advanced Encryption Standard for Securing Cognitive Radio Networks

During the last decade, the CR (Cognitive Radio) came into view as a major wireless technology to resolve the issue of spectrum secrecy and efficient spectrum utilization. However, due to unlicensed (secondary) users, there are various security threats to the CRN (Cognitive Radio Networks). Some malicious users may access the CRN and mislead the secondary users to vacate the occupied channel, which may stop the communication. In this work, we propose a new cryptographic-based algorithm, CRAES (Cognitive Radio-Advanced Encryption Standard), inspired by the traditional AES to secure the CRN. The data of the primary and secondary users is encrypted at the transmitter and decrypted at the receiver. Unlike the conventional AES, we introduce the data-dependent key-generation and shift-rows process. We also reduce the rounds of AES from 10-6 to improve the computational efficiency without compromising the overall security. The experimental results demonstrate the effectiveness of the proposed CR-AES in terms of better security, reliability, and computational efficiency

Effective Image Segmentation using Composite Energy Metric in Levelset Based Curve Evolution

Accurate segmentation of anatomical organs in medical images is a complex task due to wide interpatient variability and several acquisition dependent artefacts. Moreover, image noise, low contrast and intensity inhomogeneity in medical data further amplifies the challeng. In this work, we propose an effective yet simple algorithm based on composite energy metric for precise detection of object boundaries. A number of methods have been proposed in literature for image segmentation; however, these methods employ individual characteristics of image including gradient, regional intensity or texture map. Segmentation based on individual featres often fail for complex images, especially for medical imagery. Accordingly, we propose that the segmentation quality can be improved by integrating local and global image features in the curve evolution. This work employs the classic snake model aka active contour model; however, the curve evolution force has been updated. In contast to the conventional image-based regional intensity statistics, the proposed snake model evolves using composite image energy. Hence, the proposed method offers a greater resistance to the local optima problem as well as initialization perturbations. Experimental results for both synthetic and 2D (Two Dimensional) real clinal images are presented in this work to validate the performance of the proposed method. The performance of the proposed model is evaluated with respect to expert-based manual ground truth. Accordingly, the proposed model achieves higher accuracy in comparison to the state-of-the-art region based segmentation methods of Lankton and Yin as reported in results section

Framework for Detection and Localization of Coronary Non-Calcified Plaques in Cardiac CTA using Mean Radial Profiles

Background and Objective: The high mortality rate associated with coronary heart disease (CHD) has driven intensive research in cardiac imaging and image analysis. The advent of computed tomography angiography (CTA) has turned non-invasive diagnosis of cardiovascular anomalies into reality as calcified coronary plaques can be easily identified due to their high intensity values. However, the detection of non-calcified plaques in CTA is still a challenging problem because of lower intensity values, which are often similar to the nearby blood and muscle tissues. In this work, we propose the use of mean radial profiles for the detection of non-calcified plaques in CTA imagery. Methods: Accordingly, we computed radial profiles by averaging the image intensity in concentric rings around the vessel centreline in a first stage. In the subsequent stage, an SVM classifier is applied to identify the abnormal coronary segments. For occluded segments, we further propose a derivative-based method to localize the position and length of the plaque inside the segment. Results: A total of 32 CTA volumes were analysed and a detection accuracy of 88.4% with respect to the manual expert was achieved. The plaque localization accuracy was computed using the Dice similarity coefficient and a mean of 83.2% was achieved. Conclusion: The consistent performance for multi-vendor, multi-institution data demonstrates the reproducibility of our method across different CTA datasets with a good agreement with manual expert annotations

Framework for Detection and Localization of Coronary Non-Calcified Plaques in Cardiac CTA using Mean Radial Profiles

Background and Objective: The high mortality rate associated with coronary heart disease (CHD) has driven intensive research in cardiac imaging and image analysis. The advent of computed tomography angiography (CTA) has turned non-invasive diagnosis of cardiovascular anomalies into reality as calcified coronary plaques can be easily identified due to their high intensity values. However, the detection of non-calcified plaques in CTA is still a challenging problem because of lower intensity values, which are often similar to the nearby blood and muscle tissues. In this work, we propose the use of mean radial profiles for the detection of non-calcified plaques in CTA imagery. Methods: Accordingly, we computed radial profiles by averaging the image intensity in concentric rings around the vessel centreline in a first stage. In the subsequent stage, an SVM classifier is applied to identify the abnormal coronary segments. For occluded segments, we further propose a derivative-based method to localize the position and length of the plaque inside the segment. Results: A total of 32 CTA volumes were analysed and a detection accuracy of 88.4% with respect to the manual expert was achieved. The plaque localization accuracy was computed using the Dice similarity coefficient and a mean of 83.2% was achieved. Conclusion: The consistent performance for multi-vendor, multi-institution data demonstrates the reproducibility of our method across different CTA datasets with a good agreement with manual expert annotations

Artificial Urdu Text Detection and Localization from Individual Video Frames

In current era of technology, information acquisition from images and videos become most important task due to the rapid development of data mining and machine learning.The information can be either textual, visual, or combination of these. Text appearing in images or videos is a significant source of information and plays a vital role to perceive it. Developing a unified method to detect the text is hard, as textual properties (i.e. font, size, color, illumination, orientation, etc.) may vary with the complex background. So far, multimedia and computer vision community unable yet to standardize any ideal approach to extract the text smoothly. In this paper, a novel method is proposed to detect and localize artificial Urdu text in individual video frames. Firstly, Sobel and Canny edge detection operators are applied to input frame and are merged with MSER (Maximally Stable Extremal Region) detected regions. Next, geometric constraints are applied to eliminate obvious non-text regions with large and small variations. Further refining of non-text regions is achieved by stroke width transform. SVM (Support Vector Machine) classifier is trained to classify text and non-text objects. Finally, bounding boxes are used to localize the text.Experimental results show that the proposed method is robust and efficient than state-of-the-art methods

A Bilateral Filter Based Post-Processing Approach for Supervised Spectral-Spatial Hyperspectral Image Classification

To effectively improve the performance of representation based classifier, a spatial spectral joint classification post-processing approach is proposed, based on the application of edge preserving BF (Bilateral Filtering) method. The proposed framework includes two key processes: (1) the classifier (such as SRC, CRC, or KSRC) based on sparse representation of each pixel is used to obtain softclassified probabilities belonging to each information class for each pixel; (2) spatial spectral joint BF for the soft-classified probabilities map. It is aimed to integrate context-aware information for each pixel class labels. Under the spatial guidance image, extracted from the three principle component, a BF is employed to get the refined probability maps. The BF considers not only the spatial distance but it also considers the image context-aware distance which significantly improves the classification results. Finally, the class label is obtained by choosing the maximum probability criteria. The experimental results on three benchmark hyperspectral data sets showed that the “local smoothing” is efficient and has a potential to achieve high classification accuracy. All the algorithms are implemented with equal number of labeled samples and comparative results are presented in terms of visual classification map and numerical classification results. The major advantages of proposed method are: it is simple, non-iterative and easy to implement. Hence, the advantages lead to significant usage in real applications

Association of the 9p21.3 locus with risk of first-ever myocardial infarction in Pakistanis: Case-control study in South Asia and updated meta-analysis of Europeans

Objective: To examine variants at the 9p21 locus in a case-control study of acute myocardial infarction (MI) in Pakistanis and to perform an updated meta-analysis of published studies in people of European ancestry.Methods and results: A total of 1851 patients with first-ever confirmed MI and 1903 controls were genotyped for 89 tagging single-nucleotide polymorphisms at locus 9p21, including the lead variant (rs1333049) identified by the Wellcome Trust Case Control Consortium. Minor allele frequencies and extent of linkage disequilibrium observed in Pakistanis were broadly similar to those seen in Europeans. In the Pakistani study, 6 variants were associated with MI (P\u3c10(-2)) in the initial sample set, and in an additional 741 cases and 674 controls in whom further genotyping was performed for these variants. For Pakistanis, the odds ratio for MI was 1.13 (95% CI, 1.05 to 1.22; P=2 x 10(-3)) for each copy of the C allele at rs1333049. In comparison, a meta-analysis of studies in Europeans yielded an odds ratio of 1.31 (95% CI, 1.26 to 1.37) for the same variant (P=1 x 10(-3) for heterogeneity). Meta-analyses of 23 variants, in up to 38,250 cases and 84,820 controls generally yielded higher values in Europeans than in Pakistanis.Conclusions: To our knowledge, this study provides the first demonstration that variants at the 9p21 locus are significantly associated with MI risk in Pakistanis. However, association signals at this locus were weaker in Pakistanis than those in European studies

Genetic determinants of major blood lipids in Pakistanis compared with Europeans.

BACKGROUND: Evidence is sparse about the genetic determinants of major lipids in Pakistanis. METHODS AND RESULTS: Variants (n=45 000) across 2000 genes were assessed in 3200 Pakistanis and compared with 2450 Germans using the same gene array and similar lipid assays. We also did a meta-analysis of selected lipid-related variants in Europeans. Pakistani genetic architecture was distinct from that of several ethnic groups represented in international reference samples. Forty-one variants at 14 loci were significantly associated with levels of HDL-C, triglyceride, or LDL-C. The most significant lipid-related variants identified among Pakistanis corresponded to genes previously shown to be relevant to Europeans, such as CETP associated with HDL-C levels (rs711752; P<10(-13)), APOA5/ZNF259 (rs651821; P<10(-13)) and GCKR (rs1260326; P<10(-13)) with triglyceride levels; and CELSR2 variants with LDL-C levels (rs646776; P<10(-9)). For Pakistanis, these 41 variants explained 6.2%, 7.1%, and 0.9% of the variation in HDL-C, triglyceride, and LDL-C, respectively. Compared with Europeans, the allele frequency of rs662799 in APOA5 among Pakistanis was higher and its impact on triglyceride concentration was greater (P-value for difference <10(-4)). CONCLUSIONS: Several lipid-related genetic variants are common to Pakistanis and Europeans, though they explain only a modest proportion of population variation in lipid concentration. Allelic frequencies and effect sizes of lipid-related variants can differ between Pakistanis and Europeans