(CORN)\u3csup\u3e2\u3c/sup\u3e: Correlation-Based Cooperative Spectrum Sensing in Cognitive Radio Networks

In this paper, (CORN)2, a correlation-based, optimal sensing scheduling algorithm is developed for cognitive radio networks to minimize energy consumption. A sensing quality metric is defined as a measure of the correctness of spectral availability information. The optimal scheduling algorithm is shown to minimize the cost of sensing (e.g., energy consumption, sensing duration) while meeting the sensing quality requirements. To this end, (CORN)2 utilizes a novel sensing deficiency virtual queue concept and exploits the correlation between spectrum measurements of a particular secondary user and its collaborating neighbors. The proposed algorithm is further proved to achieve a distributed and optimal solution under certain, easily satisfied assumptions. In addition to the theoretically proved performance guarantees, the proposed algorithm is also evaluated through simulations

Cooperative Spectrum Sensing in Cognitive Radio Networks Using Multidimensional Correlations

In this paper, a multidimensional-correlation-based sensing scheduling algorithm, (CORN)2, is developed for cognitive radio networks to minimize energy consumption. A sensing quality metric is defined as a measure of the correctness of spectral availability information based on the fact that spectrum sensing information at a given space and time can represent spectrum information at a different point in space and time. The scheduling algorithm is shown to achieve a cost of sensing (e.g., energy consumption, sensing duration) arbitrarily close to the possible minimum, while meeting the sensing quality requirements. To this end, (CORN)2 utilizes a novel sensing deficiency virtual queue concept and exploits the correlation between spectrum measurements of a particular secondary user and its collaborating neighbors. The proposed algorithm is proved to achieve a distributed and arbitrarily close to optimal solution under certain, easily satisfied assumptions. Furthermore, a distributed Selective-(CORN)2 (S-(CORN)2) is introduced by extending the distributed algorithm to allow secondary users to select collaboration neighbors in densely populated cognitive radio networks. In addition to the theoretically proved performance guarantees, the algorithms are evaluated through simulations

Cooperative Spectrum Sensing in Cognitive Radio Networks Using Multidimensional Correlations

In this paper, a multidimensional-correlation-based sensing scheduling algorithm, (CORN)2, is developed for cognitive radio networks to minimize energy consumption. A sensing quality metric is defined as a measure of the correctness of spectral availability information based on the fact that spectrum sensing information at a given space and time can represent spectrum information at a different point in space and time. The scheduling algorithm is shown to achieve a cost of sensing (e.g., energy consumption, sensing duration) arbitrarily close to the possible minimum, while meeting the sensing quality requirements. To this end, (CORN)2 utilizes a novel sensing deficiency virtual queue concept and exploits the correlation between spectrum measurements of a particular secondary user and its collaborating neighbors. The proposed algorithm is proved to achieve a distributed and arbitrarily close to optimal solution under certain, easily satisfied assumptions. Furthermore, a distributed Selective-(CORN)2 (S-(CORN)2) is introduced by extending the distributed algorithm to allow secondary users to select collaboration neighbors in densely populated cognitive radio networks. In addition to the theoretically proved performance guarantees, the algorithms are evaluated through simulations

Classical scrapie prions in ovine blood are associated with B lymphocytes and platelet-rich plasma

<p>Abstract</p> <p>Background</p> <p>Classical scrapie is a naturally occurring transmissible spongiform encephalopathy of sheep and goats characterized by cellular accumulation of abnormal isoforms of prion protein (PrP^Sc) in the central nervous system and the follicles of peripheral lymphoid tissues. Previous studies have shown that the whole blood and buffy coat blood fraction of scrapie infected sheep harbor prion infectivity. Although PrP^Schas been detected in peripheral blood mononuclear cells (PBMCs), plasma, and more recently within a subpopulation of B lymphocytes, the infectivity status of these cells and plasma in sheep remains unknown. Therefore, the objective of this study was to determine whether circulating PBMCs, B lymphocytes and platelets from classical scrapie infected sheep harbor prion infectivity using a sheep bioassay.</p> <p>Results</p> <p>Serial rectal mucosal biopsy and immunohistochemistry were used to detect preclinical infection in lambs transfused with whole blood or blood cell fractions from preclinical or clinical scrapie infected sheep. PrP^Scimmunolabeling was detected in antemortem rectal and postmortem lymphoid tissues from recipient lambs receiving PBMCs (15/15), CD72⁺B lymphocytes (3/3), CD21⁺B lymphocytes (3/3) or platelet-rich plasma (2/3) fractions. As expected, whole blood (11/13) and buffy coat (5/5) recipients showed positive PrP^Sclabeling in lymphoid follicles. However, at 549 days post-transfusion, PrP^Scwas not detected in rectal or other lymphoid tissues in three sheep receiving platelet-poor plasma fraction.</p> <p>Conclusions</p> <p>Prion infectivity was detected in circulating PBMCs, CD72⁺pan B lymphocytes, the CD21⁺subpopulation of B lymphocytes and platelet-rich plasma of classical scrapie infected sheep using a sheep bioassay. Combining platelets with B lymphocytes might enhance PrP^Scdetection levels in blood samples.</p

Non-Invasive Microstructure and Morphology Investigation of the Mouse Lung: Qualitative Description and Quantitative Measurement

BACKGROUND: Early detection of lung cancer is known to improve the chances of successful treatment. However, lungs are soft tissues with complex three-dimensional configuration. Conventional X-ray imaging is based purely on absorption resulting in very low contrast when imaging soft tissues without contrast agents. It is difficult to obtain adequate information of lung lesions from conventional X-ray imaging. METHODS: In this study, a recently emerged imaging technique, in-line X-ray phase contrast imaging (IL-XPCI) was used. This powerful technique enabled high-resolution investigations of soft tissues without contrast agents. We applied IL-XPCI to observe the lungs in an intact mouse for the purpose of defining quantitatively the micro-structures in lung. FINDINGS: The three-dimensional model of the lung was successfully established, which provided an excellent view of lung airways. We highlighted the use of IL-XPCI in the visualization and assessment of alveoli which had rarely been studied in three dimensions (3D). The precise view of individual alveolus was achieved. The morphological parameters, such as diameter and alveolar surface area were measured. These parameters were of great importance in the diagnosis of diseases related to alveolus and alveolar scar. CONCLUSION: Our results indicated that IL-XPCI had the ability to represent complex anatomical structures in lung. This offered a new perspective on the diagnosis of respiratory disease and may guide future work in the study of respiratory mechanism on the alveoli level