A Mixed Line Rate(MLR)optical network is a good candidate for a core backbone network because of its ability to provide diverse line rates to effectively accommodate traffic demands with heterogeneous bandwidth requir...A Mixed Line Rate(MLR)optical network is a good candidate for a core backbone network because of its ability to provide diverse line rates to effectively accommodate traffic demands with heterogeneous bandwidth requirements.Because of the deleterious effects of physical impairments,there is a maximum transmission reach for optical signals before they have to be regenerated.Being expensive devices,regenerators are expected to be sparsely located and used in such a network,called a translucent optical network.In this paper,we consider the Grooming,Routing,and Wavelength Assignment(GRWA)problem so that the Quality of Transmission(QoT)for connections is satisfied,and the network-level performance metric of blocking probability is minimized.Cross-layer heuristics to effectively allocate the sparse regenerators in MLR networks are developed,and extensive simulation results are presented to demonstrate their effectiveness.展开更多
Video reconstruction quality largely depends on the ability of employed sparse domain to adequately represent the underlying video in Distributed Compressed Video Sensing (DCVS). In this paper, we propose a novel dyna...Video reconstruction quality largely depends on the ability of employed sparse domain to adequately represent the underlying video in Distributed Compressed Video Sensing (DCVS). In this paper, we propose a novel dynamic global-Principal Component Analysis (PCA) sparse representation algorithm for video based on the sparse-land model and nonlocal similarity. First, grouping by matching is realized at the decoder from key frames that are previously recovered. Second, we apply PCA to each group (sub-dataset) to compute the principle components from which the sub-dictionary is constructed. Finally, the non-key frames are reconstructed from random measurement data using a Compressed Sensing (CS) reconstruction algorithm with sparse regularization. Experimental results show that our algorithm has a better performance compared with the DCT and K-SVD dictionaries.展开更多
基金supported in part by National Science Foundation (NSF) under Grants No. CNS-0915795 and No.CNS-0916890
文摘A Mixed Line Rate(MLR)optical network is a good candidate for a core backbone network because of its ability to provide diverse line rates to effectively accommodate traffic demands with heterogeneous bandwidth requirements.Because of the deleterious effects of physical impairments,there is a maximum transmission reach for optical signals before they have to be regenerated.Being expensive devices,regenerators are expected to be sparsely located and used in such a network,called a translucent optical network.In this paper,we consider the Grooming,Routing,and Wavelength Assignment(GRWA)problem so that the Quality of Transmission(QoT)for connections is satisfied,and the network-level performance metric of blocking probability is minimized.Cross-layer heuristics to effectively allocate the sparse regenerators in MLR networks are developed,and extensive simulation results are presented to demonstrate their effectiveness.
基金supported by the Innovation Project of Graduate Students of Jiangsu Province, China under Grants No. CXZZ12_0466, No. CXZZ11_0390the National Natural Science Foundation of China under Grants No. 61071091, No. 61271240, No. 61201160, No. 61172118+2 种基金the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province, China under Grant No. 12KJB510019the Science and Technology Research Program of Hubei Provincial Department of Education under Grants No. D20121408, No. D20121402the Program for Research Innovation of Nanjing Institute of Technology Project under Grant No. CKJ20110006
文摘Video reconstruction quality largely depends on the ability of employed sparse domain to adequately represent the underlying video in Distributed Compressed Video Sensing (DCVS). In this paper, we propose a novel dynamic global-Principal Component Analysis (PCA) sparse representation algorithm for video based on the sparse-land model and nonlocal similarity. First, grouping by matching is realized at the decoder from key frames that are previously recovered. Second, we apply PCA to each group (sub-dataset) to compute the principle components from which the sub-dictionary is constructed. Finally, the non-key frames are reconstructed from random measurement data using a Compressed Sensing (CS) reconstruction algorithm with sparse regularization. Experimental results show that our algorithm has a better performance compared with the DCT and K-SVD dictionaries.
