This paper focuses on compressive sensing (CS)-based hyperspectral imaging (HSI). A band-by-band reconstruction approach, namely prior image constrained compressive sensing (PICCS)-based HSI, is proposed. Furthermore, a more effective PICCS model is built in this paper. Each hyperspectral band is reconstructed based on the previous one, which utilizes not only the sparsity of each hyperspectral band in a certain basis but also the similarity between two consecutive bands. Moreover, compared with the algorithms which reconstruct all the hyperspectral bands simultaneously, PICCS-based HSI reduces the requirements for computational ability and computational memory of the receivers. In addition, compared with the independent band-by-band reconstruction algorithms and tensor-SL0-based HSI, PICCS-based HSI significantly reduces the number of measurements with similar or better reconstruction quality. The convergence of the two algorithms is proved and some simulations are provided to illustrate their effectiveness.
The major goal of TE (traffic engineering) is to facilitate efficient and reliable network operations while simultaneously optimizing network utilization and traffic performance. Therefore it is necessary to introduce TE mechanism to ISPs’ (Internet Service Providers’) networks with the purpose of improving network performance and reducing costs. In this paper, a novel TE mechanism based on GMPLS (Generalized Multi-Protocol Label Switching) is proposed and analyzed in detail, and is demonstrated on a network with IP over ASON (automatically switched optical network) architecture. The ASON layer acts as the server of IP layer which uses traditional IP protocols, and realizes TE mechanism through the method of dynamically changing bandwidth seen by IP layer through UNI (User-Network Interface). Simulation results have shown that the model has a superior operational agility to the conventional method and lower congestion probability in certain conditions.