The unsupervised hyperspectral unmixing (HU) technique, also known as blind HU, directly decomposes the mixed pixels of a hyperspectral image (HSI) into a combination of endmembers weighted by their fractional abundances, without relying on a spectral library. To perform the blind HU, nonnegative matrix factorization (NMF) and its extensions have been widely used to improve the performance of the abundances, with sparse constraints and spatial information about the abundances. However, there are only a few works that exploit the prior information or constraints on the endmembers in the NMF-based methods for the blind HU. The linear mixing model (LMM) assumption assumes that the endmembers should be uncorrelated. Unfortunately, the spectra of HSI are highly correlated in practice, making it challenging to decompose the mixed spectra using only spatial information. Therefore, it is necessary to consider the proper constraints on the endmembers to obtain endmembers with higher accuracy from HSI. This paper first introduces the mutual incoherence (MIC) regularization to promote incoherence between endmembers, that is, to minimize the correlation between the estimated endmembers. Then, the relative total variation (RTV) regularization is introduced on the abundances to exploit the spatial structure of the HSI. Finally, the MRNMF algorithm based on NMF is proposed for the HU task by using multiplicative iteration rule. The experimental results on synthetic and real datasets show that the proposed MRNMF outperforms the state-of-the-art blind HU algorithms.

The concept of effective bandwidth can be utilized to estimate the amount of bandwidth that should be allocated to a source in order to meet a QoS requirement. Several different effective bandwidth estimators have been defined in the literature; however it is necessary to ensure that these estimators are practically implementable and feasible in realistic network environments. This necessity serves as our motivation to implement several estimators in a realistic network in order to evaluate the use of online measurement-based resource allocation schemes. In this paper, we describe our implementation of three resource allocation schemes within the realtime traffic flow measurement architecture. We compare our results of emulation to previous simulation results in order to compare the accuracy and performance of the schemes. Finally, we demonstrate that these schemes are feasible to be implemented in network hardware to be utilized in self-sizing high-speed networks.

Congestion detection and control mechanism is an integrated function of any quality of service scheme to provide differentiated services during network congestion. This paper presents a new approach to congestion detection and avoidance based on real-time traffic flow measurement and the statistics of index of dispersion for counts. This approach passively monitors the underlying network traffic flow, and detects or predicts the network congestion without relying on transport-layer protocols or feedback signaling. An experimentation of applying this scheme in a validation testbed is presented with results illustrating the effectiveness of the approach in practical scenarios.

Optical burst switching (OBS) has been touted as a suitable technique to support the bursty IP datagrams over the future all-optical networks. This paper presents an information model on edge nodes of such an OBS network to provide management information at both optical burst layer and traditional WDM transport layer to understand, operate and manage these edge nodes. Managed objects corresponding to various components and resources inside edge nodes are illustrated. The structure of management information base (MIB) under the SNMP management interface for managing the identified parameters is also presented.