We present a detailed analysis of the JIT, JET, and Horizon wavelength reservation schemes for optical burst switched (OBS) networks. Our analysis accounts for several important parameters, including the burst offset length, and the optical switching and hardware processing overheads associated with bursts as they travel across the network. The contributions of our work include: (i) analytical models of JET and Horizon (on a single OBS node) that are more accurate than previously published ones, and which are valid for general burst length and offset length distributions; (ii) the determination of the regions of parameter values in which a more complex reservation scheme reduces to a simpler one; and (iii) a new reservation scheme, JIT + , which is as simple to implement as JIT, but whose performance tracks that of Horizon and JET. We compare the performance of the four wavelength reservation schemes on a single OBS node, as well as on a path of OBS nodes with cross traffic, under various sets of parameter values. Our major finding is that, under reasonable assumptions regarding the current and future state-of-the-art in optical switch and electronic hardware technologies, the simplicity of JIT and JIT + seem to outweigh any performance benefits of Horizon and JET.

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Abstract-The prime motivation of our work is to balance the inherent trade-off between the resource consumption and the accuracy of the target tracking in wireless sensor networks. Toward this objective, the study goes through three phases. First, a cluster-based scheme is exploited. At every sampling instant, only one cluster of sensors that located in the proximity of the target is activated, whereas the other sensors are inactive. To activate the most appropriate cluster, we propose a nonmyopic rule, which is based on not only the target state prediction but also its future tendency. Second, the variational filtering algorithm is capable of precise tracking even in the highly nonlinear case. Furthermore, since the measurement incorporation and the approximation of the filtering distribution are jointly performed by variational calculus, an effective and lossless compression is achieved. The intercluster information exchange is thus reduced to one single Gaussian statistic, dramatically cutting down the resource consumption. Third, a binary proximity observation model is employed by the activated slave sensors to reduce the energy consumption and to minimize the intracluster communication. Finally, the effectiveness of the proposed approach is evaluated and compared with the state-of-the-art algorithms in terms of tracking accuracy, internode communication, and computation complexity.

It is usually assumed that optical burst switching (OBS) networks use the shortest path routing along with next-hop burst forwarding. The shortest path routing minimizes delay and optimizes utilization of resources, however, it often causes certain links to become congested while others remain underutilized. In a bufferless OBS network in which burst drop probability is the primary metric of interest, the existence of a few highly congested links could lead to unacceptable performance for the entire network. We take a traffic engineering approach to path selection in OBS networks with the objective of balancing the traffic across the network links to reduce congestion and to improve overall performance. We present an approximate integer linear optimization problem as well as a simple integer relaxation heuristic to solve the problem efficiently for large networks. Numerical results demonstrate that our approach is effective in reducing the network-wide burst drop probability, in many cases significantly, over the shortest path routing.

ObjectiveTo explore the electroencephalogram (EEG) characteristics in patients with chronic fatigue syndrome (CFS) using brain electrical activity mapping (BEAM) and EEG nonlinear dynamical analysis.MethodsForty-seven outpatients were selected over a 3-month period and divided into an observation group (24 outpatients) and a control group (23 outpatients) by using the non-probability sampling method. All the patients were given a routine EEG. The BEAM and the correlation dimension changes were analyzed to characterize the EEG features.Results1) BEAM results indicated that the energy values of δ, θ, and α1 waves significantly increased in the observation group, compared with the control group (P<0.05, P<0.01, respectively), which suggests that the brain electrical activities in CFS patients were significantly reduced and stayed in an inhibitory state; 2) the increase of δ, θ, and α1 energy values in the right frontal and left occipital regions was more significant than other encephalic regions in CFS patients, indicating the region-specific encephalic distribution; 3) the correlation dimension in the observation group was significantly lower than the control group, suggesting decreased EEG complexity in CFS patients.ConclusionThe spontaneous brain electrical activities in CFS patients were significantly reduced. The abnormal changes in the cerebral functions were localized at the right frontal and left occipital regions in CFS patients.