Divisible load scheduling on single-level tree networks with buffer constraints

Scheduling a divisible load on a heterogeneous single-level tree network with processors having finite-size buffers is addressed. We first present the closed-form solutions for the case when the available buffer size at each site is assumed to be infinite. Then we analyze the case when these buffer sizes are of finite size. For the first time in the domain of DLT (divisible load theory) literature, the problem of scheduling with finite-size buffers is addressed. For this case, we present a novel algorithm, referred to as incremental balancing strategy, to obtain an optimal load distribution. Algorithm IBS adopts a strategy to feed the divisible load in a step-by-step incremental balancing fashion by taking advantage of the available closed-form solutions of the optimal scheduling for the case without buffer size constraints. Based on the rigorous mathematical analysis, a number of interesting and useful properties exhibited by the algorithm are proven. We present a very useful discussion on the implications of this problem on the effect of sequencing discussed in the literature. Also, the impact of Rule A, a rule that obtains a reduced optimal network to achieve optimal processing time by eliminating a redundant set of processor-link pairs, is also discussed. Numerical examples are presented.     

Multiaspect classification of airborne targets via physics-basedHMMs and matching pursuits

Wideband electromagnetic fields scattered from N distinct target-sensor orientations are employed for classification of airborne targets. Each of the scattered waveforms is parsed via physics-based matching pursuits, yielding N feature vectors. The feature vectors are submitted to a hidden Markov model (HMM), each state of which is characterized by a set of target-sensor orientations over which the associated feature vectors are relatively stationary. The N feature vectors extracted from the multiaspect scattering data implicitly sample N states of the target (some states may be sampled more than once), with the state sequence modeled statistically as a Markov process, resulting in an HMM due to the “hidden” or unknown target orientation. In the work presented here, the state-dependent probability of observing a given feature vector is modeled via physics-motivated linear distributions, in lieu of the traditional Gaussian mixtures applied in classical HMMs. Further, we develop a scheme that yields autonomous definitions for the aspect-dependent HMM states. The paradigm is applied to synthetic scattering data for two simple targets