dimensions: the “enstrophy cascade, ” for which the spectral exponent α = 3, and the “inverse energy cascade, ” for which the spectral exponent α = 5/3. We find that the functional relation between the frictional drag f and the Reynolds number Re depends on the spectral exponent: where α = 3, f ∝ Re−1/2

by Utsaw Kumar The presence of inexpensive and powerful sensing and communication devices has made it possible to deploy large scale distributed systems for a variety of applications. Interactions among different components of such a system include communication of information and controlling

in partial ful2llment of the requirements for the degree of

, and complete, in the sense that all possible solutions get accurately bounded, irrespectively of whether the analyzed linkage is rigid or mobile. The problem is tackled by formulating a system of linear, parabolic, and hyperbolic equations, which is here solved by a new strategy exploiting its structure

of the signal complexity. In practice, this enables lower sampling rates that can be more easily achieved by current hardware designs. The primary bottleneck that limits ADC sam-pling rates is quantization, i.e., higher bit-depths impose lower sampling rates. Thus, the decreased sampling rates of CS ADCs

ACKNOWLEDGMENTS I would like to give many thanks to Dr. Shengli Fu and Dr. Yan Huang as my advi-

-energy description can be obtained for braneworld scenarios, allowing for a number of generalizations to the original models. This method is applied to orbifolded and uncompactified N-brane models, deriving an effective four-dimensional action. The parameter space of this theory is constrained using observational

Effective support for custom proof automation is essential for large-scale interactive proof develop-ment. However, existing languages for automation via tactics either (a) provide no way to specify the behavior of tactics within the base logic of the accompanying theorem prover, or (b) rely

in recent years An implementation of RMT has now been completed and parallelized on workstations, mid-sized parallel machines and on HECToR. Success was due to the use of a mature R-matrix parallel code for the Inner Region, and the use of the HELIUM finite-difference code for the Outer Region HELIUM

considered in performance predictions. Most existing approaches use established prediction models [3, 18] to estimate the performance of already existing complex software systems. Their main focus lies on the questions: i) ”How can we automatically derive or extract the models we need? ” and ii