Browsing by Subject "Model parameters"
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Item Open Access Discriminative fine-grained mixing for adaptive compression of data streams(Institute of Electrical and Electronics Engineers, 2014) Gedik, B.This paper introduces an adaptive compression algorithm for transfer of data streams across operators in stream processing systems. The algorithm is adaptive in the sense that it can adjust the amount of compression applied based on the bandwidth, CPU, and workload availability. It is discriminative in the sense that it can judiciously apply partial compression by selecting a subset of attributes that can provide good reduction in the used bandwidth at a low cost. The algorithm relies on the significant differences that exist among stream attributes with respect to their relative sizes, compression ratios, compression costs, and their amenability to application of custom compressors. As part of this study, we present a modeling of uniform and discriminative mixing, and provide various greedy algorithms and associated metrics to locate an effective setting when model parameters are available at run-time. Furthermore, we provide online and adaptive algorithms for real-world systems in which system parameters that can be measured at run-time are limited. We present a detailed experimental study that illustrates the superiority of discriminative mixing over uniform mixing. © 2013 IEEE.Item Open Access A novel equivalent circuit model for CMUTs(IEEE, 2009-09) Oğuz, H. Kağan; Olcum, Selim; Senlik, Muhammed N.; Atalar, Abdullah; Köymen, HayrettinA nonlinear equivalent circuit for immersed transmitting capacitive micromachined ultrasonic transducers (CMUTs) is presented. The velocity profile across the CMUT surface maintains the same form over a wide frequency range. This property and the profile are used to model both the electromechanical conversion and the mechanical section. The model parameters are calculated considering the root mean square of the velocity distribution on the membrane surface as the through variable. The new model is compared with the FEM simulation results. The new model predicts the CMUT performance very accurately. ©2009 IEEE.