Battery Charge Applications Based on Wide Output Voltage Range

In this study, high efficiency design procedure of a phase shifted full bridge (PSFB) converter is presented for on-board electrical vehicle (EV) battery charger. Presented design methodology used lithium-ion battery cells because of their high voltage and current rates compared to a lead-acid battery cells. In this case, PSFB converter can be regulated wide range output voltage with while its soft switching operation is maintained. The basic operation principles of PSFB converter is defined and its soft switching operation requirements are given. To evaluate the performance of the converter over wide output voltage range, zero voltage switching (ZVS) operation of converter is discussed based on dead time requirement. To improve efficiency, the snubber inductance effects on soft switching over wide output voltage range are evaluated. Finally, operation of the PSFB converter is validated experimentally with a prototype which has 42-54 V/15 A output range at 200 kHz switching frequency

Projections for Neutral Di-Boson and Di-Higgs Interactions at FCC-he Collider

As a high energy e-p collider, FCC-he, has been recently proposed with sufficient energy options to investigate Higgs couplings. To analyse the sensitivity on the Higgs boson couplings, we focus spesifically on the CP-even and CP-odd Wilson coefficients with $hhZZ\:$and $hh\gamma\gamma\:$ four-point interactions of Higgs boson with Effective Lagrangian Model through the process $e^{-}p\to hhje^{-}$ . We simulate the related processes in FCC-he, with 60 GeV and 120 GeV $e^{-}$ beams and 50 TeV proton beam collisions. We present the exclusion limits on these couplings both for 68% and 95% C.L. in terms of integrated luminosities.Comment: 18 pages, 20 figures, 3 table

Distributed data association for multi-target tracking in sensor networks

Associating sensor measurements with target tracks is a fundamental and challenging problem in multi-target tracking. The problem is even more challenging in the context of sensor networks, since association is coupled across the network, yet centralized data processing is in general infeasible due to power and bandwidth limitations. Hence efficient, distributed solutions are needed. We propose techniques based on graphical models to efficiently solve such data association problems in sensor networks. Our approach scales well with the number of sensor nodes in the network, and it is well--suited for distributed implementation. Distributed inference is realized by a message--passing algorithm which requires iterative, parallel exchange of information among neighboring nodes on the graph. So as to address trade--offs between inference performance and communication costs, we also propose a communication--sensitive form of message--passing that is capable of achieving near--optimal performance using far less communication. We demonstrate the effectiveness of our approach with experiments on simulated data

With four Standard Model families, the LHC could discover the Higgs boson with a few fb^-1

The existence of a 4th SM family would produce a large enhancement of the gluon fusion channel of Higgs boson production at hadron colliders. In this case, the SM Higgs boson could be seen at the CERN Large Hadron Collider (LHC) via the golden mode (H->4l) with an integral luminosity of only a few fb^-1.Comment: 7 pages, 2 figures, 2 tables, references updated in v

Segmentation of the evolving left ventricle by learning the dynamics

We propose a method for recursive segmentation of the left ventricle (LV) across a temporal sequence of magnetic resonance (MR) images. The approach involves a technique for learning the LV boundary dynamics together with a particle-based inference algorithm on a loopy graphical model capturing the temporal periodicity of the heart. The dynamic system state is a low-dimensional representation of the boundary, and boundary estimation involves incorporating curve evolution into state estimation. By formulating the problem as one of state estimation, the segmentation at each particular time is based not only on the data observed at that instant, but also on predictions based on past and future boundary estimates. We assess and demonstrate the effectiveness of the proposed framework on a large data set of breath-hold cardiac MR image sequences

Learning the dynamics and time-recursive boundary detection of deformable objects

We propose a principled framework for recursively segmenting deformable objects across a sequence of frames. We demonstrate the usefulness of this method on left ventricular segmentation across a cardiac cycle. The approach involves a technique for learning the system dynamics together with methods of particle-based smoothing as well as non-parametric belief propagation on a loopy graphical model capturing the temporal periodicity of the heart. The dynamic system state is a low-dimensional representation of the boundary, and the boundary estimation involves incorporating curve evolution into recursive state estimation. By formulating the problem as one of state estimation, the segmentation at each particular time is based not only on the data observed at that instant, but also on predictions based on past and future boundary estimates. Although the paper focuses on left ventricle segmentation, the method generalizes to temporally segmenting any deformable object

Analysis of heat transfer and entropy generation for a low-Peclet-number microtube flow using a second-order slip model: an extended-Graetz problem

Cataloged from PDF version of article.The classical Graetz problem, which is the problem of the hydrodynamically developed, thermally developing laminar flow of an incompressible fluid inside a tube neglecting axial conduction and viscous dissipation, is one of the fundamental problems of internal-flow studies. This study is an extension of the Graetz problem to include the rarefaction effect, viscous dissipation term and axial conduction with a constant wall temperature thermal boundary condition. The energy equation is solved to determine the temperature field analytically using general eigenfunction expansion with a fully developed velocity profile. To analyze the low-Peclet-number nature of the flow, the flow domain is extended from −∞ to +∞. To model the rarefaction effect, a second-order slip model is implemented. The temperature distribution, local Nusselt number, and local entropy generation are determined in terms of confluent hypergeometric functions. This kind of theoretical study is important for a fundamental understanding of the convective heat transfer characteristics of flows at the microscale and for the optimum design of thermal systems, which includes convective heat transfer at the microscale, especially operating at low Reynolds number

Design and low-power implementation of an adaptive image rejection receiver

This paper deals with and details the design and implementation of a low-power; hardware-efficient adaptive self-calibrating image rejection receiver based on blind-source-separation that alleviates the RF analog front-end impairments. Hybrid strength-reduced and re-scheduled data-flow, low-power implementation of the adaptive self-calibration algorithm is developed and its efficiency is demonstrated through simulation case studies. A behavioral and structural model is developed in Matlab as well as a low-level architectural design in VHDL providing valuable test benches for the performance measures undertaken on the detailed algorithms and structures