Generalized Kernel-based Visual Tracking

In this work we generalize the plain MS trackers and attempt to overcome standard mean shift trackers' two limitations. It is well known that modeling and maintaining a representation of a target object is an important component of a successful visual tracker. However, little work has been done on building a robust template model for kernel-based MS tracking. In contrast to building a template from a single frame, we train a robust object representation model from a large amount of data. Tracking is viewed as a binary classification problem, and a discriminative classification rule is learned to distinguish between the object and background. We adopt a support vector machine (SVM) for training. The tracker is then implemented by maximizing the classification score. An iterative optimization scheme very similar to MS is derived for this purpose.Comment: 12 page

Large Eddy Simulations of gaseous flames in gas turbine combustion chambers

Recent developments in numerical schemes, turbulent combustion models and the regular increase of computing power allow Large Eddy Simulation (LES) to be applied to real industrial burners. In this paper, two types of LES in complex geometry combustors and of specific interest for aeronautical gas turbine burners are reviewed: (1) laboratory-scale combustors, without compressor or turbine, in which advanced measurements are possible and (2) combustion chambers of existing engines operated in realistic operating conditions. Laboratory-scale burners are designed to assess modeling and funda- mental flow aspects in controlled configurations. They are necessary to gauge LES strategies and identify potential limitations. In specific circumstances, they even offer near model-free or DNS-like LES computations. LES in real engines illustrate the potential of the approach in the context of industrial burners but are more difficult to validate due to the limited set of available measurements. Usual approaches for turbulence and combustion sub-grid models including chemistry modeling are first recalled. Limiting cases and range of validity of the models are specifically recalled before a discussion on the numerical breakthrough which have allowed LES to be applied to these complex cases. Specific issues linked to real gas turbine chambers are discussed: multi-perforation, complex acoustic impedances at inlet and outlet, annular chambers.. Examples are provided for mean flow predictions (velocity, temperature and species) as well as unsteady mechanisms (quenching, ignition, combustion instabil- ities). Finally, potential perspectives are proposed to further improve the use of LES for real gas turbine combustor designs

Harnessing optical micro-combs for microwave photonics

In the past decade, optical frequency combs generated by high-Q micro-resonators, or micro-combs, which feature compact device footprints, high energy efficiency, and high-repetition-rates in broad optical bandwidths, have led to a revolution in a wide range of fields including metrology, mode-locked lasers, telecommunications, RF photonics, spectroscopy, sensing, and quantum optics. Among these, an application that has attracted great interest is the use of micro-combs for RF photonics, where they offer enhanced functionalities as well as reduced size and power consumption over other approaches. This article reviews the recent advances in this emerging field. We provide an overview of the main achievements that have been obtained to date, and highlight the strong potential of micro-combs for RF photonics applications. We also discuss some of the open challenges and limitations that need to be met for practical applications.Comment: 32 Pages, 13 Figures, 172 Reference

Stochastic-based adaptive control vibration control for MACE II

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/76388/1/AIAA-2001-4644-483.pd

Integral transform solutions for atmospheric pollutant dispersion

A transient two-dimensional advection–diffusion model describing the turbulent dispersion of pollutants in the atmosphere has been solved via the Generalized Integral Transform Technique (GITT), by two different schemes. The first approach performs numerical integration of the transformed system using available routines for initial value problems with automatic error control. In spite of the time-consuming character of such a scheme, its flexibility allows the handling of problems involving time-dependent meteorological parameters such as wind speed and eddy diffusivities. The second approach works fully analytically being thus intrinsically more robust and economic, although not directly applicable in dealing with time-dependent parameters. For the test problem used in this work, both methods agree very well with each other, as well as with a known analytical solution for a simpler formulation used as benchmark. The impact of the longitudinal diffusivity on the stiffness of the ordinary differential equation (ODE) system arising from the integral transformation has been assessed through the processing time demanded to solve it when the numerical approach is used. The observed CPU times show that the analytical approach is clearly preferable unless the problem involves time-dependent parameters.Indisponível

Sparsity in Linear Predictive Coding of Speech

nrpages: 197status: publishe

Overhead-optimization of pilot-based digital signal processing for flexible high spectral efficiency transmission

We present a low-complexity fully pilot-based digital signal processing (DSP) chain designed for high spectral efficiency optical transmission systems. We study the performance of the individual pilot algorithms in simulations before demonstrating transmission of a 51 724 Gbaud PM-64QAM superchannel over distances reaching 1000 km. We present an overhead optimization technique using the system achievable information rate to find the optimal balance between increased performance and throughput reduction from adding additional DSP pilots. Using the optimal overhead of 2.4%, we report 9.3 (8.3) bits/s/Hz spectral efficiency, or equivalently 11.9 (10.6) Tb/s superchannel throughput, after 480 (960) km of transmission over 80 km spans with EDFA-only amplification. Moreover, we show that the optimum overhead depends only weakly on transmission distance, concluding that back-to-back optimization is sufficient for all studied distances. Our results show that pilot-based DSP combined with overhead optimization can increase the robustness and performance of systems using advanced modulation formats while still maintaining state-of-the-art spectral efficiency and multi-Tb/s throughput

Data-driven deconvolution for large eddy simulations of Kraichnan turbulence

In this article, we demonstrate the use of artificial neural networks as optimal maps which are utilized for convolution and deconvolution of coarse-grained fields to account for sub-grid scale turbulence effects. We demonstrate that an effective eddy-viscosity is predicted by our purely data-driven large eddy simulation framework without explicit utilization of phenomenological arguments. In addition, our data-driven framework precludes the knowledge of true sub-grid stress information during the training phase due to its focus on estimating an effective filter and its inverse so that grid-resolved variables may be related to direct numerical simulation data statistically. The proposed predictive framework is also combined with a statistical truncation mechanism for ensuring numerical realizability in an explicit formulation. Through this we seek to unite structural and functional modeling strategies for modeling non-linear partial differential equations using reduced degrees of freedom. Both a priori and a posteriori results are shown for a two-dimensional decaying turbulence case in addition to a detailed description of validation and testing. A hyperparameter sensitivity study also shows that the proposed dual network framework simplifies learning complexity and is viable with exceedingly simple network architectures. Our findings indicate that the proposed framework approximates a robust and stable sub-grid closure which compares favorably to the Smagorinsky and Leith hypotheses for capturing the theoretical $k^{-3}$ scaling in Kraichnan turbulence