271 research outputs found
Nonlinearity Induced Critical Coupling
We study a critically coupled system (Opt. Lett., \textbf{32}, 1483 (2007))
with a Kerr-nonlinear spacer layer. Nonlinearity is shown to inhibit
null-scattering in a critically coupled system at low powers. However, a system
detuned from critical coupling can exhibit near-complete suppression of
scattering by means of nonlinearity-induced changes in refractive index. Our
studies reveal clearly an important aspect of critical coupling as a delicate
balance in both the amplitude and the phase relations, while a nonlinear
resonance in dispersive bistability concerns only the phase
Superpixel Convolutional Networks using Bilateral Inceptions
In this paper we propose a CNN architecture for semantic image segmentation.
We introduce a new 'bilateral inception' module that can be inserted in
existing CNN architectures and performs bilateral filtering, at multiple
feature-scales, between superpixels in an image. The feature spaces for
bilateral filtering and other parameters of the module are learned end-to-end
using standard backpropagation techniques. The bilateral inception module
addresses two issues that arise with general CNN segmentation architectures.
First, this module propagates information between (super) pixels while
respecting image edges, thus using the structured information of the problem
for improved results. Second, the layer recovers a full resolution segmentation
result from the lower resolution solution of a CNN. In the experiments, we
modify several existing CNN architectures by inserting our inception module
between the last CNN (1x1 convolution) layers. Empirical results on three
different datasets show reliable improvements not only in comparison to the
baseline networks, but also in comparison to several dense-pixel prediction
techniques such as CRFs, while being competitive in time.Comment: European Conference on Computer Vision (ECCV), 201
Model Adaptation with Synthetic and Real Data for Semantic Dense Foggy Scene Understanding
This work addresses the problem of semantic scene understanding under dense
fog. Although considerable progress has been made in semantic scene
understanding, it is mainly related to clear-weather scenes. Extending
recognition methods to adverse weather conditions such as fog is crucial for
outdoor applications. In this paper, we propose a novel method, named
Curriculum Model Adaptation (CMAda), which gradually adapts a semantic
segmentation model from light synthetic fog to dense real fog in multiple
steps, using both synthetic and real foggy data. In addition, we present three
other main stand-alone contributions: 1) a novel method to add synthetic fog to
real, clear-weather scenes using semantic input; 2) a new fog density
estimator; 3) the Foggy Zurich dataset comprising real foggy images,
with pixel-level semantic annotations for images with dense fog. Our
experiments show that 1) our fog simulation slightly outperforms a
state-of-the-art competing simulation with respect to the task of semantic
foggy scene understanding (SFSU); 2) CMAda improves the performance of
state-of-the-art models for SFSU significantly by leveraging unlabeled real
foggy data. The datasets and code are publicly available.Comment: final version, ECCV 201
Experimental evidence of non-Amontons behaviour at a multicontact interface
We report on normal stress field measurements at the multicontact interface
between a rough elastomeric film and a smooth glass sphere under normal load,
using an original MEMS-based stress sensing device. These measurements are
compared to Finite Elements Method calculations with boundary conditions
obeying locally Amontons' rigid-plastic-like friction law with a uniform
friction coefficient. In dry contact conditions, significant deviations are
observed which decrease with increasing load. In lubricated conditions, the
measured profile recovers almost perfectly the predicted profile. These results
are interpreted as a consequence of the finite compliance of the multicontact
interface, a mechanism which is not taken into account in Amontons' law
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A Multilevel DC to Three-Phase AC Architecture for Photovoltaic Power Plants
This paper presents a photovoltaic (PV) inverter architecture composed of stackable dc to three-phase ac converter blocks. Several such blocks, each containing a converter power stage and controls, are connected in series on their ac sides to obtain transformerless medium-voltage ac interfaces for PV power plants. The series-connected structure is made possible by a quadruple active bridge dc-dc converter that provides isolation between the PV input and each of the three ac-side phases within each block. Furthermore, since incoming PV power is transferred as constant balanced three-phase ac power, instantaneous input-output power balance bypasses the need for bulk energy storage. To streamline implementation and maximize system scalability and resilience, decentralized block-level controllers accomplish dc-link voltage regulation, maximum power point tracking, and ac-side power sharing without centralized means. The proposed architecture is validated by simulations of a PV string to medium-voltage ac system consisting of six blocks and on a proof-of-concept hardware prototype that consists of three cascaded converter blocks.</p
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