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 $3808$ real foggy images, with pixel-level semantic annotations for $16$ 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

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