Green's function retrieval and fluctuations of cross density of states in multiple scattering media

In this article we derive the average and the variance of the cross-correlation of a noise wavefield. The noise cross-correlation function (NCF) is widely used to passively estimate the Green's function between two probes and is proportional to the cross density of states (CDOS) in photonic and plasmonic systems. We first explain from the ladder approximation how the diffusion halo plays the role of secondary sources to reconstruct the mean Green's function. We then show that fluctuations of NCF are governed by several non-Gaussian correlations. An infinite-range NCF correlation dominates CDOS fluctuations and proves that NCF is not a self averaging quantity with respect to the plurality of noise sources. The link between these correlations and the intensity ones is highlighted. These results are supported by numerical simulations and are of importance for passive imaging applications and material science.Comment: 5 pages, 4 figures, 1 supplemental materia

MIMO feedback and application to detection

International audienceThe feedback effect is well known but unwanted, by sound engineers. It results from a feedback loop between a microphone and a loudspeaker. Recently, it has been shown that we can take benefit of this effect to estimate with a very good accuracy some parameters such as sound speed. More recently, some experimental results has shown the effect of a local perturbation on the top of an ultrasonic wavewguide. Here we generalize the concept to MIMO (Multiple Input Multiple Output) system where the feedback effect occurs between an array of emitters and an array of receivers. We propose to model the MIMO feedback effect by introducing a feedback matrix. Thanks to the singular decomposition of this matrix times the transfert matrix, we are able to predict the spatial dependence of the feedback effect either on the emitting array and on the receiving array. In a second part, we present experimental results that are obtained with an array of about 10 microphones and an array of about 10 loudspeakers. Several feedback matrices have been tested. One of them is inspired from time reversal. We have applied this technique to detect a person who goes across this acoustic barrier

Turning Optical Complex Media into Universal Reconfigurable Linear Operators by Wavefront Shaping

Performing linear operations using optical devices is a crucial building block in many fields ranging from telecommunication to optical analogue computation and machine learning. For many of these applications, key requirements are robustness to fabrication inaccuracies and reconfigurability. Current designs of custom-tailored photonic devices or coherent photonic circuits only partially satisfy these needs. Here, we propose a way to perform linear operations by using complex optical media such as multimode fibers or thin scattering layers as a computational platform driven by wavefront shaping. Given a large random transmission matrix (TM) representing light propagation in such a medium, we can extract a desired smaller linear operator by finding suitable input and output projectors. We discuss fundamental upper bounds on the size of the linear transformations our approach can achieve and provide an experimental demonstration. For the latter, first we retrieve the complex medium's TM with a non-interferometric phase retrieval method. Then, we take advantage of the large number of degrees of freedom to find input wavefronts using a Spatial Light Modulator (SLM) that cause the system, composed of the SLM and the complex medium, to act as a desired complex-valued linear operator on the optical field. We experimentally build several $16\times16$ complex-valued operators, and are able to switch from one to another at will. Our technique offers the prospect of reconfigurable, robust and easy-to-fabricate linear optical analogue computation units

Surveillance acoustique des cavités à risque de fontis et d'effondrements localisés

National audienceIt is very difficult to monitor sinkholes and local collapses from underground using the classical geotechnical instrumentation since the location of such pre-existing phenomena cannot be easily approached or forecast in time in wide and complex underground cavities. INERIS developed and tested an acoustic method to detect, localize and characterize rock falls with the help of a few sensors.Les cavités souterraines de faible profondeur, naturelles ou anthropiques, peuvent être à l'origine de risques de mouvements de terrains par fontis ou par effondrement localisé. Ce phénomène touche l'ensemble du territoire national. Dans l'attente d'un traitement, une surveillance peut permettre de gérer le risque. Jusqu'à présent, cette surveillance était essentiellement réalisée par inspection visuelle et par instrumentation géotechnique conventionnelle. Cette démarche présentant plusieurs limites dans le suivi des phénomènes dans la continuité et d'exposition des équipes intervenantes, il était important d'examiner de nouvelles solutions instrumentales

Crashing with disorder: Reaching the precision limit with tensor-based wavefront shaping

Perturbations in complex media, due to their own dynamical evolution or to external effects, are often seen as detrimental. Therefore, a common strategy, especially for telecommunication and imaging applications, is to limit the sensitivity to those perturbations in order to avoid them. Here, we instead consider crashing straight into them in order to maximize the interaction between light and the perturbations and thus produce the largest change in output intensity. Our work hinges on the innovative use of tensor-based techniques, presently at the forefront of machine learning explorations, to study intensity-based measurements where its quadratic relationship to the field prevents the use of standard matrix methods. With this tensor-based framework, we are able to identify the optimal crashing channel which maximizes the change in its output intensity distribution and the Fisher information encoded in it about a given perturbation. We further demonstrate experimentally its superiority for robust and precise sensing applications. Additionally, we derive the appropriate strategy to reach the precision limit for intensity-based measurements leading to an increase in Fisher information by more than four orders of magnitude with respect to the mean for random wavefronts when measured with the pixels of a camera

Resonant Metalenses for Breaking the Diffraction Barrier

We introduce the resonant metalens, a cluster of coupled subwavelength resonators. Dispersion allows the conversion of subwavelength wavefields into temporal signatures while the Purcell effect permits an efficient radiation of this information in the far-field. The study of an array of resonant wires using microwaves provides a physical understanding of the underlying mechanism. We experimentally demonstrate imaging and focusing from the far-field with resolutions far below the diffraction limit. This concept is realizable at any frequency where subwavelength resonators can be designed.Comment: 4 pages, 3 figure