Grenoble Institute of Technology

About: Grenoble Institute of Technology is a education organization based out in Grenoble, France. It is known for research contribution in the topics: Hyperspectral imaging & Geology. The organization has 3427 authors who have published 5345 publications receiving 137158 citations. The organization is also known as: Grenoble INP.

A Watermarking-Based Method for Informed Source Separation of Audio Signals With a Single Sensor

Abstract: In this paper, the issue of audio source separation from a single channel is addressed, i.e., the estimation of several source signals from a single observation of their mixture. This challenging problem is tackled with a specific two levels coder-decoder configuration. At the coder, source signals are assumed to be available before the mix is processed. Each source signal is characterized by a set of parameters that provide additional information useful for separation. We propose an original method using a watermarking technique to imperceptibly embed this information about the source signals into the mix signal. At the decoder, the watermark is extracted from the mix signal to enable an end-user who has no access to the original sources to separate these signals from their mixture. Hence, we call this separation process informed source separation (ISS). Thereby, several instruments or voice signals can be segregated from a single piece of music to enable post-mixing processing such as volume control, echo addition, spatialization, or timbre transformation. Good performances are obtained for the separation of up to four source signals, from mixtures of speech or music signals. Promising results open up new perspectives in both under-determined source separation and audio watermarking domains.

Design of Antennas for UHF RFID Tags

Abstract: This paper is mainly dedicated to the design of radio-frequency identification (RFID) tags, particularly the antennas that allow the tag to be fed, communicate, and exchange data with the reader. Good performance tags require optimized antenna that take into account numerous constraints as well as the environment of the application under consideration. Both conventional tags and robust tags are discussed. This paper also discusses how to transform a tag into an RFID sensor.

Spatio-Temporal Control of LbL Films for Biomedical Applications: From 2D to 3D.

Abstract: Introduced in the '90s by Prof. Moehwald, Lvov, and Decher, the layer-by-layer (LbL) assembly of polyelectrolytes has become a popular technique to engineer various types of objects such as films, capsules and free standing membranes, with an unprecedented control at the nanometer and micrometer scales. The LbL technique allows to engineer biofunctional surface coatings, which may be dedicated to biomedical applications in vivo but also to fundamental studies and diagnosis in vitro. Initially mostly developed as 2D coatings and hollow capsules, the range of complex objects created by the LbL technique has greatly expanded in the past 10 years. In this Review, the aim is to highlight the recent progress in the field of LbL films for biomedical applications and to discuss the various ways to spatially and temporally control the biochemical and mechanical properties of multilayers. In particular, three major developments of LbL films are discussed: 1) the new methods and templates to engineer LbL films and control cellular processes from adhesion to differentiation, 2) the major ways to achieve temporal control by chemical, biological and physical triggers and, 3) the combinations of LbL technique, cells and scaffolds for repairing 3D tissues, including cardio-vascular devices, bone implants and neuro-prosthetic devices.

Hyperspectral super-resolution of locally low rank images from complementary multisource data

Abstract: Remote sensing hyperspectral images (HSI) are quite often locally low rank, in the sense that the spectral vectors acquired from a given spatial neighborhood belong to a low dimensional subspace/manifold. This has been recently exploited for the fusion of low spatial resolution HSI with high spatial resolution multispectral images (MSI) in order to obtain super-resolution HSI. Most approaches adopt an unmixing or a matrix factorization perspective. The derived methods have led to state-of-the-art results when the spectral information lies in a low dimensional subspace/manifold. However, if the subspace/manifold dimensionality spanned by the complete data set is large, the performance of these methods decrease mainly because the underlying sparse regression is severely ill-posed. In this paper, we propose a local approach to cope with this difficulty. Fundamentally, we exploit the fact that real world HSI are locally low rank, to partition the image into patches and solve the data fusion problem independently for each patch. This way, in each patch the subspace/manifold dimensionality is low enough to obtain useful super-resolution. We explore two alternatives to define the local regions, using sliding windows and binary partition trees. The effectiveness of the proposed approach is illustrated with synthetic and semi-real data.