Search

Your search keyword '"GIPSA Pôle Sciences des Données (GIPSA-PSD)"' showing total 89 results
Start Over Author "GIPSA Pôle Sciences des Données (GIPSA-PSD)" Language undetermined
89 results on '"GIPSA Pôle Sciences des Données (GIPSA-PSD)"'

1. SHCNet: A semi-supervised hypergraph convolutional networks based on relevant feature selection for hyperspectral image classification

Author

Akrem Sellami, Mohamed Farah, Mauro Dalla Mura, Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Laboratoire de recherche en Génie Logiciel, Applications distribuées, Systèmes décisionnels et Imagerie intelligente [Manouba] (RIADI), École Nationale des Sciences de l'Informatique [Manouba] (ENSI), Université de la Manouba [Tunisie] (UMA)-Université de la Manouba [Tunisie] (UMA), GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), Institut Universitaire de France (IUF), and Ministère de l'Education nationale, de l’Enseignement supérieur et de la Recherche (M.E.N.E.S.R.)

Subjects
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Artificial Intelligence, hyperspectral image classification, Signal Processing, Unsupervised feature selection, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Computer Vision and Pattern Recognition, hypergraph convolutional network, Software, dimensionality reduction, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

International audience; Hyperspectral imagery classification is a challenging task due to the large number of spectral bands, and low number of labeled samples. To overcome these issues, we propose a novel approach for hyperspectral image classification based on feature selection and semi-supervised hypergraph convolutional network working with small number of labeled samples. Firstly, we propose a new unsupervised feature selection method based on an information theoretic criterion. Relevant spectral features are automatically selected while preserving the physical properties of hyperspectral data. Secondly, we construct a spectro-spatial hypergraph in order to represent the complex relationships between pixels. Finally, we propose a semi-supervised hypergraph convolutional network which integrates local vertex features and hypergraph topology in the convolutional layers. The aim of this step is to preserve the spectro-spatial features and to cope with the high correlation between hypernodes during classification. The main advantage of the proposed approach is to allow the automatic selection of relevant spectral bands while preserving the spatial and spectral features. In addition, by accounting for the relationship between pixels leads to improved classification results even when the number of labeled samples is low. Experiments are conducted on two real hyperspectral images show that the proposed approach reaches competitive good performances, and achieves better classification performances compared to state-of-the-art methods.

Published
2023

2. Hyperspectral Image Super-Resolution via Deep Spatiospectral Attention Convolutional Neural Networks

Author

Gemine Vivone, Jocelyn Chanussot, Jin-Fan Hu, Ting-Zhu Huang, Tai-Xiang Jiang, Liang-Jian Deng, University of Electronic Science and Technology of China [Chengdu] (UESTC), Southwestern University of Finance and Economics [Chengdu, China], Institute of Methodologies for Environmental Analysis of the National Research Council (IMAA), National Research Council of Italy | Consiglio Nazionale delle Ricerche (CNR), GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), Apprentissage de modèles à partir de données massives (Thoth), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), and ANR-19-P3IA-0003,MIAI,MIAI @ Grenoble Alpes(2019)

Subjects
Source code, Hyperspectral imaging, Mean squared error, Computer Networks and Communications, Computer science, media_common.quotation_subject, Multispectral image, Tensors, Superresolution, Convolutional neural network, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Artificial Intelligence, Robustness (computer science), Computer architecture, Image resolution, media_common, Spatial resolution, Network architecture, Training data, Learning systems, business.industry, Pattern recognition, Computer Science Applications, Artificial intelligence, business, Software
Abstract

International audience; Hyperspectral images (HSIs) are of crucial importance in order to better understand features from a large number of spectral channels. Restricted by its inner imaging mechanism, the spatial resolution is often limited for HSIs. To alleviate this issue, in this work, we propose a simple and efficient architecture of deep convolutional neural networks to fuse a low-resolution HSI (LR-HSI) and a high-resolution multispectral image (HR-MSI), yielding a high-resolution HSI (HR-HSI). The network is designed to preserve both spatial and spectral information thanks to a new architecture based on: 1) the use of the LR-HSI at the HR-MSI’s scale to get an output with satisfied spectral preservation and 2) the application of the attention and pixelShuffle modules to extract information, aiming to output high-quality spatial details. Finally, a plain mean squared error loss function is used to measure the performance during the training. Extensive experiments demonstrate that the proposed network architecture achieves the best performance (both qualitatively and quantitatively) compared with recent state-of-the-art HSI super-resolution approaches. Moreover, other significant advantages can be pointed out by the use of the proposed approach, such as a better network generalization ability, a limited computational burden, and the robustness with respect to the number of training samples. Please find the source code and pretrained models from https://liangjiandeng.github.io/Projects_Res/HSRnet_2021tnnls.html .

Published
2022

3. Simulation of Spinal Muscle Control in Human Gait Using OpenSim

Author

Julien Frère, Patrick HENAFF, Andrii Shachykov, Analysis and modeling of neural systems by a system neuroscience approach (NEUROSYS), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Complex Systems, Artificial Intelligence & Robotics (LORIA - AIS), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), GIPSA - Analyse et Modification de l'homme en mouvement : biomécanique, cognition, vocologie (GIPSA-MOVE), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), and Université Grenoble Alpes (UGA)

Subjects
CPG, [SDV]Life Sciences [q-bio], human walking, OpenSim, musculoskeletal model
Abstract

International audience; This paper presents a neuro-musculoskeletal simulation approach to human gait based on an original model of central pattern generator and the muscle synergy approach. The controller, shown here, aims at simplifying movement control by reducing the number of parameters to optimize. The model of the simplified motor coordination chain was built, from the midbrain through spinal neurons to the muscles allowing of simulating some neural and muscular values of gait, hard to obtain otherwise. The work also includes a bio-inspired reflexbased balance controller designed from knowledge of spinal gait. The used anatomic musculoskeletal model has been implemented in OpenSim platform. Time simulations show the ability of the platform to be able to produce different gait patterns including nominal and disturbed. Both gaits are controlled by the same bio-inspired closed-loop CPG-and reflex-based circuitries. After 2 second standing phase, the model was able to walk for about 5 meters with 10 steps. 2 steps were disrupted in the second simulation.

Published
2022

4. An Optimization Procedure for Robust Regression-Based Pansharpening

Author

Marco Carpentiero, Gemine Vivone, Rocco Restaino, Paolo Addesso, Jocelyn Chanussot, University of Salerno (UNISA), Institute of Methodologies for Environmental Analysis of the National Research Council (IMAA), National Research Council of Italy | Consiglio Nazionale delle Ricerche (CNR), GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), Apprentissage de modèles à partir de données massives (Thoth), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), and ANR-19-P3IA-0003,MIAI,MIAI @ Grenoble Alpes(2019)

Subjects
Optimization, regression-based data fusion, Sensors, pansharpening, Maximum likelihood estimation, Pansharpening, Estimation, Laplace equations, Electronic mail, Image fusion, pyramidal decomposition, remote sensing, General Earth and Planetary Sciences, Electrical and Electronic Engineering, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; Model-based approaches to pansharpening still constitute a class of widely employed methods, thanks to their straightforward applicability to many problems, dispensing the user from time-consuming training phases. The injection scheme based on an accurate estimation (exploiting regression) of the relationship between the details contained in the panchromatic (PAN) image and those required for the enhancement of the multispectral (MS) image represents the most updated approach to this problem, being characterized by both theoretical and practical optimality. We elaborated on this scheme by designing a procedure for estimating the key parameters required for the optimal setting of such a regression-based approach. We tested this approach on several datasets acquired by the WorldView satellites comparing the proposed approach with a benchmark consisting of some state-of-the-art pansharpening methods.

Published
2022

5. N-Cluster Loss and Hard Sample Generative Deep Metric Learning for PolSAR Image Classification

Author

Licheng Jiao, Bo Ren, Biao Hou, Shuang Wang, Chen Yang, Jocelyn Chanussot, Yue Hu, Xidian University, GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), Apprentissage de modèles à partir de données massives (Thoth), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), and ANR-19-P3IA-0003,MIAI,MIAI @ Grenoble Alpes(2019)

Subjects
Measurement, Discriminator, Similarity (geometry), Contextual image classification, business.industry, Computer science, Feature vector, Deep learning, Pattern recognition, Sample (statistics), Scattering, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Task analysis, Metric (mathematics), Feature extraction, Training, General Earth and Planetary Sciences, Artificial intelligence, Electrical and Electronic Engineering, Convergence, business, Focus (optics), Data mining
Abstract

International audience; Deep learning works normally in PolSAR image classification because the complex terrain scattering characteristic results in large intraclass differences and high interclass similarity. Deep metric learning (DML) aims to make the features keep a closer intraclass and a farther interclass distance. Therefore, we introduce DML and then propose an N-cluster generative adversarial net (N-cluster GAN) framework for PolSAR image classification. However, existing DML losses mainly focus on the relationship between individual samples in feature space. Hence, we propose N-cluster loss that pays more attention to the overall structure of all samples. Meanwhile, traditional hard negative sample mining methods occupy lots of computational resources. In addition, the hard level of the negative samples will affect the model’s performance. Therefore, we explore a new method based on a GAN framework to replace the sample mining. Positive N-cluster loss is added to the discriminator ( D ), and a negative one is added to the generator ( G ). In this way, D will possess better classification ability, and G can produce hard negative samples for D . Then, the hard level of the generated negative samples will change with the discrimination of D , which is appropriate for the proposed model. N-cluster loss can be directly calculated through the extracted features rather than redundant data preparation. The proposed model is verified on four PolSAR datasets from two aspects of the loss function and negative samples mining. Then, it achieves competitive performance compared with state-of-the-art algorithms.

Published
2022

6. PDFL: Polarimetric Decomposition Feature Learning via Deep Autoencoder

Author

Chen Yang, Biao Hou, Qian Wu, Bo Ren, Jocelyn Chanussot, Shuang Wang, Licheng Jiao, Xidian University, Air Force Engineering University [Xi'an] (AFEU), GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), Apprentissage de modèles à partir de données massives (Thoth), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), and ANR-19-P3IA-0003,MIAI,MIAI @ Grenoble Alpes(2019)

Subjects
Scattering, Mathematical models, Matrix decomposition, Covariance matrices, Decoding, Data models, General Earth and Planetary Sciences, Electrical and Electronic Engineering, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Representation learning
Abstract

International audience; Model-based polarimetric target decomposition (TD) generally solves scattering components and parameters under a preset decomposition base; then, decomposition features are also obtained. However, a preset base could not be adjusted according to different scenes. Furthermore, solving the polarimetric parameters needs exploring additional information or considering limiting conditions to build equations, which is hard and easily brings negative effects into decomposition features. To this end, we regard the TD as a process of learning decomposition base and features by deep learning. Then, the polarimetric decomposition feature learning (PDFL) model is proposed in this article. Strictly, this model is not an incoherent TD method but a learning-based method. It does not need to construct the parameter solution equations or fixed base. Then, the decomposition base and feature can be adaptively learned according to the scattering characteristics of the current dataset. Due to the characteristics of unsupervised reconstruction, the deep autoencoder (DAE) is used as the model foundation. Then, some adjustments and constraints are utilized to make the DAE fit closely with TD. The encoder extracts the latent vector from polarimetric synthetic aperture radar (PolSAR) data, and then, the decoder reconstructs pseudodata on this latent vector. The reconstruction can be regarded as the inverse process of TD, so the base matrix of the decoder and the latent vector indicate the learned decomposition base and features when the model converges. The effectiveness of PDFL is verified on simulated and real PolSAR datasets. Compared with representative algorithms, the proposed model gains more discriminative features and achieves competitive performance on terrain classification and segmentation tasks.

Published
2022

7. Reconstruction Error-Based Decomposition Feature Selection for PolSAR Image

Author

Biao Hou, Chen Yang, Xianpeng Guo, Licheng Jiao, Bo Ren, Jocelyn Chanussot, Shuang Wang, Xidian University, GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), Apprentissage de modèles à partir de données massives (Thoth), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), and ANR-19-P3IA-0003,MIAI,MIAI @ Grenoble Alpes(2019)

Subjects
business.industry, Computer science, Data models, Synthetic aperture radar, Feature selection, Pattern recognition, Image (mathematics), Scattering, Reconstruction error, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Task analysis, Image reconstruction, Decomposition (computer science), Feature extraction, Training, General Earth and Planetary Sciences, Artificial intelligence, Electrical and Electronic Engineering, business
Abstract

International audience; Target decomposition features are the cornerstone of subsequent analyses for PolSAR images. Generally, adopting single or several decomposition algorithms limits the representation ability for original terrain characteristics. Using all the existing decomposition features, however, will definitely increase computational complexity. Besides, some features even have a negative effect on the following tasks. To address these problems, a sparse variational autoencoder feature selection framework (SVAE-FS) is proposed in this article. In detail, the encoder transforms the original feature set into latent space and then decoder reconstructs the corresponding pseudo set on this latent space. Similarly, a pseudo subset is subsequently obtained by the SVAE. The discrepancy, namely reconstruction error, between the pseudo set and the pseudo subset is taken as an evaluation criterion which reflects the feature representation ability of pseudo subset. Sparse constraint in the encoder makes the representative features stand out. Meanwhile, the linear feature transformation layer of the encoder enables the SVAE to evaluate different scale subsets without repeated training. Finally, a greedy selection approach with search scale K is proposed to find the suboptimal subset. This procedure not only reduces time consumption, but also ensures the performance of the subset. The selected features are analyzed on four real PolSAR datasets according to the terrain scattering characteristics. Furthermore, these features have achieved competitive performance on three PolSAR image tasks.

Published
2022

8. Convolutional Neural Networks for Multimodal Remote Sensing Data Classification

Author

Jocelyn Chanussot, Danfeng Hong, Xin Wu, Beijing Institute of Technology (BIT), Chinese Academy of Sciences [Beijing] (CAS), GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), Apprentissage de modèles à partir de données massives (Thoth), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), and ANR-19-P3IA-0003,MIAI,MIAI @ Grenoble Alpes(2019)

Subjects
Synthetic aperture radar, Hyperspectral imaging, business.industry, Computer science, Deep learning, Data classification, Network architecture, Ranging, Convolutional neural network, Laser radar, Lidar, Modal, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Task analysis, Feature extraction, General Earth and Planetary Sciences, Convolutional neural networks, Artificial intelligence, Electrical and Electronic Engineering, business, Remote sensing
Abstract

International audience; In recent years, enormous research has been made to improve the classification performance of single-modal remote sensing (RS) data. However, with the ever-growing availability of RS data acquired from satellite or airborne platforms, simultaneous processing and analysis of multimodal RS data pose a new challenge to researchers in the RS community. To this end, we propose a deep-learning-based new framework for multimodal RS data classification, where convolutional neural networks (CNNs) are taken as a backbone with an advanced cross-channel reconstruction module, called CCR-Net. As the name suggests, CCR-Net learns more compact fusion representations of different RS data sources by the means of the reconstruction strategy across modalities that can mutually exchange information in a more effective way. Extensive experiments conducted on two multimodal RS datasets, including hyperspectral (HS) and light detection and ranging (LiDAR) data, i.e., the Houston2013 dataset, and HS and synthetic aperture radar (SAR) data, i.e., the Berlin dataset, demonstrate the effectiveness and superiority of the proposed CCR-Net in comparison with several state-of-the-art multimodal RS data classification methods. The codes will be openly and freely available at https://github.com/danfenghong/IEEE_TGRS_CCR-Net for the sake of reproducibility.

Published
2022

9. Predictive and Symbolic Control: Performance and Safety for Non-linear Systems

Author

Azaki, Zakeye, Girard, Antoine, Olaru, Sorin, Girard, Antoine, Towards programmable cyber-physical systems: a symbolic control approach - PROCSYS - - H2020 Pilier ERC2017-09-01 - 2022-08-31 - 725144 - VALID, GIPSA - COntrol, PErception, Robots, navigation and Intelligent Computing (GIPSA-COPERNIC), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), Laboratoire des signaux et systèmes (L2S), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), and European Project: 725144,H2020 Pilier ERC,PROCSYS(2017)

Subjects
[INFO.INFO-SY] Computer Science [cs]/Systems and Control [cs.SY], Recursive feasibility, [SPI.AUTO] Engineering Sciences [physics]/Automatic, Control and Systems Engineering, Non-linear systems, Controlled invariant set, [INFO.INFO-SY]Computer Science [cs]/Systems and Control [cs.SY], [MATH.MATH-OC] Mathematics [math]/Optimization and Control [math.OC], [MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC], Model predictive control, Symbolic control, Terminal constraints, [SPI.AUTO]Engineering Sciences [physics]/Automatic
Abstract

International audience; Although Model Predictive Control (MPC) has proven its e ciency in the process operation and it is known for its high performance, it also su ers from design limitation. One of the fundamental issues for such an optimization-based technique is the di culty to guarantee recursive feasibility in the absence of terminal constraints, or alternatively, the complexity of design when a certain basin of a raction or a controlled invariant set needs to be certi ed for the closed loop in the most general nonlinear se ing. Based on symbolic control techniques, this paper proposes a simple and guaranteed solution for such problems. As a main result, a Symbolically guided Model Predictive Control scheme is developed. is controller is an improved version of the generic MPC approach such that the recursive feasibility is guaranteed through appending time-varying terminal constraints, carefully designed using the symbolic control approach, to the optimization problem of the original MPC formulation.

Published
2022

10. CyCU-Net: Cycle-Consistency Unmixing Network by Learning Cascaded Autoencoders

Author

Jocelyn Chanussot, Zhu Han, Lianru Gao, Danfeng Hong, Bing Zhang, Chinese Academy of Sciences [Beijing] (CAS), German Aerospace Center (DLR), GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), Apprentissage de modèles à partir de données massives (Thoth), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), and ANR-19-P3IA-0003,MIAI,MIAI @ Grenoble Alpes(2019)

Subjects
remote sensing (RS), business.industry, Computer science, Deep learning, 0211 other engineering and technologies, Process (computing), Hyperspectral imaging, Pattern recognition, 02 engineering and technology, cycle consistency, Net (mathematics), Autoencoder, Cascaded autoencoders (AEs), hyperspectral unmixing (HU), Term (time), self-perception, Consistency (database systems), deep learning (DL), [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Curve fitting, General Earth and Planetary Sciences, Artificial intelligence, Electrical and Electronic Engineering, business, 021101 geological & geomatics engineering
Abstract

International audience; In recent years, deep learning (DL) has attracted increasing attention in hyperspectral unmixing (HU) applications due to its powerful learning and data fitting ability. The autoencoder (AE) framework, as an unmixing baseline network, achieves good performance in HU by automatically learning low-dimensional embeddings and reconstructing data. Nevertheless, the conventional AE-based architecture, which focuses more on the pixel-level reconstruction loss, tends to lose some significant detailed information of certain materials (e.g., material-related properties) in the reconstruction process. Therefore, inspired by the perception mechanism, we propose a cycle-consistency unmixing network, called CyCU-Net, by learning two cascaded AEs in an end-to-end fashion, to enhance the unmixing performance more effectively. CyCU-Net is capable of reducing the detailed and material-related information loss in the process of reconstruction by relaxing the original pixel-level reconstruction assumption to cycle consistency dominated by the cascaded AEs. More specifically, cycle consistency can be achieved by a newly proposed self-perception loss, which consists of two spectral reconstruction terms and one abundance reconstruction term. By taking advantage of the self-perception loss in the network, the high-level semantic information can be well preserved in the unmixing process. Moreover, we investigate the performance gain of CyCU-Net with extensive ablation studies. Experimental results on one synthetic and three real hyperspectral data sets demonstrate the effectiveness and competitiveness of the proposed CyCU-Net in comparison with several state-of-the-art unmixing algorithms.

Published
2022

11. Bayesian Unmixing of Hyperspectral Image Sequence With Composite Priors for Abundance and Endmember Variability

Author

Qian Du, Jocelyn Chanussot, Hongyi Liu, Zhihui Wei, Zebin Wu, Youkang Lu, Nanjing University of Science and Technology (NJUST), Mississippi State University [Mississippi], GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), Apprentissage de modèles à partir de données massives (Thoth), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), and Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )

Subjects
Endmember, Hyperspectral imaging, Posterior probability, Bayesian probability, Principal component analysis, Statistics::Machine Learning, symbols.namesake, Image sequences, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Abundance (ecology), Prior probability, Electrical and Electronic Engineering, Lighting, Mathematics, business.industry, Markov processes, Monte Carlo methods, Pattern recognition, Markov chain Monte Carlo, Bayes methods, Computer Science::Computer Vision and Pattern Recognition, symbols, General Earth and Planetary Sciences, Artificial intelligence, Likelihood function, business
Abstract

International audience; A hyperspectral image sequence can be obtained at different time in the same region from a hyperspectral sensor. The environmental change usually leads to variation in endmember reflectance, which has an important influence on unmixing process. In this article, a Bayesian unmixing model considering spectral variability for hyperspectral sequence is proposed, in which composite prior distributions of abundance and endmember variability are developed. The abundance priors consider the continuity of abundance in the temporal and spatial domains, simultaneously. Specifically, in the spatial domain, a data-adaptive variance of the abundance prior distribution is put forward based on local spatial difference. Moreover, the priors of endmember variability in temporal continuity and spectral smoothness are also exploited. Finally, a joint posterior distribution is obtained by the likelihood function and the parameter prior distributions, which can be calculated by the Markov chain Monte Carlo (MCMC) algorithm. Experiments on synthetic and real data sets demonstrate the effectiveness of the proposed approach in terms of abundance, endmember, and its variability estimation accuracy.

Published
2022

12. Hyperspectral Pansharpening With Adaptive Feature Modulation-Based Detail Injection Network

Author

Yunsong Li, Yuxuan Zheng, Jiaojiao Li, Rui Song, Jocelyn Chanussot, Xidian University, University College of London [London] (UCL), GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), Apprentissage de modèles à partir de données massives (Thoth), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), and ANR-19-P3IA-0003,MIAI,MIAI @ Grenoble Alpes(2019)

Subjects
Modulation, Spatial resolution, Image resolution, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Feature extraction, General Earth and Planetary Sciences, Pansharpening, Electrical and Electronic Engineering, Data mining, Convolution
Abstract

International audience; Recently, deep learning-based methodologies have attained unprecedented performance in hyperspectral (HS) pansharpening, which aims to improve the spatial quality of HS images (HSIs) by making use of details extracted from the high-resolution panchromatic (HR-PAN) image. However, it remains challenging to incorporate the details into the pansharpened image effectively, while alleviating the spectral distortion simultaneously. To tackle this problem, in this article, we propose an adaptive feature modulation-based detail injection network (AFM-DIN) for HS pansharpening, which mainly consists of four phases: high-frequency details generation of the HR-PAN image, multiscale feature extraction of the upsampled HSI, AFM-based detail injection, and reconstruction of the HR-HSI. First, a novel octave convolution unit is employed to decompose the HR-PAN image into high and low frequencies, and then merge the high-frequency features together to generate the comprehensive PAN-details. Second, the spatial and spectral separable 3-D convolution units with multiple kernel sizes are designed to extract multiscale features of the upsampled HSI in a computationally efficient manner. Subsequently, by taking the critical PAN-details as prior, the proposed AFM module is able to not only incorporate the detail information effectively, but also adjust the injected details adaptively to ensure the spectral fidelity. Finally, the anticipated HR-HSI is obtained through adding the upsampled HSI to the predicted HSI-details reconstructed from informative modulated features. Extensive comparison experiments with several state-of-the-arts conducted on simulated and real HS data sets demonstrate that our proposed AFM-DIN can achieve superior pansharpening accuracy in both spatial and spectral aspects.

Published
2022

13. CNN-Based Hyperspectral Pansharpening With Arbitrary Resolution

Author

Lin He, Jiawei Zhu, Jun Li, Antonio Plaza, Jocelyn Chanussot, Zhuliang Yu, South China University of Technology [Guangzhou] (SCUT), Sun Yat-Sen University [Guangzhou] (SYSU), Universidad de Extremadura - University of Extremadura (UEX), GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), Apprentissage de modèles à partir de données massives (Thoth), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Laboratoire Jean Kuntzmann (LJK), and ANR-19-P3IA-0003,MIAI,MIAI @ Grenoble Alpes(2019)

Subjects
Spatial resolution, Hyperspectral imaging, Task analysis, Image reconstruction, Training, General Earth and Planetary Sciences, Convolutional neural networks, Pansharpening, Electrical and Electronic Engineering, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; Traditional hyperspectral (HS) pansharpening aims at fusing a HS image with its panchromatic (PAN) counterpart, to bring the spatial resolution of the HS image to that of the PAN image. However, in many practical applications, arbitrary resolution HS (ARHS) pansharpening is required, where the HS and PAN images need to be integrated to generate a pansharpened HS image with arbitrary resolution (usually higher than that of the PAN image). Such an innovative task brings forth new challenges for the pansharpening technique, mainly including how to reconstruct HS images beyond the training scale and how to guarantee spectral fidelity at any spatial resolutions. To tackle the challenges, we present a novel convolutional neural network (CNN)-based method for ARHS pansharpening called ARHS-CNN. It is based on a two-step relay optimization process, which is associated with a multilevel enhancement subnetwork and a rescaling subnetwork. With a careful design following the thread, our ARHS-CNN is able to pansharpen HS images to any spatial resolutions using just a single CNN model trained on a limited number of scales while meantime to keep spectral fidelity at those resolutions, which wins an obvious advantage over traditional pansharpening methods. Experimental results obtained on several datasets verify the excellent performance of our ARHS-CNN method.

Published
2022

14. ArbRPN: A Bidirectional Recurrent Pansharpening Network for Multispectral Images With Arbitrary Numbers of Bands

Author

Xiaomin Yang, Zhibing Lai, Gemine Vivone, Gwanggil Jeon, Jocelyn Chanussot, Lihui Chen, Sichuan University [Chengdu] (SCU), Institute of Methodologies for Environmental Analysis of the National Research Council (IMAA), National Research Council of Italy | Consiglio Nazionale delle Ricerche (CNR), Incheon National University, Chinese Academy of Sciences [Beijing] (CAS), GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), Apprentissage de modèles à partir de données massives (Thoth), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), and ANR-19-P3IA-0003,MIAI,MIAI @ Grenoble Alpes(2019)

Subjects
business.industry, Computer science, Multispectral image, pansharpening, Pattern recognition, multispec- tral (MS) images, image fusion, remote sensing, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Deep learning (DL), recurrent neural networks (RNNs), General Earth and Planetary Sciences, Artificial intelligence, Electrical and Electronic Engineering, business
Abstract

International audience; Although the performance of pansharpening has been significantly improved by advanced deep-learning (DL) technologies in recent years, most DL-based methods fail to process multispectral (MS) images with arbitrary numbers of bands by a single model. Consequently, it is inevitable to train separate models for MS images with different numbers of bands, which is time- and storage-consuming as well as inefficient in practice. To tackle the above problem, we propose a bidirectional recurrent pansharpening network (named ArbRPN) for MS images with arbitrary numbers of bands. Our ArbRPN can dynamically reconstruct high-resolution (HR) MS images with different numbers of bands by adaptively changing the number of recurrence to the number of bands of the low-resolution (LR) MS images. Leveraging on the ability of the ArbRPN to process MS images with any number of bands, one can even customize the bands to be pansharpened. Moreover, to achieve superior performance, spectral discrepancy and dependence are considered in the ArbRPN. Details from the panchromatic (PAN) image are adaptively injected into the fused product according to the captured spectral dependence. Furthermore, training strategies of existing DL-based pansharpening methods can only group MS images with a constant number of bands into mini-batches. Therefore, we present a mask-based training method (called mask-training) to solve this problem. Benefiting from the mask-training, our ArbRPN can achieve superior performance and robustness during pansharpening. Extensive experiments show the superior performance of our ArbRPN with respect to the state-of-the-art (SOTA) methods applied to MS images with different numbers of bands. The code of our ArbRPN is available on https://github.com/Lihui-Chen/ArbRPN.git.

Published
2022

15. Tethered Drone-Based Airborne Wind Energy System Launching and Retrieving

Author

Jonathan Dumon, Ahmad Hably, Alexandre Sarazin, Amaury Nègre, Audrey Schanen, Nacim Meslem, GIPSA - COntrol, PErception, Robots, navigation and Intelligent Computing (GIPSA-COPERNIC), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), GIPSA-Services (GIPSA-Services), GIPSA - Modelling and Optimal Decision for Uncertain Systems (GIPSA-MODUS), and GIPSA Pôle Automatique et Diagnostic (GIPSA-PAD)

Subjects
0209 industrial biotechnology, Wind power, Angle of attack, business.industry, 020209 energy, Applied Mathematics, Aerospace Engineering, 02 engineering and technology, Rudder, Drone, Takeoff and landing, [SPI]Engineering Sciences [physics], Ground station, 020901 industrial engineering & automation, Space and Planetary Science, Control and Systems Engineering, 0202 electrical engineering, electronic engineering, information engineering, Environmental science, Electrical and Electronic Engineering, business, ComputingMilieux_MISCELLANEOUS, Marine engineering
Abstract

International audience

Published
2021

16. Minimalistic in-flight odometry based on two optic flow sensors along a bouncing trajectory

Author

Bergantin, Lucia, Coquet, Charles, Nègre, Amaury, Raharijaona, Thibaut, Marchand, Nicolas, Ruffier, Franck, Aix Marseille Université (AMU), Institut des Sciences du Mouvement Etienne Jules Marey (ISM), Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), GIPSA-Services (GIPSA-Services), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM), GIPSA - COntrol, PErception, Robots, navigation and Intelligent Computing (GIPSA-COPERNIC), GIPSA Pôle Sciences des Données (GIPSA-PSD), and Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab)

Subjects
[INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], [SPI.AUTO]Engineering Sciences [physics]/Automatic
Abstract

International audience; Estimating the distance traveled while navigating in a GPS-deprived environment is key for aerial robotic applications. For drones, this issue is often coupled with weight and computational power constraints, from which stems the importance of minimalistic equipment. In this study, we present a visual odometry strategy based solely on two optic flow magnitudes perceived by two optic flow sensors oriented at ±30° on either side of a drone's vertical axis. As results, (i) we measured the local optic flow divergence and the local translational optic flow respectively as the subtraction and the sum of the two optic flow magnitudes perceived (ii) we validated experimentally the visual odometer on a hexarotor oscillating upand-down while following a 50m-long circular trajectory under three illuminance conditions (117lux, 814lux and 1518lux). The measured optic flow divergence was used to estimate the flight height by means of an Extended Kalman Filter. The estimated flight height scaled the measured translational optic flow, which was integrated to perform minimalistic visual odometry.

Published
2022

17. Onboard dynamic RGB‐D simultaneous localization and mapping for mobile robot navigation

Author

Bruce Canovas, Amaury Nègre, Michèle Rombaut, GIPSA - COntrol, PErception, Robots, navigation and Intelligent Computing (GIPSA-COPERNIC), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), and GIPSA-Services (GIPSA-Services)

Subjects
dynamic environment, TK7800-8360, General Computer Science, Computer science, TK5101-6720, 02 engineering and technology, Simultaneous localization and mapping, 01 natural sciences, rgb‐d, 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Computer vision, Electrical and Electronic Engineering, navigation, Dense SLAM, business.industry, 010401 analytical chemistry, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], RGB-D, 020207 software engineering, Mobile robot navigation, 0104 chemical sciences, Electronic, Optical and Magnetic Materials, mobile robotics, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Telecommunication, Artificial intelligence, Electronics, business
Abstract

International audience; Although the actual visual simultaneous localization and mapping (SLAM) algorithms provide highly accurate tracking and mapping, most algorithms are too heavy to run live on embedded devices. In addition, the maps they produce are often unsuitable for path planning. To mitigate these issues, we propose a completely closed-loop online dense RGB-D SLAM algorithm targeting autonomous indoor mobile robot navigation tasks. The proposed algorithm runs live on an NVIDIA Jetson board embedded on a two-wheel differential-drive robot. It exhibits lightweight three-dimensional mapping, room-scale consistency, accurate pose tracking, and robustness to moving objects. Further, we introduce a navigation strategy based on the proposed algorithm. Experimental results demonstrate the robustness of the proposed SLAM algorithm, its computational efficiency, and its benefits for on-the-fly navigation while mapping.

Published
2021

18. An attention-fused network for semantic segmentation of very-high-resolution remote sensing imagery

Author

Xiuping Jia, Shanshan Li, Xuan Yang, Jocelyn Chanussot, Baipeng Li, Zhengchao Chen, Bing Zhang, Pan Chen, Chinese Academy of Sciences [Beijing] (CAS), GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), Apprentissage de modèles à partir de données massives (Thoth), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Australian Defence Force Academy [Canberra] (ADFA), and ANR-19-P3IA-0003,MIAI,MIAI @ Grenoble Alpes(2019)

Subjects
FOS: Computer and information sciences, 010504 meteorology & atmospheric sciences, Computer science, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, 0211 other engineering and technologies, Convolutional neural network, 02 engineering and technology, Very-high-resolution imagery, 01 natural sciences, Segmentation, Computers in Earth Sciences, Engineering (miscellaneous), 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, Block (data storage), Attention-fused network, business.industry, Deep learning, ISPRS, Sensor fusion, Semantic segmentation, Atomic and Molecular Physics, and Optics, Computer Science Applications, Artificial intelligence, F1 score, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Encoder, Multipath propagation
Abstract

Semantic segmentation is an essential part of deep learning. In recent years, with the development of remote sensing big data, semantic segmentation has been increasingly used in remote sensing. Deep convolutional neural networks (DCNNs) face the challenge of feature fusion: very-high-resolution remote sensing image multisource data fusion can increase the network's learnable information, which is conducive to correctly classifying target objects by DCNNs; simultaneously, the fusion of high-level abstract features and low-level spatial features can improve the classification accuracy at the border between target objects. In this paper, we propose a multipath encoder structure to extract features of multipath inputs, a multipath attention-fused block module to fuse multipath features, and a refinement attention-fused block module to fuse high-level abstract features and low-level spatial features. Furthermore, we propose a novel convolutional neural network architecture, named attention-fused network (AFNet). Based on our AFNet, we achieve state-of-the-art performance with an overall accuracy of 91.7% and a mean F1 score of 90.96% on the ISPRS Vaihingen 2D dataset and an overall accuracy of 92.1% and a mean F1 score of 93.44% on the ISPRS Potsdam 2D dataset., Comment: 35 pages. Published by ISPRS Journal of Photogrammetry and Remote Sensing

Published
2021

19. ImSPOC: A Novel Compact Hyperspectral Camera for the Monitoring of Atmospheric Gases

Author

Gousset, Silvère, Picone, Daniele, Le Coarer, Etienne, Rodrigo, Juana, Voisin, D., Croizé, Laurence, Ferrec, Yann, Dalla Mura, Mauro, Institut de Planétologie et d'Astrophysique de Grenoble (IPAG), Centre National d'Études Spatiales [Toulouse] (CNES)-Observatoire des Sciences de l'Univers de Grenoble (OSUG ), Institut national des sciences de l'Univers (INSU - CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Grenoble Alpes (UGA)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Grenoble Alpes (UGA)-Météo-France, GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), Institut des Géosciences de l’Environnement (IGE), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), DOTA, ONERA, Université Paris Saclay [Palaiseau], ONERA-Université Paris-Saclay, Institut Universitaire de France (IUF), Ministère de l'Education nationale, de l’Enseignement supérieur et de la Recherche (M.E.N.E.S.R.), and Tokyo Institute of Technology [Tokyo] (TITECH)

Subjects
Accident prevention, [PHYS]Physics [physics], Hyperspectral imaging, Monitoring, SPECTROSCOPIE, Hyperspectral imaging sensor, Miniaturized sensor, [SPI]Engineering Sciences [physics], Gas detector, SURVEILLANCE, Fourier transform, Gas monitoring, IMAGERIE HYPERSPECTRALE, DETECTION GAZ, Spectroscopy, PREVENTION ACCIDENT
Abstract

International audience; There is an increasing industrial and scientific demand for large scale monitoring of gas concentration, e.g., to abide with constraints on pollutants for environmental safety. This paper aims to show the potential of the very recent ImSPOC technology for gas monitoring. We present the ImSPOC operating principle, some of the developed prototypes and two illustrative examples in the monitoring of methane and carbon dioxide in controlled environments.

Published
2022

20. Decoupled Control of a Twin Hull-Based Unmanned Surface Vehicle Using a Linear Parameter Varying Approach

Author

Echrak Chnib, Olivier Sename, Francesco Ferrante, Eduardo S. Rodriguez Canales, Juan C. Cutipa Luque, Ferrante, Francesco, GIPSA - COntrol, PErception, Robots, navigation and Intelligent Computing (GIPSA-COPERNIC), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), GIPSA - Infinite Dimensional Dynamics (GIPSA-INFINITY), GIPSA Pôle Automatique et Diagnostic (GIPSA-PAD), and Universidad Nacional de San Agust ́ın de Arequipa

Subjects
Decoupled control, [INFO.INFO-AU]Computer Science [cs]/Automatic Control Engineering, ∞, Unmanned surface vehicle (USV), [INFO.INFO-AU] Computer Science [cs]/Automatic Control Engineering, robust control, ComputingMilieux_MISCELLANEOUS, Linear parameter varying (LPV)
Abstract

International audience

Published
2022

21. Blind Hyperspectral Unmixing Based on Graph Total Variation Regularization

Author

Jocelyn T. Chi, Harlin Lee, Jocelyn Chanussot, Andrea L. Bertozzi, Lucas Drumetz, Jing Qin, Yifei Lou, Department of Mathematics, University of Kentucky, Lexington, Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, Department of Statistics, North Carolina State University, Raleigh, Equipe Observations Signal & Environnement (Lab-STICC_OSE), Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), Institut Mines-Télécom [Paris] (IMT)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-Institut Mines-Télécom [Paris] (IMT)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL), Département Mathematical and Electrical Engineering (IMT Atlantique - MEE), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), Department of Mathematical Sciences, University of Texas at Dallas, Richardson, Department of Mathematics [UCLA], University of California [Los Angeles] (UCLA), University of California-University of California, and ANR-19-P3IA-0003,MIAI,MIAI @ Grenoble Alpes(2019)

Subjects
Computer science, 0211 other engineering and technologies, Nystrom method, 02 engineering and technology, blind hyperspectral unmixing, Geological & Geomatics Engineering, Blind hyperspectral unmixing, Regularization (mathematics), Spectral line, graph total variation, graph Laplacian, 0202 electrical engineering, electronic engineering, information engineering, Electrical and Electronic Engineering, Image resolution, 021101 geological & geomatics engineering, Pixel, business.industry, alternating direction method of multipliers, Hyperspectral imaging, Pattern recognition, Nyström method, Total variation denoising, Alternating direction method of multipliers, Geophysics, Geomatic Engineering, General Earth and Planetary Sciences, Graph (abstract data type), 020201 artificial intelligence & image processing, Artificial intelligence, Laplacian matrix, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; Remote sensing data from hyperspectral cameras suffer from limited spatial resolution, in which a single pixel of a hyperspectral image may contain information from several materials in the field of view. Blind hyperspectral image unmixing is the process of identifying the pure spectra of individual materials (i.e., endmembers) and their proportions (i.e., abundances) at each pixel. In this paper, we propose a novel blind hyperspectral unmixing model based on the graph total variation (gTV) regularization, which can be solved efficiently by the alternating direction method of multipliers (ADMM). To further alleviate the computational cost, we apply the Nyström method to approximate a fully-connected graph by a small subset of sampled points. Furthermore, we adopt the Merriman-Bence-Osher (MBO) scheme to solve the gTV-involved subproblem in ADMM by decomposing a grayscale image into a bit-wise form. A variety of numerical experiments on synthetic and real hyperspectral images are conducted, showcasing the potential of the proposed method in terms of identification accuracy and computational efficiency.

Published
2021

22. Hydrophone Array Optimization, Conception, and Validation for Localization of Acoustic Sources in Deep-Sea Mining

Author

Arthur Finez, Simon Bouley, Florent Fayet, Barbara Nicolas, Jerome Mars, Valentin Baron, GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), Imagerie Ultrasonore, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), MicrodB, OSEAN S.A.S [Le Pradet], Osean, Nicolas, Barbara, Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), and Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
Beamforming, Ground truth, source localization, Hydrophone, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, 010505 oceanography, Computer science, Mechanical Engineering, Acoustics, 020206 networking & telecommunications, Ocean Engineering, 02 engineering and technology, 01 natural sciences, deep-sea mining, Deep sea mining, underwater acoustics, Noise, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, 0202 electrical engineering, electronic engineering, information engineering, 14. Life underwater, Electrical and Electronic Engineering, Underwater, Underwater acoustics, Image resolution, 0105 earth and related environmental sciences
Abstract

International audience; As the mining of deep-sea natural deposits is becoming cost competitive compared to similar land-based mining, companies have started to dig into the seabeds to collect minerals. However, the acoustic contribution of this activity in the surrounding environment can be significant. To predict the impact of such noise, the starting point is to localize and quantify the sources that create it. In this study, a 3-D prototype acoustic array to perform this localization and quantification is designed, built, and deployed at sea for validation of its localization capacities. The design method performs a two-step study to define the array shape and select the hydrophone arrangement over it, under harsh constraints. Each step relies on two metrics to rank the candidates: the maximum sidelobe level, and the spatial resolution. These are computed on conventional beamforming maps for simulated sources that represent excavation machines on the ground. The shape is first determined to be the one that yields steady maximum sidelobe value levels over frequency. Second, the hydrophone arrangement that achieves the lowest maximum sidelobe level while limiting the spatial resolution is selected. This leads to a tip down conical array with 21 hydrophones, of about 3 m in height and diameter, and this is manufactured and used during an experimental campaign in the Mediterranean Sea. The experimental localization maps show strong agreement between the estimated source position and its ground truth. A more detailed comparison between simulated and real performances confirms accurate array conception and realization. Thus, this design procedure provides an efficient underwater acoustic array for monitoring deep-sea mining, the localization capacities of which are validated in a real-life setting.

Published
2021

23. Keypoint-Based Local Descriptors for Target Recognition in SAR Images: A Comparative Analysis

Author

Ganggang Dong, Hongwei Liu, Jocelyn Chanussot, National University of Defense Technology [China], Duke University [Durham], GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), and ANR-19-P3IA-0003,MIAI,MIAI @ Grenoble Alpes(2019)

Subjects
Synthetic aperture radar, comparative analysis, General Computer Science, Computer science, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 0211 other engineering and technologies, pose configuration, 02 engineering and technology, law.invention, radar target recognition, raw intensity values, law, Radar imaging, holistic features, 0202 electrical engineering, electronic engineering, information engineering, Computer vision, Electrical and Electronic Engineering, Radar, Instrumentation, 021101 geological & geomatics engineering, business.industry, imaging parameters, SAR images, keypoint-based local descriptors, General Earth and Planetary Sciences, Clutter, 020201 artificial intelligence & image processing, Artificial intelligence, business, Articulation (phonetics), [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; Though widely studied over the years, radar target recognition is still far from being solved. Most earlier works rely on holistic features or raw intensity values, which are sensitive to the real-world sources of variability such as changes of pose, configuration, and imaging parameters; articulation; and occlusion. To solve these problems, this article resorts to the local descriptors around keypoints.

Published
2021

24. A Comparison of Mobile VR Display Running on an Ordinary Smartphone With Standard PC Display for P300-BCI Stimulus Presentation

Author

Grégoire Cattan, Cesar Mendoza, Marco Congedo, Anton Andreev, Interaction Homme Machine Technologie (IHMTEK), GIPSA - Vision and Brain Signal Processing (GIPSA-VIBS), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), and GIPSA-Services (GIPSA-Services)

Subjects
FOS: Computer and information sciences, Computer science, Computer Science - Human-Computer Interaction, Electroencephalography, [SCCO]Cognitive science, Computer Science - Graphics, 0302 clinical medicine, Human–computer interaction, P300, Virtual Reality VR, Réalité Virtuelle RV, Electroencéphalographie EEG, medicine.diagnostic_test, 05 social sciences, Brain-Computer Interfaces BCI, Interface Cerveau-Machine ICM, [INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR], Electroencephalograhy EEG, ACM: H.: Information Systems/H.5: INFORMATION INTERFACES AND PRESENTATION (e.g., HCI), Potentiel évoqué, [INFO.INFO-ES]Computer Science [cs]/Embedded Systems, [SCCO.COMP]Cognitive science/Computer science, Virtual reality, Stimulus (physiology), 050105 experimental psychology, Eeg synchronization, Human-Computer Interaction (cs.HC), Gaming, 03 medical and health sciences, InformationSystems_MODELSANDPRINCIPLES, Artificial Intelligence, medicine, Event-related potential ERP, 0501 psychology and cognitive sciences, Head-Mounted Devices HMD, Electrical and Electronic Engineering, Brain–computer interface, Casque de réalité virtuelle, Interface Cerveau-Ordinateur ICO, [SCCO.NEUR]Cognitive science/Neuroscience, Significant difference, Game, Graphics (cs.GR), Control and Systems Engineering, Mobile phone, Personal computer, Casque de Réalité Virtuelle Passif, Jeu vidéo, 030217 neurology & neurosurgery, Software, ACM: K.: Computing Milieux/K.8: PERSONAL COMPUTING/K.8.0: General/K.8.0.0: Games
Abstract

A brain-computer interface (BCI) based on electroencephalography (EEG) is a promising technology for enhancing virtual reality (VR) applications-in particular, for gaming. We focus on the so-called P300-BCI, a stable and accurate BCI paradigm relying on the recognition of a positive event-related potential (ERP) occurring in the EEG about 300 ms post-stimulation. We implemented a basic version of such a BCI displayed on an ordinary and affordable smartphone-based head-mounted VR device: that is, a mobile and passive VR system (with no electronic components beyond the smartphone). The mobile phone performed the stimuli presentation, EEG synchronization (tagging) and feedback display. We compared the ERPs and the accuracy of the BCI on the VR device with a traditional BCI running on a personal computer (PC). We also evaluated the impact of subjective factors on the accuracy. The study was within-subjects, with 21 participants and one session in each modality. No significant difference in BCI accuracy was found between the PC and VR systems, although the P200 ERP was significantly wider and larger in the VR system as compared to the PC system., Comment: IEEE Transactions on Games, Institute of Electrical and Electronics Engineers, In press

Published
2021

25. Coupled Convolutional Neural Network With Adaptive Response Function Learning for Unsupervised Hyperspectral Super Resolution

Author

Danfeng Hong, Lianru Gao, Bing Zhang, Jocelyn Chanussot, Ke Zheng, Ximin Cui, Wenzhi Liao, Chinese Academy of Sciences [Beijing] (CAS), IMEC (IMEC), Catholic University of Leuven - Katholieke Universiteit Leuven (KU Leuven), Flemish Institute for Technological Research (VITO), German Aerospace Center (DLR), University of Chinese Academy of Sciences [Beijing] (UCAS), China University of Mining and Technology (CUMT), GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), and ANR-19-P3IA-0003,MIAI,MIAI @ Grenoble Alpes(2019)

Subjects
FOS: Computer and information sciences, Point spread function, hyperspectral image, Computer science, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, Adaptive learning, 0211 other engineering and technologies, super-resolution, 02 engineering and technology, Convolutional neural network, Convolution, FOS: Electrical engineering, electronic engineering, information engineering, Electrical and Electronic Engineering, Image resolution, 021101 geological & geomatics engineering, autoencoder, coupled convolutional neural network, business.industry, Deep learning, Image and Video Processing (eess.IV), Hyperspectral imaging, Pattern recognition, Function (mathematics), Electrical Engineering and Systems Science - Image and Video Processing, Autoencoder, General Earth and Planetary Sciences, Artificial intelligence, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; Due to the limitations of hyperspectral imaging systems, hyperspectral imagery (HSI) often suffers from poor spatial resolution, thus hampering many applications of the imagery. Hyperspectral super resolution refers to fusing HSI and MSI to generate an image with both high spatial and high spectral resolutions. Recently, several new methods have been proposed to solve this fusion problem, and most of these methods assume that the prior information of the point spread function (PSF) and spectral response function (SRF) are known. However, in practice, this information is often limited or unavailable. In this work, an unsupervised deep learning-based fusion method-HyCoNet-that can solve the problems in HSI-MSI fusion without the prior PSF and SRF information is proposed. HyCoNet consists of three coupled autoencoder nets in which the HSI and MSI are unmixed into endmembers and abundances based on the linear unmixing model. Two special convolutional layers are designed to act as a bridge that coordinates with the three autoencoder nets, and the PSF and SRF parameters are learned adaptively in the two convolution layers during the training process. Furthermore, driven by the joint loss function, the proposed method is straightforward and easily implemented in an end-to-end training manner. The experiments performed in the study demonstrate that the proposed method performs well and produces robust results for different data sets and arbitrary PSFs and SRFs.

Published
2021

26. Reconstruction Error in Nonuniformly Sampled Approximately Sparse Signals

Author

Milos Dakovic, Cornel Ioana, Isidora Stankovic, Milos Brajovic, Ljubisa Stankovic, University of Montenegro (UCG), GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), and Université Grenoble Alpes (UGA)

Subjects
Noise measurement, Computer science, 0211 other engineering and technologies, Nonuniform sampling, Jitter, 02 engineering and technology, Domain (software engineering), symbols.namesake, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, 0202 electrical engineering, electronic engineering, information engineering, Range (statistics), Random variables, Electrical and Electronic Engineering, 021101 geological & geomatics engineering, Sensors, Sampling (statistics), Indexes, 020206 networking & telecommunications, Geotechnical Engineering and Engineering Geology, Discrete Fourier transforms, Noise, Compressed sensing, Fourier transform, symbols, Algorithm, Random variable
Abstract

International audience; With its aim to reduce the amount of sensed data and to improve the energy efficiency, compressive sensing (CS) is recently witnessing a growing research interest in remote-sensing applications. The Fourier transform domain plays a significant role as a signal-processing tool and the sparsity domain for the CS-reconstruction methods. A generalized expression for the error in the reconstruction of nonuniformly sampled, approximately sparse, or nonsparse, noisy signals in the Fourier domain is presented in this letter. This expression holds for a wide range of practically important nonuniform signal-sampling strategies, covering the uniform and completely random sampling as the special cases. Additive noise and noise-folding effects are included in the analysis. Statistical examples and two real-world examples validate the presented theory.

Published
2021

27. Geometric Multimodal Learning Based on Local Signal Expansion for Joint Diagonalization

Author

Christian Jutten, Sayyed Mohammad Saeed Ehsani, Maysam Behmanesh, Peyman Adibi, Jocelyn Chanussot, University of Isfahan, GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), GIPSA - Vision and Brain Signal Processing (GIPSA-VIBS), and ANR-19-P3IA-0003,MIAI,MIAI @ Grenoble Alpes(2019)

Subjects
multimodal signal processing, Laplacian matrices joint diagonalization, Matching (graph theory), Computer science, Generalization, Diagonalizable matrix, 02 engineering and technology, Matrix (mathematics), manifold learning, 0202 electrical engineering, electronic engineering, information engineering, Tangent space, Electrical and Electronic Engineering, Predictive learning, business.industry, Nonlinear dimensionality reduction, 020206 networking & telecommunications, Pattern recognition, Dimensionality reduction, Manifold, Multimodal learning, intrinsic tangent space, Signal Processing, Pattern recognition (psychology), Artificial intelligence, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Laplace operator
Abstract

International audience; Multimodal learning, also known as multi-view learning, data integration, or data fusion, is an emerging field in signal processing, machine learning, and pattern recognition domains. It aims at building models, learned from several related and complementary modalities, in order to increase the generalization performances of a predictive learning model. Multimodal manifold learning extends spectral or diffusion geometry-aware data analysis to multiple modalities. This can be performed through the definition of undirected graph Laplacian matrices in different modalities. However, finding common eigenbasis of multiple Laplacians is not always a relevant solution for multimodal manifold learning problems. As a matter of fact, the Laplacians of all modalities are not simultaneously diagonalizable in many real-world problems due to the major differences between the different modalities. In this paper, we propose a multimodal manifold learning approach based on intrinsic local tangent spaces of underlying data manifolds in order to discover the local geometrical structure around matching and mismatching samples in different modalities in sparse diagonalization problems. This approach searches for approximate common eigenbasis of Laplacian matrices by expanding the signal of limited existing information about matching and mismatching samples of different modalities to their on-manifold neighbors. Experiments on synthetic and real-world datasets in supervised, unsupervised, and semi-supervised problems demonstrate the superiority of our proposed approach over existing state-of-the-art related methods.

Published
2021

28. Modified Tensor Distance-Based Multiview Spectral Embedding for PolSAR Land Cover Classification

Author

Biao Hou, Jocelyn Chanussot, Licheng Jiao, Bo Ren, Xidian University, GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), Apprentissage de modèles à partir de données massives (Thoth), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), and Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )

Subjects
Synthetic aperture radar, Computer science, Feature extraction, 0211 other engineering and technologies, Polarimetry, Tensors, 02 engineering and technology, Land cover, Backscatter, Speckle pattern, Speckle, Tensor, Electrical and Electronic Engineering, 021101 geological & geomatics engineering, Measurement, Pixel, business.industry, Pattern recognition, Geotechnical Engineering and Engineering Geology, Computer Science::Computer Vision and Pattern Recognition, Task analysis, Embedding, Artificial intelligence, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Classifier (UML)
Abstract

International audience; This letter proposes a novel method for combining multiview features in polarimetric synthetic aperture radar (PolSAR) for land cover classification. It is well-known that feature extraction and classifier design are two significant steps in machine learning methods for PolSAR data interpretation. Each PolSAR pixel can be represented in different feature spaces, such as polarimetric data scattering, or the polarimetric target decomposition spaces. In this letter, a tensor-based multiview embedding algorithm is proposed to fuse those features from different spaces in order to obtain a distinctive set of features for the subsequent classification. Based on the pixel-based classification tasks, a modified tensor distance (MTD) is designed to accurately calculate the distance between tensors. It emphasizes the importance of the central pixel, and decreases the influence of the neighbors in the feature patch when calculating tensor distance. Furthermore, the complementary properties of different views are exploited by an MTD measured tensor multiview spectral embedding method, so as to obtain relevant low-dimensional features. Compared with state-of-the-art methods, the validation and effectiveness of the proposed method is demonstrated on two real PolSAR data sets.

Published
2020

29. 3-D Interface for the P300 Speller BCI

Author

Christian Jutten, Saman Noorzadeh, Bertrand Rivet, GIPSA - Vision and Brain Signal Processing (GIPSA-VIBS), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), Shahid Beheshti University, and ANR-19-P3IA-0003,MIAI,MIAI @ Grenoble Alpes(2019)

Subjects
Computer Networks and Communications, Computer science, Interface (computing), Human Factors and Ergonomics, Stereoscopy, Brain waves, Keyboards, 050105 experimental psychology, law.invention, 03 medical and health sciences, 0302 clinical medicine, Artificial Intelligence, law, 0501 psychology and cognitive sciences, Computer vision, Stereo image processing, Brain–computer interface, Virtual keyboard, business.industry, 05 social sciences, Computer Science Applications, Human-Computer Interaction, Control and Systems Engineering, Signal Processing, Performance evaluation, Ergonomics, Artificial intelligence, Brain-computer interfaces, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, 030217 neurology & neurosurgery
Abstract

International audience; A brain–computer interface (BCI) is a common device for communication between the human brain and a computer. This article investigates the efficiency of using a 3-D interface for BCI machines. For this purpose, the P300 speller, which is a BCI device that enables the user to spell characters on a screen using brain waves, is modified. The classical virtual keyboard of the P300 speller is replaced with 3-D stereoscopic images, which enhances the ergonomic features of the device. Moreover, the flashing paradigm on a 3-D interface can affect the performance of the device in three ways: accuracy, speed, and capacity. This article proposes two different flashing paradigms called the natural 3-D and parallel 2-D interfaces and studied their effects regarding the three mentioned measures. The former flashes the planes in 3-D space, and the latter comprises flashes of parallel keyboards at different 3-D depths. The theoretical analysis of these effects is presented. The results are validated by experimental data obtained from real subjects and were compared with the classical 2-D interface. Both presented keyboards increase the speed of the device, while parallel 2-D has a better total performance than natural 3-D.

Published
2020

30. Spectral Unmixing: A Derivation of the Extended Linear Mixing Model From the Hapke Model

Author

Christian Jutten, Lucas Drumetz, Jocelyn Chanussot, Département Signal et Communications (IMT Atlantique - SC), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Lab-STICC_IMTA_CID_TOMS, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT), GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), GIPSA - Vision and Brain Signal Processing (GIPSA-VIBS), and ANR-19-P3IA-0003,MIAI,MIAI @ Grenoble Alpes(2019)

Subjects
Spectral signature, Pixel, Scattering, Computer science, spectral variability, Image and Video Processing (eess.IV), 0211 other engineering and technologies, Hyperspectral imaging, 02 engineering and technology, Electrical Engineering and Systems Science - Image and Video Processing, Geotechnical Engineering and Engineering Geology, extended linear mixing model, Spectral line, Nonlinear system, Atmospheric radiative transfer codes, Hyperspectral image unmixing, FOS: Electrical engineering, electronic engineering, information engineering, Electrical and Electronic Engineering, Biological system, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Hapke model, Mixing (physics), 021101 geological & geomatics engineering
Abstract

International audience; In hyperspectral imaging, spectral unmixing aims at decomposing the image into a set of reference spectral signatures corresponding to the materials present in the observed scene and their relative proportions in every pixel. While a linear mixing model was used for a long time, the complex nature of the physico-chemical phenomena that affect the spectra of the materials led to shift the community's attention towards algorithms accounting for the variability of the endmembers. Such intra-class variations are mainly due to local changes in the composition of the materials, and to illumination changes. In the physical remote sensing community, a popular model accounting for illumination variability is the radiative transfer model proposed by Hapke. It is however too complex to be directly used in hyperspectral unmixing in a tractable way. Instead, the Extended Linear Mixing Model (ELMM) allows to easily unmix hyperspectral data accounting for changing illumination conditions and to address nonlinear effects to some extent. In this letter, we show that the ELMM can be obtained from the Hapke model by successive simplifying physical assumptions, whose validity we experimentally examine, thus demonstrating its relevance to handle illumination induced variability in the unmixing problem. Index Terms-Hyperspectral image unmixing, spectral variability , Hapke model, extended linear mixing model.

Published
2020

31. CNN-Based Super-Resolution of Hyperspectral Images

Author

Alok Porwal, P. V. Arun, Krishna Mohan Buddhiraju, Jocelyn Chanussot, Indian Institute of Technology Bombay (IIT Bombay), GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), and ANR-19-P3IA-0003,MIAI,MIAI @ Grenoble Alpes(2019)

Subjects
Spatial resolution, Endmember, Hyperspectral imaging, Computer science, business.industry, 0211 other engineering and technologies, Convolutional codes, Pattern recognition, 02 engineering and technology, Iterative reconstruction, Spectral bands, Convolutional neural network, Superresolution, Dictionaries, Image reconstruction, General Earth and Planetary Sciences, Artificial intelligence, Deconvolution, Electrical and Electronic Engineering, Spectral resolution, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Image resolution, 021101 geological & geomatics engineering
Abstract

International audience; Single-image super-resolution (SISR) techniques attempt to reconstruct the finer resolution version of a given image from its coarser version. In the SISR of hyperspectral data sets, the simultaneous consideration of spectral bands is crucial for ensuring the spectral fidelity. However, the high spectral resolution of these data sets affects the performance of conventional approaches. This research proposes the design of 3-D convolutional neural network (CNN)-based SISR architectures that can map the spatial-spectral characteristics of hypercubes to a finer spatial resolution. The proposed approaches facilitate the simultaneous optimization of sparse codes and dictionaries with regard to the super-resolution objective. Our main hypothesis is that the consideration of spectral aspects is essential for the spatial enhancement of hyperspectral images. Also, we propose that the regularized deconvolution of a coarser-scale hypercube, using learned 3-D filters, yields the required high-resolution version. Based on these hypotheses, a convolution-deconvolution framework is proposed to super-resolve the hypercubes in parallel with the reconstruction of a set of regularizing features. Novel sparse code optimization sub-networks proposed in this article give better performance than the existing strategies. The endmember similarities and hyperspectral image prior are considered while designing the proposed loss functions. In order to improve the generalizability, a collaborative spectral unmixing strategy is employed to refine the spectral base of the super-resolved result. The spatial-spectral accuracy of the super-resolved hypercubes, in terms of the validity of regularizing features and endmembers, is explored to devise an optimal ensemble strategy. The experiments, over different data sets, confirm better accuracy of the proposed frameworks compared to the prominent approaches.

Published
2020

32. Robust set‐membership state estimator against outliers in data

Author

Ahmad Hably, Nacim Meslem, GIPSA - Modelling and Optimal Decision for Uncertain Systems (GIPSA-MODUS), GIPSA Pôle Automatique et Diagnostic (GIPSA-PAD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), GIPSA - COntrol, PErception, Robots, navigation and Intelligent Computing (GIPSA-COPERNIC), and GIPSA Pôle Sciences des Données (GIPSA-PSD)

Subjects
0209 industrial biotechnology, Control and Optimization, optimisation, Computer science, State estimator, set theory, Uncertain systems, Algorithm robustness, 02 engineering and technology, discrete time systems, Interval arithmetic, [SPI]Engineering Sciences [physics], framing property, 020901 industrial engineering & automation, Control theory, Framing (construction), Electrical and Electronic Engineering, observers, interval computation, Linear system, linear systems, Computer Science Applications, Human-Computer Interaction, uncertain systems, robust set‐membership state estimator, uncertain discrete‐time linear systems, Control and Systems Engineering, set‐valued filtering techniques, optimal interval observer, Outlier, filtering theory, Robust control, Algorithm, robust control
Abstract

International audience; Based on interval computation, a set‐membership state estimator capable to manage a certain type of outliers in measurements is proposed for uncertain discrete‐time linear systems. To achieve this purpose, two set‐valued filtering techniques are implemented in the presented state estimation algorithm. The setting up of these techniques offers two main advantages. On one hand, the convergence of the estimated state enclosures width is guaranteed and, on the other hand, the algorithm robustness against outliers in data is ensured. That is, unlike former methods, the proposed set‐valued state estimator preserves the framing property despite the presence of some false values in available sensors data. To show the efficiency and the performance of the introduced set‐valued state estimator, it is compared, through two numerical examples, with an optimal interval observer selected from the literature for its high performance.

Published
2020

33. Changes in the voice production of solo singers across concert halls

Author

Paul Luizard, Nathalie Henrich Bernardoni, Technische Universität Berlin (TU), GIPSA - Analyse et Modification de l'homme en mouvement : biomécanique, cognition, vocologie (GIPSA-MOVE), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), and Université Grenoble Alpes (UGA)

Subjects
Glottis, medicine.medical_specialty, Acoustics and Ultrasonics, Voice Quality, Microphone, Singing, Musical, Audiology, 01 natural sciences, 03 medical and health sciences, 0302 clinical medicine, Arts and Humanities (miscellaneous), 0103 physical sciences, medicine, [SHS.LANGUE]Humanities and Social Sciences/Linguistics, 030223 otorhinolaryngology, 010301 acoustics, Electroglottograph, [SPI.ACOU]Engineering Sciences [physics]/Acoustics [physics.class-ph], Acoustics, Vocal intensity, Voice production, Room acoustics, Voice, Psychology, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Timbre
Abstract

International audience; To investigate the influence of room acoustics on singing, four lyrical singers (soprano, mezzo-soprano, tenor, baritone) performed four musical pieces in eight different venues (from dry studio to reverberant church). In addition to vocal intensity measured by a near-field microphone, glottal behavior (vibratory fundamental frequency and contact quotient) was assessed by electroglottography. Statistical linear mixed models showed that the variance in vocal performance was partly explained by room acoustics. Complementary to previous results on voice musical features influenced by timbre and level of the room's response, voice production parameters were mostly influenced by spatial aspects of the room's response.

Published
2020

34. Le chant des poissons

Author

Nathalie Henrich Bernardoni, Térence Meunier, Lia Giraud, Philippe Marmottant, Laboratoire Interdisciplinaire de Physique [Saint Martin d’Hères] (LIPhy ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS), GIPSA - Analyse et Modification de l'homme en mouvement : biomécanique, cognition, vocologie (GIPSA-MOVE), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), SFR Création 2018, artiste, Chercheur indépendant, and Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)

Subjects
0106 biological sciences, biomimicry, interdisciplinarité, media_common.quotation_subject, acousmatic music, 010603 evolutionary biology, 01 natural sciences, recherche-création, 03 medical and health sciences, interdisciplinarity, chant des poissons, biomimétisme, fish chorus, organologie, musique acousmatique, organology, [SHS.LANGUE]Humanities and Social Sciences/Linguistics, 030304 developmental biology, media_common, [SPI.ACOU]Engineering Sciences [physics]/Acoustics [physics.class-ph], 0303 health sciences, General Medicine, Art, research-creation, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Humanities
Abstract

International audience; During their research-creation residencies at the LIPhy (Laboratoire Interdisciplinaire de Physique, Université Grenoble Alpes), the two artists Lia Giraud and Térence Meunier investigated the mysterious ‘fish songs’, assisted by researchers in physics of bubbles (Philippe Marmottant), voice sciences (Nathalie Henrich Bernardoni), bioacoustics and animal biology.The initial aim of the project was to reproduce the fish sounds by modelling swim bladders and the action of the associated sonic muscle in the laboratory. During their research quest, the two artists were led to question the biomimetic modelling of this phenomenon and to propose a new working methodology inspired by the concrete approach found at the heart of ‘acousmatic’ music. What if these songs were finally just a matter of ‘stick slip’?From an organological approach to biomimetic fantasies, from poetic imagination to the reality of experimental devices, this narrative explores the areas of slippage and friction between different ways of approaching and understanding a phenomenon.We have chosen to restitute this investigation through a corpus of images by means of a portfolio. These documents, collected and created during the experiments and meetings, are the common thread of our reflexion.; Au fil de leurs résidences de recherche-création au LIPhy (Laboratoire interdisciplinaire de physique, Université Grenoble-Alpes), les deux artistes Lia Giraud et Térence Meunier ont enquêté sur le mystérieux « chant des poissons », aidés par des chercheurs spécialisés en physique des bulles (Philippe Marmottant), en sciences de la voix (Nathalie Henrich Bernardoni), en bioacoustique et biologie animale.Ce projet cherchait originellement à reproduire les sons émis par les poissons en modélisant en laboratoire des vessies natatoires et l’action du muscle sonique associé. Au fil de la recherche, les deux artistes ont été amenés à interroger l’approche biomimétique de ce phénomène et à proposer une nouvelle méthodologie de travail inspirée de la démarche concrète que l’on retrouve au cœur de la musique dite « acousmatique ». Et si ces chants n’étaient finalement qu’une histoire de « stick-slip » ?De la démarche organologique aux fantasmes biomimétiques, de l’imaginaire poétique à la réalité des dispositifs expérimentaux, ce récit explore les zones de glissement et de friction entre différentes manières d’approcher et de comprendre un phénomène.Notre choix de format d’écriture s’est porté sur le portfolio, ce qui nous permet de restituer cette enquête par le biais d’un corpus d’images. Ces documents, collectés et produits lors des expérimentations et rencontres, s’offrent comme fil rouge de notre réflexion.

Published
2020

35. The nocturnal life of the great scallops (Pecten maximus, L.): First description of their natural daily valve opening cycle

Author

Elie Retailleau, Arthur Chauvaud, Gaetan Richard, Delphine Mathias, Laurent Chauvaud, Sarah Reynaud, Jerome Mars, Sylvain Chauvaud, Société d’Observation Multi-Modale de l’Environnement [Plouzané], Laboratoire des Sciences de l'Environnement Marin (LEMAR) (LEMAR), Institut de Recherche pour le Développement (IRD)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Université de Brest (UBO)-Institut Universitaire Européen de la Mer (IUEM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Institut Universitaire Européen de la Mer (IUEM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS), Département lmage et Traitement Information (IMT Atlantique - ITI), IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), and This project has received financial support from the CNRS through the MITI interdisciplinary programs.

Subjects
nocturnal activity, [SDV.EE]Life Sciences [q-bio]/Ecology, environment, Multidisciplinary, [SDE]Environmental Sciences, Pecten maximus, valvometry method, natural gaping activity
Abstract

Valvometry techniques used to monitor bivalve gaping activity have elucidated numerous relationships with environmental fluctuations, along with biological rhythms ranging from sub-daily to seasonal. Thus, a precise understanding of the natural activity of bivalves (i.e., not exposed to stressful environmental variations) is necessary as a baseline for detecting abnormal behaviors (deviations). This knowledge is also needed to reliably interpret observations of bivalve gaping behavior and associated biological processes (e.g., respiration, nutrition) acquired over time-limited periods. With this in mind, we investigated the natural daily gaping activity of the great scallop (Pecten maximus) by continuously monitoring 35 individuals in several individual tanks and in situ (Bay of Saint-Brieuc, Brittany, France) using fully autonomous Hall effect sensors. Our results revealed a circadian cycle (τ = 24.0h) in scallop gaping activity. Despite significant inter-individual variability in mean opening and cycle amplitude, almost all individuals (87.5%) exhibited nocturnal activity, with valves more open at night than during the day. A shift in light regime in the tanks triggered an instantaneous change in opening pattern, indicating that light levels strongly determine scallop activity. Based on the opening status of scallops, we also identified several gaping behaviors deviating from the regular daily pattern (lack of rhythmicity, high daytime opening), potentially reflecting physiological weakness. While further long-term studies are required to fully understand the natural activity of scallops, these findings pave the way for studies focused on the scallop response to external factors and introduce further research into the detection of abnormal behaviors. Coupling observations of diel valve gaping cycles with other daily variations in organismal and environmental parameters could help explain mechanisms driving the growth patterns of scallops observed in their shell striations. From a technical perspective, our field-based monitoring demonstrates the suitability of autonomous valvometry sensors for studying mobile subtidal bivalve activity in remote offshore environments.

Published
2023

36. Indoor and outdoor in-flight odometry based solely on optic flows with oscillatory trajectories

Author

L Bergantin, C Coquet, J Dumon, A Negre, T Raharijaona, N Marchand, F Ruffier, Institut des Sciences du Mouvement Etienne Jules Marey (ISM), Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), GIPSA-Services (GIPSA-Services), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Laboratoire de Conception Fabrication Commande (LCFC), Université de Lorraine (UL)-Arts et Métiers Sciences et Technologies, HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM), GIPSA - COntrol, PErception, Robots, navigation and Intelligent Computing (GIPSA-COPERNIC), GIPSA Pôle Sciences des Données (GIPSA-PSD), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), ANR-19-ASTR-0009,Proxilearn,Apprentissage d'un pilote automatique pour les micro-drones en espace confiné(2019), ANR-10-EQPX-0044,ROBOTEX,Réseau national de plateformes robotiques d'excellence(2010), and ANR-21-ESRE-0015,TIRREX,Infrastructure technologique pour la recherche d'excellence en robotique(2021)

Subjects
Oscillations, Indoor and outdoor flight tests, Visual odometry, Aerial field robotics, Aerospace Engineering, Optic Flow, SOFIa, Hexarotor, [SPI.AUTO]Engineering Sciences [physics]/Automatic
Abstract

International audience; Estimating distance traveled is a frequently arising problem in robotic applications designed for use in environments where GPS is only intermittently or not at all available. In UAVs, the presence of weight and computational power constraints makes it necessary to develop odometric strategies based on minimilastic equipment. In this study, a hexarotor was made to perform up-and-down oscillatory movements while flying forward in order to test a self-scaled optic flow based odometer. The resulting self-oscillatory trajectory generated series of contractions and expansions in the optic flow vector field, from which the flight height of the hexarotor could be estimated using an Extended Kalman Filter. For the odometry, the downward translational optic flow was scaled by this current visually estimated flight height before being mathematically integrated to obtain the distance traveled. Here we present three strategies based on sensor fusion requiring no, precise or rough prior knowledge of the optic flow variations generated by the sinusoidal trajectory. The “rough prior knowledge” strategy is based on the shape and timing of the variations in the optic flow. Tests were performed first in a flight arena, where the hexarotor followed a circular trajectory while oscillating up and down over a distance of about [Formula: see text] m under illuminances of [Formula: see text] lux and [Formula: see text] lux. Preliminary field tests were then performed, in which the hexarotor followed a longitudinal bouncing [Formula: see text]-long trajectory over an irregular pattern of grass.

Published
2023

37. Synchronization of acquisition devices in neuroimaging: An application using co-registration of eye movements and electroencephalography

Author

Gelu Ionescu, Aline Frey, Nathalie Guyader, Emmanuelle Kristensen, Anton Andreev, Anne Guérin-Dugué, GIPSA-Services (GIPSA-Services), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Laboratoire de Neurosciences Cognitives [Marseille] (LNC), Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU), GIPSA - Vision and Brain Signal Processing (GIPSA-VIBS), GIPSA Pôle Sciences des Données (GIPSA-PSD), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), and Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Eye Movements, [SCCO.NEUR]Cognitive science/Neuroscience, 05 social sciences, [SCCO.COMP]Cognitive science/Computer science, Experimental and Cognitive Psychology, Electroencephalography, 050105 experimental psychology, 03 medical and health sciences, [SCCO]Cognitive science, 0302 clinical medicine, Arts and Humanities (miscellaneous), Developmental and Educational Psychology, Humans, 0501 psychology and cognitive sciences, Psychology (miscellaneous), 030217 neurology & neurosurgery, General Psychology, Algorithms
Abstract

Interest in applications for the simultaneous acquisition of data from different devices is growing. In neuroscience for example, co-registration complements and overcomes some of the shortcomings of individual methods. However, precise synchronization of the different data streams involved is required before joint data analysis. Our article presents and evaluates a synchronization method which maximizes the alignment of information across time. Synchronization through common triggers is widely used in all existing methods, because it is very simple and effective. However, this solution has been found to fail in certain practical situations, namely for the spurious detection of triggers and/or when the timestamps of triggers sampled by each acquisition device are not jointly distributed linearly for the entire duration of an experiment. We propose two additional mechanisms, the "Longest Common Subsequence" algorithm and a piecewise linear regression, in order to overcome the limitations of the classical method of synchronizing common triggers. The proposed synchronization method was evaluated using both real and artificial data. Co-registrations of electroencephalographic signals (EEG) and eye movements were used for real data. We compared the effectiveness of our method to another open source method implemented using EYE-EEG toolbox. Overall, we show that our method, implemented in C++ as a DOS application, is very fast, robust and fully automatic.

Published
2021

38. Effect of simplifying the body model to compute the energy parameters in pole vaulting

Author

Johan Cassirame, Julien Frère, Romain Vanhaesebrouck, Hervé Sanchez, GIPSA - Analyse et Modification de l'homme en mouvement : biomécanique, cognition, vocologie (GIPSA-MOVE), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), Développement, Adaptation et Handicap. Régulations cardio-respiratoires et de la motricité (DevAH), Université de Lorraine (UL), Fédération Française d'Athlétisme (FFA), Laboratoire Culture, sport, santé, société - UFC (EA 4660) (C3S), Université de Franche-Comté (UFC), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC), Performance, Santé, Métrologie, Société - EA 7507 (PSMS), Université de Reims Champagne-Ardenne (URCA), and This work was supported by the French Ministry of Sport and the French Institute of Sport under Grant 16-R-14, and the French Athletics Federation.

Subjects
Kinematics, Speedup, [SDV]Life Sciences [q-bio], Computation, bepress|Life Sciences|Kinesiology, Physical Therapy, Sports Therapy and Rehabilitation, SportRxiv|Sport and Exercise Science|Performance Analysis, bepress|Life Sciences|Kinesiology|Biomechanics, Kinetic energy, Total/mechanical, 03 medical and health sciences, statistical parametric mapping, 0302 clinical medicine, SportRxiv|Sport and Exercise Science|Sport and Exercise Biomechanics, motion capture, sport performance, Orthopedics and Sports Medicine, Mathematics, Mathematical analysis, 030229 sport sciences, SportRxiv|Sport and Exercise Science, Main effect, Pairwise comparison, 030217 neurology & neurosurgery, Energy (signal processing)
Abstract

International audience; This study compared changes in the energy-time profiles in pole vaulting using several body models. Two-dimensional kinematics were collected from 20 successful vaults (5.20-6.01 m) performed by 10 athletes during a national meet. The linear and angular kinetic, potential, and total mechanical energy-time profiles were obtained from three pole vaulter models composed of 12, 5, and 3 segments (M12, M5, and M3, respectively), as well as calculated and approximated centre of mass models. One-dimensional statistical parametric mapping was used to compare these energy-time profiles from the models, while agreements of discrete energy parameters were assessed. For all the studied energy-time profiles, there was a main effect (0.05 < p < 0.001) of the models, but pairwise comparisons showed that M5 presented the lowest differences with M12 in comparison with the other models (M3 and approximated centre of mass models). In addition, M5 showed better agreement (lower bias and small effect size) with M12 for the studied energy parameters compared to the other models. Therefore, M5 may be a reliable option to simplify the body model and speed up the computation of the energy-time profiles of the pole vaulter.

Published
2021

39. The Role of Emotional Content and Perceptual Saliency During the Programming of Saccades Toward Faces

Author

Martial Mermillod, Louise Kauffmann, Carole Peyrin, Clémence Charles, Lea Entzmann, Nathalie Guyader, Juliette Lenouvel, Roman Vuillaume, Laboratoire de Psychologie et NeuroCognition (LPNC ), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), GIPSA - Vision and Brain Signal Processing (GIPSA-VIBS), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), Imagerie et Vision Artificielle [Dijon] (ImViA), Université de Bourgogne (UB), and ANR-19-P3IA-0003,MIAI,MIAI @ Grenoble Alpes(2019)

Subjects
'Happy' face, Visual perception, Cognitive Neuroscience, media_common.quotation_subject, Emotions, [SCCO.COMP]Cognitive science/Computer science, Experimental and Cognitive Psychology, 050105 experimental psychology, 03 medical and health sciences, 0302 clinical medicine, Artificial Intelligence, Saccade programming, Perception, Reaction Time, Saccades, Humans, Attention, 0501 psychology and cognitive sciences, Face detection, book, media_common, Emotional facial expressions, Time course, Facial expression, [SCCO.NEUR]Cognitive science/Neuroscience, 05 social sciences, Eye movement, book.written_work, Saccadic masking, Facial Expression, Neural computation, Eye movements, [SCCO.PSYC]Cognitive science/Psychology, Saccade, Psychology, 030217 neurology & neurosurgery, Cognitive psychology
Abstract

International audience; Previous studies have shown that the human visual system can detect a face and elicit a saccadic eye movement toward it very efficiently compared to other categories of visual stimuli. In the first experiment, we tested the influence of facial expressions on fast face detection using a saccadic choice task. Face-vehicle pairs were simultaneously presented and participants were asked to saccade toward the target (the face or the vehicle). We observed that saccades toward faces were initiated faster, and more often in the correct direction, than saccades toward vehicles, regardless of the facial expressions (happy, fearful, or neutral). We also observed that saccade endpoints on face images were lower when the face was happy and higher when it was neutral. In the second experiment, we explicitly tested the detection of facial expressions. We used a saccadic choice task with emotional-neutral pairs of faces and participants were asked to saccade toward the emotional (happy or fearful) or the neutral face. Participants were faster when they were asked to saccade toward the emotional face. They also made fewer errors, especially when the emotional face was happy. Using computational modeling, we showed that this happy face advantage can, at least partly, be explained by perceptual factors. Also, saccade endpoints were lower when the target was happy than when it was fearful. Overall, we suggest that there is no automatic prioritization of emotional faces, at least for saccades with short latencies, but that salient local face features can automatically attract attention.

Published
2021

40. Semi-supervised Classification of Hyperspectral Image through Deep Encoder-Decoder and Graph Neural Networks

Author

Imed Riadh Farah, Akrem Sellami, Mauro Dalla Mura, Refka Hanachi, University of Manouba, National School of Computer Science, Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), and Université Grenoble Alpes (UGA)

Subjects
Pixel, Computer science, business.industry, Feature vector, Deep learning, Feature extraction, Hyperspectral imaging, Pattern recognition, Autoencoder, ComputingMethodologies_PATTERNRECOGNITION, Computer Science::Computer Vision and Pattern Recognition, Graph (abstract data type), Artificial intelligence, Representation (mathematics), business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; In this research study, we deal with remote sensing data analysis over high dimensional space formed by hyperspectral images. This task is generally complex due to the large spectral, spatial richness, and mixed pixels. Thus, several spectral un-mixing methods have been proposed to discriminate mixing spectra by estimating the classes and their presence rates. However, information related to mixed pixel composition is very interesting for some applications, but it is insufficient for many others. Thus, it is necessary to have much more data about the spatial localization of the classes detected during the spectral un-mixing process. To solve the above-mentioned problem and specify the spatial location of the different land cover classes in the mixed pixel, sub-pixel mapping techniques were introduced. This manuscript presents a novel sub-pixel mapping process relying on K-SVD (K-singular value decomposition) learning and total variation as a spatial regularization parameter (SMKSVD-TV: Sub-pixel Mapping based on K-SVD dictionary learning and Total Variation). The proposed approach adopts total variation as a spatial regularization parameter, to make edges smooth, and a pre-constructed spatial dictionary with the K-SVD dictionary training algorithm to have more spatial configurations at the sub-pixel level. It was tested and validated with three real hyperspectral data. The experimental results reveal that the attributes obtained by utilizing a learned spatial dictionary with isotropic total variation allowed improving the classes sub-pixel spatial localization, while taking into account pre-learned spatial patterns. It is also clear that the K-SVD dictionary learning algorithm can be applied to construct a spatial dictionary, particularly for each data set.

Published
2021

41. Smart charging impact on electric vehicles in presence of photovoltaics

Author

Ahmad Hably, Seddik Bacha, Khaled Hajar, Baoling Guo, GIPSA - COntrol, PErception, Robots, navigation and Intelligent Computing (GIPSA-COPERNIC), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), Laboratoire de Génie Electrique de Grenoble (G2ELab ), Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), and Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Electric vehicles, RESs, 02 engineering and technology, 7. Clean energy, Automotive engineering, [SPI.AUTO]Engineering Sciences [physics]/Automatic, Charging station, Photovoltaics, 0202 electrical engineering, electronic engineering, information engineering, Wind power, business.industry, V2G, [SPI.NRJ]Engineering Sciences [physics]/Electric power, Photovoltaic system, charging station. 1, 020206 networking & telecommunications, Grid, Renewable energy, EV charging program, Electric power transmission, 13. Climate action, Environmental science, 020201 artificial intelligence & image processing, charging station, Electricity, business, plug in electric vehicles
Abstract

International audience; The emergence of electric vehicles (EVs) promises to be the turning point of sustainable energy and, in particular, renewable energy production in the world. EV Charging will generate substantial extra demand for electricity. Renewable energy, including solar and wind power, can be met in the grid technically and economically. Recent studies have shown that smart charging of EV can increase the synergy between photovoltaics (PV), electrical transmission and electricity usage, resulting in technological and economic advantages. Given the increasing emphasis on this area, this analysis summarizes an overview on smart charging studies taking into account PV power output and consumption of electricity.

Published
2021

42. A portable neurofeedback device for treating chronic subjective tinnitus: Feasibility and results of a pilot study

Author

Jonathan Schmutz, Bastien Piedfort, Alain Londero, Marco Congedo, Victor Ferat, Thomas Cervoni, Robin Guillard, Marie-Josée Fraysse, Renaud Simeon, GIPSA - Vision and Brain Signal Processing (GIPSA-VIBS), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), Hôpital Universitaire de Purpan, Hôpital Rothschild [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Zeta Technologies, Aix-Marseille Université - Faculté de pharmacie (AMU PHARM), Aix Marseille Université (AMU), Hôpital Européen Georges Pompidou, Assistance Publique-Hôpitaux de Paris (AP-HP), Centre Hospitalier Universitaire de Toulouse (CHU Toulouse), CHU Rothschild [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Winfried Schlee, Berthold Langguth, Tobias Kleinjung, Sven Vanneste, and Dirk De Ridder

Subjects
Intrusiveness, medicine.medical_specialty, Cognitive strategies, Visual analogue scale, Headset, Population, Electroencephalography, Tinnitus, 03 medical and health sciences, 0302 clinical medicine, Medicine, EEG, [SDV.MHEP.OS]Life Sciences [q-bio]/Human health and pathology/Sensory Organs, Adverse effect, education, ComputingMilieux_MISCELLANEOUS, education.field_of_study, medicine.diagnostic_test, business.industry, [SCCO.NEUR]Cognitive science/Neuroscience, Hearing loss, Neurofeedback, Physical therapy, medicine.symptom, business, 030217 neurology & neurosurgery
Abstract

Background: Several clinical studies have shown that neurofeedback (NFB) has the potential to significantly improve the quality of life of patients complaining of chronic subjective tinnitus. Yet the clinical applicability of such a therapeutic approach in the everyday practice has not been tested so far. Objective: This study aims at investigating the feasibility and efficacy of a semi-automated NFB intervention by means of a portable device that eventually could be used by the patients at home on an everyday basis. The duration of setup procedures is minimized through the use of a dry electrodes electroencephalography (EEG) headset and an automated user-interface. Methods: We conducted a pilot clinical study (non-controlled, single arm, NCT03773926 ). According to a predetermined power calculation, a homogeneous population of 33 subjects with strict inclusion criteria was enrolled. After inclusion, all patients underwent 10 NFB sessions lasting 50 min each, over a period of 5 weeks and a 3-month follow-up period. According to previous studies, the NFB training aimed at increasing the alpha-band power (8–12 Hz) in the EEG power spectrum on the averaged signal of leads FC1, FC2, F3 and F4. Tinnitus handicap inventory (THI) was used as a primary outcome measure. Secondary outcome measures were the visual analog scales (VAS) and the change of the alpha-band power within sessions and across training. Time points of assessment were before intervention (T1), after intervention (T2) and at the 3-month follow-up (T3). Results: Patient exhibited a clinically significant decrease of the THI score, with a 23% decrease (N = 28) on average between T1 and T2 and a 31% decrease (N = 25) between T1 and T3. A significant increase of the alpha-band power within sessions was observed. No significant increase of the alpha-band power across sessions was observed. For the 19 subjects where sufficient data were exploitable, a significant correlation was found between the evolution of the alpha-band training across sessions and the evolution of the THI between T1 and T2. The sessions were well tolerated and no adverse effect was reported. Conclusion: This study suggests that neurofeedback has potential to suit everyday clinical practice with the goal to significantly reduce tinnitus intrusiveness. The merits and limitations of this NFB procedure are discussed, especially with respect to the choice of EEG electrodes to ensure a good signal quality.

Published
2021

43. Data-Dependent Scaling of CNN’s First Layer for Improved Image Manipulation Detection

Author

Kai Wang, Ivan Castillo Camacho, GIPSA - Apprentissage, Classification, Traitement d'Images et de Vidéos (GIPSA-ACTIV), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), and Université Grenoble Alpes (UGA)

Subjects
021110 strategic, defence & security studies, Computer science, business.industry, [INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM], ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 0211 other engineering and technologies, Stability (learning theory), Initialization, Pattern recognition, 02 engineering and technology, Filter (signal processing), Convolutional neural network, Data flow diagram, 0202 electrical engineering, electronic engineering, information engineering, Median filter, 020201 artificial intelligence & image processing, Artificial intelligence, Noise (video), Performance improvement, business
Abstract

International audience; Convolutional Neural Networks (CNNs) have become an effective tool to detect image manipulation operations, e.g., noise addition, median filtering and JPEG compression. In this paper, we propose a simple and practical method for adjusting the CNN's first layer, based on a proper scaling of first-layer filters with a data-dependent approach. The key idea is to keep the stability of the variance of data flow in a CNN. We also present studies on the output variance for convolutional filter, which are the basis of our proposed scaling. The proposed method can cope well with different first-layer initialization algorithms and different CNN architectures. The experiments are performed with two challenging forensic problems, i.e., a multi-class classification problem of a group of manipulation operations and a binary detection problem of JPEG compression with high quality factor, both on relatively small image patches. Experimental results show the utility of our method with a noticeable and consistent performance improvement after scaling.

Published
2021

44. C18O, 13CO, and 12CO abundances and excitation temperatures in the Orion B molecular cloud

Author

Emeric Bron, Marc-Antoine Miville-Deschenes, Pierre Gratier, Jérôme Pety, Jan H. Orkisz, Nicolas Peretto, Maxime Vono, Evelyne Roueff, Antoine Roueff, Albrecht Sievers, Javier R. Goicoechea, Jacques Le Bourlot, Victor de Souza Magalhaes, Jouni Kainulainen, Annie Hughes, Franck Petit, Viviana V. Guzmán, Maryvonne Gerin, Harvey S. Liszt, François Levrier, Antoine Marchal, Jocelyn Chanussot, David Languignon, Sébastien Bardeau, M. Gaudel, Pierre Chainais, Centre National de la Recherche Scientifique (France), Max Planck Society, Instituto Geográfico Nacional (España), European Space Agency, Centre National D'Etudes Spatiales (France), Agencia Estatal de Investigación (España), PhyTI (PhyTI), Institut FRESNEL (FRESNEL), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Etude du Rayonnement et de la Matière en Astrophysique et Atmosphères = Laboratory for Studies of Radiation and Matter in Astrophysics and Atmospheres (LERMA), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY), FORMATION STELLAIRE 2020, Laboratoire d'Astrophysique de Bordeaux [Pessac] (LAB), Université de Bordeaux (UB)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Astrophysique, Laboratoire de physique de l'ENS - ENS Paris (LPENS), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)-Département de Physique de l'ENS-PSL, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)-Département de Physique de l'ENS-PSL, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Instituto de Física Fundamental [Madrid] (IFF), Consejo Superior de Investigaciones Científicas [Madrid] (CSIC), Onsala Space Observatory (OSO), Chalmers University of Technology [Göteborg], Institut de RadioAstronomie Millimétrique (IRAM), Centre National de la Recherche Scientifique (CNRS), Signal et Communications (IRIT-SC), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Toulouse Mind & Brain Institut (TMBI), Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT), GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Pontificia Universidad Católica de Chile (UC), Institut de recherche en astrophysique et planétologie (IRAP), Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), Laboratoire Univers et Théories (LUTH (UMR_8102)), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), National Radio Astronomy Observatory [Charlottesville] (NRAO), National Radio Astronomy Observatory (NRAO), Canadian Institute for Theoretical Astrophysics (CITA), Astrophysique Interprétation Modélisation (AIM (UMR_7158 / UMR_E_9005 / UM_112)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Cardiff University, ANR-19-P3IA-0003,MIAI,MIAI @ Grenoble Alpes(2019), Laboratoire d'Etude du Rayonnement et de la Matière en Astrophysique (LERMA (UMR_8112)), Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Sorbonne Université (SU)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY), Laboratoire de physique de l'ENS - ENS Paris (LPENS (UMR_8023)), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), Onsala Space Observatory, Université Toulouse 1 Capitole (UT1)-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1)-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées, Ecole Centrale de Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS)-Université Fédérale Toulouse Midi-Pyrénées-Centre National d'Études Spatiales [Toulouse] (CNES)-Météo France-Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS)-Université Fédérale Toulouse Midi-Pyrénées-Centre National d'Études Spatiales [Toulouse] (CNES)-Météo France-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)-École normale supérieure - Paris (ENS Paris), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Institut national des sciences de l'Univers (INSU - CNRS)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS), Université de Lille-Ecole Centrale de Lille-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)-Observatoire de Paris, and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut national des sciences de l'Univers (INSU - CNRS)

Subjects
statistical [Methods], Astrophysics, 7. Clean energy, 01 natural sciences, Molecular physics, ISM: clouds, Spectral line, Methods: data analysis, 0103 physical sciences, Radiative transfer, Astrophysics::Solar and Stellar Astrophysics, Isotopologue, [INFO]Computer Science [cs], data analysis [Methods], 010303 astronomy & astrophysics, molecules [ISM], Astrophysics::Galaxy Astrophysics, Methods: statistical, Line (formation), Physics, 010308 nuclear & particles physics, Molecular cloud, Estimator, Velocity dispersion, Astronomy and Astrophysics, ISM: molecules, 3. Good health, 13. Climate action, Space and Planetary Science, [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph], clouds [ISM], Excitation
Abstract

27 pags., 23 figs., 3 tabs., Context. CO isotopologue transitions are routinely observed in molecular clouds for the purpose of probing the column density of the gas and the elemental ratios of carbon and oxygen, in addition to tracing the kinematics of the environment. Aims. Our study is aimed at estimating the abundances, excitation temperatures, velocity field, and velocity dispersions of the three main CO isotopologues towards a subset of the Orion B molecular cloud, which includes IC 434, NGC 2023, and the Horsehead pillar. Methods. We used the Cramer Rao bound (CRB) technique to analyze and estimate the precision of the physical parameters in the framework of local-thermodynamic-equilibrium (LTE) excitation and radiative transfer with added white Gaussian noise. We propose a maximum likelihood estimator to infer the physical conditions from the 1-0 and 2-1 transitions of CO isotopologues. Simulations show that this estimator is unbiased and proves efficient for a common range of excitation temperatures and column densities (Tex > 6 K, N > 1014-1015 cm-2). Results. Contrary to general assumptions, the various CO isotopologues have distinct excitation temperatures and the line intensity ratios between different isotopologues do not accurately reflect the column density ratios. We find mean fractional abundances that are consistent with previous determinations towards other molecular clouds. However, significant local deviations are inferred, not only in regions exposed to the UV radiation field, but also in shielded regions. These deviations result from the competition between selective photodissociation, chemical fractionation, and depletion on grain surfaces. We observe that the velocity dispersion of the C18O emission is 10% smaller than that of 13CO. The substantial gain resulting from the simultaneous analysis of two different rotational transitions of the same species is rigorously quantified. Conclusions. The CRB technique is a promising avenue for analyzing the estimation of physical parameters from the fit of spectral lines. Future works will generalize its application to non-LTE excitation and radiative transfer methods., This work is based on observations carried out under project numbers 019-13, 022-14, 145-14, 122-15, 018-16, and finally the large program number 124-16 with the IRAM 30m telescope. IRAM is supported by INSU/CNRS (France), MPG (Germany) and IGN (Spain). This research also used data from the Herschel Gould Belt survey (HGBS) project (http:// gouldbelt-herschel.cea.fr). The HGBS is a Herschel Key Programme jointly carried out by SPIRE Specialist Astronomy Group 3 (SAG 3), scientists of several institutes in the PACS Consortium (CEA Saclay, INAF-IFSI Rome and INAF-Arcetri, KU Leuven, MPIA Heidelberg), and scientists of the Herschel Science Center (HSC). We thank CIAS for their hospitality during the many workshops devoted to the ORION-B project. This project has received financial support from the CNRS through the MITI interdisciplinary programs. This work was supported in part by the Programme National “Physique et Chimie du Milieu Interstellaire” (PCMI) of CNRS/INSU with INC/INP, co-funded by CEA and CNES. JRG thanks Spanish MICI for funding support under grant AYA2017- 85111-P. Finally, we thank the anonymous referee for helpful comments on the manuscript.

Published
2020

45. U-Net Neural Network for Heartbeat Detection in Ballistocardiography

Author

Jean Bergounioux, François Jouen, Bertrand Rivet, Sophie Achard, Guillaume Cathelain, École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL), GIPSA - Vision and Brain Signal Processing (GIPSA-VIBS), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), Modèles statistiques bayésiens et des valeurs extrêmes pour données structurées et de grande dimension (STATIFY), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), and École Pratique des Hautes Études (EPHE)

Subjects
Adult, Adolescent, Computer science, Vital signs, 030204 cardiovascular system & hematology, Ballistocardiography, 03 medical and health sciences, Young Adult, 0302 clinical medicine, Heart Rate, Heart rate monitoring, medicine, Humans, Respiratory system, Child, Aged, Artificial neural network, medicine.diagnostic_test, Noise (signal processing), business.industry, Heartbeat detection, Infant, Newborn, Pattern recognition, Signal Processing, Computer-Assisted, Middle Aged, 3. Good health, Artificial intelligence, Neural Networks, Computer, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, 030217 neurology & neurosurgery, Algorithms
Abstract

International audience; Monitoring vital signs of neonates can be harmful and lead to developmental troubles. Ballistocardiography, a contactless heart rate monitoring method, has the potential to reduce this monitoring pain. However, signal processing is uneasy due to noise, inherent physiological variability and artifacts (e.g. respiratory amplitude modulation and body position shifts). We propose a new heartbeat detection method using neural networks to learn this variability. A U-Net model takes thirty-second-long records as inputs and acts like a nonlinear filter. For each record, it outputs the samples probabilities of belonging to IJK segments. A heartbeat detection algorithm finally detects heartbeats from those segments, based on a distance criterion. The U-Net has been trained on 30 healthy subjects and tested on 10 healthy subjects, from 8 to 74 years old. Heartbeats have been detected with 92% precision and 80% recall, with possible optimization in the future to achieve better performance.

Published
2020

46. New Approach of UAV Movement Detection and Characterization Using Advanced Signal Processing Methods Based on UWB Sensing

Author

Angela Digulescu, Cristina Despina-Stoian, Denis Stănescu, Florin Popescu, Florin Enache, Cornel Ioana, Emanuel Rădoi, Iulian Rîncu, Alexandru Șerbănescu, Académie Technique Militaire [Bucarest] (ATM), Univ Brest, CNRS, UMR 6285, Lab-sticc, F-29200 Brest, France, Université de Brest (UBO), GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), Lab-STICC_UBO_CACS_COM, Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université de Brest (UBO)-Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT), and Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC)

Subjects
LSS UAV, movement map, lcsh:TP1-1185, drone, lcsh:Chemical technology, signal processing, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, UWB sensing, Article, RPA
Abstract

International audience; In the last years, the commercial drone/unmanned aerial vehicles market has grown due to their technological performances (provided by the multiple onboard available sensors), low price, and ease of use. Being very attractive for an increasing number of applications, their presence represents a major issue for public or classified areas with a special status, because of the rising number of incidents. Our paper proposes a new approach for the drone movement detection and characterization based on the ultra-wide band (UWB) sensing system and advanced signal processing methods. This approach characterizes the movement of the drone using classical methods such as correlation, envelope detection, time-scale analysis, but also a new method, the recurrence plot analysis. The obtained results are compared in terms of movement map accuracy and required computation time in order to offer a future starting point for the drone intrusion detection.

Published
2020

47. 3D Visualization of Body Motion in Speed Climbing

Author

Franck Quaine, Lionel Reveret, Pierre Legreneur, Sylvain Chapelle, Calcul des Variations, Géométrie, Image (CVGI), Laboratoire Jean Kuntzmann (LJK), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Fédération Française de la Montagne et de l'Escalade, GIPSA - Analyse et Modification de l'homme en mouvement : biomécanique, cognition, vocologie (GIPSA-MOVE), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Laboratoire Interuniversitaire de Biologie de la Motricité (LIBM ), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Université Jean Monnet [Saint-Étienne] (UJM)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry]), and Université de Lyon-Université de Lyon-Université Jean Monnet - Saint-Étienne (UJM)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])

Subjects
Motion analysis, lcsh:BF1-990, Tracking (particle physics), biomechanics, 050105 experimental psychology, Motion (physics), [SPI]Engineering Sciences [physics], 03 medical and health sciences, 0302 clinical medicine, video analysis, Psychology, 0501 psychology and cognitive sciences, Computer vision, Polygon mesh, General Psychology, Original Research, Plane (geometry), business.industry, motion analysis, 05 social sciences, speed climbing, Visualization, lcsh:Psychology, 3D visualization, center of mass, Climbing, Artificial intelligence, business, 030217 neurology & neurosurgery, Energy (signal processing)
Abstract

International audience; Speed climbing involves an optimization of the velocity of the ascent and the trajectory path during performance. Consequently, any amount of energy spent in the two other directions than vertical, namely the lateral direction and the direction perpendicular to the wall plane, is a potential loss of performance. To assess this principle, we present a study on 3D motion analysis and its 3D visualization for a subject during a speed climbing performance. The fundamentals of geometrical measurement in 3D require to integrate multiple 2D cues, at least two, in order to extract 3D information. First results with two drones following an athlete's ascent show that a 3D velocity profile can be provided from the tracking of a marker on the harness, pointing critical phases in the ascent where the vertical speed is not dominant any more. We further investigate 3D motion of full body using markerless video-based tracking. Our approach is based on a full body 3D avatar model of the climber, represented as a 3D mesh. This model and its deformation are learned in a laboratory studio. The learning needs to be done only once. Result is a manifold embedding of the 3D mesh and its deformations, which can be used afterwards to perform registration onto video of performance of speed climbing. The results of the tracking is an inference of the 3D mesh aligned onto videos of speed climbing performance. From this 3D mesh, we deduce an estimation of the center of mass (COM). We show that this estimation from 3D mesh differs from the usual approximation of the COM as a marker on the harness. In particular, the 3D mesh COM takes into account the whole body movement such as the influence of the limbs which is not detected by a marker on the harness.

Published
2020

48. Pursuit-evasion Game for Nonholonomic Mobile Robots With Obstacle Avoidance using NMPC

Author

Ahmad Hably, Mukhtar Sani, Bogdan Robu, GIPSA - COntrol, PErception, Robots, navigation and Intelligent Computing (GIPSA-COPERNIC), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), GIPSA - Modelling and Optimal Decision for Uncertain Systems (GIPSA-MODUS), and GIPSA Pôle Automatique et Diagnostic (GIPSA-PAD)

Subjects
0301 basic medicine, Computer Science::Computer Science and Game Theory, Optimization problem, Computer science, ComputingMilieux_PERSONALCOMPUTING, Pursuer, Mobile robot, [SPI.AUTO]Engineering Sciences [physics]/Automatic, 03 medical and health sciences, symbols.namesake, 030104 developmental biology, 0302 clinical medicine, Control theory, Nash equilibrium, Best response, Obstacle avoidance, Differential game, symbols, Pursuit-evasion, 030217 neurology & neurosurgery
Abstract

International audience; In this work, non-cooperative competitive games between two unmanned ground robots using Nonlinear Model Predictive Control (NMPC) while incorporating obstacle avoidance techniques are studied. The objective of the first player (pursuer) is to minimize the relative distance and orientation between itself and the second player (evader) while avoiding obstacles, whereas the evader does the opposite. The Pursuit-Evasion Game (PEG) being a typical class of a differential game is formulated as a zero-sum game with two homogeneous players in five different game scenarios. The objective function of each player is formulated as a double optimization problem and is solved separately using NMPC techniques. The optimal trajectory of each player is computed iteratively by considering the best response of the opponent player. The level of information is assumed to be symmetric. Simulations of various scenarios show the winning possibility of each player.

Published
2020

49. Toward Cybersecurity of Unmanned Aircraft System operations under 'Specific' category

Author

Jean-Marc Thiriet, Amin El Mrabti, Trung Duc Tran, Nicolas Marchand, GIPSA - Safe, Controlled and Monitored Systems (GIPSA-SAFE), GIPSA Pôle Automatique et Diagnostic (GIPSA-PAD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), GIPSA - COntrol, PErception, Robots, navigation and Intelligent Computing (GIPSA-COPERNIC), GIPSA Pôle Sciences des Données (GIPSA-PSD), SOGILIS company [Grenoble], and Association Nationale Recherche Technologique (ANRT), 33 rue Rennequin -75017 Paris- SOGILIS company (France), 4 Avenue Doyen Louis Weil, 38000 Grenoble

Subjects
Cybersecurity, Computer science, Rulemaking, 020206 networking & telecommunications, 02 engineering and technology, SORA, Computer security, computer.software_genre, Critical infrastructure, Aviation safety, [INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR], Safety risk, Privacy, Specific Operation, Agency (sociology), 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], media_common.cataloged_instance, 020201 artificial intelligence & image processing, UAS, European union, Risk assessment, computer, media_common
Abstract

International audience; Nowadays, the increasing number of Unmanned Aircraft System (UAS) operations raises public concerns on cybersecurity issues. Therefore, it requires a methodology to address these issues during the UAS development. The Specific Operation Risk Assessment (SORA) is a risk assessment methodology developed by Joint Authorities for Rulemaking on Unmanned Aircraft System (JARUS). This methodology is endorsed by the European Union Aviation Safety Agency (EASA) as an acceptable means to fulfill the requirements of EU regulation related to UAS operations under the Specific category. The original SORA methodology focus on Safety risk scenarios only, which relate to unintentional threats and the harms to people’s life. In this paper, we introduce our solution to extend the methodology toward cybersecurity aspects. The extended methodology concerns risk scenarios relating to intentional digital threats and some other harms (e.g privacy violation, damage to critical infrastructure). A part of this solution is developed and is presented in this paper.

Published
2020

50. Dimensionality Transcending: A Method for Merging BCI Datasets With Different Dimensionalities

Author

Pedro Rodrigues, Christian Jutten, Marco Congedo, GIPSA - Vision and Brain Signal Processing (GIPSA-VIBS), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), and Université Grenoble Alpes (UGA)

Subjects
Matching (statistics), Brain-Computer Interface, Computer science, 0206 medical engineering, Biomedical Engineering, 02 engineering and technology, Positive-definite matrix, Riemannian geometry, Symmetric positive definite matrices, Matrix (mathematics), symbols.namesake, Discriminative model, Symmetric matrix, Heterogeneous domain adaptation, EEG, Electrodes, Brain–computer interface, business.industry, Pattern recognition, Electroencephalography, 020601 biomedical engineering, Manifold, Data point, Brain-Computer Interfaces, symbols, Probability distribution, [SDV.IB]Life Sciences [q-bio]/Bioengineering, Artificial intelligence, business, Focus (optics), Transfer of learning, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Curse of dimensionality
Abstract

International audience; Objective: We present a transfer learning method for datasets with different dimensionalities, coming from different experimental setups but representing the same physical phenomena. We focus on the case where the data points are symmetric positive definite (SPD) matrices describing the statistical behavior of EEG-based brain computer interfaces (BCI).Method: Our proposal uses a two-step procedure that transforms the data points so that they become matched in terms of dimensionality and statistical distribution. In the dimensionality matching step, we use isometric transformations to map each dataset into a common space without changing their geometric structures. The statistical matching is done using a domain adaptation technique adapted for the intrinsic geometry of the space where the datasets are defined.Results: We illustrate our proposal on time series obtained from BCI systems with different experimental setups (e.g., different number of electrodes, different placement of electrodes). The results show that the proposed method can be used to transfer discriminative information between BCI recordings that, in principle, would be incompatible.Conclusion and significance: Such findings pave the way to a new generation of BCI systems capable of reusing information and learning from several sources of data despite differences in their electrodes positioning.

Published
2020

Catalog

Books, media, physical & digital resources
See catalog results