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4. On Designing and Implementing Agro-ecology IoT Applications: Issues from Applied Research Projects

Author

Sandro Bimonte, Amina Belhassena, Christophe Cariou, Jean Laneurit, Rim Moussa, Gerard Chalhoub, Robert Wrembel, Gauthier Picard, Ladjel Bellatreche, Alexandre Journaux, Thierry Heirman, Ali Hassan, Stefano Rizzi, Jean-Pierre George, Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université de Carthage - University of Carthage, Université Clermont Auvergne (UCA), Poznan University of Technology (PUT), ONERA / DTIS, Université de Toulouse [Toulouse], ONERA-PRES Université de Toulouse, Ecole Nationale Supérieure de Mécanique et d'Aérotechnique [Poitiers] (ISAE-ENSMA), Umanis France, Partenaires INRAE, DISI, University Bologna, Italy, Sandro Bimonte, Amina Belhassena, Christophe Cariou, Jean Laneurit, Rim Moussa, Gerard Chalhoub, Robert Wrembel, Gauthier Picard, Ladjel Bellatreche, Alexandre Journaux, Thierry Heirman, Ali Hassan, Stefano Rizzi, Jean-Pierre Georgé, Technologies et systèmes d'information pour les agrosystèmes (UR TSCF), Université de Clermont-Ferrand, Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes (LIMOS), Ecole Nationale Supérieure des Mines de St Etienne (ENSM ST-ETIENNE)-Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne (UCA)-Institut national polytechnique Clermont Auvergne (INP Clermont Auvergne), Université Clermont Auvergne (UCA)-Université Clermont Auvergne (UCA), Génétique Physiologie et Systèmes d'Elevage (GenPhySE ), Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-École nationale supérieure agronomique de Toulouse (ENSAT), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Alma Mater Studiorum Università di Bologna [Bologna] (UNIBO), Systèmes Multi-Agents Coopératifs (IRIT-SMAC), 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), IEEE Comp Soc, Griffith Univ, Destinat Gold Coast, ANR-19-LCV2-0011,Tiara,Vers des robots intelligents et adaptables pour l'agriculture(2019), and ANR-16-IDEX-0001,CAP 20-25,CAP 20-25(2016)

Subjects
[PHYS]Physics [physics], Big data, smart farming, [SPI]Engineering Sciences [physics], Agriculture robots, Internet of Things, Big data, smart farming, Agriculture robots, Internet of Things, [INFO]Computer Science [cs], [MATH]Mathematics [math]
Abstract

International audience; The design and implementation of agro-ecology IoT applications is a non-trivial task since the data processed in such applications are typically complex and heterogeneous. Moreover, these applications are implemented using different systems and technologies, over complex IoT communication network layers (edge, fog, cloud). The existing system design methods fail to effectively represent data in such a scenario. In this position paper we report and discuss the open issues for a new, dedicated design method, based on our initial experience in implementing an agro-ecology IoT system.

Published
2021

5. Predicting Heart Failure patient events by exploiting saliva and breath biomarkers information

Author

Tripoliti, Evanthia Eleftherios, Karanasiou, Georgia Spiridon, Kalatzis, Fanis Georgios, Fotiadis, Dimitrios Ioannis, Ghimenti, Silvia, Lomonaco, Tommaso, Bellagambi, Francesca, Fuoco, Roger, Naka, Katerina Kyriakos, Bechlioulis, Aris, Scali, Maria Chiara, Errachid, Abdelhamid, Department of Biomedical Research, University of Ioannina, Department of Chemistry and Industrial Chemistry - University of Pisa, Dept Cardiol, Azienda Osped Univ Pisana - Cardiothorac & Vasc Dept, University of Pisa - Università di Pisa, Micro & Nanobiotechnologies, Institut des Sciences Analytiques (ISA), Institut de Chimie du CNRS (INC)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), This work is supported by the HEARTEN project that has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 643694., IEEE, IEEE Comp Soc, Biol & Artificial Intelligence Soc, European Project: 643694,H2020,H2020-PHC-2014-single-stage,HEARTEN(2015), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), and Université de Lyon-Université de Lyon-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)

Subjects
event prediction, [SDV.BIO]Life Sciences [q-bio]/Biotechnology, breath biomarkers, heart failure, [CHIM]Chemical Sciences, data mining, [SDV.IB.BIO]Life Sciences [q-bio]/Bioengineering/Biomaterials, saliva biomarkers
Abstract

International audience; The aim of this work is to present a machine learning based method for the prediction of adverse events (mortality and relapses) in patients with heart failure (HF) by exploiting, for the first time, measurements of breath and saliva biomarkers (Tumor Necrosis Factor Alpha, Cortisol and Acetone). Data from 27 patients are used in the study and the prediction of adverse events is achieved with high accuracy (77%) using the Rotation Forest algorithm. As in the near future, biomarkers can be measured at home, together with other physiological data, the accurate prediction of adverse events on the basis of home based measurements can revolutionize HF management.

Published
2017

6. A Comparative Study of descriptors and detectors in Multispectral Face Recognition

Author

Mamadou Diarra, Adou Kablan Jérôme, Gouton Pierre, Université Félix Houphouët-Boigny ( UFHB ), Laboratoire Electronique, Informatique et Image ( Le2i ), Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique ( CNRS ), Sponsor(s):IEEE, IEEE Comp Soc, Univ Bourgogne, CNRS, ACM SIGAPP, IFIP, Natl Res Council Italy, Inst High Performance Comp & Networking, Univ Naples Federico II, Univ Milan, Univ Bourgogne, Lab Elect Image Informatique Res Grp, Univ Stvdiorvm Mediolanensis, Distretto Alta Tecnologia Beni Culturali, ACM, Yetongnon, K, Dipanda, A, DePietro, RCG, Gallo, L, Université Félix Houphouët-Boigny (UFHB), Laboratoire Electronique, Informatique et Image [UMR6306] (Le2i), Université de Bourgogne (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Arts et Métiers (ENSAM), Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Université de Bourgogne (UB)-École Nationale Supérieure d'Arts et Métiers (ENSAM), and HESAM Université (HESAM)-HESAM Université (HESAM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique (CNRS)

Subjects
HARRIS, [ INFO ] Computer Science [cs], Computer science, 3D single-object recognition, Multispectral image, Feature extraction, 0211 other engineering and technologies, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 02 engineering and technology, Facial recognition system, Object-class detection, [SPI]Engineering Sciences [physics], ACM: H.: Information Systems, FREAK, 0202 electrical engineering, electronic engineering, information engineering, [ SPI ] Engineering Sciences [physics], Three-dimensional face recognition, Computer vision, [INFO]Computer Science [cs], BRISK, 021110 strategic, defence & security studies, business.industry, multispectral face recognition, SURF, ACM : H.: Information Systems, Pattern recognition, visible, Object detection, ComputingMethodologies_PATTERNRECOGNITION, Feature (computer vision), infrared, 020201 artificial intelligence & image processing, Artificial intelligence, business
Abstract

International audience; Feature extraction is widely used in many computer vision problems such as object detection, image retrieval, object and scene recognition, etc. Feature extraction based on local feature detectors and descriptors requires a good compromise between the detectors and the descriptors. This paper presents a set of tools which are BRISK, FAST, FREAK, HARRIS and SURF for multispectral face recognition which is an alternative to conventional face recognition. The multispectral images are obtained by fusion of thermal and visible images.

Published
2016

7. A GRID ENABLED PSE FOR MEDICAL IMAGING: EXPERIENCES ON MEDIGRID

Author

BOCCIA, VANIA, D'AMORE, LUISA, M. Guarracino, LACCETTI, GIULIANO, MURLI, ALMERICO, IEEE Comp. Soc., Boccia, Vania, D'Amore, Luisa, M., Guarracino, Laccetti, Giuliano, and Murli, Almerico

Abstract

A key issue in designing applications codes which are able to effectively take advantage of grid computing environments is to deal and manage all the software components complexity, this is even more true for medical imaging applications. We are concerned with development of a medical imaging PSE for advanced and grid computing environments. Specifically, MedIGrid is a distributed application for the management, processing and visualization of biomedical images that integrates a set of software and hardware components, or, more specifically, a set of grid collaborative applications useful to nuclear doctors. It results from the interaction of computing scientists devoted to the design and deployment of new tomographic reconstruction algorithms techniques, computer scientists researchers in the field of distributed and parallel architectures and physicians involved in nuclear medicine. We'll describe MedIGrid general architecture, as well as improvements and enhancements, essentially regarding the semantic power of the GUI (the Portal) (its language and its realization), and the entire phase of monitoring and migration of the parallel computational kernel for images reconstruction, mainly based on a performance contract inked taking into account historical performances and an ad hoc developed performance model

Published
2005

8. Optimal Window and Lattice in Gabor Transform. Application to Audio Analysis

Author

Christoph Wiesmeyr, Benjamin Ricaud, Bruno Torrésani, Guillaume Stempfel, Darian Onchis-Moaca, Hélène Lachambre, Petcu, D, Watt, S, Zaharie, D, Kovacs, L, Negru, V, Ida, T, Jebelean, T, GENESIS S.A., LTS2 - EPFL, Ecole Polytechnique Fédérale de Lausanne (EPFL), Institut de Mathématiques de Marseille (I2M), Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU), Numerical Harmonic Analysis Group, Faculty of Mathematics (NuHAG), Universität Wien, Univ Vest Timisoara, IEEE Comp Soc, European Project: 255931,EC:FP7:ICT,FP7-ICT-2009-C,UNLOCX(2010), and Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
FOS: Computer and information sciences, Sound (cs.SD), Speech recognition, 010103 numerical & computational mathematics, 02 engineering and technology, Gabor transform, computer.software_genre, 01 natural sciences, Computer Science - Sound, Window function, Article, Chirped Gaussian window, Optimal window, Scale space implementation, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Discrete Gabor Transform, 0202 electrical engineering, electronic engineering, information engineering, 0101 mathematics, Audio signal processing, Mathematics, Audio signal, Short-time Fourier transform, 020206 networking & telecommunications, Optimal lattice, Time–frequency analysis, Audio analyzer, Algorithm, computer
Abstract

International audience; This article deals with the use of optimal lattice and optimal window in Discrete Gabor Transform computation. In the case of a generalized Gaussian window, extending earlier contributions, we introduce an additional local window adaptation technique for non-stationary signals. We illustrate our approach and the earlier one by addressing three time-frequency analysis problems to show the improvements achieved by the use of optimal lattice and window: close frequencies distinction, frequency estimation and SNR estimation. The results are presented, when possible, with real world audio signals.

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