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315 results on '"GUILLAUD M"'

1. Performance of Interference Alignment in Clustered Wireless Ad Hoc Networks

Author

Tresch, R. and Guillaud, M.

Subjects
Computer Science - Information Theory
Abstract

Spatial interference alignment among a finite number of users is proposed as a technique to increase the probability of successful transmission in an interference limited clustered wireless ad hoc network. Using techniques from stochastic geometry, we build on the work of Ganti and Haenggi dealing with Poisson cluster processes with a fixed number of cluster points and provide a numerically integrable expression for the outage probability using an intra-cluster interference alignment strategy with multiplexing gain one. For a special network setting we derive a closed-form upper bound. We demonstrate significant performance gains compared to single-antenna systems without local cooperation., Comment: 5 pages

Published
2010

2. Maximum Entropy MIMO Wireless Channel Models

Author

Guillaud, M., Debbah, M., and Moustakas, A. L.

Subjects
Computer Science - Information Theory
Abstract

In this contribution, models of wireless channels are derived from the maximum entropy principle, for several cases where only limited information about the propagation environment is available. First, analytical models are derived for the cases where certain parameters (channel energy, average energy, spatial correlation matrix) are known deterministically. Frequently, these parameters are unknown (typically because the received energy or the spatial correlation varies with the user position), but still known to represent meaningful system characteristics. In these cases, analytical channel models are derived by assigning entropy-maximizing distributions to these parameters, and marginalizing them out. For the MIMO case with spatial correlation, we show that the distribution of the covariance matrices is conveniently handled through its eigenvalues. The entropy-maximizing distribution of the covariance matrix is shown to be a Wishart distribution. Furthermore, the corresponding probability density function of the channel matrix is shown to be described analytically by a function of the channel Frobenius norm. This technique can provide channel models incorporating the effect of shadow fading and spatial correlation between antennas without the need to assume explicit values for these parameters. The results are compared in terms of mutual information to the classical i.i.d. Gaussian model., Comment: Submitted to IEEE Trans. Information Theory Dec. 2006; 16 pages, 4 figures

Published
2006

9. 6G white paper on localization and sensing

Author

de Lima, C. (Carlos), Belot, D. (Didier), Berkvens, R. (Rafael), Bourdoux, A. (Andre), Dardari, D. (Davide), Guillaud, M. (Maxime), Isomursu, M. (Minna), Lohan, E.-S. (Elena-Simona), Miao, Y. (Yang), Barreto, A. N. (Andre Noll), Aziz, M. R. (Muhammad Reza Kahar), Saloranta, J. (Jani), Sanguanpuak, T. (Tachporn), Sarieddeen, H. (Hadi), Seco-Granados, G. (Gonzalo), Suutala, J. (Jaakko), Svensson, T. (Tommy), Valkama, M. (Mikko), Wymeersch, H. (Henk), and van Liempd, B. (Barend)

Abstract

Executive Summary This white paper explores future localization and sensing opportunities for beyond fifth generation (5G) wireless communication systems by identifying key technology enablers and discussing their underlying challenges, implementation issues, and identifying potential solutions. In addition, we present exciting new opportunities for localization and sensing applications, which will disrupt traditional design principles and revolutionize the way we live, interact with our environment, and do business. In contrast to 5G and earlier generations, localization and sensing will be built-in from the outset to both cope with specific applications and use cases, and to support flexible and seamless connectivity. Following the trend initiated in the 5G new radio (NR) systems, sixth generation (6G) will continue to develop towards even higher frequency ranges, wider bandwidths, and massive antenna arrays. In turn, this will enable sensing solutions with very fine range, Doppler and angular resolutions, as well as localization to cm-level degree of accuracy. Moreover, new materials, device types, and reconfigurable surfaces will allow network operators to reshape and control the electromagnetic response of the environment. At the same time, machine learning and artificial intelligence will leverage the unprecedented availability of data and computing resources to tackle the biggest and hardest problems in wireless communication systems. 6G systems will be truly intelligent wireless systems that will not only provide ubiquitous communication but also empower high accuracy localization and high-resolution sensing services. They will become the catalyst for this revolution by bringing about a unique new set of features and service capabilities, where localization and sensing will coexist with communication, continuously sharing the available resources in time, frequency and space. Applications such as THz imaging and spectroscopy have the potential to provide continuous, real-time physiological information via dynamic, non-invasive, contactless measurements for future digital health technologies. 6G simultaneous localization and mapping (SLAM) methods will not only enable advanced cross reality (XR) applications but also enhance the navigation of autonomous objects such as vehicles and drones. In convergent 6G radar and communication systems, both passive and active radars will simultaneously use and share information, to provide a rich and accurate virtual image of the environment. In 6G, intelligent context-aware networks will be capable of exploiting localization and sensing information to optimize deployment, operation, and energy usage with no or limited human intervention. This white paper concludes by highlighting foundational research challenges, as well as implications and opportunities related to privacy, security, and trust. Addressing these challenges will undoubtedly require an interdisciplinary and concerted effort from the research community.

Published
2020

10. Convergent communication, sensing and localization in 6G systems:an overview of technologies, opportunities and challenges

Author

Lima, C. D. (Carlos De), Belot, D. (Didier), Berkvens, R. (Rafael), Bourdoux, A. (André), Dardari, D. (Davide), Guillaud, M. (Maxime), Isomursu, M. (Minna), Lohan, E.-S. (Elena-Simona), Miao, Y. (Yang), Barreto, A. N. (Andre Noll), Aziz, M. R. (Muhammad Reza Kahar), Saloranta, J. (Jani), Sanguanpuak, T. (Tachporn), Sarieddeen, H. (Hadi), Seco-Granados, G. (Gonzalo), Suutala, J. (Jaakko), Svensson, T. (Tommy), Valkama, M. (Mikko), van Liempd, B. (Barend), Wymeersch, H. (Henk), Lima, C. D. (Carlos De), Belot, D. (Didier), Berkvens, R. (Rafael), Bourdoux, A. (André), Dardari, D. (Davide), Guillaud, M. (Maxime), Isomursu, M. (Minna), Lohan, E.-S. (Elena-Simona), Miao, Y. (Yang), Barreto, A. N. (Andre Noll), Aziz, M. R. (Muhammad Reza Kahar), Saloranta, J. (Jani), Sanguanpuak, T. (Tachporn), Sarieddeen, H. (Hadi), Seco-Granados, G. (Gonzalo), Suutala, J. (Jaakko), Svensson, T. (Tommy), Valkama, M. (Mikko), van Liempd, B. (Barend), and Wymeersch, H. (Henk)

Abstract

Herein, we focus on convergent 6G communication, localization and sensing systems by identifying key technology enablers, discussing their underlying challenges, implementation issues, and recommending potential solutions. Moreover, we discuss exciting new opportunities for integrated localization and sensing applications, which will disrupt traditional design principles and revolutionize the way we live, interact with our environment, and do business. Regarding potential enabling technologies, 6G will continue to develop towards even higher frequency ranges, wider bandwidths, and massive antenna arrays. In turn, this will enable sensing solutions with very fine range, Doppler, and angular resolutions, as well as localization to cm-level degree of accuracy. Besides, new materials, device types, and reconfigurable surfaces will allow network operators to reshape and control the electromagnetic response of the environment. At the same time, machine learning and artificial intelligence will leverage the unprecedented availability of data and computing resources to tackle the biggest and hardest problems in wireless communication systems. As a result, 6G will be truly intelligent wireless systems that will provide not only ubiquitous communication but also empower high accuracy localization and high-resolution sensing services. They will become the catalyst for this revolution by bringing about a unique new set of features and service capabilities, where localization and sensing will coexist with communication, continuously sharing the available resources in time, frequency, and space. This work concludes by highlighting foundational research challenges, as well as implications and opportunities related to privacy, security, and trust.

Published
2021

11. A Machine-Learning Model to Predict Axillary Node Positivity from Clinical Characteristics and Large-Scale DNA Organization Features in Breast Cancer Biopsy Specimens.

Author

Alabi, N., Guillaud, M., Hallani, S. El, Inaba, F., and Nichol, A.

Subjects
*SENTINEL lymph node biopsy, *LYMPHATIC metastasis, *RECEIVER operating characteristic curves, *BREAST biopsy, *NUCLEAR DNA
Abstract

Breast cancer is the leading cause of cancer-related fatalities among women globally. Although sentinel lymph node biopsy (SLNB) is used routinely for clinically node-negative patients, over 60% lack axillary lymph node metastases, so SLNB upstages only a minority. Consequently, there has been ongoing research into non-invasive methods, such as ultrasound, MRI, and PET, which are increasingly used in staging axillary lymph nodes. Large-scale DNA organization (LDO) analysis involves measuring various characteristics of nuclear DNA organization, including size, shape, and chromatin organization texture. LDO analysis has shown correlations with diverse clinical outcomes in multiple cancers, including survival in breast cancer patients. We hypothesized that a machine learning-based tool to predict axillary lymph node metastases using LDO would perform better than nomograms based on routine clinical features. This study utilized a training cohort of tissue microarray (TMA) core samples from 347 breast cancer patients and an external validation cohort from a different hospital comprising TMA core samples from 157 patients. The TMA slides underwent staining with Feulgen-thionin, followed by automated segmentation of cell nuclei and selection of intact, in-focus nuclei. LDO features of each nucleus were calculated and input into a Random Forest model to distinguish between nuclei from patients with and without lymph node metastases. The predictions for each patient were amalgamated into a score and combined with clinical features in the Random Forest model to classify their lymph node metastasis status. During model training, several LDO features, such as nuclear radius and intensity, were identified as crucial in predicting axillary lymph node metastases. The LDO features emerged as the most important variables in the Random Forest model trained on LDO and clinical features. On the external validation set, the area under the receiver operating characteristic curve (AUC) for the model trained using LDO features alone was 0.711, and the AUC for the model trained on clinical features alone was 0.741. The AUC of our final model that used both clinical and LDO features was 0.775, outperforming the established Memorial Sloan Kettering and MD Anderson nomograms for node positivity on the same data. Our model identified several key nuclear features that can predict lymph node metastasis status from a biopsy. Our study presents the first histology-based, explainable machine-learning model capable of predicting axillary lymph node metastases. [ABSTRACT FROM AUTHOR]

Published
2024
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19. Endocardite et incitation à la surveillance bucco-dentaire

Author

Blatier J-F, Chabert R, Nakache P, and Guillaud M

Abstract

Objectif : L’Union regionale des caisses d’Assurance maladie de Rhone-Alpes a conduit une action de promotion de la surveillance bucco-dentaire chez les malades a risque d’endocardite infectieuse (MREI). L’objectif etait d’evaluer l’efficacite de la diffusion de documents d’information aux MREI et aux medecins traitants avec audit-retour.Methodes : Les beneficiaires de l’Assurance maladie âges de 5 a 70 ans, exoneres du ticket moderateur en 2004 pour une maladie a risque d’endocardite infectieuse, ont ete repartis aleatoirement en fonction de leur arrondissement de residence en un groupe temoin et un groupe intervention. Differents documents d’information ont ete adresses aux MREI du groupe intervention ainsi qu’a leur medecin traitant. La surveillance buccodentaire a ete etudiee a partir des prestations remboursees pendant un an.Resultats : La surveillance biannuelle concernait 13,6 % de l’effectif du groupe intervention contre 14,1 % du groupe temoin (p unilateral = 0,44). Apres ajustement, l’information n’etait pas significativement associee a la surveillance biannuelle (OR = 0,95 ; IC 95 % = [0,55 ; 1,65]).De meme, l’information n’etait pas significativement associee a la surveillance annuelle (OR = 1,22 ; IC 95 % = [0,90 ; 1,65]) alors que la mention de surveillance dentaire sur le protocole de soins l’etait tres fortement (OR = 4,30 ; IC 95 % = [1,36 ; 13,54]). Les MREI a haut risque etaient moins bien surveilles (OR = 0,70 ; IC 95 % = [0,53 ; 0,94]).Conclusion : Les actions de prevention devraient inciter a faire mentionner la surveillance sur le protocole de soins, et a privilegier les MREI a haut risque.Prat Organ Soins 2010;41(1):23-32

Published
2010
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32. Fluorescence Visualization and Imaging: A Tool for Improving Patient Outcomes for Oral and (Hopefully) Other Cancers

Author

MacAulay, Calum, primary, Poh, Catherine, additional, Durham, J. Scott, additional, Zhang, Lewei, additional, Lam, Sylvia F., additional, Rosin, Miriam P., additional, Guillaud, M, additional, McAlpine, J, additional, Ehlen, T, additional, Miller, D, additional, van Niekerk, D, additional, Follen, M, additional, and Lane, Pierre M., additional

Published
2013
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