Renseignement d'origine électromagnétique

Résumé

vignette|400px|Dupuy-de-Lôme, navire collecteur de renseignements d'origine électromagnétique de la Marine nationale française. Le renseignement d'origine électromagnétique ou ROEM (en anglais : signals intelligence ou SIGINT) est un renseignement dont les sources d'information sont des signaux électromagnétiques : communications utilisant les ondes (radio, satellitaire), émissions d'ondes faites par un radar ou par des instruments de télémesure. Le plus célèbre réseau ROEM est le système Echelon, développé principalement par des États anglo-saxons dans le cadre du traité UKUSA de 1946, et connu du grand public depuis les années 1990. Outre les écoutes téléphoniques, le ROEM comprend donc la surveillance des télégrammes, des télécopieurs, des courriers électroniques et autres sortes de communications électroniques, facilitant l'espionnage industriel et posant d'évidents problèmes de respect de la vie privée. Par extension, le ROEM désigne toutes les activités liées à la collecte et à

Source officielle

https://fr.wikipedia.org/wiki/Renseignement_d'origine_%C3%A9lectromagn%C3%A9tique

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Publications associées (25)

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In the last years, remote health monitoring is becoming an essential branch of health care with the rapid development of wearable sensors technology. To meet the demand of new more complex applications and ensuring adequate battery lifetime, wearable sensors have evolved into multicore systems with advanced power-saving capabilities and additional heterogeneous components. In this article, we present an approach that applies optimization and parallelization techniques uncovered by modern ultralow power (ULP) platforms in the SW layers with the goal of improving the mapping and reducing the energy consumption of biomedical applications. Additionally, we investigate the benefit of integrating domain-specific accelerators to further reduce the energy consumption of the most computationally expensive kernels. Using 30-s excerpts of signals from two public databases, we apply the proposed optimization techniques on well-known modules of biomedical benchmarks from the state-of-the-art and two complete applications. We observe speed-ups of 5.17x and energy savings of 41.6% for the multicore implementation using a cluster of 8 cores with respect to single-core wearable sensor designs when processing a standard 12-lead electrocardiogram (ECG) signal analysis. Additionally, we conclude that the minimum workload required to take advantage of parallelization for a heartbeat classifier corresponds to the processing of 3-lead ECG signals, with a speed-up of 2.96x and energy savings of 19.3%. Moreover, we observe additional energy savings of up to 7.75% and 16.8% by applying power management and memory scaling to the multicore implementation of the 3-lead beat classifier and 12-lead ECG analysis, respectively. Finally, by integrating hardware (HW) acceleration we observe overall energy savings of up to 51.3% for the 12-lead ECG analysis.

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

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Modular Design and Optimization of Biomedical Applications for Ultra-Low Power Heterogeneous Platforms

David Atienza Alonso, Luca Benini, Elisabetta De Giovanni, Benoît Walter Denkinger, Simone Machetti, Fabio Montagna, Miguel Peon Quiros

In the last years, remote health monitoring is becoming an essential branch of health care with the rapid development of wearable sensors technology. To meet the demand of new more complex applications and ensuring adequate battery lifetime, wearable sensors have evolved into multi-core systems with advanced power-saving capabilities and additional heterogeneous components. In this paper, we present an approach that applies optimization and parallelization techniques uncovered by modern ultra-low power platforms in the SW layers with the goal of improving the mapping and reducing the energy consumption of biomedical applications. Additionally, we investigate the benefit of integrating domain-specific accelerators to further reduce the energy consumption of the most computationally expensive kernels. Using 30-second excerpts of signals from two public databases, we apply the proposed optimization techniques on well-known modules of biomedical benchmarks from the state-of-the-art and two complete applications. We observe speed-ups of 5.17x and energy savings of 41.6% for the multi-core implementation using a cluster of 8 cores with respect to single-core wearable sensor designs when processing a standard 12-lead ECG signal analysis. Additionally, we conclude that the minimum workload required to take advantage of parallelization for a hearbeat classifier corresponds to the processing of 3-lead ECG signals, with a speed-up of 2.96x and energy savings of 19.3%. Moreover, we observe additional energy savings of up to 7.75% and 16.8% by applying power management and memory scaling to the multi-core implementation of the 3-lead beat classifier and 12-lead ECG analysis, respectively. Finally, by integrating hardware (HW) acceleration we observe overall energy savings of up to 51.3% for the 12-lead ECG analysis.

2020

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One of the greatest challenges in the design of modern wearable devices is energy efficiency. While data processing and communication have received a lot of attention from the industry and academia, leading to highly efficient microcontrollers and transmission devices, sensor data acquisition in medical devices is still based on a conservative paradigm that requires regular sampling at the Nyquist rate of the target signal. This requirement is usually excessive for signals that are typically sparse and highly non-stationary, leading to data overload and a waste of resources in the full processing pipeline. In this work we propose a new system to create event-based heart-rate analysis devices, including a novel algorithm for QRS detection that is able to process electrocardiogram signals acquired irregularly and much below the theoretically-required Nyquist rate. This technique allows us to drastically reduce the average sampling frequency of the signal and, hence, the energy needed to process it and extract the relevant information. We implemented both the proposed event-based algorithm and a state-of-the-art version based on regular sampling on an ultra-low power hardware platform, and the experimental results show that the event-based version reduces the energy consumption in runtime up to 15.6 times, while the detection performance is maintained at an average F1 score of 99.5%.

IEEE2020

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Modular Design and Optimization of Biomedical Applications for Ultra-Low Power Heterogeneous Platforms

David Atienza Alonso, Luca Benini, Elisabetta De Giovanni, Benoît Walter Denkinger, Simone Machetti, Fabio Montagna, Miguel Peon Quiros

In the last years, remote health monitoring is becoming an essential branch of health care with the rapid development of wearable sensors technology. To meet the demand of new more complex applications and ensuring adequate battery lifetime, wearable sensors have evolved into multi-core systems with advanced power-saving capabilities and additional heterogeneous components. In this paper, we present an approach that applies optimization and parallelization techniques uncovered by modern ultra-low power platforms in the SW layers with the goal of improving the mapping and reducing the energy consumption of biomedical applications. Additionally, we investigate the benefit of integrating domain-specific accelerators to further reduce the energy consumption of the most computationally expensive kernels. Using 30-second excerpts of signals from two public databases, we apply the proposed optimization techniques on well-known modules of biomedical benchmarks from the state-of-the-art and two complete applications. We observe speed-ups of 5.17x and energy savings of 41.6% for the multi-core implementation using a cluster of 8 cores with respect to single-core wearable sensor designs when processing a standard 12-lead ECG signal analysis. Additionally, we conclude that the minimum workload required to take advantage of parallelization for a hearbeat classifier corresponds to the processing of 3-lead ECG signals, with a speed-up of 2.96x and energy savings of 19.3%. Moreover, we observe additional energy savings of up to 7.75% and 16.8% by applying power management and memory scaling to the multi-core implementation of the 3-lead beat classifier and 12-lead ECG analysis, respectively. Finally, by integrating hardware (HW) acceleration we observe overall energy savings of up to 51.3% for the 12-lead ECG analysis.

2020