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Record Nr. |
UNINA9910495693003321 |
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Autore |
Albert Jean-Pierre |
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Titolo |
Désir n’a repos : Hommage à Danielle Bohler / / Florence Bouchet, Danièle James-Raoul |
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Pubbl/distr/stampa |
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Pessac, : Presses Universitaires de Bordeaux, 2020 |
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ISBN |
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Descrizione fisica |
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1 online resource (456 p.) |
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Collana |
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Altri autori (Persone) |
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ArchibaldElizabeth |
Bedos-RezakBrigitte Miriam |
BelmontNicole |
BouchetFlorence |
CecchettoCéline |
ChardenetVirginie |
Colombo TimelliMaria |
DemartiniDominique |
DemaulesMireille |
DoudetEstelle |
DulacLiliane |
Estripeaut-BourjacMarie |
Ferlampin-AcherChristine |
FritzJean-Marie |
GrosselMarie-Geneviève |
James-RaoulDanièle |
JeayMadeleine |
KuonPeter |
Lecuppre-DesjardinÉlodie |
LefèvreSylvie |
Mathey-MailleLaurence |
Milland-BoveBénédicte |
Moulinier-BrogiLaurence |
NinanClaire Le |
PastoureauMichel |
PeyletGérard |
RenoChristine |
RichardsJeffrey |
RuheDoris |
SultanAgathe |
Vine DurlingNancy |
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Soggetti |
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Anthropology |
anthropologie |
imaginaire |
histoire |
représentation |
symbolique |
féminin |
Moyen Âge |
norme |
féminité |
femme |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Sommario/riassunto |
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Ce volume Desir n’a repos, offert à Danielle Bohler par ses amis, collègues et anciens étudiants, propose une trentaine d’articles organisés selon trois champs de recherches universitaires au carrefour des disciplines, dans lesquels Danielle Bohler s’est investie et s’est illustrée, avec prédilection mais sans exclusive, tout au long de sa carrière à la Sorbonne Nouvelle-Paris 3, puis Michel de Montaigne Bordeaux 3 : imaginaire et symbolique ; normes, histoire et anthropologie ; femmes, féminin et féminité. Derrière l’hommage à une carrière bien remplie et le témoignage d’amitié à une collègue qui a déployé une chaleureuse énergie au service des études médiévales, cet ouvrage veut être un état des lieux, un bilan d’étape et une ouverture sur les recherches menées actuellement sur le monde du Moyen Âge, ses mentalités et ses représentations, ses gens et ses œuvres, dans leur richesse, leur mouvance et leur diversité, sans cesse renouvelées. |
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2. |
Record Nr. |
UNINA9910566462503321 |
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Autore |
Abbod Maysam |
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Titolo |
Advanced Signal Processing in Wearable Sensors for Health Monitoring |
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Pubbl/distr/stampa |
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Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
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Descrizione fisica |
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1 online resource (206 p.) |
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Soggetti |
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History of engineering & technology |
Technology: general issues |
History of engineering and technology |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Sommario/riassunto |
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Smart, wearables devices on a miniature scale are becoming increasingly widely available, typically in the form of smart watches and other connected devices. Consequently, devices to assist in measurements such as electroencephalography (EEG), electrocardiogram (ECG), electromyography (EMG), blood pressure (BP), photoplethysmography (PPG), heart rhythm, respiration rate, apnoea, and motion detection are becoming more available, and play a significant role in healthcare monitoring. The industry is placing great emphasis on making these devices and technologies available on smart devices such as phones and watches. Such measurements are clinically and scientifically useful for real-time monitoring, long-term care, and diagnosis and therapeutic techniques. However, a pertaining issue is that recorded data are usually noisy, contain many artefacts, and are affected by external factors such as movements and physical conditions. In order to obtain accurate and meaningful indicators, the signal has to be processed and conditioned such that the measurements are accurate and free from noise and disturbances. In this context, many researchers have utilized recent technological advances in wearable sensors and signal processing to develop smart and accurate wearable devices for clinical applications. The processing and analysis of physiological signals is a key issue for these smart |
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wearable devices. Consequently, ongoing work in this field of study includes research on filtration, quality checking, signal transformation and decomposition, feature extraction and, most recently, machine learning-based methods. |
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