Analytical Methodologies for the Control of Particle-Phase Polycyclic Aromatic Compounds from Diesel Engine Exhaust

International audienc

Linking initial soil bacterial diversity and polycyclic aromatic hydrocarbons (PAHs) degradation potential

International audienc

CirdoX: an On/Off-line Multisource Speech and Sound Analysis Software

International audienceVocal User Interfaces in domestic environments recently gained interest in the speech processing community. This interest is due to the opportunity of using it in the framework of Ambient Assisted Living both for home automation (vocal command) and for call for help in case of distress situations, i.e. after a fall. CIRDOX, which is a modular software, is able to analyse online the audio environment in a home, to extract the uttered sentences and then to process them thanks to an ASR module. Moreover, this system perfoms non-speech audio event classification; in this case, specific models must be trained. The software is designed to be modular and to process on-line the audio multichannel stream. Some exemples of studies in which CIRDOX was involved are described. They were operated in real environment, namely a Living lab environment. Keywords: audio and speech processing, natural language and multimodal interactions, Ambient Assisted Living (AAL)

Self-assembled monolayers of bisphosphonates: Influence of side chain steric hindrance

Bisphosphonates form self-assembled monolayers (SAMs) spontaneously on stainless steel, silicon, and titanium oxidized surfaces. We used contact angle measurements, atomic force microscopy, and X-ray reflectivity analysis to study the formation of SAMs on a model surface of ultraflat titanium (rms=0.2 nm). The results were extended to standard materials (mechanically polished titanium, stainless steel, and silicon) and showed that water-soluble bisphosphonic perfluoropolyether can easily form SAMs, with 100% surface coverage and a layer thickness of less than 3 nm. Hydrophobic (water contact angle >110° on stainless steel or titanium) and lipophobic (methylene iodide contact angle >105° on titanium) properties are discussed in terms of industrial applications

The CIRDO Corpus: Comprehensive Audio/Video Database of Domestic Falls of Elderly People

International audienceAmbient Assisted Living aims at enhancing the quality of life of older and disabled people at home thanks to Smart Homes. In particular, regarding elderly living alone at home, the detection of distress situation after a fall is very important to reassure this kind of population. However, many studies do not include tests in real settings, because data collection in this domain is very expensive and challenging and because of the few available data sets. The CIRDOcorpus is a dataset recorded in realistic conditions in DOMUS, a fully equipped Smart Home with microphones and home automation sensors, in which participants performed scenarios including real falls on a carpet and calls for help. These scenarios were elaborated thanks to a field study involving elderly persons. Experiments related in a first part to distress detection in real-time using audio and speech analysis and in a second part to fall detection using video analysis are presented. Results show the difficulty of the task. The database can be used as standardized database by researchers to evaluate and compare their systems for elderly person's assistance. Keywords: audio and video data set, multimodal corpus, natural language and multimodal interaction, Ambient Assisted Living (AAL), distress situation

Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows

Background: Predicting progression from a stage of Mild Cognitive Impairment to dementia is a major pursuit in current research. It is broadly accepted that cognition declines with a continuum between MCI and dementia. As such, cohorts of MCI patients are usually heterogeneous, containing patients at different stages of the neurodegenerative process. This hampers the prognostic task. Nevertheless, when learning prognostic models, most studies use the entire cohort of MCI patients regardless of their disease stages. In this paper, we propose a Time Windows approach to predict conversion to dementia, learning with patients stratified using time windows, thus fine-tuning the prognosis regarding the time to conversion. Methods: In the proposed Time Windows approach, we grouped patients based on the clinical information of whether they converted (converter MCI) or remained MCI (stable MCI) within a specific time window. We tested time windows of 2, 3, 4 and 5 years. We developed a prognostic model for each time window using clinical and neuropsychological data and compared this approach with the commonly used in the literature, where all patients are used to learn the models, named as First Last approach. This enables to move from the traditional question "Will a MCI patient convert to dementia somewhere in the future" to the question "Will a MCI patient convert to dementia in a specific time window". Results: The proposed Time Windows approach outperformed the First Last approach. The results showed that we can predict conversion to dementia as early as 5 years before the event with an AUC of 0.88 in the cross-validation set and 0.76 in an independent validation set. Conclusions: Prognostic models using time windows have higher performance when predicting progression from MCI to dementia, when compared to the prognostic approach commonly used in the literature. Furthermore, the proposed Time Windows approach is more relevant from a clinical point of view, predicting conversion within a temporal interval rather than sometime in the future and allowing clinicians to timely adjust treatments and clinical appointments.FCT under the Neuroclinomics2 project [PTDC/EEI-SII/1937/2014, SFRH/BD/95846/2013]; INESC-ID plurianual [UID/CEC/50021/2013]; LASIGE Research Unit [UID/CEC/00408/2013

Metabolic Syndrome and Risk for Incident Alzheimer's Disease or Vascular Dementia: The Three-City Study

OBJECTIVE—Associations between metabolic syndrome and its individual components with risk of incident dementia and its different subtypes are inconsistent

Electrode surface treatment and electrochemical impedance spectroscopy study on carbon/carbon supercapacitors

Power improvement in supercapacitors is mainly related to lowering the internal impedance. The real part of the impedance at a given frequency is called ESR (equivalent series resistance). Several contributions are included in the ESR: the electrolyte resistance (including the separator), the active material resistance (with both ionic and electronic parts) and the active material/current collector interface resistance. The first two contributions have been intensively described and studied by many authors. The first part of this paper is focused on the use of surface treatments as a way to decrease the active material/current collector impedance. Al current collector foils have been treated following a two-step procedure: electrochemical etching and sol-gel coating by a highly-covering, conducting carbonaceous material. It aims to increase the Al foil/active material surface contact leading to lower resistance. In a second part, carbon-carbon supercapacitor impedance is discussed in term of complex capacitance and complex power from electrochemical impedance spectroscopy data. This representation permits extraction of a relaxation time constant that provides important information on supercapacitor behaviour. The influence of carbon nanotubes addition on electrochemical performance of carbon/carbon supercapacitors has also been studied by electrochemical impedance spectroscopy

On Distant Speech Recognition for Home Automation

The official version of this draft is available at Springer via http://dx.doi.org/10.1007/978-3-319-16226-3_7International audienceIn the framework of Ambient Assisted Living, home automation may be a solution for helping elderly people living alone at home. This study is part of the Sweet-Home project which aims at developing a new home automation system based on voice command to improve support and well-being of people in loss of autonomy. The goal of the study is vocal order recognition with a focus on two aspects: distance speech recognition and sentence spotting. Several ASR techniques were evaluated on a realistic corpus acquired in a 4-room flat equipped with microphones set in the ceiling. This distant speech French corpus was recorded with 21 speakers who acted scenarios of activities of daily living. Techniques acting at the decoding stage, such as our novel approach called Driven Decoding Algorithm (DDA), gave better speech recognition results than the baseline and other approaches. This solution which uses the two best SNR channels and a priori knowledge (voice commands and distress sentences) has demonstrated an increase in recognition rate without introducing false alarms