Search results for "Neural"

showing 10 items of 2783 documents

Acoustic characterization of Silica aerogel clamped plates for perfect absorption purpose

2017

International audience; Silica aerogel has been widely studied as bulk material for its extremely low density and thermal conductivity. Plates or membranes made of this extremely soft materials exhibits interesting properties for sound absorption. A novel signal processing method for the characterization of an acoustic metamaterial made of silica aerogel clamped plates is presented. The acoustic impedance of a silica aerogel clamped plate is derived from the elastic theory for the flexural waves, while the transfer matrix method is used to model reflection and transmission coefficients of a single plate. Experimental results are obtained by using an acoustic impedance tube. The difference b…

Absorption (acoustics)Materials scienceAcoustics and UltrasonicsPhysics::Instrumentation and DetectorsTransfer-matrix method (optics)Physics::Optics01 natural sciencesCondensed Matter::Disordered Systems and Neural Networks03 medical and health sciences0302 clinical medicineThermal conductivityArts and Humanities (miscellaneous)0103 physical sciencesReflection coefficientComposite material030223 otorhinolaryngology010301 acousticsComputingMilieux_MISCELLANEOUSMetamaterialAerogel[PHYS.MECA]Physics [physics]/Mechanics [physics][PHYS.MECA.ACOU]Physics [physics]/Mechanics [physics]/Acoustics [physics.class-ph]Condensed Matter::Soft Condensed MatterReflection (mathematics)[PHYS.MECA] Physics [physics]/Mechanics [physics]Acoustic impedance[PHYS.MECA.ACOU] Physics [physics]/Mechanics [physics]/Acoustics [physics.class-ph]
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OH-related Infrared Absorption Bands in Oxide Glasses

2005

We report the infrared activity, in the spectral region of the OH stretching modes, of different composite silicate glasses whose chemical composition is established by X-ray fluorescence measurements. The analysis of the absorption line profiles is made in terms of different spectral contributions, Gaussian in shape. The comparison with analogous spectra obtained in vitreous silica samples with impurity concentrations < 100 part per million moles is evidence of the effects of the different oxides on the vibrational properties of the OH groups. In particular, for oxide glasses a red shift of the composite band at about 3670 cm(-1), assigned to the OH stretching modes of free Si-OH groups an…

Absorption spectroscopyInfraredFTIR AbsorptionOxide glasseOxideAnalytical chemistryX-ray fluorescenceInfrared spectroscopyCondensed Matter PhysicsCondensed Matter::Disordered Systems and Neural NetworksSylanol groupsSilicateSpectral lineSettore FIS/07 - Fisica Applicata(Beni Culturali Ambientali Biol.e Medicin)Settore FIS/03 - Fisica Della MateriaElectronic Optical and Magnetic Materialschemistry.chemical_compoundchemistryImpurityHydroxyl groupFTIR spectroscopy.Materials ChemistryCeramics and Compositessilicate glasse
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Combining gestures and vocalizations to imitate sounds

2015

International audience; Communicating about sounds is a difficult task without a technical language, and naïve speakers often rely on different kinds of non-linguistic vocalizations and body gestures (Lemaitre et al. 2014). Previous work has independently studied how effectively people describe sounds with gestures or vocalizations (Caramiaux, 2014, Lemaitre and Rocchesso, 2014). However, speech communication studies suggest a more intimate link between the two processes (Kendon, 2004). Our study thus focused on the combination of manual gestures and non-speech vocalizations in the communication of sounds. We first collected a large database of vocal and gestural imitations of a variety of …

Acoustics and UltrasonicsComputer scienceInformationSystems_INFORMATIONINTERFACESANDPRESENTATION(e.g.HCI)Speech recognition02 engineering and technologyRepresentation (arts)[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing[INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE][INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]Loudness[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI][SCCO]Cognitive science0202 electrical engineering electronic engineering information engineering[ INFO.INFO-NE ] Computer Science [cs]/Neural and Evolutionary Computing [cs.NE]050107 human factorsComputingMilieux_MISCELLANEOUSSound (medical instrument)05 social sciences[ SHS.ANTHRO-SE ] Humanities and Social Sciences/Social Anthropology and ethnology[INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA][ SCCO.COMP ] Cognitive science/Computer science[SCCO.PSYC] Cognitive science/Psychology[INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD][ SCCO.NEUR ] Cognitive science/Neuroscience[SCCO.PSYC]Cognitive science/Psychology[ INFO.EIAH ] Computer Science [cs]/Technology for Human Learning[ INFO.INFO-MA ] Computer Science [cs]/Multiagent Systems [cs.MA][INFO.EIAH]Computer Science [cs]/Technology for Human Learning[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingGesture[ SHS.MUSIQ ] Humanities and Social Sciences/Musicology and performing artsAcoustics[SCCO.COMP]Cognitive science/Computer scienceArts and Humanities (miscellaneous)[ INFO.INFO-HC ] Computer Science [cs]/Human-Computer Interaction [cs.HC]0501 psychology and cognitive sciences[ INFO.INFO-CL ] Computer Science [cs]/Computation and Language [cs.CL][INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC]Set (psychology)[ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI][SPI.ACOU]Engineering Sciences [physics]/Acoustics [physics.class-ph][SPI.ACOU] Engineering Sciences [physics]/Acoustics [physics.class-ph][SHS.MUSIQ]Humanities and Social Sciences/Musicology and performing arts[ INFO.INFO-ET ] Computer Science [cs]/Emerging Technologies [cs.ET][SCCO.NEUR]Cognitive science/Neuroscience020207 software engineering[SHS.ANTHRO-SE]Humanities and Social Sciences/Social Anthropology and ethnologyVariety (linguistics)loudness[INFO.INFO-ET]Computer Science [cs]/Emerging Technologies [cs.ET]Noise (video)[ INFO.INFO-SD ] Computer Science [cs]/Sound [cs.SD]
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Comparing identification of vocal imitations and computational sketches of everyday sounds

2016

International audience; Sounds are notably difficult to describe. It is thus not surprising that human speakers often use many imitative vocalizations to communicate about sounds. In practice,vocal imitations of non-speech everyday sounds (e.g. the sound of a car passing by) arevery effective: listeners identify sounds better with vocal imitations than with verbal descriptions, despite the fact that vocal imitations are often inaccurate, constrained by the human vocal apparatus. The present study investigated the semantic representations evoked by vocal imitations by experimentally quantifying how well listeners could match sounds to category labels. Itcompared two different types of sounds…

Acoustics and UltrasonicsComputer science[ SHS.MUSIQ ] Humanities and Social Sciences/Musicology and performing artsSpeech recognitionAcoustics[SCCO.COMP]Cognitive science/Computer science[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing[INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE][INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL][INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI][SPI]Engineering Sciences [physics][SCCO]Cognitive scienceArts and Humanities (miscellaneous)[ INFO.INFO-HC ] Computer Science [cs]/Human-Computer Interaction [cs.HC][ INFO.INFO-CL ] Computer Science [cs]/Computation and Language [cs.CL][INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC][ INFO.INFO-NE ] Computer Science [cs]/Neural and Evolutionary Computing [cs.NE][ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]ComputingMilieux_MISCELLANEOUSSound (medical instrument)[ INFO.INFO-ET ] Computer Science [cs]/Emerging Technologies [cs.ET][SHS.MUSIQ]Humanities and Social Sciences/Musicology and performing arts[SCCO.NEUR]Cognitive science/Neuroscience[SHS.ANTHRO-SE]Humanities and Social Sciences/Social Anthropology and ethnologyIdentification (information)[ SHS.ANTHRO-SE ] Humanities and Social Sciences/Social Anthropology and ethnology[INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA][ SCCO.COMP ] Cognitive science/Computer science[ SCCO.NEUR ] Cognitive science/Neuroscience[INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD][ INFO.EIAH ] Computer Science [cs]/Technology for Human Learning[ INFO.INFO-MA ] Computer Science [cs]/Multiagent Systems [cs.MA][INFO.INFO-ET]Computer Science [cs]/Emerging Technologies [cs.ET][INFO.EIAH]Computer Science [cs]/Technology for Human Learning[ INFO.INFO-SD ] Computer Science [cs]/Sound [cs.SD][SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
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Mini-COVIDNet: Efficient Lightweight Deep Neural Network for Ultrasound Based Point-of-Care Detection of COVID-19

2021

Lung ultrasound (US) imaging has the potential to be an effective point-of-care test for detection of COVID-19, due to its ease of operation with minimal personal protection equipment along with easy disinfection. The current state-of-the-art deep learning models for detection of COVID-19 are heavy models that may not be easy to deploy in commonly utilized mobile platforms in point-of-care testing. In this work, we develop a lightweight mobile friendly efficient deep learning model for detection of COVID-19 using lung US images. Three different classes including COVID-19, pneumonia, and healthy were included in this task. The developed network, named as Mini-COVIDNet, was bench-marked with …

Acoustics and UltrasonicsCoronavirus disease 2019 (COVID-19)Computer sciencePoint-of-Care SystemsLatency (audio)detectionlung ultrasound (US) imaging01 natural sciences0103 physical sciencesImage Interpretation Computer-AssistedComputer-Assisted/methodsHumansElectrical and Electronic Engineering010301 acousticsInstrumentationImage InterpretationPoint of careUltrasonographyArtificial neural networkbusiness.industrySARS-CoV-2Deep learningImage Interpretation Computer-Assisted/methodsVDP::Technology: 500COVID-19deep learningUltrasonography/methodsLung ultrasoundCoronavirusTask (computing)point-of-care testingSoftware deploymentEmbedded systemCOVID-19/diagnostic imagingArtificial intelligencebusiness
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An ASSOM neural network to represent actions performed by an autonomous agent

1997

An ASSOM neural network to describe the action performed by an autonomous reactive agent is proposed. The neural network receives in input the sequences of data acquired by the agent internal sensors and it classifies them by generating the corresponding symbolic assertions. Experimental results performed on a RWI B12 autonomous robot are reported.

Action (philosophy)Artificial neural networkComputer sciencebusiness.industryControl systemAutonomous agentRoboticsComputingMethodologies_GENERALArtificial intelligenceAutonomous robotbusiness
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Dropping out of school as a meaningful action for adolescents with social, emotional and behavioural difficulties

2013

This study examines and discusses dropping out of school related to adolescents with social, emotional and behavioural difficulties (SEBD). It is based on in-depth interviews of 10 adolescents between the ages of 16 and 20, three girls and two boys with internalised problems, and two girls and three boys with extroverted behavioural problems. Given this group of students' challenges at school, the aim of this paper is to explore the narratives of this adolescent group as they relate to the significance they attach to their dropout behaviour. An additional objective is to draw attention to what these findings are likely to mean for implementing preventive practices in school. Results show th…

Action (philosophy)Interpersonal competenceeducationSocial emotional learningAdolescent groupContext (language use)NarrativeOut of schoolPsychologyDropout (neural networks)EducationDevelopmental psychologyJournal of Research in Special Educational Needs
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Rapid developmental switch in the mechanisms driving early cortical columnar networks

2006

The immature cerebral cortex self-organizes into local neuronal clusters long before it is activated by patterned sensory inputs. In the cortical anlage of newborn mammals, neurons coassemble through electrical or chemical synapses either spontaneously or by activation of transmitter-gated receptors. The neuronal network and the cellular mechanisms underlying this cortical self-organization process during early development are not completely understood. Here we show in an intact in vitro preparation of the immature mouse cerebral cortex that neurons are functionally coupled in local clusters by means of propagating network oscillations in the beta frequency range. In the newborn mouse, this…

Action PotentialsSensory systemBiologyReceptors N-Methyl-D-AspartateSynapseMiceSubplatemedicineBiological neural networkAnimalsReceptorNeuronsMultidisciplinaryGap junctionGap JunctionsSomatosensory CortexElectrophysiologyMice Inbred C57BLElectrophysiologymedicine.anatomical_structureBiochemistryAnimals NewbornCerebral cortexSynapsesNMDA receptorCarbacholNeuronCortical columnNeurosciencee-Neuroforum
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A New Adaptive Neural Harmonic Compensator for Inverter Fed Distributed Generation

2004

This paper deals with the command of inverters in DG (distributed generation) systems by use of linear neural networks in such a way that, with a slight upgrade of their control software, they can be used also to compensate for the harmonic distortion in the node where they are connected (local compensation), that is in the in the point of common coupling (PCC). To this purpose a neural estimator based on linear neurons (ADALINEs) has been developed which is able to act as a selective noise cancellers for each harmonic of the node voltage. The use of linear neurons permits the drawbacks of classical neural networks to be overcome and moreover the neural estimator is easy to implement, thus …

Active filtersseries activePower qualityharmonic compensationneural networks.Distributed generation
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Is the nonREM–REM sleep cycle reset by forced awakenings from REM sleep?

2002

In selective REM sleep deprivation (SRSD), the occurrence of stage REM is repeatedly interrupted by short awakenings. Typically, the interventions aggregate in clusters resembling the REM episodes in undisturbed sleep. This salient phenomenon can easily be explained if the nonREM–REM sleep process is continued during the periods of forced wakefulness. However, earlier studies have alternatively suggested that awakenings from sleep might rather discontinue and reset the ultradian process. Theoretically, the two explanations predict a different distribution of REM episode duration. We evaluated 117 SRSD treatment nights recorded from 14 depressive inpatients receiving low dosages of Trimipram…

Activity CyclesMaleSelective REM sleep deprivationPolysomnographyAudiologyBehavioral NeuroscienceNIGHTSleep onset REM episodeDEPRIVATIONSlow-wave sleepmedia_commonDEPRESSIVE PATIENTSmedicine.diagnostic_testDepressionmusculoskeletal neural and ocular physiologyTRIMIPRAMINEMiddle AgedAntidepressive AgentsAnesthesiaLATENCIESFemaleWakefulnessArousalPsychologyAlgorithmspsychological phenomena and processesmedicine.drugVigilance (psychology)Adultmedicine.medical_specialtyREM episodePolysomnographymedia_common.quotation_subjectRapid eye movement sleepSleep REMExperimental and Cognitive PsychologyNon-rapid eye movement sleepmental disordersmedicineHumansWakefulnessMODULATIONUltradian rhythmINTERRUPTIONARTIFICIAL NEURAL NETWORKSRECOGNITIONTrimipramineUltradian processSleep cycleSleepEYE-MOVEMENT SLEEPPhysiology &amp; Behavior
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