Acoustic signatures of communicative dimensions in codified mother-infant interactions

2021 ◽  
Vol 150 (6) ◽  
pp. 4429-4437
Author(s):  
Simone Falk ◽  
Nicolas Audibert
Keyword(s):  
2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Anas Alfarsi ◽  
Céline Caillet ◽  
Garry Fawbert ◽  
Simon Lawrence ◽  
Jacob Krüse ◽  
...  

AbstractThe trade in falsified medicine has increased significantly and it is estimated that global falsified sales have reached $100 billion in 2020. The EU Falsified Medicines Directive states that falsified medicines do not only reach patients through illegal routes but also via the legal supply chain. Falsified medicines can contain harmful ingredients. They can also contain too little or too much active ingredient or no active ingredient at all. BARDS (Broadband Acoustic Resonance Dissolution Spectroscopy) harnesses an acoustic phenomenon associated with the dissolution of a sample (tablet or powder). The resulting acoustic spectrum is unique and intrinsic to the sample and can be used as an identifier or signature profile. BARDS was evaluated in this study to determine whether a product is falsified or genuine in a rapid manner and at lower cost than many existing technologies. A range of genuine and falsified medicines, including falsified antimalarial tablets from south-east Asia, were tested, and compared to their counterpart genuine products. Significant differences between genuine and falsified doses were found in their acoustic signatures as they disintegrate and dissolve. Principal component analysis was employed to differentiate between the genuine and falsified medicines. This demonstrates that the tablets and capsules included here have intrinsic acoustic signatures which could be used to screen the quality of medicines.


Author(s):  
D.L. Patel ◽  
S.I. Bloch ◽  
P.A. Dayton ◽  
K.V. Ferrara

Author(s):  
Chris Dawson ◽  
Stuart Inkpen ◽  
Chris Nolan ◽  
David Bonnell

Many different approaches have been adopted for identifying leaks in pipelines. Leak detection systems, however, generally suffer from a number of difficulties and limitations. For existing and new pipelines, these inevitably force significant trade-offs to be made between detection accuracy, operational range, responsiveness, deployment cost, system reliability, and overall effectiveness. Existing leak detection systems frequently rely on the measurement of secondary effects such as temperature changes, acoustic signatures or flow differences to infer the existence of a leak. This paper presents an alternative approach to leak detection employing electromagnetic measurements of the material in the vicinity of the pipeline that can potentially overcome some of the difficulties encountered with existing approaches. This sensing technique makes direct measurements of the material near the pipeline resulting in reliable detection and minimal risk of false alarms. The technology has been used successfully in other industries to make critical measurements of materials under challenging circumstances. A number of prototype sensors were constructed using this technology and they were tested by an independent research laboratory. The test results show that sensors based on this technique exhibit a strong capability to detect oil, and to distinguish oil from water (a key challenge with in-situ sensors).


2011 ◽  
Vol 324 ◽  
pp. 93-96 ◽  
Author(s):  
Amel Gacem ◽  
A. Doghmane ◽  
Z. Hadjoub

The determination of the characteristics and properties of thin films deposited on substrates is necessary in any device application in various fields. Adequate mechanical properties are highly required for the majority of surface waves and semiconductor devices. In this context, modelling the ultrasonic-material interaction, we present results of simulation curves of acoustic signatures for multiple thin film/substrate combinations. The results obtained on several structures (Al, SiO2, ZnO, Cu, AlN, SiC and Cr)/(Al2O3, Si, Cu or Quartz) showed a velocity dispersion of the Rayleigh wave as a function of layer thickness. The development of a theoretical calculation model based on the acoustic behaviour of these structures has enabled us to quantify the dispersive evolution (positive and negative) density. Thus, we have established a universal relationship describing the density-thickness variation. In addition, networks of dispersion curves, representing the evolution of elasticity modulus (Young and shear), were determined. These charts can be used to extract the influence of thickness of layers on the variation of elastic constants


Geophysics ◽  
2008 ◽  
Vol 73 (1) ◽  
pp. E15-E33 ◽  
Author(s):  
Andrey Bakulin ◽  
Alexander Sidorov ◽  
Boris Kashtan ◽  
Mikko Jaaskelainen

Deepwater production is challenged by well underperformance issues that are hard to diagnose early on and expensive to deal with later. Problems are amplified by reliance on a few complex wells with sophisticated sand-control media. New downhole data are required for better understanding and prevention of production impairment. We introduce real-time completion monitoring (RTCM), a new nonintrusive surveillance method that uses acoustic signals sent via the fluid column to identify permeability impairment in sand-screened completions. The signals are carried by tube waves that move borehole fluid back and forth radially across the completion layers. Such tube waves are capable of instant testing of the presence or absence of fluid communication across the completion and are sensitive to changes occurring in sand screens, gravel sand, perforations, and possibly in the reservoir. The part of the completion that has different impairment from its neighbors will carry tube waves with modified signatures (velocity, attenuation) and will produce a reflection from the boundary where impairment changes. We conduct a laboratory experiment with a model of a completed horizontal borehole and focus on effects of sand-screen permeability on transmitted and reflected acoustic signatures. These new findings form the basis of an RTCM method that can be thought of as “miniaturized” 4D seismic and as a “permanent log” in an individual wellbore. We present experiments with a fiber-optic acoustic system that suggest a nonintrusive way to install downhole sensors on the pipe in realistic completions and thus implement real-time surveillance with RTCM.


Author(s):  
Brian Skoglind ◽  
Travis Roberts ◽  
Sourabh Karmakar ◽  
Cameron Turner ◽  
Laine Mears

Abstract Electrical connections in consumer products are typically made manually rather than through automated assembly systems due to the high variety of connector types and connector positions, and the soft flexible nature of their structures. Manual connections are prone to failure through missed or improper connections in the assembly process and can lead to unexpected downtime and expensive rework. Past approaches for registering connection success such as vision verification or Augmented Reality have shown limited ability to verify correct connection state. However, the feasibility of an acoustic-based verification system for electrical connector confirmation has not been extensively researched. One of the major problems preventing acoustic based verification in a manufacturing or assembly environment is the typically low signal to noise ratio (SNR) between the sound of an electrical connection and the diverse soundscape of the plant. In this study, a physical means of background noise mitigation and signature amplification are investigated in order to increase the SNR between the electrical connection and the plant soundscape in order to improve detection. The concept is that an increase in the SNR will lead to an improvement in the accuracy and robustness of an acoustic event detection and classification system. Digital filtering has been used in the past to deal with low SNRs, however, it runs the risk of filtering out potential important features for classification. A sensor platform is designed to filter out and reduce background noise from the plant without effecting the raw acoustic signal of the electrical connection, and an automated detection algorithm is presented. The solution is over 75% effective at detecting and classifying connections.


2004 ◽  
Author(s):  
Thyagaraju Damarla ◽  
Tien Pham ◽  
Douglas Lake

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