Jonathon Miegel – jmiegel@swin.edu.au
Philip Branch – pbranch@swin.edu.au
Swinburne University of Technology
John St
Hawthorn, VIC 3122, AU

Peter Blamey – peter.blamey@blameysaunders.com.au
Blamey Saunders hears
364 Albert Street
East Melbourne, VIC 3002, AU

Popular version of paper 3pPA5
Presented Wednesday afternoon, May 09, 2018
175^th ASA Meeting, Minneapolis

Hearing loss affects 10% of the global population to some degree but only 20% of sufferers receive treatment^1,2. Hearing aids are the most common treatments for hearing loss, with longer battery life and improved ease of use identified as the most desirable advances that will improve acceptance^3,4,5. Our research addresses both these issues.

Modern hearing aids have dramatically shrunk in size over the years. This is a positive development since a small hearing aid is less apparent and more comfortable than has been the case in the past. However, with smaller size has come new problems. Controls for modern hearing aids are now much harder to place on the actual device and smaller controls have become increasingly difficult to use, especially for those with low dexterity. Small switches and additional accessories have been the main ways to interact with hearing aids, with increasing adoption of Bluetooth Low Energy (BLE) for connections with smart phones.

The use of BLE and other radio frequency technologies requires additional hardware within the hearing aid, which increases both its price and power consumption. Our work addresses this problem by using high frequency sound waves and ultrasound to communicate between a smart phone and hearing aid (Figure 1). Using hardware already present on the hearing aid allows our technology to be implemented on both old and new hearing aids without any additional hardware costs.

Figure 1 –  An illustration of acoustic wireless communication between a smart phone and a hearing aid.

Our work investigated the performance of multiple communication techniques operating at frequencies within the inaudible range of 16 to 24 kHz. To reduce power consumption, the highly efficient audio processing capabilities of the hearing aid were used alongside simple manipulations of the audio signal. These simple manipulations modulate the amplitude and frequency of the sound waves to transmit binary data.

We were able to transmit 48 bits of data over a range of 3 metres while consuming less power than BLE. While 48 bits of data is relatively small compared to data sent via radio frequency transmissions, it represents multiple commands for the remote operation of two hearing aids. These commands can be used to adjust the volume as well as change program settings for different listening scenarios.

There are benefits to using sound waves as a communication channel for other body worn devices apart from hearing aids. The limited transmission range of high frequency audio provides security through proximity as any potential attacker must be within close range and line of sight to conduct an attack. The prevalence of audio technology in personal electronic devices also has the potential for a universal communication medium across varying platforms.

As hardware on both the transmitting and receiving sides of the acoustic channel continues to develop for the core purpose of each technology, acoustic wireless communication will continue to improve as an option for controlling hearing aid technology and other body worn devices.

References
¹ N. Oishi and J. Schacht, “Emerging treatments for noise-induced hearing loss,” Expert Opin. Emerg. Dr. 16, 235-245 (2011).

² Hartley, D., E. Rochtchina, P. Newall, M. Golding, and P. Mitchell, “Use of hearing aids and assistive listening devices in an older Australian population” Journal of the American Academy of Audiology, 21, 642-653 (2010).

³ S. Kochkin, “MarkeTrak VIII: Consumer satisfaction with hearing aids is slowly increasing,” The Hearing Journal 63, 19-20 (2010).

⁴ S. Kochkin, “MarkeTrak VIII Mini-BTEs tap new market, users more satisfied,” The Hearing Journal 64, 17-18 (2011).

⁵ S. Kochkin, “MarkeTrak VIII: The key influencing factors in hearing aid purchase intent,” Hearing Review 19, 12-25 (2012).

Andi Petculescu – andi@louisiana.edu
University of Louisiana at Lafayette
Lafayette, Louisiana, US

Popular version of paper 2aPA6 “An acoustic approach to assess natural gas quality in real time.”
Presented Tuesday morning, December 5, 2017, 11:00 AM-11:20, Balcony L
174th ASA in New Orleans

Infrared laser spectroscopy offers amazing measurement resolution for gas sensing applications, ranging between 1 part per million (ppm) down to a few parts per billion (ppb).

There are applications, however, that require sensor hardware able to operate in harsh conditions, without the need for periodic maintenance or recalibration. Examples are monitoring of natural gas composition in transport pipes, explosive gas accumulation in grain silos, and ethylene concentration in greenhouse environments. A robust alternative is embodied by gas-coupled acoustic sensing. Such gas sensors operate on the principle that sound waves are intimately coupled to the gas under study hence any perturbation on the latter will affect i) how fast the waves can travel and ii) how much energy they lose during propagation. The former effect is represented by the so-called speed of sound, which is the typical “workhorse” of acoustic sensing. The reason the sound speed of a gas mixture changes with composition is because it depends on two gas parameters beside temperature. The first parameter is the mass of the molecules forming the gas mixture; the second parameter is the heat capacity, describing the ability of the gas to follow, via the amount of heat exchanged, the temperature oscillations accompanying the sound wave. All commercial gas-coupled sonic gas monitors rely solely on the dependence of sound speed on molecular mass. This traditional approach, however, can only sense relative changes in the speed of sound hence in mean molecular mass; thus it cannot do a truly quantitative analysis. Heat capacity, on the other hand, is the thermodynamic “footprint” of the amount of energy exchanged during molecular collisions. It therefore opens up the possibility to perform quantitative gas sensing. Furthermore, the attenuation coefficient, which describes how fast energy is lost from the coherent (“acoustic”) motion to incoherent (random) behavior of the gas molecules, has largely been ignored. We have shown that measurements of sound speed and attenuation at only two acoustic frequencies can be used to infer the intermolecular energy transfer rates, depending on the species present in the gas. The foundation of our model is summarized in the pyramid of Figure 1. One can either predict the sound speed and attenuation if the composition is known (bottom-to-top arrow) or perform quantitative analysis or sensing based on measured sound speed and attenuation (top-to-bottom arrow).

Figure 1. The prediction/sensing pyramid of molecular acoustics. Direct problem: prediction of sound wave propagation (speed and attenuation). Inverse problem: quantifying a gas mixture from measured sound speed and attenuation.

We are developing physics-based algorithms that not only quantify a gas mixture but also help identify contaminant species in a base gas. With the right optimization, the algorithms can be used in real time to measure the composition of piped natural gas as well as its degree of contamination by CO₂, N₂, O₂ and other species. It is these features that have sparked the interest of the gas flow-metering industry. Figure 2 shows model predictions and experimental data for the attenuation coefficient for mixtures of nitrogen in methane (Fig. 2a) and ethylene in nitrogen (Fig. 2b).

Figure 2. The normalized (dimensionless) attenuation coefficient in mixtures of N₂ in CH₄ (a) and C₂H₄ in N₂ (b). Solid lines–theory; symbols–measurements

The sensing algorithm that we named “Quantitative Acoustic Relaxational Spectroscopy” (QARS) is based on a purely geometric interpretation of the frequency-dependent heat capacity of the mixture of polyatomic molecules. This characteristic makes it highly amenable to implementation as a robust real-time sensing/monitoring technique. The results of the algorithm are shown in Figure 3, for a nitrogen-methane mixture. The example shows how the normalized attenuation curve arising from intermolecular exchanges is reconstructed (or synthesized) from data at just two frequencies. The prediction of the first-principles model (dashed line) shows two relaxation times: the main one of approximately 50 us (=1/20000 Hz^-1) and a secondary one around 1 ms (=1/1000 Hz^-1). Probing the gas with only two frequencies yields the main relaxation process, around 20000 Hz, from which the composition of the mixture can be inferred with relatively high accuracy.

Figure 3. The normalized (dimensionless) attenuation as a function of frequency. Dashed line–theoretical prediction; solid line–reconstructed curve.

Kenneth A. Pestka II – pestkaka@longwood.edu
Jacob W. Hull – ‪jacob.hull@live.longwood.edu
Jonathan D. Buckley – ‪jonathan.buckley@live.longwood.edu

Department of Chemistry and Physics
Longwood University
Farmville, Virginia, 23909, USA

Stephen J. Kalista Jr. –kaliss@rpi.edu
Department of Biomedical Engineering,
Rensselaer Polytechnic Institute
Troy, New York, 12180, USA

Popular version of paper 5aPA3
Presented Friday morning, May 11, 2018
175^th ASA Meeting, Minneapolis, MN

In our lab at Longwood University we have recently used Resonant Acoustic and Ultrasonic Spectroscopy to improve our understanding of a self-healing thermal plastic ionomer composed of polyethylene co-methacrylic acid (EMAA-0.6Na) both before and after damage [1]. Resonant Ultrasound Spectroscopy (RUS) is a prodigious technique ideally suited for the characterization and determination of the elastic properties of novel materials, especially those that are often only accessible in small sample sizes or with exotic attributes, and EMAA-0.6Na is among one of the more exotic materials [1,2]. EMAA-0.6Na is a thermal plastic material that is capable of autonomously self-healing after energetic impact and even after penetration by a bullet [3].

Material samples, including those composed of EMAA-0.6Na, exhibit normal modes of vibration and resonant frequencies that are governed by their sample geometry, mass and elastic properties, as illustrated in Fig. 1. The standard RUS approach uses an initial set of approximate elastic constants as input parameters in a computer program to calculate a set of theoretical resonant frequencies. The resulting theoretically calculated resonant frequencies are then iteratively adjusted and compared to the experimentally measured resonant frequencies in order to determine the actual elastic properties of a material.

Figure 1. 3D-model of a self-healing EMAA-0.6Na sample illustrating the first six vibrational modes.

However, EMAA-60Na is a relatively soft material, leading to sample resonances that are often difficult to isolate and identify. A partial spectrum from an EMAA-0.6Na sample is shown in Fig. 2. In order to extract individual resonant frequencies a multiple peak-fitting algorithm was used as shown in Fig. 2 (b).

Figure 2. Undamaged Sample Behavior: Time dependence of the partial resonant spectrum of an approximately 7✕7.5✕1.4 mm3 EMAA sample over 48 hours (a). Lorentzian multi-peak fit to the signal used to extract individual resonances (b). Time evolution of the resonant frequencies at approximately 8.7 kHz (c) and 9.8 kHz (d) for the undamaged EMAA sample, adapted from[1].

Interestingly, the resonant frequencies of undamaged EMAA-0.6Na samples changed over time as shown in Fig. 2(c) and 2(d), but the observed rate of elastic evolution was quite gradual. However, once the samples were damaged, in this case by a 3mm pinch punch hammered directly into approximately 1mm thick samples, dramatic changes occurred in the resonant spectrum, as shown in Fig. 3. Using this approach we were able to determine the approximate healing timescale of several EMAA-0.6Na samples after exposure to damage.

Figure 3. Partial time dependent spectrum of an approximately 7✕7.5✕1.4 mm3 EMAA sample before damage (a) and after damage (b). The lorentzian multi-peak fits are shown just after damage (c) and over an hour after damage (d), adapted from [1].

Building on this approach we have been able to identify a sufficient number resonant frequencies of undamaged EMAA-0.6Na samples to determine the complete material elastic constants. In addition, it should be possible to assess the evolution of EMAA-0.6Na elastic constants for both undamaged and damaged samples, with the ultimate goal of quantifying the material parameters and environmental conditions that most significantly affect the elastic and self-healing behavior of this unusual material.

[1] Pestka II, K. A., Buckley, J. D., Kalista Jr., S. J., Bowers, N. R., Elastic evolution of a self-healing ionomer observed via acoustic and ultrasonic resonant spectroscopy Rep. vol. 7, Article number: 14417 (2017). doi:10.1038/s41598-017-14321-z

[2] Migliori, A. and Maynard, J. D. Implementation of a modern resonant ultrasound spectroscopy system for the measurement of the elastic moduli of small solid specimens.  Rev. Sci. Instrum. 76, 121301 (2005).

[3] S. J. Kalista, T.C. Ward, Self-Healing of Poly(ethylene-co-methacrylic acid) Copolymers Following Ballistic Puncture, Proceedings of the First International Conference on Self Healing Materials, Noorwijk aan Zee, The Netherlands: Springer (2007).

Gabriel Dumy– gabriel.dumy@espci.fr
Mauricio Hoyos – mauricio.hoyos@espci.fr
Jean-Luc Aider – jean-luc.aider@espci.fr
ESPCI Paris – PMMH Lab
10 rue Vauquelin
Paris, 75005, FRANCE

Popular version of paper 3aPA7, “Investigation on a novel photoacoustofluidic effect”

Presented Wednesday morning, December 6, 2017, 11:00-11:15 AM, Balcony L

174th ASA Meeting, New Orleans

Amongst the various ways of manipulating suspensions, acoustic levitation is one of the most practical yet not very known to the public. Allowing for contactless concentration of microscopic bodies (from particles to living cells) in fluids (whether it be air, water, blood…), this technique only requires a small amount of power and materials. It is thus smaller and less power consuming than other technologies using magnetic or electric fields for instance and does not require any preliminary tagging.

Acoustic levitation occurs when using standing ultrasonic waves trapped between two reflecting walls. If the ultrasonic wavelength _ac is matched to the distance between the two walls (it has to be a certain number of the half wavelength), then an acoustic pressure field forces the particles or cells to move toward the region where the acoustic pressure is minimal (this region is called a pressure node) [1]. Once the particles or cells have reached the pressure node, they can be kept in so-called “acoustic levitation” as long as needed. They are literally trapped in an “acoustic tweezer”. Using this method, it is easy to force cells or particles to create large clusters or aggregates than can be kept in acoustic levitation as long as the ultrasonic field is on.

What happens if we illuminate the aforementioned aggregates of fluorescent particles or cells with a strong monochromatic (only one color) optic wave? If this wave is absorbed by the levitating objects, then the previously very stable aggregate explodes.

We can observe that the particles are now ejected from the illuminated aggregate at great speed from its periphery. But they are still kept in acoustic levitation, which is not affected by the introduction of light.

We determined that the key parameter is the absorption of light by the levitating objects because the explosions happened even with non-fluorescent particles. Moreover, this phenomenon exhibits a strong coupling between light and sound, as it needs the two sources of energy to be present at the same time to occur. If the particles are not in acoustic levitation, on the bottom of the cavity or floating in the suspending medium, even a very strong light does not move them. Without the adequate illumination, we only observe a classical acoustic aggregation process.

Using this light absorption property together with acoustic levitation opens the way to more complex and challenging experiments, like advanced manipulations of micro-objects in acoustic levitation or fast and highly selective sorting of mixed suspensions, since we can discriminate these particles not only on their mechanical properties but also on their optic ones.

We did preliminary experiments with living cells. We observed that human red blood cells (RBCs), having a strong absorption of blue light, could be easily manipulated by both sounds and light. We were able to break up RBCs aggregates very quickly. As a matter of fact, this new effect coupling both acoustics and light suggests all new perspectives for living cells manipulation and sorting, like cell washing (removing unwanted cells from the target cell).  Indeed, most of the living cells absorb light at different wavelengths and can already be manipulated using acoustic fields. This discovery should allow very selective manipulations and/or sorting of living cells in a very simple and easy way, using a low-cost setup.

Figure 1. Illustration of the acoustic manipulation of suspensions. A suspension is first focused under the influence of the vertical acoustic pressure field in red (a and b). Once in the pressure node, the suspension is radially aggregated c) by secondary acoustic forces [2]. On d), when we enlighten the stable aggregate with an adequate wavelength, this one laterally explodes.

Figure 2. (videos missing): Explosion (red_explosion) of the previously formed aggregate of 1.6 polystyrene beads, that are red fluorescent, by a green light. Explosion (green_explosion) of an aggregate of 1.7µm green fluorescent polystyrene beads by a blue light.

Figure 3 (videos missing): Illustration of the separation potential of the phenomenon. We take an aggregate (a) that is a mix of two kind of polystyrene particles with same diameter, one absorbing blue light and fluorescing green (b), the other absorbing green light and fluorescing red (c), that we cannot separate by acoustics alone. We expose this aggregate to blue light for 10 seconds. On the bottom row is shown the effect of this light, we effectively separated the blue absorbing particles (e) from the green absorbing one (f).

Movie missing – describes the observation from the top of the regular acoustic aggregation process of a suspension of 1.6µm polystyrene beads.

[1] K. Yosioka and Y. Kawasima, “Acoustic radiation pressure on a compressible sphere,” Acustica, vol. 5, pp. 167–173, 1955.

[2] G. Whitworth, M. A. Grundy, and W. T. Coakley, “Transport and harvesting of suspended particles using modulated ultrasound,” Ultrasonics, vol. 29, pp. 439–444, 1991.